\documentclass[12pt]{article} \usepackage{amsmath} \usepackage{amsfonts} \usepackage{epsfig} \usepackage{setspace} \usepackage{lscape} \usepackage{natbib} \usepackage{epigraph} \usepackage{verbatim} \usepackage{url} %\usepackage{harvard} \bibliographystyle{agsm} %\citationstyle{dcu} %\citationstyle{chicago} %\bibliographystyle{harvard} %\bibliographystyle{apalike} %\usepackage{rotating} \special{papersize=8.5in,11in} \addtolength{\oddsidemargin}{-.5in} \addtolength{\evensidemargin}{-.5in} \addtolength{\textwidth}{1in} \addtolength{\topmargin}{-.5in} \addtolength{\textheight}{1in} \makeatletter \def\@sect#1#2#3#4#5#6[#7]#8{\ifnum #2>\c@secnumdepth \let\@svsec\@empty\else \refstepcounter{#1}\edef\@svsec{\csname the#1\endcsname. \hskip 0.4em}\fi \@tempskipa #5\relax \ifdim \@tempskipa>\z@ \begingroup #6\relax \@hangfrom{\hskip #3\relax\@svsec}{\interlinepenalty \@M #8\par}% \endgroup \csname #1mark\endcsname{#7}\addcontentsline {toc}{#1}{\ifnum #2>\c@secnumdepth \else \protect\numberline{\csname the#1\endcsname}\fi #7}\else \def\@svsechd{#6\hskip #3\relax %% \relax added 2 May 90 \@svsec #8\csname #1mark\endcsname {#7}\addcontentsline {toc}{#1}{\ifnum #2>\c@secnumdepth \else \protect\numberline{\csname the#1\endcsname}\fi #7}}\fi \@xsect{#5}} \makeatother \makeatletter \renewcommand{\section}{\@startsection{section}{1}{0mm}{-\baselineskip}{0.25\baselineskip}{\centering\normalfont\normalsize\scshape}} \renewcommand{\subsection}{\@startsection{subsection}{2}{0mm}{-\baselineskip}{0.25\baselineskip}{\raggedright\normalfont\normalsize\scshape}} \renewcommand{\subsubsection}{\@startsection{subsubsection}{3}{0mm}{-\baselineskip}{0.25\baselineskip}{\raggedright\normalfont\small\scshape}} \def\@begintheorem#1#2{\trivlist \item[\hskip \labelsep{\bf #1\ #2:}]\it} \makeatother \makeatletter \def\monthname{\ifcase\month\or January\or February\or March\or April\or May\or June\or July\or August\or September\or October\or November\or December\fi} \makeatother \renewenvironment{epigraphwidth}{\setlength{13.5cm}} \renewcommand{\thesection}{\Roman{section}} \renewcommand{\thesubsection}{\Alph{subsection}} \renewenvironment{abstract} {\begin{center}\normalsize\textsc{}% \end{center}\begin{quote}\normalsize} {\end{quote}} \renewcommand{\appendix}{\footnotesize\parindent 0cm\setlength{\parskip}{\medskipamount}\setcounter{equation}{0}% \renewcommand{\theequation}{A.\arabic{equation}}} \newtheorem{theorem}{\small\sc Theorem}[section] \newtheorem{proposition}{\small\sc Proposition}[section] \newtheorem{assumption}{\small\sc Assumption}[section] \newtheorem{lemma}{\small\sc Lemma}[section] \newtheorem{corollary}{\small\sc Corollary}[section] \newcommand{\indep}{\perp\!\!\!\!\perp} \begin{document} \begin{titlepage} \vspace*{0.2cm} \setcounter{page}{0} %\vskip 30pt \vskip 10pt \begin{center}% {\Large \sc Political Capital:\\The (Mostly) Mediocre Performance of Congressional Stock Portfolios, 2004-2008 % Local Gains in Congressional % Political Investing: The Common Stock Investments of Members of Congress 2004-2008 \\ %\vskip 1.5em% \vspace{.5cm} \par}% \vskip 1em% {\large \lineskip .75em% \begin{tabular}[t]{c}% % Andrew Eggers -- Yale University \\ % Jens Hainmueller -- Massachusetts Institute of Technology \end{tabular}\par}% \vskip 1.5em% % {\small First version: May 2008\\ % This version: \monthname \ \number\year \par}% % \vskip 1.0em% {\monthname \, \number\day,\, \number\year} \par \vskip .2em% \end{center}\par \begin{abstract} We examine common stock investments made by members of Congress between 2004 and 2008. We find that that the average stock portfolio in Congress underperformed market indices by 2-3\% per year, suggesting that members of Congress are not the savvy insiders depicted in previous research but instead are generally mediocre investors. % on average. % quite ordinary in their mediocrity. We also find that members invest disproportionately in local companies and companies that provide them with campaign contributions, and that members do better when they invest in these companies. (Investments members make in companies headquartered in their own districts outperform the market by over 4\% per year.) % Members of Congress are remarkable as investors, however, in two main respects: a) they invest disproportionately in companies to which they are politically connected, and %b) their investments in companies headquartered in their own districts outperform the market by over 4\% per year. Our findings suggest that informational advantages enjoyed by members of Congress as investors arise primarily from their relationships with local companies, and that widespread concerns about corrupt and self-serving investing behavior in Congress have been misplaced. % question is whether those advantages are themselves corrupt % % %the degree to which they favor companies headquartered in their own districts %and the performance of these local investments % %% an extensive, newly-collected dataset of the investment portfolios of members of Congress between 2004 and 2008. %We find that members' politically-connected investments \--- those investments that they make in companies headquartered in their districts %and companies from which they receive campaign contributions \--- outperform the rest of their portfolios, and that %members invest disproportionately in these connected companies. Investments in local companies beat the market on average. Overall, however, %members of Congress are poor investors: % fairly dismal performance: %% in contrast to an earlier study asserting that members of Congress %%beat the market by 15\% per year, % we find that that the Congressional stock portfolio in this period underperforms market indices by 2-3\% per year. %% In contrast to an earlier study suggesting that members of Congress were making lucrative trades that %While recent public attention to Congressional investing has centered on the concern that members of Congress were growing rich by %trading on political information, our findings suggest that it is perhaps the ineptitude of Congressional investors, rather than their corruption, that is worthy of attention. \end{abstract} %invest in contributors and firms they regulate partly in order to cement political deals.% but not in trading on firms they oversee through committee assignments; meanwhile, members seem to \vspace*{0.25cm} %\vfill % \footnoterule % {\footnotesize %\noindent Andrew Eggers, Post-Doctoral Fellow, Leitner Program of Yale University. Email: andrew.eggers@yale.edu. Jens Hainmueller, %Assistant Professor, MIT Department of Political Science. E-mail: jhainm@mit.edu. Authors are listed in alphabetical order %and contributed equally. The authors recognize Harvard's Institute %for Quantitative Social Science (IQSS), who generously provided funding for this project.\\ %\indent We thank Adam Berinsky, Ryan Bubb, Justin Grimmer, Michael Hiscox, Gary King, Gabe Lenz, Ken Shepsle, Alberto Tomba, Jim Snyder, and seminar participants at Harvard, MIT, Princeton, Stanford, Yale, and the London School of Economics for helpful comments. For excellent research assistance we thank Thi Theu Dinh and Seth Dickinson. %We would especially like to thank the Center for Responsive Politics for sharing data. The usual disclaimer %applies. \vspace*{0.2cm} %\noindent\footnotesize %} \end{titlepage} \newpage \setcounter{page}{1} \addtolength{\baselineskip}{0.5\baselineskip} %\epigraph{\footnotesize ``Trying to take money out of politics is like trying to take jumping out of basketball.''}{\footnotesize Bill Bradley} %\epigraph{\footnotesize ``"} % %{\footnotesize } \section{Introduction} % change this later. Are members of Congress good investors? There is substantial reason to think they might be. Anecdotes abound of members of Congress (and their staffers) trading stocks based on information they acquire through their political positions. (Senator Dick Durbin, for example, reportedly sold stocks in September of 2008 just after a closed-door meeting in which senior leaders of the Federal Reserve and Treasury Department told Durbin and other Congressional leaders that the developing financial crisis was more serious than widely understood.)\footnote{James Rowley. ``Durbin Invests With Buffett After Funds Sale Amid Market Plunge.'' \emph{Bloomberg}. June 13, 2009. Other anecdotal evidence appears in Joy Ward, ``Taking Stock in Congress", \emph{Mother Jones}, Sept./Oct. 1995, and Brody Mullins, Tom McGinty, and Jason Zweig, ``Congressional Staffers Gain From Trading in Stocks," \emph{Wall Street Journal}, October 11, 2010.} % Secretary of the Treasury Henry Paulson and Federal Reserve Chairman Ben Bernanke.) \begin{comment} Research in political economy suggests that stock prices are sensitive to political events and firms' connections to powerful political actors both in the U.S. \citep{roberts1990dst,jayachandran2006je,goldman2008dpc,goldman2008b} and in other countries \citep{fisman2001evp,johnson2003cac,khwaja2005lfp,faccio2006pcf,ferguson2008bhv}, suggesting that savvy political insiders would face plenty of arbitrage opportunities based on their advance knowledge of political developments. % cites Recent research in empirical finance also suggests that more highly connected investors perform better \citep{cohen2008swi}, and members of Congress would seem to be among the most connected investors. % cite \end{comment} Consistent with these anecdotes, \citet{ziobrowski2004arc} % suggestive evidence,a previous academic study found that Senators' stock trades in the 1990s showed %examining stock trades made by Senators in the 1990s confirms that members %displayed uncanny timing, concluding that Senators took advantage of a ``definite informational advantage" over other investors. % \citep[pg.~16]. %on privileged in anticipation of legislative events, % cites % % %Research in empirical finance indicates that well-connected % %Do members of Congress beat the market? % % %Do members of Congress beat the market? financially benefit from their political positions? Anecdotal evidence would suggest they might, as does a bunch of literature in political economy about the effects of politicians on firms. Say something about how it is unethical but not illegal for members of Congress to use political position for personal benefit. Even if they are not taking advantage of political stuff they are sophisticated and well-connected individuals. Consistent with all of this, a study based on 1990s said they do on the average beat the market. % %This matters because % %In this paper we do this really comprehensive look at investments of members of Congress. We look at performance overall and of subsets. % %Examining the investing performance of members of Congress is valuable in part because it speaks to a debate about ethical behavior in Congress. But it also not just as a muckracking exercisse % %On the evening of September 18 of 2008 then-Treasury Secretary Henry Paulson and Federal Reserve Chairman Ben Bernanke called an emergency, closed-door meeting that laid out the devastating ramifications of the financial crisis before congressional leaders. On the next day, at least 10 senators traded stock or mutual funds related to the finance industry; among them senator Richard Durbin who had attended the meeting and personally transferred close to \$50,000 worth of stocks and mutual funds into safer investments using his electronic trading account as stocks subsequently plummeted.\footnote{Several media reported on this story as an example of congressional insider trading. See for example: James Rowley. ``Durbin Invests With Buffett After Funds Sale Amid Market Plunge.'' \emph{Bloomberg}. June 13, 2009.} % %Anecdotes like this have raised concerns over potential insider trading in Congress. According to testimony by Representative Louise Slaughter ``Congress and the federal government are now so enmeshed in the operations of our financial markets that the potential for abuse by members of Congress, congressional staff and federal employees is staggering.''\footnote{Testimony before the House Financial Services Oversight and Investigations quoted in Mike Lillis ``Insider Trading Bill Looks to Hold Congress to Corporate Standard'' \emph{Washington Independent}. July 14, 2009.} Recent research in political economy provides substantial reason to believe that members of Congress could be extraordinarily good investors. A long and growing list of political economy papers show that firm values are sensitive to political factors, both in the US \citep{roberts1990dst,jayachandran2006je,goldman2008dpc,goldman2008b} and in other countries \citep{fisman2001evp,johnson2003cac,khwaja2005lfp,faccio2006pcf,ferguson2008bhv}. %If these studies do not greatly overstate the impact of politicians on stock prices, %%and if politicians at least occasionally take advantage of their %% inside knowledge of future political events, %then members of Congress (who presumably know the effects of at least some of their actions before the %market does) should be able to handsomely profit from information arbitrage. As Representative Brian Baird puts it ``From corporate tax breaks to the next big government contract, valuable information that can drive financial markets is constantly at our fingertips.''\footnote{Press release from Rep. Baird. December 16, 2005. Accessible at \url{http://www.baird.house.gov/index.php?option=com_content&task=view&id=281&Itemid=107}.} % %% handsomely profiting from their political positions. %In addition, recent research in empirical finance \citep{cohen2008swi} suggests that mutual fund managers %with more connections to senior corporate management %perform better as investors. If valuable investment information comes in part simply %from interaction with senior managers, one would expect members of Congress (who spend a large amount of time asking business leaders for money or being asked by them for legislation) to be capable of soundly outperforming the average investor. In this paper, we perform the most comprehensive study to date of the common stock investments of members of the U.S. Congress. Using financial disclosures filed between 2004 and 2008, we reconstruct the daily holdings of the 422 members of the House and Senate who reported owning U.S. stocks in this period, and we use standard methods from empirical finance to assess the % should we say "newest" instead of "standard"? performance of these portfolios against market benchmarks. Our motivation for doing so is in part to provide a more complete answer to the question of whether members of Congress unethically (or even illegally) convert their political positions into superior portfolio returns. The perception that they do so, fueled both by anecdotes and by \citet{ziobrowski2004arc}, has provoked the repeated introduction of legislation to forbid members from trading stocks on the basis of political ``insider information,"\footnote{The ``Stop Trading on Congressional Knowledge'' (STOCK) Act has been repeatedly introduced since 2006 by Reps Slaughter and Baird. It is currently legal for members of Congress to own stocks and to trade them based on political knowledge, but using one's political position for personal gain violates Congressional ethics rules. For more on policy issues surrounding stock trading by members of Congress, see \cite{George2008} and \cite{jerke2010cashing}} and the results of this paper should inform public debate on this issue. Our analysis also %But by looking at the portfolios of politicians we also seek to contributes to at least two broader lines of inquiry, one in political economy and one in empirical finance. In examining whether members of Congress financially benefit from political information, we add to a growing political economy literature measuring the economic value of holding political office \citep{diermeier2005pem,eggers2009mps,lenzgetting}, which in turn informs a mostly theoretical literature about the factors determining who enters politics \citep{caselli2004bp,messner2004paying,besley2005ps}; our findings also indirectly relate to a large body of work in political economy measuring the impact of political events and political connections on financial markets \citep{roberts1990dst,jayachandran2006je,goldman2008dpc,goldman2008b,fisman2001evp,johnson2003cac,khwaja2005lfp,faccio2006pcf,ferguson2008bhv}. In evaluating the investing success of a set of highly-connected investors, we also provide evidence relevant to literatures in empirical finance examining the portfolio choices and performance of individual investors \citep{barber2000thy,huberman2001familiarity,barber2007all,barber2008just}, the information value of personal connections between investors and corporate management \citep{cohen2008swi}, and the existence of an information advantage from investing in local stocks \citep{coval1999hbh,coval2001gii,ivkovic2005local,seasholes2009there}. What we find is that, contrary to expectations of politicians as savvy insiders, members of Congress are in fact rather poor investors. We find that the average Congressional portfolio underperformed the market index by 2-3\% per year (before expenses) during the period we examine. In dollar terms, \$100 invested in an index fund in January 2004 would have yielded \$80 by the end of 2008; the same \$100 invested like the average investor in Congress %dollar-for-dollar to mimic %members of Congress would have yielded only about \$70. % As one indication of the poor overall performance of members of Congress as investors in the 2004\--2008 period, We find underperformance using a variety of specifications and weighting approaches, and not just for Congress as a whole but separately for both the House and the Senate, Democrats and Republicans, members of power committees, and groups of members stratified by wealth, portfolio size, and turnover. We also carry out our analyses on individual members and confirm that member-level excess returns are distributed symmetrically and centered below zero, which further increases our confidence that the underperformance we find is a widespread pattern and not limited to a few outliers. Performance relative to the market was if anything slightly better in 2004-2006 than in 2007-2008, suggesting that on average members of Congress did not capitalize on the unusually active role of the government in the economy during the latter period. %While our findings of overall underperformance %Our findings suggest that members of Congress are on par with average individual investors, who have been shown to underperform the market on average %While surprising in the context of expectations of politicians as savvy and self-serving, our findings suggest line findings from other studies which suggest that average retail investors that move away from indexing tend to do poorly relative to the market %\citep{barber2000thy,} and even professional investors often fail to systematically earn excess returns \citep{carhart1997pmf}. Our finding that members of Congress underperform market indices % of overall underperformance contrasts sharply with the sole prior study of Congressional investments, \citet{ziobrowski2004arc}, who analyze stock trades reported by members of the Senate in the period 1993\--1998. % (their sample includes about 30 Senators on average). They conclude that a portfolio constructed from members' transactions would have beaten market indices by as much as 12\% per year, a performance far better than that found for mutual fund managers \citep{carhart1997pmf}, hedge fund managers \citep{fung2008hedge}, and corporate insiders \citep{jeng2003eri}. \citet{ziobrowski2004arc}'s finding has been cited in numerous articles and reports emphasizing the ethical problems of Congressional investing\footnote{Articles and broadcasts citing \citet{ziobrowski2004arc} include \emph{The New Yorker}'s {}``Financial Page'' of October 31, 2005; {}``An Ethics Quagmire: Senators Beat the Stock Market \--- and Get Rich \---- With Insider Information,'' \emph{Washington Spectator} January 1, 2006; ``Nieman Watchdog -- Questions the press should ask,'' March 10, 2006; R. Foster Winans, ``Let Everyone Use What Wall Street Knows," \emph{The New York Times}, March 13, 2007; NPR's \emph{Marketplace} on September 17, 2009 (\url{http://marketplace.publicradio.org/display/web/2009/09/17/pm-inside-dope/}); Brody Mullins and Jason Zweig, ``For Bill on Lawmaker Trading, Delay Is Long and Short of It", \emph{The Wall Street Journal}, May 5, 2010; ``Policy, portfolios and the investor lawmaker", \emph{The Washington Post}, November 23, 2009.} and was featured in testimony before the House Financial Services Committee in July of 2009.\footnote{Available at \url{http://www.house.gov/apps/list/hearing/financialsvcs_dem/ziobrowski_testimony.pdf}; accessed Sept. 8, 2010.} % WOULD BE GREAT TO FIND A PLACE FOR THIS: Whereas the findings of \citet{ziobrowski2004arc} could only reasonably be explained by unethical and perhaps illegal investing behavior, our findings are consistent with the view that members of Congress are basically ordinary investors whose portfolios do not particularly benefit from their political positions. %and has provoked calls for legislation to prevent members of Congress from investing on the basis of their political knowledge.\footnote{The ``Stop Trading on Congressional Knowledge'' (STOCK) Act has been repeatedly introduced since 2006 by Reps Slaughter and Baird. For more on policy issues surrounding stock trading by members of Congress, see \cite{George2008} and \cite{jerke2010cashing}.} %While most of our analysis takes a more comprehensive %We are unable to explain the difference between our results and those of \citet{ziobrowski2004arc}, other than to say that our study examines a more recent period. % they examine a different period. % and something must have changed. While most of our findings are based on a more comprehensive view of members' investments than was used by \citet{ziobrowski2004arc} (namely, our main analysis is based on actual positions held by members rather than a portfolio constructed solely from trades), the discrepancy between our results and those of \citet{ziobrowski2004arc} persists when we perform their precise procedure using our data. The difference between our findings must therefore be the result of a reduction in the informational advantages of members of Congress between the period they study (1993-1998) and the period we study (2004-2008), a decrease in members' willingness to act on these informational advantages (perhaps because of increased scrutiny applied to their investments), or simply a change in luck. While we provide some evidence that speaks to the relative importance of these different possible accounts, we leave to future work the task of producing a detailed explanation of why members of Congress appear to have been investing geniuses in the 1990s but not in the 2004-2008 period.\footnote{Professor Ziobrowski declined to share any of the data used in his study or even such basic information as the names of the members of the Senate whose investments they analyzed.} %% AE: I wonder if this is a little too strong. % in the investing environment of politicians between the 1990s %It is possible that members of Congress have become more circumspect about profiting from political information since the Ziobrowski study, %Our best explanation for the difference is that the extraordinarily large abnormal returns in \citet{ziobrowski2004arc}'s study were the result of luck rather than investing skill, although we cannot rule out a change in the circumstances of Congressional investing between the 1990s and the 2000s (i.e. a reduction in the informational advantages of members of Congress and/or their willingness to use this information to their own financial benefit) or simply computational error. %\footnote{Professor Ziobrowski declined to share any of the data used in his study or even such basic information as the names of the members of the Senate whose investments they analyzed.} % or simply computational error %In order to account for the differences we asked Alan Ziobrowski for more detail on his data. Unfortunately, professor Ziobrowski is unwilling to share any details, including even basic information such as the names of the 30 Senators that are included in his sample.} %One possible reason for the difference between our overall findings and those of \citet{ziobrowski2004arc} is that we use all available information to reconstruct members' actual portfolios (holdings and transactions) while \citet{ziobrowski2004arc} analyze only their transactions (based on an arbitrary assumption about fixed holding periods). %If the portfolio contains many holdings that are not or only rarely traded, then any analysis soled based on transactions is likely to be uninformative about the overall portfolio performance. Transaction based analysis then primarily speaks to the canny timing of the trades. We find little evidence of abnormal returns when we replicate \citet{ziobrowski2004arc}'s transaction-based approach as closely as possible with our data. We therefore conclude that the performance of investors in the Senate has changed from the 1990s to today, and that this is due to either a change in the circumstances % (market conditions, the composition of the Senate, or Congressional ethics) or simply an end to an uncanny run of good luck.\footnote{In order to account for the differences we asked Alan Ziobrowski for more detail on his data. Unfortunately, professor Ziobrowski is unwilling to share any details, including even basic information such as the names of the 30 Senators that are included in his sample.} Our overall findings suggest that members of Congress are not the savvy and self-serving insiders portrayed by \citet{ziobrowski2004arc} but that, in terms of overall performance, they are little different from run-of-the-mill individual investors, who have been shown to perform on average at or below market indices \citep{barber2000thy,barber2008just}. Whereas the large excess returns reported in \citet{ziobrowski2004arc} have provoked public criticisms of the ethics of members of Congress, the poor performance we report may if anything inspire concern about their judgment. Members of Congress could have avoided losing on average almost 10\% of their portfolios over the 2004\--2008 period (before expenses) merely by following widely-available %investing advice and investing in a low\--cost index fund. Not only would this course of action have preserved a large chunk of their portfolios, it would also have shielded them from possible criticisms of political ``insider trading." % from their political positions. %that their investment positions affect their legislative decisions (and vice versa). %In this paper we do not attempt to answer that question \--- how investments affect politics \--- but we do %investigate the rever We next investigate the relationship between members' political positions and their investment decisions. %\footnote{We leave for future work the complementary project of investigating the relationship between members' investment positions and their political decisions.} % reader is expecting "legislative decisions" Remarkably, %It may be, in fact, that political relationships between members and the companies in which they invest explain the %When we dig a bit deeper into members' portfolios, however, it becomes clear that members of Congress differ from ordinary investors in important respects. %First, we find that members invest about 16 times as much in a company if it is located in their district (or state, for Senators) than otherwise, controlling for member and company fixed effects. A similar ``local bias" has been found for other types of investors, but the magnitude of the bias we find among members of Congress is around twice as large as that found for %a sample of individual investors \citep{ivkovic2005local} and over 10 times as much as that found for mutual fund managers \citep{coval1999hbh}.