Vol 66 October 2011 No Editor Co-Editor CAMPBELL R HARVEY Duke University JOHN GRAHAM Duke University Associate Editors VIRAL ACHARYA New York University FRANCIS A LONGSTAFF University of California, Los Angeles ANAT R ADMATI Stanford University HANNO LUSTIG University of California, Los Angeles ANDREW ANG Columbia University ANDREW METRICK Yale University KERRY BACK Rice University TOBIAS J MOSKOWITZ University of Chicago MALCOLM BAKER Harvard University DAVID K MUSTO University of Pennsylvania NICHOLAS C BARBERIS Yale University STEFAN NAGEL Stanford University NITTAI K BERGMAN Massachusetts Institute of Technology TERRANCE ODEAN University of California, Berkeley HENDRIK BESSEMBINDER University of Utah CHRISTINE A PARLOUR University of California, Berkeley MICHAEL W BRANDT Duke University ´ L˘ UBOS˘ PASTOR University of Chicago ALON BRAV Duke University LASSE H PEDERSEN New York University MARKUS K BRUNNERMEIER Princeton University MITCHELL A PETERSEN Northwestern University DAVID A CHAPMAN Boston College MANJU PURI Duke University MIKHAIL CHERNOV London School of Economics RAGHURAM RAJAN University of Chicago JENNIFER S CONRAD University of North Carolina FRANCESCA CORNELLI London Business School BERNARD DUMAS INSEAD BURTON HOLLIFIELD Carnegie Mellon University HARRISON HONG Princeton University NARASIMHAN JEGADEESH Emory University WEI JIANG Columbia University STEVEN N KAPLAN University of Chicago JONATHAN M KARPOFF University of Washington ARVIND KRISHNAMURTHY Northwestern University MICHAEL LEMMON University of Utah JOSHUA RAUH Northwestern University MICHAEL R ROBERTS University of Pennsylvania ANTOINETTE SCHOAR Massachusetts Institute of Technology HENRI SERVAES London Business School ANIL SHIVDASANI University of North Carolina RICHARD STANTON University of California, Berkeley ANNETTE VISSING-JORGENSEN Northwestern University ANDREW WINTON University of Minnesota Business Manager DAVID H PYLE University of California, Berkeley Assistant Editor WENDY WASHBURN HELP DESK The Latest Information Our World Wide Web Site For the latest information about the journal, about our annual meeting, or about other announcements of interest to our membership, consult our web site at http://www.afajof.org Subscription Questions or Problems Address Changes Journal Customer Services: For ordering information, claims, and any enquiry concerning your journal subscription, please go to interscience.wiley.com/support or contact your nearest office Americas: Email: cs-journals@wiley.com; 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email: pyle@haas.berkeley.edu Volume 66 CONTENTS for OCTOBER 2011 No ARTICLES In Search of Attention ZHI DA, JOSEPH ENGELBERG, and PENGJIE GAO Free Cash Flow, Issuance Costs, and Stock Prices JEAN-PAUL DE´ CAMPS, THOMAS MARIOTTI, JEAN-CHARLES ROCHET, ´ VILLENEUVE and STEPHANE A Unified Theory of Tobin’s q, Corporate Investment, Financing, and Risk Management PATRICK BOLTON, HUI CHEN, and NENG WANG Nonbinding Voting for Shareholder Proposals DORON LEVIT and NADYA MALENKO The Real and Financial Implications of Corporate Hedging MURILLO CAMPELLO, CHEN LIN, YUE MA, and HONG ZOU Concentrating on Governance DALIDA KADYRZHANOVA and MATTHEW RHODES-KROPF 1461 1501 1545 1579 1615 1649 Overconfidence and Early-Life Experiences: The Effect of Managerial Traits on Corporate Financial Policies ULRIKE MALMENDIER, GEOFFREY TATE, and JON YAN 1687 Overconfidence, Compensation Contracts, and Capital Budgeting SIMON GERVAIS, J B HEATON, and TERRANCE ODEAN 1735 Are Incentive Contracts Rigged by Powerful CEOs? ADAIR MORSE, VIKRAM NANDA, and AMIT SERU 1779 Motivating Innovation GUSTAVO MANSO 1823 MISCELLANEA 1861 THE JOURNAL OF FINANCE • VOL LXVI, NO • OCTOBER 2011 In Search of Attention ZHI DA, JOSEPH ENGELBERG, and PENGJIE GAO∗ ABSTRACT We propose a new and direct measure of investor attention using search frequency in Google (Search Volume Index (SVI)) In a sample of Russell 3000 stocks from 2004 to 2008, we find that SVI (1) is correlated with but different from existing proxies of investor attention; (2) captures investor attention in a more timely fashion and (3) likely measures the attention of retail investors An increase in SVI predicts higher stock prices in the next weeks and an eventual price reversal within the year It also contributes to the large first-day return and long-run underperformance of IPO stocks What information consumes is rather obvious: it consumes the attention of its recipients Hence, a wealth of information creates a poverty of attention and a need to allocate that attention efficiently among the overabundance of information sources that might consume it “Designing Organizations for an Information-Rich World,” in Martin Greenberger, Computers, Communication, and the Public Interest [Baltimore, MD: The Johns Hopkins Press, 1971, 40–41] Herbert Simon, Nobel Laureate in Economics TRADITIONAL ASSET PRICING models assume that information is instantaneously incorporated into prices when it arrives This assumption requires that ∗ Da is with University of Notre Dame, Engelberg is with the University of California at San Diego, and Gao is with University of Notre Dame We thank Nick Barberis; Robert Battalio; Andriy Bodnaruk; Zhiwu Chen; Jennifer Conrad; Shane Corwin; Mark Greenblatt; Campbell