\footnote{In \citet{ivkovic2005local}, ``local" means a radius of 250 miles; in \citet{coval1999hbh} it means a radius of 100 km (62 miles). The median Congressional district has an area of just over 2000 square miles which, if it were a circle, would have a radius of about 25 miles; even considering that in many cases the local area in these papers would be largely ocean, the area we consider is smaller. The stronger local bias we find could therefore reflect the fact that our definition of ``local" is more restrictive.} Also intriguing is the fact that members of Congress invest about 5 times as much in a company if its PAC contributes to their election campaigns than otherwise, controlling for whether the company is headquartered in the member's district. % para break here? The apparent ``political" bias of members' investments raises the possibility that members of Congress invest in local companies and contributors in part to establish or maintain political relationships. In particular, a member may invest in local companies and potential contributors in order to convince them that he shares their regulatory goals, hoping that this would convince them to provide him with political and financial support in return. (\citet{tahounrole} explores this possibility in depth.) % which may make it more likely that those companies would %signal their willingness to be publicly associated with these companies or to %internalize their regulatory objectives; % members may find these investments to be useful when they ask these local companies and contributors for further political and financial support. To the extent that these investments are made for political and not financial reasons, they may drag down the average performance of members' portfolios, which would help to explain the poor overall performance we observe. What we find, however, is that members' connected investments actually \emph{outperform} the rest of their portfolios. % %disproportionately in companies that contribute to their election campaigns. % % % %disproportionately in companies headquartered in their districts (or states, for Senators): % % % %to which they are politically connected \--- those companies that are headquartered in their %districts or contribute to their election campaigns. On average and controlling for firm and member characteristics, members invest about 16 times in a company if it is %located in their district than otherwise, %%as much in companies located in their district as in other similar companies, %and about 5 times as much if the company contributed to their election campaigns. % this is based on the column 1 regression %Individual investors and mutual fund %managers have been found in other studies to favor local companies in their portfolios as well, but the magnitude of local bias that we find is around twice as large as that found %for individuals \citep{ivkovic2005local} and over 10 times as much as that found for mutual fund managers \citep{coval1999hbh}. % %The strength of the bias toward in-constituency firms, and the existence %of bias toward contributors, provides our first clear evidence that members of Congress are political investors. % %One could imagine that members' bias %away from indexing %toward companies to which they are politically connected might explain %the poor average performance of the Congressional portfolio: if members invested so heavily in local companies and campaign contributors % companies to which they were politically connected %not for financial reasons but in order to extract electoral or financial support (perhaps by bonding themselves to these companies' interests or signaling their aligned preferences), %then we might expect these investments to perform poorly. Looking at the performance of members' connected portfolios leads us to our second finding distinguishing members of Congress from other %investors. %% We find that members' connected investments outperform the rest of their portfolios. %We find that A portfolio of holdings where the company contributed money to the member's election campaigns performs as well as the market, as does a portfolio of holdings where the company lobbied the member's committee; most remarkably, a portfolio of holdings where the company is headquartered in the member's constituency robustly outperforms the market by about 4.5\% per year. This finding, like the overall underperformance just discussed, is robust to various specifications including estimating excess returns individually for each member, which yields a symmetric distribution of member-level excess returns clearly centered above zero. Whereas the overall finding of poor portfolio performance makes members of Congress look like typical individual investors at best, the impressive performance of their local investments puts members of Congress in a class of their own: %strikingly distinguishes members of Congress from ordinary investors. recent studies have found that neither individual investors \citep{seasholes2009there} nor mutual fund managers \citep{coval2001gii}\footnote{\citet{coval2001gii} find a local advantage before 1985 but not since.} enjoy a performance premium on their local investments, which suggests that members' local advantage reflects unusually valuable information about the economic circumstances of local companies. %Our finding that the local investments of members of Congress outperformed the market in this period in fact provides one of very few examples in empirical finance % of a substantial premium arising from investor knowledge. Geographical proximity does apparently yield informational advantages, at least when one's local networks and expertise are as developed as those of members of Congress. %our finding %we document %that members of Congress beat the market when they invest in companies headquartered in their districts suggests that (in contrast to recent work on individual investors \citep{seasholes2009there} and mutual fund managers \citep{coval2001gii}) geographical proximity does yield informational advantages, at least when one's local networks and expertise are as developed as those of members of Congress. This evidence indicates that members do play some role in incorporating information into prices. While we cannot say for certain what explains the robust performance of members' local investments, we provide evidence to suggest that it is based on general knowledge of local companies and the environment in which they operate, rather than time-sensitive knowledge about e.g. earnings announcements or political events. In particular, we examine instances where members traded local and non-local stocks, and find that local trades do not seem to have been better timed than other trades, based on the performance of traded stocks during various periods (one day, two weeks, and five weeks) following the trade. This suggests that the local premium we find is based not on stock tips or non-public legislative plans but rather on general but not-widely-shared knowledge of the quality of the management of local companies or the types of projects in which they are engaged. %Mostly our findings in this respect are negative: the overall findings do not suggest that members systematically exploit information about firms or political events, nor do our analyses of members' portfolios indicate that they disproportionately invest in local companies and contributors for political rather than financial reasons, nor do we find evidence that members invest disproportionately in companies that they are responsible for regulating. Together, these findings suggest that, at least in their investing behavior, members take less advantage of their connections to firms than has previously been supposed. The overall picture we present is thus a somewhat subtle one. Members of Congress seem to benefit as investors from knowledge of companies to which they are politically connected (and particularly those headquartered in their districts), and they appear to take advantage of this knowledge by investing disproportionately in those companies. The poor performance of the rest of their portfolios, however, drags down their overall returns. Members of Congress would have benefited financially from investing even more intensively in local companies (and other companies to which they were politically connected) while shifting the rest of their portfolios into passive index funds. %Our paper contributes to at least three distinct literatures. First and most directly, it provides empirical evidence relevant to a policy debate about self-dealing in Congress. Whereas the findings of \citet{ziobrowski2004arc} could only reasonably be explained by unethical and perhaps illegal investing behavior, our findings are consistent with the view that members of Congress are basically ordinary investors whose portfolios do not particularly benefit from their political positions. Second, it contributes to a literature examining the investing behavior and performance of various types of individuals. Our examination of the unusual set of investors who serve in Congress confirms earlier findings that individuals generally come up short of the market. At the same time, our finding %%we document %that members of Congress beat the market when they invest in companies headquartered in their districts suggests that (in contrast to recent work on individual investors \citep{seasholes2009there} and mutual fund managers \citep{coval2001gii}) geographical proximity does yield informational advantages, at least when one's local networks and expertise are as developed as those of members of Congress. This evidence indicates that members do play some role in incorporating information into prices. Third, our work contributes to a growing literature in political economy research examining the relationship between politicians and firms. Mostly our findings in this respect are negative: the overall findings do not suggest that members systematically exploit information about firms or political events, nor do our analyses of members' portfolios indicate that they disproportionately invest in local companies and contributors for political rather than financial reasons, nor do we find evidence that members invest disproportionately in companies that they are responsible for regulating. Together, these findings suggest that, at least in their investing behavior, members take less advantage of their connections to firms than has previously been supposed. % a little unsatisfying %This finding suggest that Members of Congress are unusual compared to retail investors, who also tend to skew their stock holdings towards local companies but do not earn any excess returns on their local holdings (Seasholes and Zhu, 2009). While the home bias for individual investors is primarily driven by an increased familiarity with local companies, our analysis suggests that the home bias for Members of Congress may be driven largely by the fact that Members do have value-relevant information about local stocks. % that unusually good luck . %In contrast to earlier findings, our analysis suggests that, in terms of overall investment performance, %members of Congress are less like corporate insiders, who \citet{jeng2003eri} find earn large excess returns, %and more like average individuals, whose overconfidence, loss aversion, and failure to diversify have been documented and linked to poor performance in a %growing number of studies \citep{odean1999investors,barber2000thy,barber2007all,barber2008just,goetzmann2008equity}. %The fact that members invest disproportionately in companies to which they are politically connected, and that these investments do %somewhat better than the rest of their portfolios, does lend a whiff of corrupt opportunism to their investing performance, but %overall we are left with the impression that members of Congress are more remarkable for their mediocrity as investors. %%the overall impression we are left with is of a set of investors who are quite pedestrian % quotidian %%in their mediocrity. After describing our data in the next section, we assess the overall performance of Congressional investors and subsets thereof, comparing this performance to that documented in other studies including \citet{ziobrowski2004arc}. We then divide members' portfolios according to connections between companies and members and assess how much members invest in connected companies, how well these investments perform, and what that suggests about members' interactions with firms to which they are politically connected. % We then %place our results in the context of previous work on Congressional investing and other types of investors and consider possible explanations for the %performance of members' portfolios. We then conclude by weighing some implications of our findings. %The investment record of Members of Congress is not merely a story of overconfidence and mundane underperformance. % Connected portfolio performance; weights in the portfolio. Able to confirm that it's not just that these companies perform well. % %On the other hand, the unimpressive average performance %of members of Congress suggests that a set of powerful individuals with responsibility for American fiscal and economic policy have %largely failed to internalize the lessons of decades of empirical finance about the futility of stock-picking. Perhaps more damning, %while the overall performance of Congressional portfolios is poor, members appear to do better when they invest in companies %that are politically connected to them \--- companies that are headquartered in their constituency, companies whose PAC contributed money to their election %campaigns, and %% introduction needs to handle: %% policy relevance, previous findings, very briefly, description of what we do, implications. \begin{comment} Research in empirical finance tells us that average investors do poorly relative to the market \citep{barber2000thy} and even professional investors fail to systematically earn excess returns \citep{carhart1997pmf}. In this paper, we examine the portfolio choices and investment returns of members of Congress, a class of investors whose political roles put them in an informationally privileged position but also impose unusual constraints on their investments. In contrast to a previous paper analyzing Congressional investments using older and less complete data \citep{ziobrowski2004arc}, we do not find that members of Congress on average beat the market in the period 2004-2008. We do however find intriguing differences in returns across members and types of investments, which, combined with evidence that members tend to overweight local companies and campaign supporters in their portfolios, improves our understanding of how politicians trade off conflicting financial and political incentives while in office. %% AE: this needs to reflect more of the new findings -- the poor overall performance Why study the investments of members of Congress? %% AE: prob not nec to motivate in this way? Leaving aside the considerable public interest in the question of whether legislators personally benefit from their political positions (in violation of ethics rules), the investment choices and performance of members of Congress may provide political scientists with valuable indirect evidence about the way firms and legislators interact. Like all investors, members of Congress presumably invest in stocks in order to preserve and increase their wealth. But members of Congress differ from other investors in two principal ways that guide our investigation. First, they may possess unusually valuable market-relevant information about public companies and the regulations that affect them. Second, their political success depends to some extent on soliciting political support and campaign contributions from corporations and their stakeholders, and their investments (which after all are public) may play a role in establishing connections with public companies. In short, the investment choices and investment performance of members of Congress provide valuable clues about what kind of informational advantages members may possess and how they use those advantages, %the extent to which members possess and use informational advantages, as well as whether and in what ways they use their investments to cement political relationships with firms and their stakeholders. %the way in %which they trade off these advantages against political considerations including the desire to solicit corporate support. %This is work in progress, but Our findings indicate that members of Congress do not on average outperform market indices, in contrast to the sole previous study of Congressional stock investments \citep{ziobrowski2004arc}. But members appear to invest in a way that reflects political considerations, strongly overweighting local firms, firms that give campaign contributions, and firms with business before the member's own committees. The performance of these connected portfolios also diverges from market benchmarks: members' local investments outperform the market by about 4\% a year, indicating substantial informational advantages from proximity and relationships with supporters, while their investments in contributing companies underperform the market by about the same amount, suggesting that members invest in contributors in part to cement political relationships. \end{comment} %MORE HERE. %\section{Preliminary Discussion: What is Political about Politicians' Investments?} %Despite evidence that both amateur and professional investors do not systematically beat market indices, %typical investors underperform the market (as well as theoretical arguments about market efficiency), %recent research in political economy provides substantial reason to believe that members of Congress could be extraordinarily good investors. %A substantial and growing list of papers show that firm values are very sensitive to political factors: %\begin{itemize} %\item \citet{roberts1990dst} finds that the death of the ranking Democrat on the Senate Armed Service Committee resulted in lower stock value of firms located in the Senator's state and higher stock values of firms connected to the Senator who inherited his position on the committee. %\item \citet{jayachandran2006je} finds that the market value of Republican-connected firms dropped when Senator Jeffords unexpectedly departed the Republican Party in 2001, shifting the Senate majority to the Democrats. %\item \citet{goldman2008dpc} and \citet{goldman2008b} show that companies that announce the appointment of a politically-connected director experience a positive abnormal return and that politically connected firms are more likely to secure procurement contracts. %\footnote{In related work, \citet{agrawal2001sod} find that companies that have larger dealings with the government also have a larger number of board members with political experience and \citet{kroszner1998igc} demonstrate that interest group political action committees (PACs) donate more to politicians who are members of committees overseeing their industries.} %\end{itemize} %Comparable evidence abounds for other countries as well \citep{fisman2001evp,johnson2003cac,khwaja2005lfp,faccio2006pcf,ferguson2008bhv}. %The picture presented by all of these studies is that politicians can %significantly impact firm values. Presumably, politicians know about the impact of their own actions and those of other politicians with whom they work. If these %studies do not greatly overstate the impact of politicians on stock prices, an investment-minded member of Congress could handsomely profit from information arbitrage. %Politicians may enjoy additional informational advantages simply by being in close contact with corporate executives and industry lobbyists as part of their legislating and fundraising routines. %%While overall dismissive of the opportunity to achieve sustained excess returns, some r %Recent research in empirical finance suggests that mutual fund managers do better %when they invest in companies to which they are connected %through geographic proximity \citep{coval2001gii}\footnote{Although not in recent years; see below.} or personal ties to executives \citep{cohen2008swi}. Members of Congress necessarily have large personal networks %and frequent contact with corporate executives and lobbyists. Whether the firm approaches the legislator asking for policy favors or the legislator %approaches the firm asking for campaign donations, the firm may reveal information about its market prospects (either intentionally or unintentionally) that %the legislator could act on in her own investments. %% generally, there is a problem here of where to get into this theory. the standard thing would be intro brings up issues quickly, related lit, then "model" %%\begin{comment} %%The prospect that members of Congress may be good investors is bolstered by \citet{ziobrowski2004arc}, %%which concludes based on an analysis of transactions reported 1991-1996 that Senators experienced large abnormal returns. %%%based on analysis of transactions reported 1991-1996. %%As an indication of the uncanny timing exhibited by Senators in the period they consider, %%stocks that Senators sold outperformed the %%market by roughly 25 percent during the 12 months prior to the sell date and %%remained fairly flat thereafter, while stocks that they purchased %%beat the market by only 3 percent prior to the buying date and by %%almost 28 percent in the following year. %%%In subgroup analysis they fail to find a difference in the returns %%%of Democrats and Republicans, and if anything junior Senators %%%appear to be smarter traders. %%The Ziobrowski et al paper generated attention in the media% %%\footnote{The study was cited on the \emph{New Yorker}'s {}``Financial Page'' %%of October 31, 2005; it was described in a \emph{Washington Spectator %%}article on January 1, 2006, {}``An Ethics Quagmire: Senators Beat %%the Stock Market\textemdash{}and Get Rich\textemdash{}With Insider %%Information''; and it was featured on {}``Nieman Watchdog -- Questions %%the press should ask'' on March 10, 2006.% %%} and in Congress itself.\footnote{The ``Stop Trading on Congressional Knowledge'' (STOCK) Act was introduced in 2006 as H.R. 5015 by Reps Slaughter and Baird and reintroduced %%in 2007 (by the same members) as H.R. 2341. The bill proposed to prohibit %%members of Congress, congressional staffers, and members of the executive %%branch from trading on {}``material non-public information,'' defined %%as information members acquire as a result of their employment by %%the federal government. For more on policy issues surrounding stock %%trading by members of Congress, see \cite{George2008}.} Their explanation for Senators' %%outstanding performance is that they appear to be %%profiting from their privileged access to information and their influence over policy outcomes. %%\end{comment} % where should we talk about ZIobrowski? too many places as it is. depends on whether we want to have a related lit section or waht. %While members of Congress likely enjoy considerable information advantages because of their political power, %they also face a number of constraints arising from their need to appeal to political constituents. %We focus on three such constraints, which we will refer to as ``signaling," ``bonding," and ``ethics." %%In addition to the unusual advantages members of Congress likely enjoy because of their political power, %%there are also a number of constraints that they face arising from their need to appeal to political constituents. %%We focus on three such constraints, which we will refer to as ``signaling," ``bonding," and ``ethics." %% the signaling value of their portfolio choices and the % do not apply to other investors. %%We focus %%We point out two other ways in which investments might be related to politics: signaling and incentive alignment. %\subsubsection*{Signaling} %Members' investments are public (which is why this paper is possible) and occasionally subject to %journalistic scrutiny (e.g., \citet{boller1995}). To voters, firms, and other politicians, %a member's stock holdings may convey a signal about the member's policy preferences or ideology. %Suppose politicians agree on the expected return of a particular tobacco company's %stock but differ in their opposition to tobacco companies: some are pro-tobacco and some are anti-tobacco. %Anti-tobacco types experience a higher private cost of owning tobacco stock, perhaps because % they dislike the idea of indirectly supporting the firm or %of the cognitive dissonance of having a financial stake in a company they dislike. % or because (in the case of a politician) he is involved in a repeated game with anti-tobacco voters. %this is a bit murky? %Suppose that there is also a group of constituents who oppose the tobacco industry and want to elect an anti-tobacco politician. %%Constituents also have types and they want to vote for politicians of the same type. %%A group of pro-tobacco constituents wants to elect a pro-tobacco representative but cannot directly observe politicians' types. %If the private cost to anti-tobacco politicians of owning %tobacco stock is high enough, there may be a separating equilibrium where only pro-tobacco politicians choose %to own the stock and the anti-tobacco constituents vote for politicians who refuse to own tobacco stock.\footnote{In terms of Spence's educational signaling model, the constituents are the employers, the politicians are the workers, \emph{not} buying stock is the costly credential, and the anti-tobacco politicians are the high ability types for whom the credential is less costly.} %because they face lower private costs of doing so, and pro-tobacco constituents support stockholding politicians. %%\begin{comment} %%Signaling would suggest that politicians from different parties would own different portfolios. %%Consistent with this, our data shows that Republicans are much more likely to hold stock in Altria, Exxon Mobil, and Monsanto, among other companies; this is consistent with %%the fact that these firms are frequent targets of attack from environmental and public health interests of the political left. %%Table \ref{tab:partisan_companies} lists the top five Democratic and Republican companies, ranked according to the z-score on the log odds ratio comparing the number of Republicans and Democrats holding the stock \citep{monroe:fwl}. Portfolio choices would probably be less correlated with party if they remained private. %%%If portfolios were private, presumably politicians would be more likely to agree on %%%To put the ``signaling" idea a bit more prosaically, Democratic members would %%%probably face harsh criticism if they owned these stocks; if they were private citizens no one would know that they owned %%\end{comment} %In addition to signaling political ideology, investments might be thought to signal preferences about public service versus private gain: %by abstaining from using political knowledge to bank windfall profits, politicians may signal to voters that they prioritize public service. Consistent with the idea that members constrain themselves in order to send a signal to voters, \citet{ziobrowski2004arc} find smaller abnormal %returns in 1996 and 1997, which they suggest may be due to the unfavorable %media attention drawn to well-timed stocks trades among members by \cite{boller1995}. %To the extent that signaling is indeed a significant determinant of members' investment portfolios, we might expect %average returns to be modest, since basic portfolio theory tells us that restricting possible %investments (particularly based on non-financial considerations) cannot enhance returns. %CITE % %\subsubsection*{Bonding} %The second political constraint on members' investment decisions also comes from their relationship with voters and constituent firms. %%Politicians may also take on equity stakes specifically to align their policy preferences with the preferences of constituents. %As is widely discussed in the political economy literature, %CITES %politicians face a commitment problem with respect to voters and potential campaign donors. %Suppose that a firm is considering offering a campaign contribution to a politician, but is unsure of whether the politician %will pursue its interests in the legislature. This is clearly an incomplete contracting situation: it is impossible to write down all of %the ways in which the politician could serve or not serve the firm's interests, and at any rate courts would not enforce such a \emph{quid pro quo}. %The firm may then find it beneficial to require the politician to take an equity stake in the company, bringing the firm's and the politician's %policy interests into closer alignment.\footnote{It is standard for corporate directors to be required to %own large equity stakes in the companies on whose boards they serve in order to reduce slack in the shareholder/director relationship. Directors %are usually contractually required to hold the stock, which brings us back to the commitment problem here: it may not be time consistent for the politician to continue holding the company's stock once the firm's check has been cashed.} %CITE} %In short, members may take on equity in constituent companies in order %to make policy promises credible. % and possible a political exchange of support for representation. %If the politician is able to influence policy to help the firm in a way the market did not anticipate, we might expect these investments to be profitable; %in an inefficient market, though, there can be no such expectation: since the politician engages in the informal contract %principally to earn political returns, it may be that the \emph{ex ante} financial returns are nonexistent or negative. %\subsubsection*{Ethics} %The final political constraint is ethics regulation. Members of Congress face no special restrictions on their investment choices (other than the requirement to file annual disclosures), %but ethics rules state broadly that members should not financially profit from their political positions (\emph{Code of Conduct}, 2005). %\citep{code_of_conduct}. %A member of Congress who invested very aggressively might face ethics charges in addition to journalistic scrutiny \citep{boller1995}. \section{Data}% and Methods} Our study is based on common stock holdings and transactions reported by members of the U.S. Senate and House of Representatives between January 2004 and December 2008. As a result of the 1978 Ethics in Government Act, members of Congress are required to disclose their stock investments (as well as real estate and other investments, liabilities, and outside income and employment) and those of spouses and dependent children in annual filings known as Financial Disclosure Reports.\footnote{Our analysis includes all holdings and trades reported by members, including those owned by spouses and dependent children. Members may also choose to create qualified blind trusts, which are managed on their behalf and whose holdings are unknown to the member. In our data 20 members report qualified blind trusts.} This paper is the product of using these reports to reconstruct members' actual portfolios %(a painstaking process described in detail below) and evaluating the performance of those portfolios using standard methods from empirical finance. % do we want to say the methods are the "newest" or something? \subsection{Reconstructing Portfolios from Disclosure Forms} Members of Congress are required to submit disclosure reports each spring, detailing their year-end holdings as well as all transactions made during the year. Since 2004 the Center for Responsive Politics (\texttt{www.opensecrets.org}) has transcribed the reports, and since 2008 they have made this data freely available. We thus received the data as a pair of spreadsheets, one with a row for each of the 111,101 transactions recorded and another with a row for each of the 169,828 year-end holdings recorded. % A considerable amount of work remained, however, to reconstruct members' portfolios from this data. %After matching the named assets and transactions to publicly listed companies (and manually checking apparent financial stocks in order to identify and remove %savings accounts, IRAs, and insurance policies), we imputed The first task in converting this raw data to stock portfolios was to identify the companies in which members hold stocks. The disclosure reports %FDRs do not identify holdings in standardized ways (e.g. an investment in Bank of America common stock may be described as ``Bank of America," ``Bank America Common Stock," ``Banc of America," or ``BOA"); we used search utilities provided by Google Finance and the Center for Research on Security Prices (CRSP) to link variously described assets to actual companies. Even more challenging, the descriptions may not precisely distinguish between stock holdings and other types of assets such as corporate bonds, mortgages, or bank accounts. To reduce the risk of misclassifying savings accounts as stock investments, we hand-checked the disclosure report for each apparent financial stock to determine whether other clues (such as columns reporting dividend or investment income) could distinguish stocks from other types of assets. The next task was to impute a dollar value for each holding and trade reported. The law requires only that %% AE: check that XXXX members report the value of their investments in broad value bands (e.g. \$15,000 \--- \$50,000) rather than exact dollar amounts.\footnote{Value band cutpoints are at \$1,000, \$15,000, \$50,000, \$100,000, \$250,000, \$500,000, \$1,000,000, \$5,000,000, \$10,000,000 and \$25,000,000, and a top category captures all investments of \$50,000,000 or more in value.} In order to impute precise values for investments reported in these bands, we took advantage of the fact that we do know the precise value of a sizable minority of reported investments \---- those cases in which a member submitted an annual statement from a bank or investment manager rather than filling out the official forms.\footnote{This information is available for about 25\% of the transactions in the dataset and about 8\% of the year-end holdings.} We used these investments to fit a distribution of precise values and, for each investment for which we know only the band, %used the distribution's band mean as value for imputation. an imputed investments in each band, we impute the expected value of the precise\--value distribution within that band.\footnote{This approach is inspired by the imputation method proposed in \cite{milyo1999electoral}.} %\footnote{By comparison, Ziobrowski et al impute the midpoint of each band, which is higher than our imputed value for every band except the lowest.} For the highest band (investments over \$50,000,000), of which there are fewer than 100 holdings and 5 trades in our estimation sample, we impute the value of \$50,000,000.%Because we have too little data to fit an accurate distribution, and in order to curb the influence of extremely large holdings. % \footnote{By comparison, Ziobrowski et al top-code at \$250,000, which as we show in the appendix impacts estimates of average returns (particularly value-weighted returns) but does not change the overall conclusions.} % (To follow previous work on Congressional investing, we focus only on stocks traded in the major U.S. exchanges: NYSE, NASDAQ, and AMEX.) %% does this deserve more entries? Having linked each holding and trade to a company and imputed dollar values, it remained to reconstruct the day-by-day stock portfolio. Our approach in reconstructing a portfolio from the disclosure reports %FDRs was to start at the last day of each year, for which the reports provide the entire portfolio (i.e. the year-end holdings), and work backward to the beginning of the year, adjusting the portfolio each day to reflect purchases and sales as well as fluctuations in value due to security price changes. (In other words, each portfolio is rebalanced on a daily basis.\footnote{\citet{barber2000thy} show that ignoring intra-month timing of trades makes little difference in their overall return calculations, but we see no reason not to calculate daily returns, % first and then aggregate to the monthly level, % ignore fine-grained temporal information, particularly given the short time-frame in which information arbitrage would likely take place.}) For example, suppose a member reported holding \$10,000 of stock in Company A at the end of the year and reported purchasing \$5,000 of stock in Company A on June 1. This member's portfolio on January 1 of that year is estimated by calculating what \$10,000 in Company A stock was worth on June 1 (based on the return between June 1 and the end of the year), subtracting \$5,000, and then calculating what that value was worth on January 1. In this way we calculate dollar value holdings for every member of every stock on each day between January 1, 2004 and December 31, 2008. \subsection{A Glimpse at Congressional Portfolios} \begin{comment} Figure \ref{fig:kerry} provides a glimpse of the reconstructed portfolio for one of the largest investors in Congress, John Kerry (D-MA). The top panel plots the daily total portfolio value as computed by our approach; the bottom panel records cumulative purchases and sales starting January 1, 2004. The overall trajectory of Kerry's portfolio value reflects the market's gradual rise until the middle of 2007 followed by decline culminating in the crash at the end of 2008. % the broad market in the period we examine: a gradual It also shows episodes where Kerry purchased or sold a large amount of stock: jumps in March and June of 2007, and drops in May of 2007 and March of 2008, reflect large net buys and sales (respectively), as seen in the lower panel. In addition, we see a sharp jump on January 1, 2006, and a sharp drop on January 1, 2008. In some cases, we see jumps between years like this because a member reports some holdings in one year that he neglects (presumably unintentionally) to report in another; in other cases, such jump might happen because of the limitations of imputing precise values based on value bands. (The apparent drop of about \$40,000,000 in Kerry's portfolio value from 2007 to 2008, for example, could happen if about ten holdings in Kerry's portfolio declined in value from just above \$5 million at the end of 2007 to just below \$5 million at the end of 2008; even if the actual value drop were fairly small, the jump from one band to another would create a large apparent drop at the beginning of 2008 because of the large difference between the imputed values for investments in those bands.) \end{comment} Our data covers disclosure reports from 650 members who served in the House and Senate between 2004 and 2008. Of these members, 422 reported holding a stock listed on NYSE, NASDAQ, or AMEX at some point during that period. Overall the dataset includes 29,778 reported end-of-year holdings and 48,309 reported transactions. A total of 2,581 companies are represented in the dataset; together these companies make up about 94\% of the total capitalization of these three exchanges over our sample period. % The holdings and trades we analyze include those reported by members but owned by spouses and dependent children. Table \ref{tab:summarystats} provides summary statistics describing the portfolios of the 422 members of Congress whose investments appear in our dataset. % or the annual averages of the Congressional portfolios of the 453 active members over the 2004-2008 period. For each member, we calculate the value and number of holdings and transactions in each year and then average across years to get member-level averages. As indicated in the left panel of Table \ref{tab:summarystats}, member portfolio sizes range from \$501 (for a member who reported a single stock in the lowest value band) to \$140 million, the average reported by Jane Harman.\footnote{The performance of Jane Harman's portfolio was unusually poor, largely due to a \$50+ million position in Harman Industries that dropped about 1/3 in value in January of 2008 after the release of negative news. Because of the large size of her portfolio and the consequent large downward influence of her performance on aggregate excess returns, we exclude her from subsequent analyses unless otherwise noted. Including Harman not surprisingly has little effect on estimates of the performance of the average member but yield lower estimated performance when we weight by portfolio size.} % Estimated returns with Harman included not surprisingly look quite similar when we look at the performance of the average member but are substantially lower when we weight by portfolio size.} %in the central panel-based analysis and in the average-member analysis provided in the appendix, but are substantially lower in the aggregate (value-weighted) analysis provided in the appendix. } %NB: we could probably do without this if we do the panel stuff. % The bad performance of Harman's portfolio is largley explained by a \$50 million position (28\% of her portfolio) in Harman Industries which on January 14, 2008 dropped about 1/3 in value. See ``Harman Shares Tumble After Forecast," \emph{Reuters}, Jan 14, 2008.} % who is closely followed by John Kerry \$116 million John Kerry. The distribution of stock holdings is strongly skewed: the median member on average holds stocks worth about \$93,000 in 5 stocks, while the average member holds about \$1.7 million in 19 stocks. The right panel of Table \ref{tab:summarystats} indicates that the distribution of annual transactions across members is also quite right-skewed: %transactions reported by members in our dataset and aggregated to the yearly frequency. %shows the reported sell and buy transactions aggregated to an annual average for each member. %This distribution is equally right-skewed: the average member buys and sells %an average of 18 and 22 stocks per year (respectively), worth about \$402,000 and \$619,000; the median member buys and sells 2 and 3 stocks worth about \$17,000 and \$40,000. % (median) member buys on average about 18 (2) stocks worth about \$397,000 (\$16,000) and sells 22 (3) stocks worth about \$611,000 (\$39,000). The presence of a number of very large portfolios in the data suggests that conclusions about the performance of Congress as a whole will be sensitive to whether individual-level performances are weighted equally across members or by portfolio size. As described below, our analysis focuses on the average member-month, but we also provide estimates that weight by value and number of holdings; in the appendix, we also provide estimates of the return on aggregate portfolios that are either weighted equally across members or weighted by portfolio value. %% here we should ad numbers on average turnover probably %%The figure makes clear that the distribution of stock holdings in Congress is strongly skewed to the right. The majority of members in our dataset repor %%stock holdings of less than \$100,000, and 189 report holdings below \$50,000; on the top end, sixty-nine members report annual stock portfolios %worth more than \$1,000,000, and thirteen report portfolios worth more than \$10,000,000. The number of holdings reported per year is also skewed: half of the %members in our dataset report fewer than 5 holdings per year, while seventeen report more than 100. \section{Do Members Beat the Market?} We now turn to the task of assessing the performance of the common stock investments of members of Congress between 2004 and 2008. \subsection{Methods} % \subsection{Portfolio Returns from Portfolio Holdings} To compare Congressional stock portfolios to the market benchmark, we adopt the standard calendar-time approach (e.g. \citet{barber2000thy}) of regressing risk-adjusted member returns on a set of controls including the return on a market index. % the holding-based risk-adjusted portfolio returns (i.e. the return on a member's portfolio minus the risk-free rate) on a set of controls including the return on a market index. Following \citet{Hoechle2009} and \cite{seasholes2009there} (and in contrast to earlier work including \citet{barber2000thy} and \citet{ziobrowski2004arc}) we carry out our main analysis via a panel regression that estimates the average monthly excess return across members and time, conditional on the standard controls. %Following standard procedure in the literature, we conduct our portfolio regressions at a monthly frequency and aggregate each member's daily portfolio returns to the monthly level. %% cites to indicate that this is standard In particular, we aggregate each member's daily portfolio returns to the monthly level and then fit the widely-used Carhart Four-Factor model (an extension of the Fama-French Three-Factor model): \[ R_{i,t} - R^f_t = \alpha + \beta_1\big(R^m_t - R^f_t\big) + \beta_2 \mathrm{SMB}_t + \beta_3 \mathrm{HML}_t + \beta_4 \mathrm{MOM}_t + \epsilon_{i,t}\] where $R_{i,t}$ is the return on the portfolio of member $i$ in month $t$, $R^m_t$ is the return on a market index, $R^f_t$ is the ``risk-free rate" or return on U.S. Treasury Bills, and the other controls are passive portfolios noted in the empirical finance literature for diverging from the overall market. $\mathrm{SMB}_t$ is the return on a hedged portfolio that is long in small companies and short in big companies (``small-minus-big"), $\mathrm{HML}_t$ is the return on a hedged portfolio that is long in high book-to-market companies and short in low book-to-market companies (``high-minus-low"), and $\mathrm{MOM}_t$ \citep{carhart1997pmf} is the return on a hedged portfolio that is long in companies with the best performance in the previous year and short in the companies with the worst performance in the previous year. We obtained each control series and data on the risk-free rate from Kenneth R. French's website.\footnote{\url{http://mba.tuck.dartmouth.edu/pages/faculty/ken.french/data_library.html}} The intercept $\alpha$ in this panel regression is our estimate of the monthly average abnormal portfolio return across members; we also report estimates where we weight members by portfolio size and number of holdings. %% awkward. necessary? In order to account for the cross-sectional correlation in portfolio returns we compute robust standard errors clustered by month (see \cite{seasholes2009there}). The approach just outlined appears to be the current state of the art in empirical finance and is our preferred specification, but for the sake of robustness and comparability with previous studies we carry out a variety of specifications and weighting schemes and, because the findings from the various specifications are quite similar, we report the results in the appendix. We run the panel analysis using the CAPM model, which includes the market index as a single control. We also carry out all analyses with the approach employed by \citet{barber2000thy} and \citet{ziobrowski2004arc}, among others, which involves aggregating all individual portfolio returns up to a single time series and then running the Carhart Four-Factor or CAPM regression. In these aggregate analyses, we report results employing two approaches for aggregating member portfolio returns \--- one that weights each member equally and another that weights each member by her portfolio size. %Our results for these aggregate regressions look at portfolios created by equally weighting each member and portfolios created by weighting each member by the size of his or her portfolio. %In creating these aggregate portfolios, we We report these aggregate portfolio results that aggregate monthly returns across members by weighting them equally (the result of which we call the ``equal-weighted aggregate portfolio") and proportional to portfolio value (which we call the ``value-weighted aggregate portfolio"). As shown in \cite{Hoechle2009} the panel approach on which we focus is numerically identical to the equal-weighted aggregate portfolio approach as long as the panel is balanced; when it is not, the weighting implied by the panel regression is more natural in our view.\footnote{The panel regression weights every investor-month equally, while the aggregated approach weights every month equally regardless of how many investors are present in each month. Standard errors also differ between the panel and aggregated approach depending on the intra-cluster correlation in the panel regression. See \cite{Hoechle2009} for a discussion.} The key point is that the findings from the various specifications we employ produce the same conclusions about the investing performance of members of Congress, which means that the reader can focus on the smaller set of main results we report. %aggregated approach for the average investor return except that the panel regression provides a more natural weighting and is therefore our preferred approach %We replicate the panel regressions using the CAPM model which includes the market index as a single control and the results are very similar to those obtained from the more conservative Carhart model. We have also replicated the analysis using an alternative approach where for each month the portfolio returns are first aggregated across members (either using the value-weighted or the equal member weighted average). The Carhart and CAPM model are then fit to a single time series of average monthly portfolio returns. Not surprisingly, the results from these tests based on aggregated data are similar to the ones obtained from the disaggregated panel regressions. In fact, as shown in \cite{Hoechle2009} the panel approach is numerically identical to the aggregated approach for the average investor return except that the panel regression provides a more natural weighting and is therefore our preferred approach.\footnote{The panel regression weights every investor-month equally, while the aggregated approach weights every month equally regardless of how many investors are present in each month. See \cite{Hoechle2009} for a discussion. \cite{seasholes2009there} also relies on the panel approach.} Finally, we also conduct an analysis of transaction-based portfolio returns. %In the appendix we also replicate the portfolio regressions using a more disaggregated panel approach at the member-month level, where we cluster the standard errors by month to account for cross-sectional correlations in portfolio-returns (\cite{seasholes2009there}). As expected, the alpha estimates from these disaggregated panel models are generally very close to the estimates for the average Congressional portfolio (the small differences result from the fact that the panel regression weights every member-month equally, while the aggregated regression first averages returns across members and then weights every month equally). \begin{comment} As a check of the integrity of our approach, we produced our own value-weighted portfolio weight matrix $\mathbf{w}^m$ (using daily closing prices and shares outstanding from CRSP), % to construct a market capitalization-based weight matrix $\mathbf{w}^m$), computed the portfolio return series $\mathbf{R^m}$ from $\mathbf{w}^m$ and $\mathbf{r}$, aggregated daily returns to a monthly frequency, and confirmed that the abnormal return $\alpha$ on this portfolio was essentially zero in both CAPM and Carhart Four-Factor regressions. % (It was not precisely zero because our dataset includes only those firms that members reported holding, whereas the market index used in the CAPM and Carhart regressions includes all stocks on the NYSE, NASDAQ, and AMEX exchanges; the difference is negligible because the stocks held at some point by members of Congress in our period collectively make up a large proportion of the overall market.) % The stocks on our dataset make up approximately XXX percent of the total market value of these exchanges, which helps explain the similarity of the standard market index and the one produced we produced using market capitalizations of firms in our sample.) This test confirms the soundness of the machinery by which we produce daily portfolio returns from portfolio weights, aggregate them to the monthly level, and compare them to the market index. It also indicates that differences in performance we observe between Congressional portfolios and the market index are not due to differences between the universe of companies in the market index and the subset that were held at some point by members of Congress and thus appear in our data; members could have matched the standard market index almost exactly with a value-weighted portfolio of the 2,581 companies in our dataset. % \footnote{This is a reflection of the fact that the companies that appear in the CRSP index but not in our dataset tended to be small and thus had very little weight in the value-weighted index.} \end{comment} \subsection{Results: Overall Performance} % little question whether this should come before the discussion of methods - come back to that. Before looking at abnormal returns estimated by market models, we display in Figure \ref{fig:cumret} the cumulative raw returns for the average Congressional portfolio over our period of study. The figure depicts the value over time of \$100 invested in the CRSP market index (a passive, value-weighted portfolio of stocks on the NYSE, NASDAQ, and AMEX exchanges) and the average (i.e. equal-weighted aggregate) Congressional portfolio.\footnote{For each month, we compute each member's monthly raw portfolio return and average across members; %aggregate/ the figure depicts the compound return on this series of monthly returns.} % For the aggregate Congressional portfolio the monthly returns are averaged across members such that every member is weighted by portfolio size so the overall return mimics the dollar-by-dollar investments of Congress as a whole. For the average Congressional portfolio the monthly returns are averaged across members such that every member is weighted equally so the overall return mimics the portfolio of the average member.