Harvey (the editor); David Hirshleifer; Kewei Hou; Byoung-Hyoun Hwang; Ryan Israelsen; Ravi Jagannathan; Robert Jennings; Gabriele Lepori; Dong Lou; Tim Loughran; Ernst Schaumburg; Paul Schultz; Mark Seasholes; Ann Sherman; Sophie Shive; Avanidhar Subrahmanyam; Paul Tetlock; Heather Tookes; Annette Vissing-Jorgensen; Mitch Warachka; Yu Yuan; an anonymous associate editor; two anonymous referees; and seminar participants at AQR Capital Management, HEC Montreal, Purdue University, Singapore Management University, University of California at Irvine, University of North Carolina at Chapel Hill, University of Georgia, University of Hong Kong, University of Oklahoma, University of Notre Dame, Fifth Yale Behavioral Science Conference, the 2009 NBER Market Microstructure meeting, Macquarie Global Quant Conference, 2009 Chicago Quantitative Aliance Academic Competition, 2010 American Finance Association, 2010 Crowell Memorial Prize Paper Competition, and Center of Policy and Economic Research (CEPR) European Summer Symposia for helpful comments and discussions We thank Frank Russell and Company for providing us with the historical Russell 3000 index membership data, Dow Jones & Company for providing us with the news data, Market System Incorporated (MSI) for providing us with the Dash-5 data, and IPO SCOOP for providing us with the IPO rating data We are grateful to Robert Battalio, Hyunyoung Choi, Amy Davison, Ann Sherman, and Paul Tetlock for their assistance with some of the data used in this study Xian Cai, Mei Zhao, Jianfeng Zhu, and Mendoza IT Group provided superb resesarch assistance We are responsible for remaining errors 1461 1462 The Journal of Finance R investors allocate sufficient attention to the asset In reality, attention is a scarce cognitive resource (Kahneman (1973)), and investors have limited attention Recent studies provide a theoretical framework in which limited attention can affect asset pricing statics as well as dynamics.1 When testing theories of attention, empiricists face a substantial challenge: we not have direct measures of investor attention We have indirect proxies for investor attention such as extreme returns (Barber and Odean (2008)), trading volume (Barber and Odean (2008), Gervais, Kaniel, and Mingelgrin (2001), and Hou, Peng, and Xiong (2008)), news and headlines (Barber and Odean (2008) and Yuan (2008)), advertising expense (Chemmanur and Yan (2009), Grullon, Kanatas, and Weston (2004), and Lou (2008)), and price limits (Seasholes and Wu (2007)) These proxies make the critical assumption that if a stock’s return or turnover was extreme or its name was mentioned in the news media, then investors should have paid attention to it However, return or turnover can be driven by factors unrelated to investor attention and a news article in the Wall Street Journal does not guarantee attention unless investors actually read it This is especially true in the so-called information age where “a wealth of information creates a poverty of attention.” In this paper, we propose a novel and direct measure of investor attention using aggregate search frequency in Google and then revisit the relation between investor attention and asset prices We use aggregate search frequency in Google as a measure of attention for several reasons First, Internet users commonly use a search engine to collect information, and Google continues to be the favorite Indeed, as of February 2009, Google accounted for 72.1% of all search queries performed in the United States.2 The search volume reported by Google is thus likely to be representative of the internet search behavior of the general population Second, and more critically, search is a revealed attention measure: if you search for a stock in Google, you are undoubtedly paying attention to it Therefore, aggregate search frequency in Google is a direct and unambiguous measure of attention For instance, Google’s Chief Economist Hal Varian recently suggested that search data have the potential to describe interest in a variety of economic activities in real time Choi and Varian (2009) support this claim by providing evidence that search data can predict home sales, automotive sales, and tourism Ginsberg et al (2009) similarly find that search data for 45 terms related to influenza predicted flu outbreaks to weeks before Centers for Disease Control and Prevention (CDC) reports The authors conclude that, “harnessing the collective intelligence of millions of users, Google web search logs can provide one of the most timely, broad-reaching influenza monitoring systems available today” (p 1014) Google makes the Search Volume Index (SVI) of search terms public via the product Google Trends (http://www.google.com/trends) Weekly SVI for a See, for example, Merton (1987), Sims (2003), Hirshleifer and Teoh (2003), and Peng and Xiong (2006) Source: Hitwise (http://www.hitwise.com/press-center/hitwiseHS2004/google-searches-feb-09 php) In Search of Attention 1463 search term is the number of searches for that term scaled by its time-series average Panel A of Figure plots the weekly SVI of the two search terms “diet” and “ cranberry” for January 2004 to February 2009 The news reference volumes are also plotted in the bottom of the figure SVI appears to capture attention