} % each of three portfolios in January of 2004: the market index (blue line), the aggregate (i.e. value-weighted) Congressional portfolio (dotted green line), and the average (i.e. equal-weighted) Congressional portfolio (dotted orange line). The average Congressional portfolio clearly does considerably worse than the market index: %The investments of Members of Congress underperformed the market throughout the period of our study. % , and particularly in the crash of 2008. %The market index (blue line) rose steadily from the beginning of 2004 until mid-2007 before dropping \--- gradually at first and then, starting in September of 2008, precipitously \--- such that \$100 invested in a market index (solid line) in January of 2004 would be worth about \$80 by the end of 2008, whereas invested in the %The same \$100 invested in the Congressional portfolio would be worth only about \$65, whether invested in the % aggregate Congressional portfolio (dotted green line) or the average Congressional portfolio (dotted line) it would be worth only around \$69. The underperformance is clearly not limited to the bear market and stock market crash 2007 and 2008; at the market peak in 2007 the Congressional portfolio was already about 10\% below the market on a cumulative basis since the start of 2004. Models 1-4 of Table \ref{tab:overallalphas} provide our estimates of the abnormal returns. The results are consistent with the graphical analysis. Model 1 shows that over our study period, members on average underperformed the market about .23 percentage points per month ($p=.02$), which annualizes to a yearly abnormal return of about -2.8\% with a .95 confidence interval of $[-4.9;-.5]$. This result is robust across various specifications. The poor performance is very similar when we use a random effects model with varying intercepts (model 2), weight the regression by the number of stock holdings per member-month (model 3), or weight the regression by the average value of the stock holdings per member-month (model 4). The overall returns are also similar when estimated with the CAPM model (Table A1, in the appendix) or the aggregated data regressions (Table A2). \subsection{Performance in Subgroups} Models 5-26 in Table \ref{tab:overallalphas} report the abnormal return estimates for relevant subsets of Congress. The monthly alpha estimates along with their .95 confidence interval are also visualized in Figure \ref{fig:alphasubgroups}. The results indicate that the overall underperformance is very consistent across subgroups. Republicans do slightly better than Democrats (although the difference in intercepts is not quite significant at conventional levels ($p=.22$))\footnote{To test for the differences in intercepts we fit a pooled model with a group indicator (Democrat/Republican) and its interactions with all the controls. The main effect of the group indicator then identifies the differences in alpha returns (see \cite{Hoechle2009}).} House members do slightly better than Senators, but again we do not reject the null of no difference. Members on power committees in the House or Senate\footnote{We define ``power committees" in the House as Rules, Appropriations, Ways and Means, and Commerce; in the Senate they are Appropriations, Finance, and Commerce.} do slightly better than other members, but the differences are small and statistically insignificant. The estimated excess returns are also similar for the 2004-2006 period, when the market was rising, and the 2007-2008 period, when the market fell and the government began to intervene more heavily in the economy. %indicating that performance suggesting that the increased political intervention in the economy in the wake of the crisis did not result in much different overall returns There are also no consistent differences across the group of members when we stratify the sample by seniority, net worth, portfolio size (using three equal sized bins for low, medium, and high), or pre-congressional careers.\footnote{We are grateful to Nick Carnes for providing us with the data on pre-congressional careers. A members is coded as belonging to a career category if she spent more than 60 \% of her pre-congressional career in that category. The results are very similar if other cut-points are used. See \cite{Carnes2010} for details on the career data.} The best-performing subgroup appears to be members who owned businesses before entering Congress (who we estimate beat the market by about .5\% per year), but even this group does not outperform either the market or other investors at conventional levels.\footnote{We can reject the null that former business owners earn lower returns that other members $p=.07$.} % are we making too much of this business owner result? %This superior performance is likely attributable to the fact the former business owners are more sophisticated investors than the average member. We find no such performance premium for any other pre-congressional careers (including lawyers, local politicians, or others). The comparable subgroup analyses using the CAPM model (presented in table A1 in the appendix) and the aggregated data approach (table A2) similarly show consistent underperformance across subgroups. %The last four estimates show the abnormal return for subgroups that we formed based on the members' pre-congressional careers.\footnote{We are grateful to Nick Carnes for providing us with the data on pre-congressional careers. A members is coded as belonging to a career category if she spent more than 60 \% of her pre-congressional career in that category. The results are very similar if other cut-points are used. See \cite{Carnes2010} for details on the career data.} In the only noticeable exception to the stable under-performance, members that owned a business prior to joining Congress beat the market by about .5\% per year, although the estimate is evidently not significant at conventional levels (although we can reject the null that former business owners earn lower returns that other members $p=.07$). % are we making too much of this business owner result? %This superior performance is likely attributable to the fact the former business owners are more sophisticated investors than the average member. We find no such performance premium for any other pre-congressional careers (including lawyers, local politicians, or others). The comparable subgroup analyses using the CAPM model (presented in table A1 in the appendix) and the aggregated data approach (table A2) similarly show consistent underperformance across subgrouips. The consistently negative results across subgroups indicates that our overall findings are not the artifact of a few exceptionally poor investors in Congress but rather reflects a broader underperformance across members. Notably, none of the 88 alphas we estimate (22 subgroups, each estimated four ways) is positive and significant, and only a handful of point estimates are above zero. %Republicans seem to have performed slightly better than Democrats, members with less seniority (i.e. fewer years since they were first elected to Congress) seem to have done slightly better than more senior members, and performance seems to have been better in the first three years than in the volatile last two years of our period. %Our analysis of the average portfolio also indicates that Republicans performed somewhat better and that the first three years were considerably better than the last two, but it also suggests that the Senate performed better than the House and that seniority and excess return were not particularly related. % The only positive alpha return for the aggregate portfolio of low seniority members in the 4-factor model is effectively zero at 0.004 and not robust in the CAPM or the average member portfolio for this group. %the point %estimate on the excess return is not positive for a single one of the nine subgroups we consider, calculated four different ways. \subsection{Member-Level Performance} In Figure \ref{fig:member_returns} we display estimated excess returns for each member in our dataset: estimates of alpha from a separate Carhart four-factor regression for each member. (Names are plotted only for members with relatively high or low returns or portfolio values.) A box and whiskers plot on each axis depicts the marginal distributions (the line indicates the median, the edges of the box denote the interquartile range, and the whiskers indicate the 5th and 95th percentiles). Not surprisingly, the mean monthly excess return across members at -.24 is very close to the estimated excess return from Model 1 of Table \ref{tab:overallalphas} (-.23). % and it is similar to the mean monthly return of the aggregate portfolio (-.26). The marginal distribution of returns is fairly symmetric and clearly centered below zero (the median is at -.17). % again indicating that the underperformance is very consistent across members. \subsection{Performance in Context} %\subsection{``Beating the market" in context} % put it in context from the lit. don't be surprised. % Our analysis has compared the performance of Congressional stock portfolios to a market index, the standard benchmark in empirical finance. While our finding that Congressional stock portfolios underperformed the market may be somewhat surprising based on the popular perception of politicians as savvy, well-connected, and possibly corrupt, it is consistent with a long line of empirical work documenting that even supposed investment experts do not reliably outperform market indices. An early example is \citet{cowles1933can}, who found that stock market forecasts and recommendations made by financial service firms, fire insurance companies, and the editor of the \emph{Wall Street Journal} %in the late 1920s and early 1930s tended to perform no better than what would result from random chance. In fact, every set of recommendations he examined on average did slightly worse than the market. Much subsequent research in empirical finance has examined the performance of professional fund managers, with debate focusing on whether there is evidence of any mutual fund manager consistently beating the market. %after costs have been subtracted. Some papers fail to find any evidence of stock-picking ability among managers of active mutual funds \citep{gruber1996another}; other papers find evidence of individual ability among certain mutual fund managers \citep{carhart1997pmf} or even the average mutual fund manager % beat the market on average but that expenses eliminate any gains for investors \citep{grinblatt1989mutual}. % but note that high performance is fully offset by management expenses. Several papers in recent years have documented that the portfolios of individual investors generally perform poorly (see, for example, \citet{odean1999investors,barber2000thy,barber2007all,barber2008just,goetzmann2008equity}.) A particularly interesting example is provided by \citet{barber2008just}, who analyze all trades in Taiwan over the 1995-1999 period and document a large systematic transfer of wealth from generally-inept individual investors to savvier institutional investors. Stocks sold by individuals in this sample subsequently perform better than the stocks they purchase, while the opposite is true for stocks traded by institutional investors. The results suggest that in general the stock market is a place where informed institutions take advantage of uninformed and overconfident individuals, who would be better off relying on simple indexing. It appears based on our findings that, despite the advantages of their professional situation and large network of connections, members of Congress fare no better on average than the average member of the latter category. To put our findings in perspective, we provide in Figure \ref{fig:benchmarks} a comparison of the excess return we find for members of Congress with similar findings for other subgroups of investors. Our finding suggests that members of Congress perform on par with individual investors and mutual fund managers, as measured in \citet{barber2000thy} and \citet{carhart1997pmf}, and below that of corporate insiders and hedge fund managers as found in \citet{jeng2003eri} and \citet{fung2008hedge}. %One other possibility that we consider below is that Congressional members perform poorly because they engage in political tradeoffs. For example, members may be more likely to invest in companies that contribute campaign contributions to advance some political gains at the expense of some financial loss. While at he face of it, the robustness of the underperformance across subgroups speaks against this idea, we will explicitly consider political connections between members and companies in the section further below. \subsection{Comparison to \citet{ziobrowski2004arc}} As is clear in Figure \ref{fig:benchmarks} and noted above, our finding of weak overall performance contrasts sharply with a previous widely-discussed study by \citet{ziobrowski2004arc}, who find abnormal returns among traders in the Senate in the 1990s that to our knowledge exceed those of any documented investor group. One possible explanation for this discrepancy is the difference in the type of data and methods of analysis employed: our analysis to this point has focused on the portfolio positions of members of the House and Senate, while \citet{ziobrowski2004arc}'s finding is based on an analysis of an aggregate portfolio constructed from trades made by members of the Senate. To make the most direct possible comparison, we now apply the method described in \citet{ziobrowski2004arc} to our data, such that any remaining differences should be due to changes in circumstances between the period in which the Ziobrowski study was carried out and our own period of 2004-2008. %Not only do we have a broader set of Congressional investors and data from %a more recent period, we also focus on an analysis of actual (reconstructed) portfolios held by members rather than a portfolio constructed solely from %transactions. In order to assess why we find poor investment returns in Congress while \citet{ziobrowski2004arc} had found stellar performance, %we reproduced as closely as possible \citet{ziobrowski2004arc}'s analysis using our dataset. In particular, we ignore reported end-of-year holdings and construct three portfolios based on transactions only: a buy portfolio, which holds all stocks purchased by members of Congress for 255 days following the purchase date, a sell portfolio, which holds all stocks sold by members of Congress for 255 days following the sell date, and a hedged portfolio that holds %long the purchased stocks and sells short the sold stocks (buy less sell portfolio). Like \citet{ziobrowski2004arc}, we assign precise dollar values to trades using the midpoint of the value band specified on the disclosure report, with a top-code at \$250,000. % and we aggregate member monthly portfolio returns into a single portfolio return by weighting After constructing the transaction-based portfolio and calculating daily returns, we aggregate member returns up to the monthly level and construct a single value-weighted Congressional portfolio by combining member returns in proportion to their portfolio weight. We then estimate excess returns with the CAPM and Fama-French 3-Factor models.\footnote{The Fama-French model is the Carhart 4-Factor model without the momentum term.} The last line of Figure \ref{fig:benchmarks} graphically depicts our alpha estimate for the Senate, % aggregate hedged portfolio for the which can be compared with the \citet{ziobrowski2004arc} finding that appears on the top line. The full results for the estimated excess returns on the buy sample, the sell sample, and the hedged (long/short) portfolio under the CAPM and Fama-French model for all members, Senate, and House are provided in Table A4 in the appendix. The analysis provides no evidence of informed trading; none of the coefficients are statistically significant. In separate analysis (reported in Table A5), we carry out the same regressions on portfolios similarly built from transactions but applying our own procedure to assign precise dollar values within bands (as described above) and using not just 255-day holding periods but also 1-day, 10-day, 25-day, and 140-day holding periods. %and using our own procedure to assign precise dollar values within bands. Using these transaction-based portfolios we computed alpha returns using the same methodology as outlined above. As Table A5 indicates, with some combinations of holding period, model, and weights we find evidence of good or bad trading acumen, but the overall results are consistent with the null of zero abnormal returns. Why do our results differ from those of \citet{ziobrowski2004arc}? One explanation is that circumstances may have changed between the 1990s and the 2004-2008 period we examine in a way that would explain why Senators had extremely good timing in the earlier period but not in the more recent one. % this is AE analysis of the transactions file; see code/analysis/trading_analysis.r %savvy members of Congress may have, for example, moved their assets to a qualified blind trust managed by a ``political intelligence" hedge fund. One such possible change is that the informational advantages enjoyed by members of Congress compared to the rest of the market may have declined since the 1990s. It could be, for example, that the bull market of the 1990s provided more opportunities for members of Congress to benefit from stock tips (on IPOs, for instance) than did the relatively moribund and finally panic-stricken market of the period we examine, or perhaps ``political intelligence" hedge funds now seize any arbitrage opportunities members might previously have been able to enjoy. On the other hand, %. It could be, for example, that the bull market of the 1990s provided more opportunities for members of Congress to benefit from stock tips (on IPOs, for instance) than did the relatively moribund and finally panic-stricken market of the period we examine (although the intensified involvement of the government in the financial sector and high overall market volatility in 2007 and 2008 would seem to have provided unusual opportunities for arbitrage. Another such change is that members of Congress may have become more reluctant to openly take advantage of whatever informational advantage they possess, perhaps partly as a result of heightened scrutiny due to \citet{ziobrowski2004arc}. Consistent with this explanation, Senator Barbara Boxer (who was one of the four most active traders in the Ziobrowski study) has since placed most of her assets in a qualified blind trust. (Two of the others left the Senate before our period and the other, John Warner, had unremarkable portfolio returns.) On the other hand, the number of Senators reporting trades and the number of trades reported were both larger per year in our period than in the earlier period covered by the Ziobrowski et al study, which would suggest that members of Congress have not in fact become more concerned about public criticism of their investments. % Third, the informational advantage of members of Congress compared to the rest of the market %(including generic stock-picking ability and perhaps the ability to link political events to movements in securities prices) % may have declined since the 1990s; perhaps ``political intelligence" hedge funds now seize any arbitrage opportunities members might otherwise have been able to enjoy. Logically, the other possible explanation is that the extraordinary returns found by Ziobrowski were the result of chance rather than informational advantage, i.e. that members of Congress in the 1990s were neither better informed nor more willing to take advantage of their information than members of Congress in the period we examine, but rather had better luck. Type I error is of course always a possibility in quantitative work, meaning that even if the null hypothesis is true (i.e. that members' portfolios are no better than the market) the data will sometimes tell us that it is false. Similarly, even investors with no informational advantage will sometimes perform extremely well by pure luck. %Although unsatisfying as an explanation for the difference between our findings and those of \citet{ziobrowski2004arc}, the role of chance must be considered along with the possibility that financial markets or the ethics of members of Congress have changed. It should also be noted that the findings of \citet{ziobrowski2004arc} appear to depend on the performance of a few individuals, suggesting that any informational advantage members may have enjoyed was concentrated in a few members who may have since left the Senate or changed their investing behavior. % and also suggesting that luck need not have been that widespread? Just four Senators account for nearly half of the trades in Ziobrowski et al, and the authors find abnormal returns only when examining the overall (value-weighted) Congressional portfolio, not when looking at the average member's portfolio. Further, the paper's subgroup analysis yields strikingly different returns for different subsets of the Senate, again suggesting that the performance of a small number of individuals may drive the result. This localized superior performance may itself be due to either luck or informational advantage, but the fact that it was localized suggests that our subsequent finding of unremarkable performance should be less surprising. % % % does not reflect widespread informed trading in the Senate but was rather the result of informed trading by a small number of members who have since left the chamber. Supporting this explanation is the fact that four Senators account for nearly half of the trades in \citet{ziobrowski2004arc}, and the authors fail to find a significant positive excess return when looking at the average member (i.e. when looking at a portfolio weighted equally across members as opposed to weighted by portfolio size). % % % % %An alternative explanation is that nothing significant has changed but that the large abnormal returns in \citet{ziobrowski2004arc} were the result of luck rather than skill. This is of course possible with any quantitative analysis, but we note a % % % % Finally, it may simply be a matter of chance. \citet{ziobrowski2004arc}'s analysis looked at about 1000 trades per year, reported by about thirty Senators per year (and mostly concentrated among four large investors); small samples occasionally produce extraordinary results. \section{Is the Congressional Portfolio Political?} %% need to rewrite this. Our evidence to this point %on the performance of the overall Congressional portfolio has suggested that members of Congress perform no better than the average individual investor. We now turn to a more disaggregated look at Congressional investments to assess the extent to which portfolio choices and performance measures reflect political factors linking members and companies. % Our approach is to look at %In order to assess the mix of informational advantages and political considerations that drive members' investing behavior (and particularly their portfolio bias toward local companies and contributors), we now turn to an examination of the returns of members' portfolios, paying particular attention to differences in the performance of investments made in connected and unconnected portfolios. \subsection{Connection Measures} We define three types of connections between politicians and companies in our dataset that reflect an attempt to capture important channels by which members and firms interact: %In order to assess to what extent members' portfolio choices and investment performance reflect political factors, we also collected data on connections between politicians and the companies that appear in our dataset. \begin{itemize} \item \textbf{Constituency}: We obtained the location of each company's headquarters from Compustat % Google Finance and assigned this address to a Congressional District using an API provided by \texttt{GovTrack.us}; this allows us to label whether each stock holding involved %was for a company in the owner's constituency.\footnote{For Senators, an investment is considered in-district if the company is headquartered in the Senator's state.} \item \textbf{Contributions}: We collected PAC contribution data from the FEC\footnote{Via \texttt{watchdog.net}.} and linked PACs to companies and their contributions to members (289,694 reports totalling \$466.5 million). % in total). %and identified PACs linked to the companies in our dataset by checking the name of the PAC; %%% XXX was there more to this? This allows us to record, for each stock holding, how much the company contributed to the owner's election campaigns between 2003 and 2008. \item \textbf{Committee Lobbying}: We collected data on lobbying from the Center for Responsive Politics (CRP) and linked companies to members according to the extent to which each company lobbied on legislation appearing before committees on which each member sits. In particular, for each lobbying disclosure form filed between 2003 and 2008 on behalf of a company in our dataset (238,040 reports totalling \$18.2 billion), we assessed whether any bills were mentioned under ``Specific Lobbying Issues" (as processed by CRP) and then distributed the value of the lobbying reported in that disclosure form among committees to which named bills were referred;\footnote{For example, if a report disclosing \$50,000 of lobbying expenditure by Halliburton mentioned one bill that was referred to the Agriculture Committee %and another that was referred to the Energy Committee, \$50,000 would be added to the total lobbying connection between Halliburton and every member who sits on the Agriculture Committee; if the same report mentioned two bills, one of which was referred to Agriculture and another of which was referred to Energy, then \$25,000 would be added to the total lobbying connection between Halliburton and every member who sits on the Agriculture Committee, and another \$25,000 would be added to the total lobbying connection between Halliburton and every member who sits on the Energy Committee.} this gives us an indication, for each stock holding, of how closely linked the company's lobbying priorities are to the owner's committee responsibilities. \end{itemize} \subsection{Portfolio Choice and Political Connections} To assess members' portfolio choices, we examine the weight that a member puts on a company in his portfolio as a function of the connections he has with the company. (See \citet{cohen2008swi} for another example of this kind of analysis.) In particular, we estimate a regression of the form \[ w_{ij}=\beta_0 + \beta_1 District_{ij} + \beta_2 Contributions_{ij} + \beta_3 Lobbying_{ij} + \alpha_i + \alpha_j + \varepsilon \] where $w_{ij}$ is the weight in basis points of company $j$ in member $i$'s portfolio (averaged across years for which we have the member's portfolio), $District_{ij}$ is an indicator variable that takes the value 1 if the company is headquartered in the member's district and 0 otherwise, $Contributions_{ij}$ is an indicator variable that takes the value 1 if the company's PAC contributed to the member in the period 2003-2008 and 0 otherwise, $Lobbying_{ij}$ is an indicator that takes the value 1 if the company lobbied legislation before the member's committee and 0 otherwise, and $\alpha_i$ and $\alpha_j$ are member and company fixed effects.\footnote{The average member has about %Overall about 6\% %3\% of his investments (by value) in local firms, 15\% in contributors, and 49\% in companies that lobby legislation before his committees.} %all member-company holdings in our dataset are connected by district, 18\% are connected trough any contributions, and 55\% are connected through any lobbying.} Table \ref{tab:uncondportfolio} presents the results, where model 1 reports the coefficients from the regression described above; the other models include interactions and assess other definitions of connectedness. We find a very strong skew in members' portfolio towards politically connected firms. The average portfolio weight in the data is 3.88 basis points, meaning .0388 percent of the total portfolio. Model 1 indicates that the average portfolio weight is more than 13 times higher when the company is headquartered in the member's district and about 3.5 times higher if the company has contributed to the member's election campaigns. The estimates for the lobbying connection are zero. Regression (2) includes a full battery of indicators for each possible combination of the three connections (the reference category is companies that are not connected through any of these connections). The estimates of the average portfolio weights (with their .95 confidence intervals) are visualized in Figure \ref{fig:8exp}. The average weight is about 11 times higher for companies that are connected to members by district only, about 12 times higher for companies connected by district and lobbying and 42 times higher for companies that are connected by all three. %Similarly, companies connected by contributions only still receive portfolio weights that are 4.38 times higher while the results for lobbying are much weaker. Regressions 3-5 extend this analysis by using different measures of connection, based on a binary indicator for being above the median among a member's connected companies (3) or based on a measure using the company's share of all contributions or lobbying expenditures directed to the member or his committees (4 and 5). Because all of these regressions include member and firm fixed effects, we are confident that these findings reflect the association of member-firm connections and portfolio decisions, rather than simply a correlation between member or firm characteristics and our measures of member-firm connections. Taken together these results suggest that there is a large political bias in members' portfolio choices: members place considerably larger bets in companies to which they are politically connected. The result is robust to using several additional definitions of connectedness, including different percentile- and rank-based cutoffs.\footnote{We have also replicated the analysis conditioning only on stocks that members actively choose to hold (following \citet{cohen2008swi}) and obtain very similar results (full results are in Table A6 in the appendix). For example, compared to an average weight of 279 basis points, they place an additional 274 basis points on home district firms and an additional 45 basis points on firms that provide campaign contributions on average. The overweighting is similarly increasing in the strength and combinations of the connections.} % . demonstrating that even comparing only among the stocks that members choose to actively hold, they place much larger bets on politically connected companies %Taken together these findings suggest that there is a very strong political skew in the members' portfolio weights towards contributor and local companies. In the appendix we have replicated this same finding looking only at companies that are actively held by members. The results are presented in Table B.1 are substantively identical to the ones reported here. %The biases toward constituent and contributor companies that we observe are consistent with the idea that members invest based on information about firms, as well as the signaling and bonding stories: members probably know more about local and contributor companies, and they may be particularly interested in signaling and/or aligning incentives with them. One can imagine three possible explanations for the propensity of members to invest disproportionately in local and contributor companies. First, members may invest in these companies simply because they know them. This appears to be the case for average individual investors, who invest disproportionately in local stocks but do not seem to have any particular information advantage in choosing among them. %As an indication of the local bias on individual investors, The typical U.S. household has about 30\% of its portfolio invested in stocks headquartered within a 250 mile radius of the family home, while on average only 12\% of all firms (the market) are headquartered within the same radius (see Ivkovic \& Weisbenner 2005; or Seasholes \& Zhu 2009 for a recent review). But according to the most comprehensive study of local investing patterns (Seasholes \& Zhu 2009), individual investors' local holdings do not seem to exhibit superior returns, suggesting that individuals choose these companies simply because of familiarity. % rather than because they know them, not because they possess market-relevant information about them. A second explanation is that members of Congress hold connected stocks for political reasons.\footnote{A recent paper by \citet{tahounrole} explores this phenomenon.} Members may invest in companies headquartered in their districts, or companies from which they hope to receive campaign contributions, in order to make policy promises more credible: voters may be more likely to vote for a candidate, and corporate PACs may be more likely to contribute to a candidate, when the candidate has aligned his financial incentives with their own by buying stock and thus made it more likely that he will support legislation favorable to their interests.\footnote{This reasoning requires that it is somehow difficult for members to liquidate their stock holdings in connected companies, and that members do not face too much political risk from legislating in the interests of companies in which they are invested.} If connected investments are made for political rather than financial reasons, we would not expect them to perform well. % we may expect these investment to kind of political reason, we again have no reason to think that these investments would perform well. % financial interests % in %order to align their financial interests with those of their constituents; %voters may be more inclined to re-elect a member who they %similarly, %believe preferences. Similarly, %potential contributors may be more likely to support a politician if they believe that the member's %investment in their stock makes it more likely that she will legislate with their interests in mind. In short, members may invest in %constituent or contributor companies in order to make policy promises credible. A third explanation is that members hold connected stocks because they have valuable information about those companies' economic prospects, based perhaps on interactions with the company's managers or knowledge of upcoming legislation. %based on interacting with them in the political realm. Many members of Congress entered politics from business or local office, and arrive in Washington with extensive personal and business connections to companies headquartered in their districts. Once a member is in office, these local companies remain important constituents and possible sources of campaign funding. % business careers or were otherwise actively involved %in the business community of their district, providing % Listed companies that are headquartered in a member's district %are often major employers who have extensive dealings with their Congressional representatives. Not only do these companies %often seek help from their representatives in pursuing legislative and regulatory matters, but members often seek the electoral %support of business leaders in their districts based on their local influence. Companies from which members seek financial support similarly are often closely connected to the member. These connections often involve regular interactions between corporate executives and members of Congress at social and fundraising events, as well as frequent meetings between company lobbyists and Congressional staff, all of which may provide opportunities for the member to collect market-relevant information about these connected companies. %In the case of local companies, it is likely that connections between some companies and members go back well before the member takes office, considering that many members arrive in Washington having been a prominent figure in local business and political circles. The idea that such interpersonal connections may bring market advantages has been reinforced by \citet{cohen2008swi}, who find that mutual fund managers make larger bets on companies to which they are connected through educational ties and are also more successful in these connected investments. It could also be that companies that ask for members' legislative help (whether they are local companies, contributors, or companies whose industries are overseen by a member's committees) share information that members can use to make lucrative investments. %\citet{coval1999hbh, coval2001gii} similarly find that mutual fund managers on average exhibit a modest bias toward the stocks of local companies (with the average fund manager investing a little under 7\% of her assets in companies headquartered within 100 kilometers, even though only 6.16\% of the market is located within that area); for the period before 1985 they find that these local investments perform better than non-local investments, suggesting that mutual fund managers in that period enjoyed informational benefits from proximity. In order to distinguish among possible reasons for members' preference for the stocks of local companies and companies that contributed to their election campaigns, we now turn to evaluating the performance of members' connected investments. % % CEOs and members of Congress from the same region also likely travel in the same social and business circles. If this is the case we would expect members to do better when they invest in companies to which they are politically connected, particularly through either campaign contributions or through geographical proximity. %\citet{coval1999hbh, coval2001gii} find that mutual fund managers on average exhibit a modest bias toward the stocks of local (defined as headquartered within 100 kilometers) companies, with the average fund manager investing a little under 7\% of her assets locally, even though only 6.16\% of the market is located within her local area. % %This explanation may be particularly relevant for members of congress which tend to possess private knowledge about these companies' prospects based on interacting with them as constituents and donors. Listed companies that are headquartered in a member's district are often major employers who have extensive dealings with their representatives in Washington, DC. Not only do these companies often seek help from their representatives in pursuing legislative and regulatory matters, but members often seek the electoral support of business leaders in their districts based on their local influence. CEOs and members of Congress from the same region also likely travel in the same social and business circles. If this is the case we would expect members to do better when they invest in companies to which they are politically connected, particularly through either campaign contributions or through geographical proximity. %Research on mutual fund performance \citep{cohen2008swi} indicates that mutual fund managers are more successful when investing in companies with which they have more educational ties through senior management. find a similar benefit from investing in local firms for mutual fund managers. Consistent with \cite{cohen2008swi}, \citet{coval2001gii} find that mutual fund managers enjoy a modest information advantage with respect to local companies: between 1975 and 1984, the local component of their portfolios outperformed the nonlocal component by about 2\% per year. They find no evidence, however, that fund managers' local portfolios outperformed the market after 1985. %Examining brokerage accounts in the 1991-1996 period, \citet{zhu:lbi} also finds no evidence that investors with a stronger propensity to invest locally enjoy higher returns, and shows that investment behavior seems more driven by familiarity (either through proximity or advertising) than by responses to fundamental information. %In Finland, the median non-Helsinki headquartered % rm has 12% higher weight among investors in its municipality than it does among all %Finnish investors. Finally, individuals in mainland China invest 8% more in rms from %their province-of-residence than a market-capitalization portfolio would predict. \subsection{Portfolio Performance and Political Connections} %Figure \ref{fig:cumrets} shows the cumulative raw returns for a \$100 dollar position in each average member mimicking portfolio beginning in January 2004. The connected (unconnected) average member portfolios mimics the investments of all members of Congress in connected (unconnected) stocks. We find that for the lobbying connection there is no notable difference between the returns of the connected portfolio. For the contribution connection, however, we find that the connected portfolio does about as well as the market while the unconnected portfolio, especially towards the end of the period, exhibits the familiar underperformance. Most remarkable, the portfolio consisting of members' investments in local companies robustly beats the market across the entire period; the end of period value is \$98, compared to \$80 for a market index fund. Non-local stocks in contrast exhibit the familiar underperformance. When we consider two-way connections in which local companies are also contributors or lobby the member's committee, the premium appears to grow slightly. For each type of connection, we divide each member's portfolio into two subportfolios, one in which the stocks are connected (e.g., where the company issuing the stock is headquartered in the member's constituency) and one where the stocks are not connected. We then compute for each member-month the return on the connected portfolio, the return on the unconnected portfolio, and the return on the hedged (connected minus unconnected) portfolio. Finally, we carry out our panel regression on each of the three portfolios. (See \citet{cohen2008swi} for a similar approach to assessing the role of company-investor connections in portfolio performance.) The connections we consider (and for which we report results in Table \ref{tab: connectedalphas} and in Figure \ref{fig:alphabyconnection}) include our main measures of constituency, contribution, and committee lobbying, as well as definition of lobbying and contributions based on percentile cutoffs and combinations of district and other connections. %carry out panel portfolio regressions on three In looking at stocks connected by lobbying and contributions, we consider two types of connected portfolios, one that includes investments where the company was connected to any degree to the member (i.e. whether the company provided any contributions or lobbying to that member), and another that includes investments where the company's connected lobbying expenditure (or contributions, depending on the variable) are above the median for companies connected to that member. We also consider portfolios of companies that are double-connected in the sense that they are linked to a member through two connections simultaneously (e.g. lobbying and contribution, in district and contributions, etc.). %Our measures of connectedness, explained in detail above, are ``Lobbying" (the extent to which a company lobbies legislation before a member's committees), ``Contributions" (the extent to which a company's PAC contributes to the member's election campaigns), and ``In District" (whether or not the company is headquartered in a Senate's state or Representative's district); %As a more formal test for each of the connections, we separately estimate alpha returns for the portfolios of connected stocks, the portfolio of unconnected stocks, and a hedged portfolio that is long on connected stocks and short on unconnected stocks. The results from the panel regression are provided in Table \ref{tab: connectedalphas}. The estimates for the monthly alpha returns (and their .95 confidence intervals) are visualized in Figure \ref{fig:alphabyconnection}. The remarkable finding reported in Table \ref{tab: connectedalphas} and Figure \ref{fig:alphabyconnection} is that for all definitions of connections, the connected portfolio outperforms the unconnected portfolio, such that the point estimates for the hedged portfolios are all positive. These abnormal returns on the hedged portfolio are statistically significant at conventional levels for all of the contributions and in-district connections, with alpha returns of about .16 to .18 for the contributor connections and about .48 to .57 for the in district connections. This strongly suggests that members do better when they invest in contributors and local firms. %with their investments in politically connected contributor and local firms. %Looking only at the connected portfolios, we find that members on average perform about as well as the market with firms that campaign contributions in stark contrast to the underperformance with unconnected firms. Most strikingly we find %that with their investments in companies in their home districts, members soundly beat the market when they invested in companies headquartered in their home districts, with statistically significant excess returns of about .24 to .43 per month (which annualizes to about 3-5\% per year). The size of the abnormal returns for local investments are increasing for companies that are both in-district and also gave contributions or lobbied a member's committees, which is consistent with the idea that each of these connections represents a means by which members acquire valuable information about companies. %captures some degree of valuable This suggests that members possess substantial information advantage about local companies, arguably the strongest member-company connection we consider here given that members presumably interact with local companies on a regular basis. We have also replicated all of this analysis using both the Carhart Four-Factor and the CAPM model with the aggregated data and the results are very similar (full results in table A7). How robust is the finding for the performance premium on local stocks? For each of the local connections, Figure \ref{fig:localpremium} provides box plots of the distribution of alpha estimates that are computed on a member-by-member basis for each member's connected, unconnected, and hedged portfolios. Clearly, for both the CAPM and the 4-Factor models the average member specific return robustly beats the market on the connected portfolio, and this premium increases in the two-way connections (the median alpha on the connected portfolios in the 4-factor models are, for example, .48, .66, and .66 for the in-district, in-district and contributions, and in-district and lobbying connection respectively). The fact that the connection premium is seen not just in the pooled regression but in the distribution of member-specific alphas suggests that the abnormal returns we find for local investments are not driven by a few unusual members.\footnote{We also computed returns on a passive portfolio of local stocks that were not chosen by members in their respective districts; the average alpha on these local-and-not-chosen stocks is almost exactly zero. Finally, for the contributions and lobbying connections we also considered the possibility that companies that generally gave more campaign contributions or lobbying outperformed other companies in this period. For example, the contributions-connected portfolio may have performed better not because of the specific relationships between the member and her contributor, but simply because companies that contribute generally did better than those that did not, and our member-firm connections merely pick up this overall pattern. To address this alternative explanation, we conducted the same analysis but define the connected portfolio as the set of all investments made by members in companies that gave contributions or reported lobbying to \emph{any} member during our time period. (Investments in a particular firm are thus all defined as connected or not connected, depending on the firm's PAC contribution or lobbying total.) We find no difference in the performance of the connected and unconnected portfolios defined in this way, suggesting that the portfolio of investments where the PAC contributed to the member outperforms the unconnected portfolio because of the specific relationship between the member and the firm rather than firm characteristics (results are in Table A8 in the appendix). } %% companies that were connected to any degree to the member did any lobbying of legislation before the member's committee (made any PAC contributions) and one which includes companies that were among the top half of companies that lobbied legislation before the member's committee (companies that contributed to the member's campaigns). % %Table \ref{tab:connectedunconnectedalpha} reports the alpha estimates from CAPM (Panel A) and the Carhart 4-Factor model (Panel B) for the aggregate (left side) and average (right side) Congressional portfolio. The remarkable finding is that for almost every definition of connections the connected portfolio outperforms the unconnected portfolio, such that the hedged portfolio shows a positive excess return.\footnote{The only two exceptions (of 32 comparisons) are ``Lobbying (Any)"/CAPM/aggregate and average.} The alpha returns are also statistically significantly different from zero in 13 cases. For the average member portfolios in the 4-factor model all but one hedged estimate (the one for lobbying any) is significant at conventional levels. The largest estimates occur for the contributions and the in-district connection. One could match the market index by matching dollar-for-dollar all investments made by members of Congress in this period in %companies from which they received campaign contributions. %gave campaign contributions to this member, one would easily match the market index. %One could beat the market by an annualized 4.8\% by matching dollar-for-dollar all investments made by members Congress in this period in companies headquartered in their constituencies. %%in which the member represents the constituency in which the company is headquartered, one would beat the market index by a highly significant .4 monthly excess return (with annualizes to 4.8\%). %Consistent with Figure \ref{fig:cumrets}, the estimated excess returns for local investments are even higher when we consider companies that are both in-district and also gave contributions or lobbied a member's committees. This suggests that members possess substantial information advantage about local companies, arguably the strongest member-company connection we consider here given that members presumably interact with local companies on a regular basis. The relative performance of the connected portfolio seems weaker in the case of lobbying than the other two definitions of connectedness. % \subsection{Discussion} What explains the advantage members appear to have in investing in companies to which they are politically connected (and especially in local companies)? Broadly, we see three possible channels. First, members may make trades on the basis of non-public time-sensitive information about the firm, such as an upcoming product launch; they might happen to obtain this information in the course of regular interaction with lobbyists or senior management or it might be more deliberately fed to them in return for policy favors. Second, members may make trades on the basis of time-sensitive information about the political and regulatory environment of firms to which they are connected, such as early notice about the results of an FDA trial or the inclusion of an earmark in upcoming legislation. Third, members may choose a winning portfolio of local firms based on more diffuse knowledge of these firms' management and industries gleaned from repeated interaction with those firms and long-term engagement with those industries through e.g. committee assignments. While the local premium we find is likely to be the result of these channels, we employ two strategies to attempt to say more about which ones are more important. First, we examined whether timing of trades appears to have been better for local companies than for non-local companies. (The results are reported in Table A9.) In particular, we constructed portfolios based on trades with various holding periods separately for connected and unconnected stocks (e.g. a portfolio constructed by holding each local stock bought by any member for five days after the purchase) and examined whether the returns on these transaction-based portfolios are better for connected stocks. What we find is that the local buy-minus-sell (i.e. hedged) portfolio appears to do well for the 140- and 255-day holding periods (and better than the non-local equivalent, although both point estimates and the difference between them are not significantly different from zero), but at shorter time horizons there is no evidence that the local trades were better timed. (If anything, the local trades were worse over the 5-day and 25-day windows.) This suggests that the local premium does not emerge from members' short-term trading savvy (i.e. timing) but rather from their general sense of which local companies to invest in. Second, we examined whether the local premium was larger for lower-visibility companies, where we might expect the information asymmetry between well-connected politicians and other investors to be largest. We divide the local portfolio into local companies that appeared in the S\&P 500 at some point during our period (our proxy for high visibility) and those that did not, and compare the return on a portfolio of local S\&P 500 companies to that of a portfolio of local non-S\&P 500 companies. (\citet{ivkovic2005local} and \citet{seasholes2009there} similarly test whether individual investors excel in investing in local non-S\&P 500 companies.) The results, reported in Table A10, fail to indicate a difference between local S\&P 500 and local non-S\&P 500 portfolios; if anything, the non-S\&P 500 local investments do \emph{worse}. The fact that their investments in widely covered locally companies do just as well as their investments in relatively obscure local companies suggests that members are benefiting from local information of a type that Wall Street analysts are not able to systematically uncover and arbitrage away. Together, these findings point towards an interpretation of the local premium we find. The fact that members' local trades do not appear to be particularly well timed % local premium does not seem to be based on well-timed trades suggests less need for the %that we need not be concern that members do well on their local investments through systematic corrupt or illegal behavior, such as cashing in on stock tips from constituents seeking policy favors or profiting from knowledge of impending legislation or regulatory events. The fact that their local advantage extends to widely covered companies suggests that it is members' multi-faceted and often-personal interactions with local companies that explain their advantage in investing in these companies. We speculate that members of Congress are able to make judgments about the quality of senior corporate management and other hard-to-observe characteristics of local and other connected firms by virtue of their extensive interactions with these firms in the course of campaigns and lobbying. % % % (as in our replication and extension of the Ziobrowski study, above) and measure the average performance of examining the performance of % %of stocks held for 5 days after the purchase % %a more diffuse sense of which firms are likely to do well based on % %connected companies may feed information to members as a % %We employ two strategies to make progress on this question. %First, we attempt to assess to role of good timing. % % % by looking at the performance % % %We suspect that the advantage members of Congress enjoy from investing in local %companies is based on the market-relevant information they acquire about local companies in the course of their interactions with those firms. It is also possible that the local advantage %emerges from members' knowledge of legislation that will affect local companies (e.g. earmarks), but without further evidence we are inclined to think that members of Congress resemble mutual fund managers as depicted in \citet{cohen2008swi}, in the sense that they benefit from personal connections to local companies and their senior managers. % %% contrib %The apparent advantage of investing in one's contributors may be a combination of familiarity and policy knowledge. Many contributors are local companies, and those that are not typically contribute because they seek access and influence with influential members. Either way, the contribution signals a relationship in which a member hears periodically about a firm's concerns and opportunities and also is likely to be involved with legislation affecting the firm's fortunes. % %Note that market-relevant information that members glean from local and contributing firms could be of two types. It could be public but not widely-known information, such as an important detail reported in the previous day's quarterly report; assuming less than perfect market efficiency, making investment decisions based on this information would seem to pose no legal or even ethical problems. This information could also be private, ``insider" information; while trading on this information could be lucrative for the member and sharing it might be rewarding to corporate executives hoping to attain favorable legislative or regulatory treatment, both the member and the executive could be implicated by existing insider trading laws. Unfortunately, we have no way of distinguishing between these channels; future work might look at whether members appear to make well-timed trades that anticipate earnings announcements and other corporate communications. %\begin{comment} %\subsection{Company Level Connections} % %A possible alternative explanation of our finding that the portfolio of contributions-connected companies outperforms other investments is that companies that generally gave more campaign contributions outperformed other companies in this period. In other words, the contributions-connected portfolio may have performed better not because of the specific relationships between the member and her contributor, but simply because companies that contribute generally did better than those that did not, and our member-firm connections merely pick up this overall pattern. % %To address this alternative explanation, we conduct the same analysis but define the connected portfolio as the set of all investments made by members in companies that gave contributions to any member during our time period. (Investments in a particular firm are thus all defined as connected or not connected, depending on the firm's PAC contribution total.) The results are displayed in Table \ref{tab:companyconnected}. We find no difference in the performance of the connected and unconnected portfolios defined in this way, suggesting that the portfolio of investments where the PAC contributed to the member outperforms the unconnected portfolio because of the specific relationship between the member and the firm rather than firm characteristics. %\end{comment} \begin{comment} \subsection{Implications} What have we learned about the relationship between members of Congress and firms to which they are politically connected? The strongest results relate to local companies. Our findings indicate that members invest disproportionately in local companies, and that they do this in part because they (unlike average individual investors) % or even mutual fund managers) have information about these companies that allow them to earn excess returns on these investments. While we cannot be sure of the specific nature of this knowledge (e.g. whether it is a diffuse knowledge of local companies gained through extensive and long-standing interaction with local individuals or specific knowledge of upcoming events in business or government), the fact that members' local investments perform so well (and that the local advantage is so widespread) %% need to document this I guess does suggest that relationships between members of Congress and local companies are substantial and common. %% this is not bad. The evidence on members' investments in their contributors is similar but less strong. Members disproportionately invest in these companies, and investments in contributors outperform the rest of their portfolios, but both the bias toward contributors and the apparent informational advantage from these investments is smaller than in the case of local companies. It is possible that the performance of these investments is less stellar because the desire to cement political exchanges plays more of a role in members' portfolio choices, but it is impossible to say with much confidence. % (although to a lesser extent than local companies) % this got into the conclusion -- The fact that members do not appear to do particularly well when investing in companies that lobby legislation before their committees provides some indication that members' policy information is not particularly useful as the basis for an investment strategy. Our analysis of portfolio choices also fails to find much evidence that members disproportionately invest in lobbying-connected companies, suggesting that members also do not expect to be able to profit from their connection to these companies. These results suggest that the main concern in most public discussion of \citet{ziobrowski2004arc}'s finding, as well as in the STOCK Act \--- members' use of information about pending legislative activity to enrich themselves \--- was not a major factor in members' investment performance in the 2004-2008 period.\footnote{It is possible that knowledge of more targeted legislation, such as earmark activity, accounts for the local and contributor premium.} We are hesitant to make very much of this finding, however, because we view the lobbying measure as the noisiest of our connection measures: we are unsure whether we obtain a null result because members do not take advantage of (or do not possess) market-relevant information about companies over which they have legislative power or because our measure does not faithfully capture legislative relationships. %% was "lobbying" not legislative \end{comment} \section{Conclusion} Our study of the investments of members of Congress has yielded two main findings that may appear somewhat at odds with one another. On one hand, our analysis indicates that members of Congress were mediocre investors during the 2004-2008 period that we examine, falling short of the market benchmark by 2-3\% per year. This finding contrasts with the sole previous study of Congressional investments, which found large excess returns in analysis of trades by Senators in the 1990s and has drawn considerable attention in the media and in Congress itself. On the other hand, we find that the politically-connected subset of members' portfolios outperformed the rest of their investments, and that members' investments in local companies handily outperformed the market. This finding is especially significant considering that there is no evidence of either individual investors or money managers outperforming the market in their local investments in recent decades, which suggests either that members of Congress have particularly strong local knowledge or that their valuable knowledge comes particularly from political interactions with constituents. % hmm a little unclear here We find the overall message to be consistent, however. Members of Congress are not investing geniuses. Most of what they know about political developments is probably quickly incorporated into asset prices, and many members likely recognize the possible political costs of trying to make money on whatever private political information they do possess. That their portfolios would perform only about as well as the average individual investor is therefore not entirely surprising. The one area where members of Congress are on average perhaps the most unusual compared to ordinary investors is in their extensive connections to local business leaders, who seek out their assistance with legislation and whose assistance they seek out for reelection. Our findings suggest that it is on these local investments, rather than investments in companies affected by legislation for which they have responsibility, that members are able to excel. To those who are concerned about corruption and self-serving behavior in political institutions, this study should provide relatively reassuring evidence. % about the investing behavior of members of Congress. Members do not do very well as investors over all, and while they do invest heavily in local companies and contributors, they neither invest heavily in companies they are especially responsible for regulating, nor do these investments do particularly well. % check that. Their strong performance in investing in local companies seems to emerge from extensive general knowledge of these companies rather than from time-sensitive information about firm-specific or political events. These members' constituents should perhaps be pleased that their representatives seem to understand the local economy and interact closely with local leaders. Together, these results suggest that the main concern in most public discussion of \citet{ziobrowski2004arc}'s finding, as well as in the STOCK Act \--- members' use of information about pending legislative activity to enrich themselves \--- was not a major factor in members' investment performance in the 2004-2008 period. On the other hand, our study does not inspire much confidence about the average financial savvy of members of Congress, outside of the performance of their local investments (which after all constitute only about 6\% of the average member's investments). Even considering the strong performance of members' local investments, they could have conserved their own wealth, and insulated themselves from ethical questions as well, by cashing in their stock holdings and buying passive index funds instead. % %We are hesitant to make very much of this finding, however, because we view the lobbying measure as the noisiest of our connection measures: we are unsure whether we obtain a null result because members do not take advantage of (or do not possess) market-relevant information about companies over which they have legislative power or because our measure does not faithfully capture legislative relationships. %% was "lobbying" not legislative %Based on these findings, members seeking to improve their investment returns should invest even more heavily in local companies (and perhaps other companies to which they are politically connected) and, following widely-available investment advice, convert the rest of their stock holdings to passive index funds. % %Unfortunately for them, these investments form a relatively small part of their overall portfolio and do not make up for the mediocre performance of their other investments. \newpage \begin{singlespacing} \bibliography{st} \end{singlespacing} \newpage \section*{Tables} % Some descriptive Stata \begin{table}[hbt!]\caption{\label{tab:summarystats} The common stock holdings and transactions of members of Congress - Annual Averages 2004-2008} \begin{center} \begin{tabular}{l|rr|rrrr} \hline\hline \multicolumn{ 1}{c}{} & \multicolumn{ 2}{|c|}{Holdings} & \multicolumn{ 4}{c}{Annual Transactions} \\ %\cline{4-7} \hline \multicolumn{ 1}{c}{} & \multicolumn{ 2}{|c|}{} & \multicolumn{ 2}{c}{Buys} & \multicolumn{ 2}{c}{Sells} \\ %\cline{2-3} \multicolumn{ 1}{c|}{} & \$ Value & Number & \$ Value & Number & \$ Value & Number \\ \hline Min & 501 & 1 & 0 & 0 & 0 & 0 \\ 25th Percentile & 26,424 & 2 & 0 & 0 & 11,010 & 1 \\ Median & 93,827 & 5 & 17,656 & 2 & 39,636 & 3 \\ 75th Percentile & 451,169 & 21 & 105,960 & 9 & 186,068 & 11 \\ Max & 140,767,979 & 331 & 32,253,189 & 424 & 47,615,848 & 479 \\ Mean & 1,718,091 & 19 & 401,744 & 18 & 618,942 & 22 \\ \hline \hline \multicolumn{7}{p{5.5in}}{\tiny {\it Note:} Summary statistics are annual (aggregated) averages across the 2004-2008 period based on end-of-year financial disclosure reports for 422 members of Congress that report common stocks between 2004 to 2008. Values are reported in bands and imputed based on a log-normal model that was fitted to each value band for the group of members that report exact amounts within each band (see text for details).} \end{tabular} \end{center} \end{table} \begin{landscape} \begin{table}\caption{\label{tab:overallalphas} Alpha Returns for Stock Investments of Members of Congress 2004-2008} \footnotesize \begin{tabular}{l|ccccccccccccc} \hline \hline Dependent Variable & \multicolumn{ 13}{c}{Risk-Adjusted Monthly Portfolio Return ($R_{i,t}-R_{f,t}$)} \\ Mean & \multicolumn{ 13}{c}{-.39} \\ \hline Model & (1) & (2) & (3) & (4) & (5) & (6) & (7) & (8) & (9) & (10) & (11) & (12) & (13) \\ \hline & & & & & \multicolumn{ 2}{c}{Party} & \multicolumn{ 2}{c}{Chamber} & \multicolumn{ 3}{c}{Power Committee} & \multicolumn{ 2}{c}{Period} \\ & \multicolumn{ 4}{c}{All Members} & Dems & Reps & House & Senate & House & Senate & None & 2004-06 & 2007-08 \\ \hline $R_{m,t}-R_{f,t}$ & 0.90 & 0.90 & 0.96 & 0.90 & 0.89 & 0.91 & 0.89 & 0.94 & 0.85 & 0.92 & 0.93 & 0.97 & 0.87 \\ & (0.03) & (0.03) & (0.02) & (0.03) & (0.04) & (0.04) & (0.04) & (0.03) & (0.05) & (0.04) & (0.03) & (0.06) & (0.03) \\ $SMB_t$ & 0.10 & 0.11 & 0.04 & -0.01 & 0.15 & 0.07 & 0.10 & 0.14 & 0.19 & 0.04 & 0.06 & 0.03 & -0.14 \\ & (0.05) & (0.05) & (0.03) & (0.05) & (0.07) & (0.05) & (0.06) & (0.06) & (0.07) & (0.08) & (0.04) & (0.06) & (0.08) \\ $HML_t$ & 0.21 & 0.21 & 0.08 & 0.08 & 0.15 & 0.26 & 0.23 & 0.13 & 0.24 & 0.12 & 0.21 & 0.07 & 0.29 \\ & (0.05) & (0.05) & (0.02) & (0.05) & (0.06) & (0.06) & (0.05) & (0.07) & (0.07) & (0.08) & (0.05) & (0.06) & (0.08) \\ $MOM_t$ & -0.18 & -0.18 & -0.06 & -0.08 & -0.18 & -0.19 & -0.20 & -0.11 & -0.26 & -0.08 & -0.15 & -0.05 & -0.25 \\ & (0.04) & (0.04) & (0.01) & (0.02) & (0.05) & (0.04) & (0.05) & (0.03) & (0.06) & (0.03) & (0.04) & (0.04) & (0.04) \\ Alpha & -0.23 & -0.23 & -0.20 & -0.15 & -0.30 & -0.17 & -0.26 & -0.12 & -0.26 & -0.10 & -0.24 & -0.12 & -0.28 \\ & (0.09) & (0.12) & (0.04) & (0.08) & (0.12) & (0.10) & (0.10) & (0.11) & (0.13) & (0.13) & (0.09) & (0.11) & (0.14) \\ \hline Obs & 18,388 & 18,388 & 18,388 & 18,388 & 8,621 & 9,754 & 14,475 & 3,808 & 6,847 & 2,637 & 8,904 & 11,818 & 6,570 \\ \hline Annualized Alpha & -2.76 & -2.76 & -2.4 & -1.8 & -3.6 & -2.04 & -3.12 & -1.44 & -3.12 & -1.2 & -2.88 & -1.44 & -3.36 \\ \hline & & & & & & & & & & & & & \\ & & & & & & & & & & & & & \\ \hline Model & (14) & (15) & (16) & (17) & (18) & (19) & (20) & (21) & (22) & (23) & (24) & (25) & (26) \\ \hline & \multicolumn{ 3}{c}{Seniority} & \multicolumn{ 3}{c}{Portfolio Size} & \multicolumn{ 3}{c}{Net Worth} & \multicolumn{ 4}{c}{Pre-Congressional Career} \\ & Low & Medium & High & Low & Medium & High & Low & Medium & High & Business & Lawyer & Politician & Other \\ \hline $R_{m,t}-R_{f,t}$ & 0.89 & 0.87 & 0.94 & 0.89 & 0.89 & 0.92 & 0.87 & 0.94 & 0.88 & 0.93 & 0.89 & 0.96 & 0.88 \\ & (0.06) & (0.04) & (0.02) & (0.07) & (0.04) & (0.02) & (0.06) & (0.03) & (0.03) & (0.04) & (0.04) & (0.04) & (0.04) \\ $SMB_t$ & 0.08 & 0.16 & 0.05 & 0.13 & 0.17 & 0.02 & 0.17 & 0.07 & 0.09 & 0.09 & 0.28 & 0.04 & 0.08 \\ & (0.07) & (0.05) & (0.05) & (0.07) & (0.07) & (0.03) & (0.08) & (0.05) & (0.05) & (0.08) & (0.08) & (0.09) & (0.05) \\ $HML_t$ & 0.09 & 0.23 & 0.28 & 0.28 & 0.20 & 0.16 & 0.20 & 0.19 & 0.23 & 0.19 & 0.36 & 0.17 & 0.18 \\ & (0.07) & (0.06) & (0.05) & (0.08) & (0.07) & (0.04) & (0.08) & (0.05) & (0.05) & (0.08) & (0.09) & (0.09) & (0.05) \\ $MOM_t$ & -0.16 & -0.14 & -0.24 & -0.21 & -0.23 & -0.11 & -0.28 & -0.10 & -0.18 & -0.23 & -0.11 & -0.23 & -0.18 \\ & (0.05) & (0.04) & (0.03) & (0.06) & (0.05) & (0.02) & (0.06) & (0.04) & (0.02) & (0.05) & (0.05) & (0.06) & (0.04) \\ Alpha & -0.27 & -0.22 & -0.19 & -0.15 & -0.29 & -0.24 & -0.32 & -0.13 & -0.26 & 0.04 & -0.34 & -0.21 & -0.23 \\ & (0.12) & (0.11) & (0.09) & (0.15) & (0.12) & (0.05) & (0.15) & (0.10) & (0.08) & (0.16) & (0.15) & (0.17) & (0.09) \\ \hline Obs & 5,602 & 7,171 & 5,615 & 5,422 & 6,388 & 6,578 & 5,422 & 6,483 & 6,470 & 1,131 & 2,650 & 3,407 & 11,200 \\ \hline Annualized Alpha & -3.24 & -2.64 & -2.28 & -1.8 & -3.48 & -2.88 & -3.84 & -1.56 & -3.12 & 0.48 & -4.08 & -2.52 & -2.76 \\ \hline\hline \multicolumn{14}{p{9in}}{\tiny {\it Note:} Table shows results from analysis using the monthly returns of the holdings-based calendar-time portfolios of all members of Congress that report holding common stocks during the 2004-2008 period. The dependent variable is monthly risk adjusted return of a member's holdings $R_{i,t}-R_{f,t}$ (where $R_{f,t}$ is the risk-free return from Ken French’s website). Portfolios are based on information reported in end-of-year financial disclosure reports (see text for details). Controls are the Fama and French (1993) mimicking portfolios (the market excess return ($R_{m,t}-R_{f,t}$), a zero-investment size portfolio ($SMB_t$), a zero-investment book-to-market portfolio ($HML_t$)) and the \citet{carhart1997pmf} momentum factor ($MOM_t$). Rogers standard errors (clustered by month) are provided in parenthesis. Models 1-4 present the regression for the sample of all members, where model 1 is the raw regression, model 2 includes a random effect for member, model 3 is weighted by a member's number of monthly holdings, and model 4 is weighted by a member's average value of monthly holdings. Models 5-26 report regression results for selected subgroups of members. Power committees in the House are defined as Rules, Appropriations, Ways and Means, and Commerce; in the Senate as Appropriations, Finance, and Commerce. Stratifications for seniority, portfolio size, and net worth are based on equally sized bins. Pre-congressional careers are classified based on Carnes (2010) into Business Owners, Lawyers, State or Local Politicians, and Other careers. A member is classified as belonging to an occupational category if he spent more then 60 \% of his pre-congressional career in that category.}\\ \end{tabular} \end{table} \end{landscape} \clearpage \begin{table}[hbt!]\caption{\label{tab:uncondportfolio} Portfolio Weights as a Function of Member-Firm Connections} \begin{center}\footnotesize \begin{tabular}{l|ccccc} \hline Model & (1) & (2) & (3) & (4) & (5) \\ \hline Dependent Variable: & \multicolumn{ 5}{c}{Portfolio Weight (bp)} \\ Mean: & \multicolumn{ 5}{c}{3.88} \\ \hline In District & 51.14 & 44.33 & 50.68 & 51.10 & 39.29 \\ & (8.48) & (8.71) & (8.47) & (8.43) & (8.63) \\ Lobbying (Any) & 0.09 & 0.29 & & & \\ & (0.64) & (0.63) & & & \\ Contributions (Any) & 12.64 & 17.15 & & & \\ & (2.37) & (4.72) & & & \\ In District \& Lobbying (Any) & & 36.52 & & & \\ & & (20.15) & & & \\ In District \& Contributions (Any) & & 47.25 & & & \\ & & (20.96) & & & \\ Lobbying (Any) \& Contributions (Any) & & 9.56 & & & \\ & & (2.61) & & & \\ In District \& Contributions(Any) \& Lobbying (Any) & & 166.48 & & & \\ & & (46.26) & & & \\ Lobbying ($>$ p50) & & & -0.20 & & \\ & & & (1.29) & & \\ Contributions ($>$ p50) & & & 22.06 & & \\ & & & (4.15) & & \\ Lobbying Strength & & & & -0.01 & -0.02 \\ & & & & (0.03) & (0.02) \\ Contribution Strength & & & & 0.05 & 0.04 \\ & & & & (0.01) & (0.01) \\ Lobbying Strength $\cdot$ In District & & & & & 1.38 \\ & & & & & (0.98) \\ Contribution Strength $\cdot$ In District & & & & & 0.20 \\ & & & & & (0.11) \\ \hline Member Fixed Effects & x & x & x & x & x \\ Firm Fixed Effects & x & x & x & x & x \\ \hline % N & 1,185,501 & 1,185,501 & 1,185,501 & 1,185,501 & 1,185,501 \\ N & \multicolumn{ 5}{c}{1,087,494} \\ \hline \hline \multicolumn{6}{p{5.5in}}{\tiny {\it Note:} Regression coefficients with standards errors (clustered by members) in parenthesis. The dependent variable is the \emph{portfolio weight}, i.e. the share of holdings of a firm in a member's portfolio (in basis points). Members' portfolios are computed as average holdings over the 2004-2008 period. \emph{In District} is a binary indicator for firms that are connected to a member since they are located in a member's home district. \emph{Lobbying (any)} is a binary indicator for firms that are connected to a member since they lobbied a committee on which the member served. \emph{Contributions (any)} is a binary indicator for firms that are connected to a member since they provided her with campaign contributions. \emph{Lobbying ($>$ p50)} and \emph{Contributions ($>$ p50 )} are binary indicators for firms that provided more than the median lobbying or contribution amount for each member. \emph{Lobbying Strength} and \emph{Contribution Strength} measure a firm's share of lobbying or contributions relative to each member's total lobbying or contributions (in basis points). All regressions include a full set of members and firms fixed effects (coefficients not shown here).} \end{tabular} \end{center} \end{table} \clearpage \begin{landscape} \begin{table}\caption{\label{tab: connectedalphas} Abnormal Returns for Stock Investments of Members of Congress in Politically Connected Firms 2004-2008} \footnotesize \begin{tabular}{l|c|ccc|ccc|ccc|ccc} \hline \hline Model & (1) & (2) & (3) & (4) & (5) & (6) & (7) & (8) & (9) & (10) & (11) & (12) & (13) \\ \hline & & \multicolumn{ 3}{c|}{Lobbying (Any)} & \multicolumn{ 3}{c|}{Lobbying ($>$ p50)} & \multicolumn{ 3}{c|}{Contributions (Any)} & \multicolumn{ 3}{c}{Contributions ($>$ p50)} \\ & ALL & CON & UCON & L/S & CON & UCON & L/S & CON & UCON & L/S & CON & UCON & L/S \\ \hline \hline $R_{m,t}-R_{f,t}$ & 0.90 & 0.90 & 0.94 & -0.10 & 0.88 & 0.93 & -0.10 & 0.76 & 0.93 & -0.15 & 0.73 & 0.92 & -0.20 \\ & (0.03) & (0.05) & (0.04) & (0.08) & (0.05) & (0.03) & (0.09) & (0.05) & (0.03) & (0.04) & (0.05) & (0.03) & (0.03) \\ $SMB_t$ & 0.10 & -0.08 & 0.37 & -0.40 & -0.07 & 0.27 & -0.32 & -0.07 & 0.18 & -0.25 & -0.05 & 0.15 & -0.18 \\ & (0.05) & (0.06) & (0.06) & (0.08) & (0.07) & (0.05) & (0.08) & (0.09) & (0.05) & (0.07) & (0.09) & (0.05) & (0.08) \\ $HML_t$ & 0.21 & 0.07 & 0.26 & -0.14 & 0.07 & 0.22 & -0.16 & 0.21 & 0.15 & 0.12 & 0.17 & 0.16 & 0.06 \\ & (0.05) & (0.06) & (0.06) & (0.08) & (0.06) & (0.05) & (0.08) & (0.07) & (0.05) & (0.05) & (0.07) & (0.05) & (0.06) \\ $MOM_t$ & -0.18 & -0.18 & -0.11 & -0.08 & -0.19 & -0.14 & -0.08 & -0.22 & -0.14 & -0.15 & -0.24 & -0.16 & -0.18 \\ & (0.04) & (0.04) & (0.04) & (0.05) & (0.04) & (0.04) & (0.04) & (0.05) & (0.04) & (0.04) & (0.04) & (0.04) & (0.04) \\ Alpha & -0.23 & -0.09 & -0.29 & 0.17 & -0.08 & -0.29 & 0.17 & -0.04 & -0.24 & 0.16 & -0.05 & -0.25 & 0.18 \\ & (0.09) & (0.10) & (0.10) & (0.11) & (0.11) & (0.09) & (0.12) & (0.13) & (0.08) & (0.10) & (0.12) & (0.09) & (0.11) \\ \hline N & 18,388 & 15,779 & 14,950 & 12,341 & 14,820 & 15,999 & 12,431 & 11,529 & 17,349 & 10,490 & 9,700 & 17,596 & 8,908 \\ \hline Annualized Alpha & -2.76 & -1.08 & -3.48 & 2.04 & -0.96 & -3.48 & 2.04 & -0.48 & -2.88 & 1.92 & -0.6 & -3 & 2.16 \\ \hline\hline \multicolumn{ 14}{c}{} \\ \multicolumn{ 14}{c}{} \\ \hline Model & & (14) & (15) & (16) & (17) & (18) & (19) & (20) & (21) & (22) & (23) & (24) & (25) \\ \hline & & \multicolumn{ 3}{c|}{In District} & \multicolumn{ 3}{c|}{Lobbying \& Contributions} & \multicolumn{ 3}{c|}{District \& Contributions} & \multicolumn{ 3}{c}{District \& Lobbying} \\ & & CON & UCON & L/S & CON & UCON & L/S & CON & UCON & L/S & CON & UCON & L/S \\ \hline $R_{m,t}-R_{f,t}$ & & 0.89 & 0.91 & -0.05 & 0.78 & 0.92 & -0.13 & 0.92 & 0.90 & -0.08 & 0.90 & 0.90 & -0.07 \\ & & (0.05) & (0.03) & (0.06) & (0.05) & (0.03) & (0.04) & (0.09) & (0.03) & (0.15) & (0.07) & (0.03) & (0.09) \\ $SMB_t$ & & 0.28 & 0.09 & 0.23 & -0.10 & 0.16 & -0.24 & 0.04 & 0.10 & 0.13 & 0.04 & 0.10 & 0.08 \\ & & (0.07) & (0.05) & (0.10) & (0.09) & (0.05) & (0.08) & (0.11) & (0.05) & (0.15) & (0.10) & (0.05) & (0.11) \\ $HML_t$ & & 0.23 & 0.19 & 0.02 & 0.19 & 0.16 & 0.10 & 0.04 & 0.21 & -0.10 & 0.15 & 0.21 & 0.12 \\ & & (0.07) & (0.06) & (0.10) & (0.07) & (0.05) & (0.06) & (0.13) & (0.06) & (0.20) & (0.10) & (0.05) & (0.11) \\ $MOM_t$ & & -0.21 & -0.18 & -0.05 & -0.19 & -0.16 & -0.11 & -0.23 & -0.18 & -0.14 & -0.22 & -0.18 & -0.19 \\ & & (0.06) & (0.04) & (0.06) & (0.05) & (0.04) & (0.04) & (0.07) & (0.04) & (0.08) & (0.05) & (0.04) & (0.06) \\ Alpha & & 0.24 & -0.23 & 0.48 & -0.05 & -0.22 & 0.09 & 0.39 & -0.24 & 0.57 & 0.43 & -0.24 & 0.54 \\ & & (0.12) & (0.10) & (0.15) & (0.13) & (0.09) & (0.10) & (0.17) & (0.09) & (0.21) & (0.17) & (0.09) & (0.19) \\ \hline N & & 4,607 & 18,029 & 4,248 & 10,840 & 17,494 & 9,946 & 1,826 & 18,360 & 1,798 & 2,152 & 18,360 & 2,124 \\ \hline Annualized Alpha & & 2.88 & -2.76 & 5.76 & -0.6 & -2.64 & 1.08 & 4.68 & -2.88 & 6.84 & 5.16 & -2.88 & 6.48 \\ \hline\hline \multicolumn{14}{p{8.5in}}{\tiny {\it Note:} Table shows results from analysis using the monthly returns of the holdings-based calendar-time portfolios of all members of Congress that report holding common stocks during the 2004-2008 period. The dependent variable is monthly risk adjusted return of a member's holdings of connected stocks (CON), holdings of unconnected stocks (UCON), or investments in a zero cost portfolio that holds the portfolio of connected stocks and sells short the portfolio of unconnected stocks (L/S). Connections are defined as follows: \emph{In District} connected firms are connected to a member since they are located in a member's home district. \emph{Lobbying (any)} connected firms are connected to a member since they lobbied a committee on which the member served. \emph{Contributions (any)} connected firms are connected to a member since they provided her with campaign contributions. \emph{Lobbying ($>$ p50)} and \emph{Contributions ($>$ p50 )} connected firms are connected since they provided more than the median lobbying or contribution amount for each member. Controls are the Fama and French (1993) mimicking portfolios (the market excess return ($R_{m,t}-R_{f,t}$), a zero-investment size portfolio ($SMB_t$), a zero-investment book-to-market portfolio ($HML_t$)) and the \citet{carhart1997pmf} momentum factor ($MOM_t$). Rogers standard errors (clustered by month) are provided in parenthesis.} \end{tabular} \end{table} \end{landscape} \clearpage \section*{Figures} %\begin{figure}[!hbt] %\caption{\label{fig:double_power_law} Distribution of Value of Holdings and Number of Holdings Across Members} %% \centering % \includegraphics[scale=.9]{figs/double_power_law.pdf} %\tiny{Note: Ranked value and number of holdings are shown (x-axis, bottom and top scale, respectively) for the 453 members in our dataset. For members who report holdings in more than one year, we use the average across years in which holdings are reported for that member.} %\end{figure} % %\clearpage % %\begin{figure}[!hbt] %\caption{\label{fig:kerry} Portfolio Value and Transactions for John Kerry} %% \centering % \includegraphics[scale=1]{figs/kerry_trajectory_and_net_purchases.pdf} %\tiny{Note: The top panel shows John Kerry's daily portfolio value as reported in his FDRs and imputed by our methods (described in the paper). Daily variation is due to both market fluctuations and Kerry's transactions; jumps at the end of year are due to reporting inconsistencies year-to-year and error in imputing precise values for each investment from value bands.} %\end{figure} % %\clearpage \begin{figure}[!hbt] \caption{\label{fig:cumret} Cumulative Monthly Return for Aggregate Congressional Portfolio and the Average Congressional Member} % \centering \includegraphics[scale=.55]{figs/cumret.pdf}\\ \tiny{Note: Cumulative monthly return is shown for a \$100 dollar position in the CRSP market index (a value-weighted index of stocks listed on the NYSE, AMEX, and NASDAQ) and the average Congressional portfolio beginning in January 2004. %The aggregate congressional portfolio mimics the aggregate investments of all members of Congress (value-weighted). The average Congressional portfolio return is built by averaging monthly returns across members for each month.} % is based on a portfolio that mimics the investment of the average member of Congress (equal member weighted). The market portfolio is the value weighted market portfolio from NYSE, AMEX, and NASDAQ as computed by the CRSP.} \end{figure} \begin{figure}[!hbt] \caption{\label{fig:alphasubgroups} Monthly Alpha Returns for Members of Congress} % \centering % \includegraphics[scale=.6]{figs/group_FF_all.pdf} \includegraphics[scale=.5]{figs/PanelAlphas.pdf}\\ \tiny{Note: Estimated monthly alpha returns (with .95 confidence intervals) of the holdings-based calendar-time portfolios of all members of Congress that report holding common stocks during the 2004-2008 period. Portfolios are based on information reported in end-of-year financial disclosure reports (see text for details). Alpha returns are from Carhart 4-factor panel model. The dependent variable is monthly risk adjusted return of a member's holdings $R_{i,t}-R_{f,t}$ (where $R_{f,t}$ is the risk-free return from Ken French’s website). Controls are the Fama and French (1993) mimicking portfolios (the market excess return ($R_{m,t}-R_{f,t}$), a zero-investment size portfolio ($SMB_t$), a zero-investment book-to-market portfolio ($HML_t$)) and the \citet{carhart1997pmf} momentum factor ($MOM_t$). Confidence intervals are based on Rogers standard errors (clustered by month). The first estimate is the alpha return for the sample of all members; the other estimates are for selected subgroups of members or time periods. Power committees in the House are defined as Rules, Appropriations, Ways and Means, and Commerce; in the Senate as Appropriations, Finance, and Commerce. Stratifications for seniority, portfolio size, and net worth are based on equally sized bins. Pre-congressional careers are classified based on Carnes (2010) into Business Owners, Lawyers, State or Local Politicians, and Other careers. A member is classified as belonging to an occupational category if he spent more then 60 \% of his pre-congressional career in that category.} \end{figure} \clearpage \begin{landscape} \begin{figure}[!hbt] \caption{\label{fig:member_returns} Members' Monthly Excess Returns and Average Portfolio Size 2004-2008} %\centering \includegraphics[scale=.57]{figs/returnsall_and_size_BIGLAB4FF} \tiny{Note: Monthly alpha return is Carhart 4-factor alpha obtained from a calendar time portfolio regression of each member's excess return on the Fama and French (1993) mimicking portfolios and the \citet{carhart1997pmf} momentum factor. Members with large/small returns or large/small portfolios are highlighted with labels. Box plots on the right and on top show the marginal distribution of alpha returns and portfolio sizes across members: the thick line indicates the median, the edges of the box denote the interquartile range, and the whiskers indicate the 5th and 95th percentiles.} \end{figure} \end{landscape} \clearpage \begin{figure}[!hbt] \caption{\label{fig:benchmarks} Benchmark Estimates for Different Investor Groups} \begin{centering} \includegraphics[scale=.4]{figs/benchthatshit2.pdf} \end{centering}\\ \tiny{Note: Point estimates for annual alpha returns (with .95 confidence intervals) for different investor groups compiled from different studies. The last estimate refers to our replication of the Ziobrowksi et al. (2004) approach using our data for senators.} \end{figure} \begin{figure}[!hbt] \caption{\label{fig:8exp} Portfolio Weights as a Function of Member-Firm Connections} \begin{centering} \includegraphics[scale=.4]{figs/8exp.pdf} \end{centering}\\ \tiny{Note: Point estimates (with .95 confidence intervals) for average portfolio weights (in basis points) as a function of member-firm connections based on model 2 in Table \ref{tab:uncondportfolio}.} \end{figure} \clearpage %\begin{landscape} %\begin{figure}[!hbt] %\caption{\label{fig:cumrets} Cumulative Monthly Return for Politically Connected and Unconnected Stocks} % %\centering % \includegraphics[scale=.7]{figs/allconnect} %\tiny{Note: Cumulative monthly return is shown for a \$100 dollar position in each mimicking portfolio beginning in January 2004. The connected (unconnected) portfolios mimics the investments of all members of Congress in connected (unconnected) stocks. All portfolios shown are average member portfolios. The market portfolio is the value weighted market portfolio from NYSE, AMEX, and NASDAQ as computed by the CRSP. The connections are defined as follows. \emph{In District} connected firms are connected to a member since they are located in a member's home district. \emph{Lobbying (any)} connected firms are connected to a member since they lobbied a Committee on which the member served. \emph{Contributions (any)} connected firms are connected to a member since they provided her with campaign contributions.} %\end{figure} %\end{landscape} \begin{figure}[!hbt] \caption{\label{fig:alphabyconnection} Monthly Alpha Returns for Members' Investments in Politically Connected Firms} \begin{centering} \includegraphics[scale=.55]{figs/PanelAlphasByConnection.pdf} \end{centering}\\ \tiny{Note: Estimated monthly alpha returns (with .95 confidence intervals) of the holdings-based calendar-time portfolios of all members of Congress that report holding common stocks during the 2004-2008 period. Portfolios are based on information reported in end-of-year financial disclosure reports (see text for details). Alpha returns are from Carhart 4-factor panel model. The dependent variable is monthly risk adjusted return of a member's holdings of connected stocks (CON), holdings of unconnected stocks (UCON), or investments in a zero cost portfolio that holds the portfolio of connected stocks and sells short the portfolio of unconnected stocks (L/S). Connections are defined as follows: \emph{In District} connected firms are connected to a member since they are located in a member's home district. \emph{Lobbying (any)} connected firms are connected to a member since they lobbied a committee on which the member served. \emph{Contributions (any)} connected firms are connected to a member since they provided her with campaign contributions. \emph{Lobbying ($>$ p50)} and \emph{Contributions ($>$ p50 )} connected firms are connected since they provided more than the median lobbying or contribution amount for each member. Controls are the Fama and French (1993) mimicking portfolios (the market excess return ($R_{m,t}-R_{f,t}$), a zero-investment size portfolio ($SMB_t$), a zero-investment book-to-market portfolio ($HML_t$)) and the \citet{carhart1997pmf} momentum factor ($MOM_t$). Confidence intervals are based on Rogers standard errors (clustered by month).} \end{figure} \begin{figure}[!hbt] \caption{\label{fig:localpremium} Distribution of Member Specific Returns on Locally Connected Companies} \begin{centering} \includegraphics[scale=.5]{figs/local_premium.pdf}\\ \end{centering} \tiny{Note: Box plots show the distribution of member specific monthly alpha estimates from a 4-Factor Carhart model and a CAPM respectively for locally connected and unconnected companies as well as a zero cost portfolio that holds long the connected stocks and sells short the unconnected stocks. A company is locally connected if it is headquartered in a member's district. The plot includes all members that have both connected and unconnected investments.} \end{figure} \clearpage \section*{Appendix A: Not for Publication} \singlespacing In this appendix we present additional results that are referenced in the main paper. \begin{landscape} \subsection*{A1 Alpha Returns From CAPM} Table A1 contains our replication of table \ref{tab:overallalphas} using the CAPM model. % of the monthly alpha returns using the panel regression approach on the member-month level with robust standard errors (clustered by month) \begin{table}[!hbt] \footnotesize \begin{tabular}{l|ccccccccccccc} \multicolumn{ 14}{c}{Table A1 Excess Returns for Stock Investments of Members of Congress 2004-2008 estimated with CAPM}\\ \hline \hline Dependent Variable & \multicolumn{ 13}{c}{Risk-Adjusted Monthly Portfolio Return ($R_{i,t}-R_{f,t}$)} \\ Mean & \multicolumn{ 13}{c}{-.39} \\ \hline Model & (1) & (2) & (3) & (4) & (5) & (6) & (7) & (8) & (9) & (10) & (11) & (12) & (13) \\ \hline & & & & & \multicolumn{ 2}{c}{Party} & \multicolumn{ 2}{c}{Chamber} & \multicolumn{ 3}{c}{Power Committee} & \multicolumn{ 2}{c}{Period} \\ & \multicolumn{ 4}{c}{All Members} & Dems & Reps & House & Senate & House & Senate & None & 2004-06 & 2007-08 \\ \hline $R_{m,t}-R_{f,t}$ & 0.96 & 0.96 & 0.98 & 0.90 & 0.96 & 0.96 & 0.95 & 1.00 & 0.94 & 0.95 & 0.98 & 0.96 & 0.92 \\ & (0.05) & (0.05) & (0.02) & (0.04) & (0.04) & (0.06) & (0.05) & (0.05) & (0.07) & (0.05) & (0.04) & (0.03) & (0.06) \\ Alpha & -0.27 & -0.27 & -0.21 & -0.18 & -0.36 & -0.18 & -0.30 & -0.14 & -0.33 & -0.11 & -0.26 & -0.06 & -0.70 \\ & (0.12) & (0.16) & (0.05) & (0.08) & (0.14) & (0.13) & (0.13) & (0.12) & (0.17) & (0.13) & (0.12) & (0.08) & (0.26) \\ \hline Obs & 18388 & 18388 & 18388 & 18388 & 8621 & 9754 & 14475 & 3808 & 6847 & 2637 & 8904 & 11818 & 6570 \\ \hline Annualized Alpha & -3.24 & -3.24 & -2.52 & -2.16 & -4.32 & -2.16 & -3.6 & -1.68 & -3.96 & -1.32 & -3.12 & -0.72 & -8.4 \\ \hline & & & & & & & & & & & & & \\ & & & & & & & & & & & & & \\ \hline Model & (14) & (15) & (16) & (17) & (18) & (19) & (20) & (21) & (22) & (23) & (24) & (25) & (26) \\ \hline & \multicolumn{ 3}{c}{Seniority} & \multicolumn{ 3}{c}{Portfolio Size} & \multicolumn{ 3}{c}{Net Worth} & \multicolumn{ 4}{c}{Pre-Congressional Career} \\ & Low & Medium & High & Low & Medium & High & Low & Medium & High & Business & Lawyer & Politician & Other \\ \hline $R_{m,t}-R_{f,t}$ & 0.94 & 0.93 & 1.00 & 0.96 & 0.97 & 0.95 & 0.96 & 0.98 & 0.94 & 0.99 & 0.99 & 1.01 & 0.93 \\ & (0.05) & (0.06) & (0.06) & (0.07) & (0.06) & (0.03) & (0.08) & (0.04) & (0.05) & (0.04) & (0.07) & (0.06) & (0.05) \\ Alpha & -0.33 & -0.21 & -0.26 & -0.18 & -0.35 & -0.25 & -0.42 & -0.12 & -0.29 & -0.03 & -0.26 & -0.30 & -0.28 \\ & (0.13) & (0.13) & (0.16) & (0.18) & (0.15) & (0.08) & (0.19) & (0.11) & (0.12) & (0.19) & (0.19) & (0.17) & (0.12) \\ \hline Obs & 5602 & 7171 & 5615 & 5422 & 6388 & 6578 & 5422 & 6483 & 6470 & 1131 & 2650 & 3407 & 11200 \\ \hline Annualized Alpha & -3.96 & -2.52 & -3.12 & -2.16 & -4.2 & -3 & -5.04 & -1.44 & -3.48 & -0.36 & -3.12 & -3.6 & -3.36 \\ \hline\hline \multicolumn{14}{p{9in}}{\tiny {\it Note:} Table shows results from analysis using the monthly returns of the holdings-based calendar-time portfolios of all members of Congress that report holding common stocks during the 2004-2008 period. The dependent variable is monthly risk adjusted return of a Member's holdings $R_{i,t}-R_{f,t}$ (where $R_{f,t}$ is the risk-free return from Ken French’s website). Portfolios are based on information reported in end-of-year financial disclosure reports (see text for details). Controls are the market excess return ($R_{m,t}-R_{f,t}$). Rogers standard errors (clustered by month) are provided in parenthesis. Models 1-4 present the regression for the sample of all members, where model 1 is the raw regression, model 2 includes a random effect for member, model 3 is weighted by a member's number of monthly holdings, and model 4 is weighted by a member's average value of monthly holdings. Models 5-26 report regression results for selected subgroups of members. Power committees in the House are defined as Rules, Appropriations, Ways and Means, and Commerce; in the Senate as Appropriations, Finance, and Commerce. Stratifications for seniority, portfolio size, and net worth are based on equally sized bins. Pre-congressional careers are classified based on Carnes (2010) into Business Owners, Lawyers, State or Local Politicians, and Other careers. A member is classified as belonging to an occupational category if he spent more then 60 \% of his pre-congressional career in that category.}\\ \end{tabular} \end{table} \end{landscape} \subsection*{A2 Alpha Returns With Monthly Aggregated Data} Tables A2 and A3 replicate the analysis of Table \ref{tab:overallalphas} using aggregated data, as explained in the text. Briefly, in place of our panel regressions, which estimate the average alpha across members-months, we carry out regressions that model the average monthly return on a single portfolio that is created by aggregating member returns. %the alpha returns from the holdings-based calendar-time portfolios for all members (Table A2) and selected subgroups (table A3) using the single-time series approach where monthly returns are first aggregated across members to a single monthly portfolio return. For the Aggregate Congressional Portfolio the average monthly return is computed using a value-weighted average across members; for the Average Congressional Portfolio member returns are equal-weighted across members. %% make this language consistent? Table A2 provides the results of our estimates of the abnormal return on the Congressional portfolio. Panel A shows that the average monthly abnormal return for the aggregate Congressional portfolio is negative and significant at conventional levels in both the CAPM and Carhart 4-Factor specifications. The same is true for the the average Congressional portfolio shown in Panel B. The abnormal return estimates are very similar. For the CAPM, the magnitudes suggest that the aggregate Congressional portfolio underperforms the market by an average of about .27 percentage points per month, which annualizes to a yearly excess return of about -3.2\% with a .95 confidence interval of $-5.5;-.95$; the average Congressional portfolio underperforms the market by an average of about .31 percentage points, which annualizes to a yearly excess return of about -3.8\% $[ -6.0; -1.5]$. The corresponding annualized figures for the 4-Factor model are -2.8\% $[-5.2;-.5]$ and -3.1 \% $[-5.1;-1.2]$. \begin{table}[hbt!] \begin{center}\footnotesize \begin{tabular}{lcccccc} \multicolumn{ 7}{c}{Table A2: Alpha Returns for Aggregate/Average Congressional Portfolio}\\ \hline\hline & Excess & \multicolumn{ 4}{c}{Coefficient Estimate on:} & Adjusted \\ \cline{3-6} & Return & ($R_{m,t}-R_{f,t}$) & $SMB_t$ & $HML_t$ & $MOM_t$ & $R^2$ \\ \hline \multicolumn{ 7}{c}{} \\ \multicolumn{ 7}{c}{Panel A: Gross Percentage Monthly Returns for Aggregate Congressional Portfolio} \\ \multicolumn{ 7}{c}{} \\ \hline CAPM & -0.269 & 0.925 & & & & 0.96 \\ & (0.095) & (0.038) & & & & \\ Carhart 4-Factor & -0.239 & 0.920 & -0.040 & 0.076 & -0.065 & 0.96 \\ & (0.099) & (0.037) & (0.053) & (0.055) & (0.037) & \\ \hline \multicolumn{ 7}{c}{} \\ \multicolumn{ 7}{c}{Panel B: Gross Percentage Monthly Returns for the Average Member} \\ \multicolumn{ 7}{c}{} \\ \hline CAPM & -0.319 & 0.979 & & & & 0.96 \\ & (0.093) & (0.032) & & & & \\ Carhart 4-Factor & -0.263 & 0.933 & 0.081 & 0.090 & -0.125 & 0.98 \\ & (0.080) & (0.025) & (0.042) & (0.042) & (0.030) & \\ \hline \multicolumn{7}{p{5.5in}}{\tiny {\it Note:} Table shows results from analysis using the monthly aggregate or average returns of the holdings-based calendar-time portfolios of all members of Congress that report holding common stocks during the 2004-2008 period. The dependent variable is monthly risk-adjusted return obtained from aggregating the monthly portfolio returns across members. N=60. Panel A presents results for the gross monthly return on a portfolio that mimics the aggregate investments of all members of Congress (value-weighted). Panel B presents results for the gross return on a portfolio that mimics the investment of the average member of Congress (equal member weighted). CAPM is the result from a time-series regression of the member excess return on the market excess return ($R_{m,t}-R_{f,t}$). Carhart 4-factor is the result from a time-series regression of the member excess return on the Fama and French (1993) mimicking portfolios (the market excess return, a zero-investment size portfolio ($SMB_t$), a zero-investment book-to-market portfolio ($HML_t$)) and the \citet{carhart1997pmf} momentum factor ($MOM_t$). Robust standard errors are presented in parentheses.} \end{tabular} \end{center} \end{table} \clearpage Table A3 reports the estimated abnormal returns across member subgroups using the aggregated data approach. The results are very similar to the results from the panel regression. The only noticeable exception is that the aggregate portfolio of prior business owners actually beats the market and the estimates are significant at conventional levels. Other than that all subgroups consistently underperform. % is that % %House members seem to do slightly better than Senators according to the aggregate portfolio, but the opposite is the case for the average member portfolio. % the opposite is the case. %Members on power committees in the House or Senate\footnote{We define ``power committees" in the House as Rules, Appropriations, Ways and Means, and Commerce; in the Senate they are Appropriations, Finance, and Commerce.} do slightly better than other members, but the differences are small. The estimated excess returns are also very similar for the 2004-2006 period, when the market was rising, and the 2007-2008 period, when the market fell and the government began to intervene more heavily in the economy. %indicating that performance suggesting that the increased political intervention in the economy in the wake of the crisis did not result in much different overall returns. %Republicans do slightly better than Democrats although again the differences are not quite significant at conventional levels ($p=.15$). Members with lower seniority seem to do slightly better than those with medium or high seniority. There are no consistent differences across the group of members when we stratify by net worth (high, medium, low) or portfolio size (high, medium, low). %Above all, the consistently negative results across subgroups displayed in Figure \ref{fig:alphasubgroups} suggest that our overall findings %are not the artifact of a few exceptionally poor investors in Congress but rather indicate a broader underperformance across members. Notably, only 1 out of the 72 point estimates for the excess return (18 subgroups, each estimated four ways) is positive, and that point estimate is effectively zero. % %The last four rows in Figure \ref{fig:alphasubgroups} shows alpha estimates for subgroups that we formed based on the Members pre-congressional careers.\footnote{We are grateful to Nick Carnes for providing us with the data on pre-congressional careers. A Members is coded as belonging to a career category if she spent more than 60 \% of her pre-congressional career in that category. The results are very similar if other cut-points are used. See \cite{Carnes2010} for details on the career data.} In the only noticeable exception to the stable under-performance, Members that owned a business prior to joining Congress earn higher returns. The average member returns for business owners perform about as well as the market. The aggregate returns for this group of Members even beats the market with borderline insignificant monthly excess returns of .53 ($p=0.15$) in the Carhart model and .46 ($p.=16$) in the CAPM. We find no such performance for any other pre-congressional careers (including lawyers, local politicians, or others). [ADD HERE THE COMMENT ON THE SIGNIFICANT TESTS FOR WHETHER THE AVERAGE BUSINESS OWNER DOES INDEED EARN HIGHER RETURNS] \begin{table}[hbt!] \begin{center}\scriptsize \begin{tabular}{l|cc|cc} \multicolumn{5}{c}{Table A3: Percentage Monthly Abnormal Return for Selected Subgroups}\\ & \multicolumn{ 2}{|c}{Aggregate Portfolio} & \multicolumn{ 2}{|c}{Average Member Portfolio} \\ & \multicolumn{ 2}{|c}{Excess Return} & \multicolumn{ 2}{|c}{ Excess Return} \\ \hline & \multicolumn{1}{p{1in}}{ \centering CAPM} & \multicolumn{1}{p{1in}|}{\centering 4-Factor} & \multicolumn{1}{p{1in}}{\centering CAPM} & \multicolumn{1}{p{1in}}{\centering 4-Factor} \\ \hline Democrats & -0.344 & -0.304 & -0.300 & -0.225 \\ & (0.122) & (0.126) & (0.143) & (0.118) \\ Republicans & -0.152 & -0.163 & -0.174 & -0.107 \\ & (0.143) & (0.139) & (0.156) & (0.105) \\ \hline House & -0.212 & -0.170 & -0.272 & -0.194 \\ & (0.128) & (0.134) & (0.155) & (0.114) \\ Senate & -0.334 & -0.336 & -0.103 & -0.081 \\ & (0.122) & (0.129) & (0.128) & (0.121) \\ \hline Power Committee House & -0.173 & -0.088 & -0.300 & -0.184 \\ & (0.146) & (0.144) & (0.223) & (0.149) \\ Power Committee Senate & -0.293 & -0.248 & -0.089 & -0.069 \\ & (0.139) & (0.134) & (0.095) & (0.105) \\ No Power Committee & -0.274 & -0.309 & -0.244 & -0.196 \\ & (0.117) & (0.142) & (0.110) & (0.080) \\ \hline 2004-2006 & -0.172 & -0.255 & -0.188 & -0.190 \\ & (0.098) & (0.110) & (0.067) & (0.096) \\ 2007-2008 & -0.296 & -0.216 & -0.563 & -0.329 \\ & (0.178) & (0.222) & (0.196) & (0.161) \\ \hline Seniority Low & -0.088 & 0.001 & -0.313 & -0.219 \\ & (0.129) & (0.127) & (0.143) & (0.132) \\ Seniority Medium & -0.569 & -0.625 & -0.187 & -0.159 \\ & (0.150) & (0.167) & (0.150) & (0.115) \\ Seniority High & -0.273 & -0.322 & -0.211 & -0.121 \\ & (0.168) & (0.156) & (0.161) & (0.102) \\ \hline Portfolio Size Low & -0.606 & -0.518 & -0.127 & -0.058 \\ & (0.230) & (0.229) & (0.202) & (0.162) \\ Portfolio Size Medium & -0.395 & -0.405 & -0.307 & -0.219 \\ & (0.114) & (0.121) & (0.171) & (0.132) \\ Portfolio Size High & -0.259 & -0.243 & -0.257 & -0.211 \\ & (0.095) & (0.097) & (0.090) & (0.055) \\ \hline Net Worth Low & -0.643 & -0.533 & -0.312 & -0.210 \\ & (0.185) & (0.168) & (0.222) & (0.166) \\ Net Worth Medium & -0.270 & -0.325 & -0.100 & -0.077 \\ & (0.087) & (0.088) & (0.118) & (0.108) \\ Net Worth High & -0.272 & -0.261 & -0.277 & -0.220 \\ & (0.102) & (0.103) & (0.131) & (0.082) \\ \hline Former Business Owners & 0.467 & 0.532 & -0.026 & 0.071 \\ & (0.332) & (0.362) & (0.198) & (0.167) \\ Former Lawyers & -0.245 & -0.405 & -0.213 & -0.286 \\ & (0.231) & (0.239) & (0.186) & (0.150) \\ Former Local Politicians & -0.516 & -0.451 & -0.279 & -0.142 \\ & (0.173) & (0.203) & (0.176) & (0.167) \\ Other Pre-Congressional Careers & -0.223 & -0.192 & -0.246 & -0.168 \\ & (0.109) & (0.103) & (0.143) & (0.106) \\ \hline\hline \multicolumn{5}{p{6.5in}}{\tiny {\it Note:} Alpha returns for selected subgroups with robust standard errors in parentheses. Aggregate returns/Average member returns are for portfolios that mimics the aggregate investments of all members/investments of the average member in a specific group respectively. Alpha returns from the CAPM are estimated with a time-series regression of the members' monthly excess return on the monthly market excess return. The Carhart 4-factor adds the Fama and French (1993) mimicking portfolios and the \citet{carhart1997pmf} momentum factor as controls.} \end{tabular} \end{center} \end{table} %\begin{figure}[!hbt] %\caption{\label{fig:alphasubgroups} Monthly Excess Returns by Subgroups: Aggregate Congressional Portfolio and the Average Congressional Member} %% \centering % \includegraphics[scale=.6]{figs/group_FF_both.pdf} %\tiny{Note: Monthly alpha returns (with .90 confidence intervals) from Carhart 4-factor calendar time portfolio regression for the 2004-2008 period. The aggregate congressional portfolio mimics the aggregate investments of all members of Congress (value-weighted) that belong to a specific group. Portfolio of the average congressional Member is based on a portfolio that mimics the investment of the average Member of Congress (equal Member weighted) that belong to a specific group.} %\end{figure} % %\clearpage \clearpage \subsection*{A4-A5 Alpha Returns from Transaction-Based Portfolio} Table A4 and A5 show monthly alpha returns for all members over the 2004-2008 period estimated from the transaction based calendar-time portfolios formed by mimicking the trades of all members of Congress that report holding common stocks during the 2004-2008 period. Calendar-time portfolios are formed based on stocks bought (``Buys''), and another portfolio based on stocks sold (``Sells''), and a third zero-cost portfolio that holds the portfolio of bought stocks and sells short the portfolio of sold stocks (``Long/Short''). Table A4 replicates the transaction-based portfolio returns for the value-weighted aggregate Congressional portfolios using the approach by Ziobrowski et al. (2004) where stocks are held in a calendar-time portfolio for a fixed holding period of 255 days and dollar values are imputed using band midpoints or a maximum value of \$250,000 in the highest band. Table A5 contains the results for our analysis of the transaction-based portfolio returns for the average member and aggregated congressional portfolio for various fixed holding periods. Regardless of the approach used, we find that the trades of members of Congress are not particularly well-timed. These results are consistent with the holding-based analysis. %While the stocks sold by Members on average perform slightly below market in the 140 or 255 days following the sell date, the stocks that are purchased by Members on average also underperform the market in the days after being purchased (although the estimates for the buys are almost all insignificant). As a result the returns from the long/short portfolio are not consistently positive and almost always insignificant. \begin{table}[hbt!] \begin{center}\footnotesize \begin{tabular}{lccc} \multicolumn{4}{c}{Table A4: Returns on Transaction-Based Portfolios}\\ \multicolumn{4}{c}{using Ziobrowski et. al approach}\\ \hline \hline & \multicolumn{ 3}{c}{Portfolio} \\ & Buys & Sells & Long/Short \\ \hline \multicolumn{ 4}{l}{{\it All Members:}} \\ CAPM Alpha & -0.127 & -0.187 & 0.060 \\ & (0.092) & (0.052) & (0.111) \\ Fama-French Alpha & -0.114 & -0.211 & 0.097 \\ & (0.081) & (0.048) & (0.083) \\ \hline \multicolumn{ 4}{l}{{\it Senate:}} \\ CAPM Alpha & -0.234 & -0.251 & 0.016 \\ & (0.106) & (0.089) & (0.144) \\ Fama-French Alpha & -0.248 & -0.284 & 0.036 \\ & (0.117) & (0.074) & (0.136) \\ \hline \multicolumn{ 4}{l}{House:} \\ CAPM Alpha & -0.083 & -0.104 & 0.021 \\ & (0.115) & (0.103) & (0.136) \\ Fama-French Alpha & -0.050 & -0.118 & 0.068 \\ & (0.079) & (0.097) & (0.101) \\ \hline \hline \multicolumn{4}{p{3.5in}}{\tiny {\it Note:} Table shows results from analysis using the monthly value-weighted aggregate returns of the transaction-based calendar-time portfolios formed by mimicking the trades of all members of Congress that report holding common stocks during the 2004-2008 period. Following Ziobrowski et al. (2004) stocks are held held in a calendar-time portfolio for a fixed holding period of 255 days and dollar values are imputed using band midpoints or a maximum value of \$250,000 in the highest band. Calendar-time portfolio are formed based on stocks bought (``Buys''), and another portfolio based on stocks sold (``Sells''), and a third zero-cost portfolio that holds the portfolio of bought stocks and sells short the portfolio of sold stocks (``Long/Short''). CAPM alpha is the result from a time-series regression of the portfolio excess return (i.e. raw return minus risk-free rate) on the market excess return. Fama-French alpha is the result from a time-series regression of the portfolio excess return on the three Fama and French (1993) mimicking portfolios.} \end{tabular} \end{center} \end{table} \clearpage \begin{table}[hbt!] \begin{center} \footnotesize \begin{tabular}{lc|ccc|ccc} \multicolumn{8}{c}{Table A5: Monthly Alpha Returns on Transaction-Based Portfolio}\\ \hline \hline & Holding & \multicolumn{ 3}{c}{Aggregate Portfolio} & \multicolumn{ 3}{c}{Average Portfolio} \\ & Period & Buys & Sells & Long/Short & Buys & Sells & Long/Short \\ \hline CAPM & 1 Day & 0.431 & 1.344 & -0.913 & 0.805 & 1.215 & -0.411 \\ & & (0.742) & (0.806) & (1.047) & (0.570) & (0.837) & (0.992) \\ Carhart 4 Factor & & 0.531 & 1.279 & -0.749 & 0.849 & 1.195 & -0.346 \\ & & (0.770) & (0.657) & (0.905) & (0.562) & (0.699) & (0.843) \\ \hline CAPM & 10 Days & -0.727 & 0.312 & -1.039 & -0.113 & 0.270 & -0.383 \\ & & (0.540) & (0.263) & (0.603) & (0.201) & (0.183) & (0.208) \\ Carhart 4 Factor & & -0.691 & 0.314 & -1.005 & -0.036 & 0.312 & -0.348 \\ & & (0.535) & (0.253) & (0.629) & (0.235) & (0.160) & (0.213) \\ \hline CAPM & 25 Days & -0.352 & 0.134 & -0.486 & 0.228 & 0.184 & 0.044 \\ & & (0.488) & (0.277) & (0.358) & (0.223) & (0.154) & (0.189) \\ Carhart 4 Factor & & -0.320 & 0.161 & -0.481 & 0.251 & 0.181 & 0.070 \\ & & (0.458) & (0.270) & (0.344) & (0.213) & (0.144) & (0.184) \\ \hline CAPM & 140 Days & -0.055 & -0.220 & 0.165 & -0.170 & -0.163 & -0.006 \\ & & (0.190) & (0.114) & (0.187) & (0.185) & (0.122) & (0.163) \\ Carhart 4 Factor & & -0.025 & -0.249 & 0.224 & -0.169 & -0.190 & 0.020 \\ & & (0.193) & (0.107) & (0.189) & (0.164) & (0.115) & (0.129) \\ \hline CAPM & 255 Days & -0.190 & -0.098 & -0.092 & 0.005 & -0.111 & 0.116 \\ & & (0.144) & (0.085) & (0.169) & (0.184) & (0.122) & (0.139) \\ Carhart 4 Factor & & -0.149 & -0.141 & -0.008 & -0.017 & -0.172 & 0.155 \\ & & (0.131) & (0.075) & (0.138) & (0.191) & (0.120) & (0.117) \\ \hline \hline \multicolumn{8}{p{6.2in}}{\tiny {\it Note:} Monthly alpha returns (with robust standard errors in parenthesis) for calendar time portfolios that mimics the value-weighted and equal member weighted investments in stocks bought or sold by members over the 2004-2008 period. Results are reported for fixed holding periods of 1 day, 10 days, 25 days, 140 days, and 255 days. Within reported value bands, dollar values are imputed using the lognormal model as described in the main text. Long-short is the monthly average return of a zero cost portfolio that holds the portfolio of bought stocks and sells short the portfolio of sold stocks. CAPM alpha is the result from a time-series regression of the portfolio excess return (i.e. raw return minus risk-free rate) on the market excess return. Carhart 4 Factor alpha is the result from a time-series regression of the portfolio excess return on the three Fama and French (1993) mimicking portfolios and the Carhart momentum factor.} \end{tabular} \end{center} \end{table} \clearpage \subsection*{A6 Portfolio Choice Conditional on Holding} Table A6 below replicates the portfolio choice regression, but restricts the sample to actively-held positions. The results are very similar to that from our unconditional portfolio choice analysis. Among the companies that they chose to actively hold, members on average place much larger bets in local and contributor companies. \begin{table}[hbt!] \begin{center}\footnotesize \begin{tabular}{l|ccccc} \multicolumn{6}{c}{Table A5: Portfolio Weights as a Function of Member-Firm Connections (Conditional on Holding)}\\ \hline \hline Model & (1) & (2) & (3) & (4) & (5) \\ \hline Dependent Variable: & \multicolumn{ 5}{c}{Portfolio Weight} \\ Mean: & \multicolumn{ 5}{c}{279.59} \\ \hline In District & 274.23 & 114.95 & 272.51 & 264.41 & 186.73 \\ & (87.06) & (66.64) & (87.27) & (84.62) & (81.31) \\ Lobbying (Any) & 11.80 & 14.97 & & & \\ & (16.36) & (16.22) & & & \\ Contributions (Any) & 44.53 & 80.15 & & & \\ & (21.55) & (48.95) & & & \\ In District \& Lobbying (Any) & & 339.93 & & & \\ & & (230.33) & & & \\ In District \& Contributions (Any) & & 428.58 & & & \\ & & (284.77) & & & \\ Lobbying (Any) \& Contributions (Any) & & 45.23 & & & \\ & & (26.74) & & & \\ In District \& Contributions(Any) \& Lobbying (Any) & & 509.35 & & & \\ & & (214.96) & & & \\ Lobbying ($>$ p50) & & & 3.99 & & \\ & & & (19.93) & & \\ Contributions ($>$ p50) & & & 51.94 & & \\ & & & (29.92) & & \\ Lobbying Strength & & & & 0.02 & 0.02 \\ & & & & (0.03) & (0.03) \\ Contribution Strength & & & & 0.03 & 0.02 \\ & & & & (0.02) & (0.02) \\ Lobbying Strength $\cdot$ In District & & & & & 0.02 \\ & & & & & (0.14) \\ Contribution Strength $\cdot$ In District & & & & & 0.10 \\ & & & & & (0.09) \\ \hline Members Fixed Effects & x & x & x & x & x \\ Firms Fixed Effects & x & x & x & x & x \\ \hline N & \multicolumn{ 5}{c}{ 15,093} \\ % N & 15,211 & 15,211 & 15,211 & 15,211 & 15,211 \\ \hline \multicolumn{6}{p{6.8in}}{\tiny {\it Note:} Regression coefficients with standards errors clustered by members in parenthesis. The dependent variable is the \emph{portfolio weight}, i.e. the share of holdings of a firm in a member's portfolio (in basis points). Members' portfolios are computed as average holdings over the 2004-2008 period. \emph{In District} is a binary indicator for firms that are connected to a member since they are located in a member's district. \emph{Lobbying (any)} is a binary indicator for firms that are connected to a member since they lobbied a committee on which the member served. \emph{Contributions (any)} is a binary indicator for firms that are connected to a member since they provided her with campaign contributions. \emph{Lobbying ($>$ p50)} and \emph{Contributions ($>$ p50 )} are binary indicators for firms that provided more than the median lobbying or contribution amount for each member. \emph{Lobbying Strength} and \emph{Contribution Strength} measure a firm's share of lobbying or contributions relative to each member's total lobbying or contributions (in basis points). All regressions include a full set of member and firm fixed effects (coefficients not shown here).} \end{tabular} \end{center} \end{table} \subsection*{A7 Alpha Returns for Investments in Politically Connected Stocks} Table A7 replicates the analysis of Table \ref{tab: connectedalphas} using the single-time series approach where the monthly returns are first aggregated across members (value-weighted or equal-weighted) to a single monthly portfolio return. \begin{table}[hbt!] \begin{center}\scriptsize \begin{tabular}{l|ccc|ccc} \multicolumn{7}{c}{Table A7 Monthly Abnormal Return for Connected and Unconnected Stocks}\\ \hline \hline & \multicolumn{ 3}{c}{Aggregate Congressional Portfolio} & \multicolumn{ 3}{|c}{Average Member Portfolio} \\ \hline & \scriptsize{Connected} & \scriptsize{Unconnected} & \scriptsize{Long/Short} & \scriptsize{Connected} & \scriptsize{Unconnected} & \scriptsize{Long/Short} \\ \hline \multicolumn{ 7}{c}{} \\ \multicolumn{ 7}{c}{Panel A: Excess Returns from CAPM} \\ \multicolumn{ 7}{c}{} \\ \hline Lobbying (Any) & -0.277 & -0.234 & -0.043 & -0.244 & -0.196 & -0.048 \\ & (0.138) & (0.163) & (0.232) & (0.113) & (0.130) & (0.171) \\ Lobbying ($>p50$) & -0.21 & -0.305 & 0.094 & -0.241 & -0.265 & 0.024 \\ & (0.158) & (0.129) & (0.211) & (0.128) & (0.107) & (0.154) \\ Contributions (Any) & -0.083 & -0.311 & 0.228 & -0.147 & -0.28 & 0.133 \\ & (0.234) & (0.094) & (0.230) & (0.175) & (0.086) & (0.151) \\ Contributions ($>p50$) & -0.216 & -0.277 & 0.06 & -0.14 & -0.312 & 0.172 \\ & (0.284) & (0.093) & (0.270) & (0.176) & (0.090) & (0.140) \\ Lobbying \& Contributions & -0.055 & -0.314 & 0.258 & -0.141 & -0.265 & 0.124 \\ & (0.243) & (0.094) & (0.238) & (0.163) & (0.091) & (0.136) \\ In District& 0.07 & -0.283 & 0.353 & 0.354 & -0.335 & 0.688 \\ & (0.426) & (0.088) & (0.424) & (0.192) & (0.093) & (0.173) \\ District \& Contributions & 0.277 & -0.288 & 0.564 & 0.354 & -0.327 & 0.681 \\ & (0.663) & (0.093) & (0.653) & (0.190) & (0.094) & (0.169) \\ District \& Lobbying & 0.579 & -0.298 & 0.877 & 0.433 & -0.324 & 0.757 \\ & (0.486) & (0.092) & (0.475) & (0.192) & (0.091) & (0.155) \\ \hline \multicolumn{ 7}{c}{} \\ \multicolumn{ 7}{c}{Panel B: Excess Returns from Carhart 4-Factor} \\ \multicolumn{ 7}{c}{} \\ \hline Lobbying (Any) & -0.174 & -0.31 & 0.136 & -0.124 & -0.249 & 0.125 \\ & (0.124) & (0.180) & (0.225) & (0.095) & (0.094) & (0.129) \\ Lobbying ($>p50$) & -0.111 & -0.33 & 0.219 & -0.115 & -0.264 & 0.149 \\ & (0.151) & (0.148) & (0.220) & (0.110) & (0.076) & (0.126) \\ Contributions (Any) & 0.03 & -0.302 & 0.332 & -0.019 & -0.259 & 0.239 \\ & (0.235) & (0.100) & (0.233) & (0.137) & (0.074) & (0.112) \\ Contributions ($>p50$) & -0.083 & -0.259 & 0.176 & 0.016 & -0.277 & 0.293 \\ & (0.244) & (0.101) & (0.238) & (0.139) & (0.079) & (0.118) \\ Lobbying \& Contributions & 0.052 & -0.301 & 0.353 & -0.038 & -0.227 & 0.189 \\ & (0.252) & (0.100) & (0.249) & (0.139) & (0.078) & (0.117) \\ In District & 0.044 & -0.246 & 0.29 & 0.423 & -0.272 & 0.696 \\ & (0.429) & (0.094) & (0.424) & (0.152) & (0.086) & (0.168) \\ District \& Contributions & 0.403 & -0.261 & 0.664 & 0.500 & -0.274 & 0.774 \\ & (0.681) & (0.098) & (0.679) & (0.178) & (0.084) & (0.204) \\ District \& Lobbying & 0.721 & -0.272 & 0.993 & 0.529 & -0.268 & 0.797 \\ & (0.521) & (0.097) & (0.514) & (0.173) & (0.078) & (0.177) \\ \hline \hline \multicolumn{7}{p{6in}}{\tiny {\it Note:} Monthly alpha returns for calendar time portfolios of investments in connected and unconnected stocks over the 2004-2008 period. Aggregate returns are for a portfolio that mimics the aggregate investments of all members of Congress (value-weighted) in either connected or unconnected stocks. Average member returns are for a portfolio that mimics the investments of the average member of Congress (equal member weighted) in either connected or unconnected stocks. Long-short is the monthly average return of a zero cost portfolio that holds the portfolio of connected stocks and sells short the portfolio of unconnected stocks. The connections are defined as follows: \emph{In District} connected firms are connected to a member since they are located in a member's home district. \emph{Lobbying (any)} connected firms are connected to a member since they lobbied a committee on which the member served. \emph{Contributions (any)} connected firms are connected to a member since they provided her with campaign contributions. \emph{Lobbying ($>$ p50)} and \emph{Contributions ($>$ p50 )} connected firms are connected since they provided more than the median lobbying or contribution amount for each member. CAPM is the result from a time-series regression of the member excess return on the market excess return. Carhart 4-factor is the result from a time-series regression of the member excess return on the Fama and French (1993) mimicking portfolios and the \citet{carhart1997pmf} momentum factor. Robust standard errors are presented in parentheses.} \end{tabular} \end{center} \end{table} \clearpage \subsection*{A8 Abnormal Returns for Company-Level Connected and Unconnected Stocks} Table A8 uses the aggregated, single time series approach to assess the possibility that companies that had more political connections (lobbying and contributions) systematically outperformed companies that did not. Here we label investments as connected if the company did \emph{any} lobbying/contributions (as opposed to if the company ever lobbied the committee of (or provided campaign contributions to) the member who owns the stock). The fact that the connected portfolios defined this way do not outperform the unconnected portfolios suggests that connected companies did not systematically do better; instead, it must be that members who were connected to a certain company did better investing in that company than did other members who were not connected to it, probably because they knew when to invest. \begin{table}[hbt!] \begin{center}\footnotesize \begin{tabular}{l|ccc|ccc} \multicolumn{ 7}{c}{Table A8 Abnormal Returns for Company Level Connected and Unconnected Stocks}\\ \hline \hline & \multicolumn{ 3}{c}{Aggregate Congressional Portfolio} & \multicolumn{ 3}{|c}{Average Member Portfolio} \\ \hline & \scriptsize{Connected} & \scriptsize{Unconnected} & \scriptsize{Long/Short} & \scriptsize{Connected} & \scriptsize{Unconnected} & \scriptsize{Long/Short} \\ \hline \multicolumn{ 7}{c}{} \\ \multicolumn{ 7}{c}{Panel A: Excess Returns from CAPM} \\ \multicolumn{ 7}{c}{} \\ \hline Lobbying (Any) & -0.291 & -0.152 & -0.138 & -0.247 & -0.06 & -0.187 \\ & (0.104) & (0.178) & (0.237) & (0.144) & (0.197) & (0.230) \\ Contributions (Any) & -0.295 & -0.23 & -0.064 & -0.282 & -0.062 & -0.219 \\ & (0.141) & (0.153) & (0.251) & (0.172) & (0.121) & (0.207) \\ \hline \multicolumn{ 7}{c}{} \\ \multicolumn{ 7}{c}{Panel B: Excess Returns from Carhart 4-Factor} \\ \multicolumn{ 7}{c}{} \\ \hline Lobbying (Any) & -0.236 & -0.325 & 0.089 & -0.126 & -0.154 & 0.028 \\ & (0.096) & (0.202) & (0.242) & (0.100) & (0.119) & (0.133) \\ Contributions (Any) & -0.214 & -0.313 & 0.1 & -0.108 & -0.17 & 0.062 \\ & (0.118) & (0.171) & (0.236) & (0.112) & (0.110) & (0.143) \\ \hline \hline \multicolumn{7}{p{6in}}{\tiny {\it Note:} Monthly alpha returns for calendar time portfolios of investments in connected and unconnected stocks over the 2004-2008 period. Aggregate returns are for a portfolio that mimics the aggregate investments of all members of Congress (value-weighted) in either connected or unconnected stocks. Average member returns are for a portfolio that mimics the investments of the average member of Congress (equal member weighted) in either connected or unconnected stocks. Long-short is the monthly average return of a zero cost portfolio that holds the portfolio of connected stocks and sells short the portfolio of non-connected stocks. The connections here are defined only at the company, not the company-member levels, so for all members a company is coded as connected if it provided campaign contributions (or lobbying depending on the connection) to \emph{any} member in the sample. CAPM is the result from a time-series regression of the member excess return on the market excess return. Carhart 4-factor is the result from a time-series regression of the member excess return on the Fama and French (1993) mimicking portfolios and the \citet{carhart1997pmf} momentum factor.} \end{tabular} \end{center} \end{table} \clearpage \begin{landscape} \subsection*{A9 Excess Returns on Transaction-Based Portfolios by Connection and Holding Period} Table A9 assesses whether connected trades appear to be better timed than other trades, using the same approach (aggregated, transaction-based portfolios) as Table A5. The estimated excess return on the hedged portfolio built from connected trades is generally positive for 140- and 255-day holding periods (though never significant at conventional levels) and larger than that on the hedged portfolio built from unconnected trades, but the difference is not significant. Further, connected trades are if anything worse than unconnected trades for shorter holding periods, suggesting that short-term timing does not explain the performance advantage of local holdings. \begin{table}[hbt!] \scriptsize \begin{tabular}{r|r|rrr|rrr|rrr|rrr} \hline \hline & & \multicolumn{6}{c}{Aggregate Congressional Portfolio} & \multicolumn{6}{c}{Average Member Portfolio} \\ & & \multicolumn{3}{c}{Connected} & \multicolumn{3}{c}{Unconnected} & \multicolumn{3}{c}{Connected} & \multicolumn{3}{c}{Unconnected} \\ Connection & Holding Period & Buys & Sells & L/S & Buys & Sells & L/S & Buys & Sells & L/S & Buys & Sells & L/S \\ \hline In District & 1 Day & 0.159 & -0.434 & 0.319 & 0.574 & 1.524 & -0.950 & 0.229 & -0.371 & 0.315 & 0.785 & 1.703 & -0.918 \\ & & (0.579) & (0.896) & (1.277) & (0.735) & (0.754) & (1.025) & (0.591) & (0.888) & (1.267) & (0.531) & (0.664) & (0.807) \\ In District & 10 Days & -0.599 & 0.902 & -1.357 & -0.619 & 0.329 & -0.948 & -0.344 & 0.879 & -1.120 & 0.048 & 0.357 & -0.308 \\ & & (0.851) & (0.913) & (1.279) & (0.495) & (0.385) & (0.680) & (0.736) & (0.887) & (1.156) & (0.294) & (0.268) & (0.296) \\ In District & 25 Days & 0.153 & 1.197 & -1.044 & -0.319 & 0.172 & -0.491 & 0.394 & 0.798 & -0.404 & 0.227 & 0.113 & 0.114 \\ & & (0.727) & (1.002) & (1.219) & (0.442) & (0.317) & (0.396) & (0.592) & (0.947) & (1.015) & (0.326) & (0.303) & (0.174) \\ In District & 140 Days & 0.492 & -0.670 & 1.162 & -0.034 & -0.197 & 0.163 & 0.090 & -0.246 & 0.336 & -0.163 & -0.217 & 0.054 \\ & & (0.520) & (0.609) & (0.761) & (0.194) & (0.137) & (0.205) & (0.429) & (0.390) & (0.491) & (0.184) & (0.172) & (0.166) \\ In District & 255 Days & 0.333 & -0.574 & 0.907 & -0.154 & -0.112 & -0.042 & -0.152 & -0.148 & -0.004 & -0.011 & -0.153 & 0.143 \\ & & (0.425) & (0.500) & (0.625) & (0.170) & (0.101) & (0.163) & (0.308) & (0.325) & (0.394) & (0.137) & (0.117) & (0.134) \\ \hline District \& Contributions & 1 Days & -0.498 & -0.206 & -0.379 & 0.667 & 1.755 & -1.089 & -0.484 & -0.209 & -0.352 & 0.811 & 1.737 & -0.925 \\ & & (0.720) & (0.412) & (1.084) & (0.736) & (0.749) & (0.965) & (0.717) & (0.411) & (1.077) & (0.529) & (0.655) & (0.799) \\ District \& Contributions & 10 Days & -0.480 & 1.616 & -1.595 & -0.633 & 0.390 & -1.023 & -0.187 & 1.333 & -0.705 & 0.037 & 0.375 & -0.338 \\ & & (1.010) & (1.254) & (1.658) & (0.494) & (0.381) & (0.669) & (0.961) & (1.198) & (1.414) & (0.296) & (0.267) & (0.297) \\ District \& Contributions & 25 Days & -0.704 & 0.565 & -0.914 & -0.311 & 0.202 & -0.513 & -0.134 & 0.731 & -0.391 & 0.237 & 0.181 & 0.056 \\ & & (1.403) & (1.051) & (1.608) & (0.440) & (0.318) & (0.396) & (1.322) & (1.055) & (1.628) & (0.328) & (0.301) & (0.186) \\ District \& Contributions & 140 Days & 0.164 & -0.285 & 0.420 & -0.024 & -0.244 & 0.219 & 0.184 & -0.671 & 0.924 & -0.166 & -0.203 & 0.037 \\ & & (0.994) & (0.685) & (1.057) & (0.193) & (0.130) & (0.207) & (0.705) & (0.644) & (0.779) & (0.182) & (0.168) & (0.165) \\ District \& Contributions & 255 Days & 1.538 & 0.363 & 1.222 & -0.153 & -0.134 & -0.018 & 0.734 & -0.070 & 0.898 & -0.035 & -0.171 & 0.136 \\ & & (0.959) & (0.651) & (1.071) & (0.169) & (0.095) & (0.166) & (0.581) & (0.554) & (0.664) & (0.137) & (0.109) & (0.136) \\ \hline District \& Lobbying & 1 Day & 0.395 & -0.327 & 0.516 & 0.634 & 1.792 & -1.157 & 0.535 & -0.271 & 0.590 & 0.806 & 1.764 & -0.958 \\ & & (0.548) & (0.403) & (0.756) & (0.737) & (0.752) & (0.960) & (0.547) & (0.411) & (0.769) & (0.530) & (0.654) & (0.789) \\ District \& Lobbying & 10 Days & 0.494 & 1.461 & -1.415 & -0.636 & 0.369 & -1.005 & 0.931 & 1.278 & -0.676 & 0.035 & 0.376 & -0.341 \\ & & (1.096) & (1.014) & (1.741) & (0.494) & (0.377) & (0.668) & (1.033) & (0.966) & (1.617) & (0.296) & (0.269) & (0.295) \\ District \& Lobbying & 25 Days & 0.068 & 0.653 & -0.958 & -0.323 & 0.187 & -0.509 & 0.516 & 0.290 & -0.082 & 0.228 & 0.167 & 0.061 \\ & & (0.823) & (1.006) & (1.310) & (0.441) & (0.317) & (0.394) & (0.755) & (0.971) & (1.191) & (0.329) & (0.305) & (0.181) \\ District \& Lobbying & 140 Days & 1.066 & 0.048 & 1.029 & -0.034 & -0.250 & 0.216 & 0.623 & -0.224 & 0.842 & -0.166 & -0.187 & 0.021 \\ & & (0.637) & (0.544) & (0.700) & (0.194) & (0.130) & (0.208) & (0.498) & (0.459) & (0.551) & (0.182) & (0.171) & (0.164) \\ District \& Lobbying & 255 Days & 1.182 & 0.886 & 0.287 & -0.156 & -0.146 & -0.010 & 0.277 & 0.309 & -0.043 & -0.006 & -0.172 & 0.166 \\ & & (0.496) & (0.491) & (0.616) & (0.170) & (0.095) & (0.165) & (0.395) & (0.365) & (0.491) & (0.141) & (0.110) & (0.135) \\ \end{tabular} \end{table} \end{landscape} \clearpage \subsection*{A10 Performance of Local Stocks by Firm Size} Table A10 assesses whether the local premium seems to depend on the size and visibility of the company. We apply the panel regression model (both Carhart Four-Factor and CAPM) to three portfolios of local stocks: local companies in the S\&P 500 (at some point in the 2004-2008 period), local companies not in the S\&P 500, and a hedged portfolio long in local S\&P 500 companies and short in local non-S\&P 500 companies. If the local premium were derived from members' information about low-visibility local companies, we might expect the premium to be larger for non-S\&P 500 companies than for S\&P 500 companies. We do not find a significant difference between the return on S\&P 500 and non-S\&P 500 companies; if anything the S\&P 500 companies do better. \begin{table}[hbt!] \begin{center}\footnotesize \begin{tabular}{r|ccc|ccc} \multicolumn{7}{c}{Table A10: Returns on Local Stocks in S\&P 500}\\ \hline \hline & 1 & 2 & 3 & 4 & 5 & 6 \\ \hline & \multicolumn{ 3}{c|}{Carhart 4 Factor} & \multicolumn{ 3}{c}{CAPM} \\ \hline In S\&P 500 & Yes & No & L/S & Yes & No & L/S \\ \hline $R_{m,t}-R_{f,t}$ & 0.91 & 0.93 & -0.16 & 0.95 & 1.09 & -0.25 \\ & (0.04) & (0.06) & (0.10) & (0.05) & (0.08) & (0.10) \\ $SMB_t$ & 0.07 & 0.47 & -0.32 & & & \\ & (0.07) & (0.10) & (0.16) & & & \\ $HML_t$ & 0.14 & 0.26 & 0.19 & & & \\ & (0.07) & (0.12) & (0.23) & & & \\ $MOM_t$ & -0.19 & -0.19 & 0.05 & & & \\ & (0.04) & (0.09) & (0.13) & & & \\ Alpha & 0.34 & 0.22 & 0.22 & 0.28 & 0.23 & 0.31 \\ & (0.11) & (0.20) & (0.35) & (0.14) & (0.24) & (0.29) \\ \hline N & 2767 & 2993 & 1153 & 2767 & 2993 & 1153 \\ \hline \end{tabular} \end{center} \end{table} %\subsection*{A2 Alpha Returns Estimated with Panel Regression} % %Table A.2 contains our replication of the monthly excess returns at the member-month level. To this end we run a panel regression where each Member's excess return in a given month is regressed on the Fama and French mimicking portfolios and the Carhart momentum factor (so the in contrast to the regressions presented in the main body of the paper the returns are not first averaged across members). This regression yields the average alpha return across Members. In order to account for the cross-sectional correlation of returns, we cluster the standard errors by month. The first column presents the alpha return for the sample of all Members. The monthly average return is estimated at -.23 (or 2.76 percent per year) which is almost identical to the -0.26 estimated for the average member return using the aggregated model (the tiny differences result from the fact that in the aggregated regression each month is weighted equally which in this regression each member-month is weighted equally). Models 2-23 present the alpha estimates for the different subgroups. The negative alpha returns are very stable across subgroups. The key exception here is the group of Members that owned a business prior in their pre-congressional careers (model 21). Prior business owners on average perform as well as the market with a monthly alpha of .04. % % %\begin{landscape} %\begin{table} %\footnotesize %%\begin{tabular}{l|c|ccc|ccc|ccc|ccc} %\begin{tabular}{l|ccccccccccccc} %\multicolumn{14}{c}{Table A.2: Percentage Monthly Abnormal Return}\\ %\hline % Model & (1) & (2) & (3) & (4) & (5) & (6) & (7) & (8) & (9) & (10) & (11) & (12) & (13) \\ %\hline % & & \multicolumn{ 2}{c}{Chamber} & \multicolumn{ 3}{c}{Power Committee} & \multicolumn{ 2}{c}{Period} & \multicolumn{ 2}{c}{Party} & \multicolumn{ 3}{c}{Seniority} \\ % & All & House & Senate & House & Senate & None & 2004-06 & 2007-08 & Dems & Reps & Low & Medium & High \\ %\hline % $R_{m,t}-R_{f,t}$ & 0.90 & 0.89 & 0.94 & 0.85 & 0.92 & 0.93 & 0.97 & 0.87 & 0.89 & 0.91 & 0.89 & 0.87 & 0.94 \\ % & (0.03) & (0.04) & (0.03) & (0.05) & (0.04) & (0.03) & (0.06) & (0.03) & (0.04) & (0.04) & (0.06) & (0.04) & (0.02) \\ % $SMB_t$ & 0.10 & 0.10 & 0.14 & 0.19 & 0.04 & 0.06 & 0.03 & -0.14 & 0.15 & 0.07 & 0.08 & 0.16 & 0.05 \\ % & (0.05) & (0.06) & (0.06) & (0.07) & (0.08) & (0.04) & (0.06) & (0.08) & (0.07) & (0.05) & (0.07) & (0.05) & (0.05) \\ % $HML_t$ & 0.21 & 0.23 & 0.13 & 0.24 & 0.12 & 0.21 & 0.07 & 0.29 & 0.15 & 0.26 & 0.09 & 0.23 & 0.28 \\ % & (0.05) & (0.05) & (0.07) & (0.07) & (0.08) & (0.05) & (0.06) & (0.08) & (0.06) & (0.06) & (0.07) & (0.06) & (0.05) \\ % $MOM_t$ & -0.18 & -0.20 & -0.11 & -0.26 & -0.08 & -0.15 & -0.05 & -0.25 & -0.18 & -0.19 & -0.16 & -0.14 & -0.24 \\ % & (0.04) & (0.05) & (0.03) & (0.06) & (0.03) & (0.04) & (0.04) & (0.04) & (0.05) & (0.04) & (0.05) & (0.04) & (0.03) \\ % Alpha & -0.23 & -0.26 & -0.12 & -0.26 & -0.10 & -0.24 & -0.12 & -0.28 & -0.30 & -0.17 & -0.27 & -0.22 & -0.19 \\ % & (0.09) & (0.10) & (0.11) & (0.13) & (0.13) & (0.09) & (0.11) & (0.14) & (0.12) & (0.10) & (0.12) & (0.11) & (0.09) \\ %\hline % N & 18388 & 14475 & 3808 & 6847 & 2637 & 8904 & 11818 & 6570 & 8621 & 9754 & 5602 & 7171 & 5615 \\ %\hline\hline % \multicolumn{ 14}{c}{} \\ % \multicolumn{ 14}{c}{} \\ %\hline % Model & (14) & (15) & (16) & (17) & (18) & (19) & (20) & (21) & (22) & (23) & & & \\ % \hline % & \multicolumn{ 3}{c}{Net Worth} & \multicolumn{ 3}{c}{Portfolio Size} & \multicolumn{ 4}{c}{Pre-Congressional Career} & & & \\ % & Low & Medium & High & Low & Medium & High & Other & Business & Lawyer & Politician & & & \\ % $R_{m,t}-R_{f,t}$ & 0.87 & 0.94 & 0.88 & 0.89 & 0.89 & 0.92 & 0.88 & 0.93 & 0.89 & 0.96 & & & \\ % & (0.06) & (0.03) & (0.03) & (0.07) & (0.04) & (0.02) & (0.04) & (0.04) & (0.04) & (0.04) & & & \\ % $SMB_t$ & 0.17 & 0.07 & 0.09 & 0.13 & 0.17 & 0.02 & 0.08 & 0.09 & 0.28 & 0.04 & & & \\ % & (0.08) & (0.05) & (0.05) & (0.07) & (0.07) & (0.03) & (0.05) & (0.08) & (0.08) & (0.09) & & & \\ % $HML_t$ & 0.20 & 0.19 & 0.23 & 0.28 & 0.20 & 0.16 & 0.18 & 0.19 & 0.36 & 0.17 & & & \\ % & (0.08) & (0.05) & (0.05) & (0.08) & (0.07) & (0.04) & (0.05) & (0.08) & (0.09) & (0.09) & & & \\ % $MOM_t$ & -0.28 & -0.10 & -0.18 & -0.21 & -0.23 & -0.11 & -0.18 & -0.23 & -0.11 & -0.23 & & & \\ % & (0.06) & (0.04) & (0.02) & (0.06) & (0.05) & (0.02) & (0.05) & (0.05) & (0.05) & (0.06) & & & \\ %Alpha & -0.32 & -0.13 & -0.26 & -0.15 & -0.29 & -0.24 & -0.23 & 0.04 & -0.34 & -0.21 & & & \\ % & (0.15) & (0.10) & (0.08) & (0.15) & (0.12) & (0.05) & (0.09) & (0.16) & (0.15) & (0.17) & & & \\ %\hline % N & 5422 & 6483 & 6470 & 5422 & 6388 & 6578 & 11095 & 1131 & 2650 & 3407 & & & \\ %\hline\hline %\multicolumn{14}{p{7.5in}}{\tiny {\it Note:} Regression coefficients with robust standards errors clustered by month in parenthesis. The dependent variable is the Member monthly excess return $R_{i,t}-R_{f,t}$. Returns are based on end-of-year financial disclosure reports for all members of Congress that report common stocks between 2004 to 2008. Daily portfolio returns are computed based on reported holdings and transactions and then compounded to the monthly level (see text for details). Controls are the Fama and French (1993) mimicking portfolios (the market excess return ($R_{m,t}-R_{f,t}$), a zero-investment size portfolio ($SMB_t$), a zero-investment book-to-market portfolio ($HML_t$)) and the \citet{carhart1997pmf} momentum factor ($MOM_t$). } %\end{tabular} %\end{table} %\end{landscape} % %\subsection*{A4 Alpha Returns by Member-Firm Connected with Panel Regression} % %Table A4 below replicates the monthly alpha returns for investments in connected and unconnected stocks using the panel regression approach where the unit of analysis is the member-month. The results are very similar to the aggregate level analysis presented in the main paper. On average Members perform better with politically connected companies (the estimates for the long/short portfolio are positive and mostly significant). For the contributor and the lobbying connection the investments in connected stocks perform about as well as the market. For the in district connection, the connected investments again soundly beat the market. The average monthly alpha returns are .24 ($p.=.05$) for investments in companies that are headquartered in district, .39 ($p.=.01$) for investments in companies that are in district and also contributed to the Member's campaign, and .43 ($p=.01$) for companies that are in district and reported lobbying. % % % %\clearpage % % % %\clearpage % %\section*{Appendix B} % %\begin{figure}[!hbt] %Figure A.1. Cumulative Abnormal Returns for Aggregate Congressional Portfolio and the Average Congressional Member\\ % %\centering % \includegraphics[scale=.45]{figs/all_members_ziob_mapping_holding_returns_tw} % \includegraphics[scale=.45]{figs/all_members_ziob_mapping_holding_returnshed_tw} % \includegraphics[scale=.45]{figs/all_members_ziob_mapping_holding_returns_ew} % \includegraphics[scale=.45]{figs/all_members_ziob_mapping_holding_returnshed_ew} %\tiny{Note: Cumulative Abnormal Returns (CARs) for aggregate congressional portfolio and the average congressional member. The aggregate portfolio mimics the aggregate trades of all members of Congress (value-weighted). The average congressional member portfolio mimics the trades of the average Member of Congress (equal Member weighted). The CARs are normalized such that the value is zero on the trading day zero. The left figures show the CARs for the buy and and sell portfolios. The right figures show the CARs for a zero cost portfolio that holds the portfolio of bought stocks and sells short the portfolio of sold stocks.} %\end{figure} % %\clearpage \end{document}