well The SVI for “diet” falls during the holiday season and spikes at the beginning of the year, consistent with the notion that individuals pay less attention to dieting during the holidays (November and December) but more attention in January as part of a New Year’s resolution, where as the SVI for “cranberry” spikes in November and December, coinciding with the Thanksgiving and Christmas holidays To capture attention paid towards particular stocks, we examine the SVI for stock ticker symbols (e.g., “AAPL” for Apple Computer and “MSFT” for Microsoft) After obtaining the SVI associated with stock ticker symbols for all Russell 3000 stocks, we proceed in three steps First, we investigate the relationship between SVI and existing attention measures We find that the time-series correlations between (log) SVI and alternative weekly measures of attention such as extreme returns, turnover, and news are positive on average but the level of the correlation is low In a vector autoregression (VAR) framework, we find that (log) SVI actually leads alternative measures such as extreme returns and news, consistent with the notion that investors may start to pay attention to a stock in anticipation of a news event When we focus on our main variable, abnormal SVI (ASVI), which is defined as the (log) SVI during the current week minus the (log) median SVI during the previous eight weeks, we find that the majority of the time-series and cross-sectional variation in ASVI remains unexplained by alternative measures of attention We also find that a stock’s SVI has little correlation with a news-based measure of investor sentiment Second, we examine whose attention SVI is capturing Consistent with intuition, we find strong evidence that SVI captures the attention of individual/retail investors Using retail order execution from SEC Rule 11Ac1-5 (Dash-5) reports, we find a strong and direct link between SVI changes and trading by retail investors Interestingly, across different market centers, the same increase in SVI leads to greater individual trading in the market center that typically attracts less sophisticated retail investors (i.e., Madoff) than in the market center that attracts more sophisticated retail investors (i.e., NYSE for NYSE stocks and Archipelago for NASDAQ stocks) This difference suggests that SVI likely captures the attention of less sophisticated individual investors Third, having established that SVI captures retail investor attention, we test the attention theory of Barber and Odean (2008) Barber and Odean (2008) argue that individual investors are net buyers of attention-grabbing stocks and thus an increase in individual investor attention results in temporary positive price pressure The reasoning behind their argument goes as follows When individual investors are buying, they have to choose from a large set of available alternatives However, when they are selling, they can only sell what they own This means that shocks to retail attention should lead, on average, 1464 The Journal of Finance R Figure Illustrations of Google Trends search Panel A represents the graphical output for a Google Trends search of “diet, cranberry.” The graph plots weekly aggregate search frequency (SVI) for both “diet” and “cranberry.” The SVI for “diet” is the weekly search volume for “diet” scaled by the average search volume of “diet,” while the SVI for “cranberry” is the weekly search volume for “cranberry” scaled by the average search volume of “diet.” Panel B represents the graphical output for a Google Trends search of the terms “MSFT, AAPL.” The graph plots weekly SVI for both “MSFT” and “AAPL.” The SVI for “MSFT” is the weekly search volume for “MSFT” scaled by the average search volume of “MSFT,” while the SVI for “AAPL” is the weekly search volume for “AAPL” scaled by the average search volume of “MSFT.” In Search of Attention 1465 to net buying from these uninformed traders Within the framework of Barber and Odean (2008), a positive ASVI should predict higher stock prices in the short term and price reversals in the long run Furthermore, we expect to find stronger attention-induced price pressure among stocks in which individual investor attention matters the most Our empirical results based on ASVI as a measure of retail attention strongly support the hypotheses of Barber and Odean (2008) Among our sample of Russell 3000 stocks, stocks that experience an increase in ASVI this week are associated with an outperformance of more than 30 basis points (bps) on a characteristic-adjusted basis during the subsequent two weeks This initial positive price pressure is almost completely reversed by the end of the year In addition, we find such price pressure to be stronger among Russell 3000 stocks that are traded more by individual investors The fact that we document strong price pressure associated with SVI even after controlling for a battery of alternative attention measures highlights the incremental value of SVI In fact, ASVI is the only variable to predict both a significant initial price increase and a subsequent price reversal A natural venue to test the retail attention hypothesis is a stock’s initial public offering (IPO) IPOs follow the pattern predicted by the attention-induced price pressure hypothesis As studied in Loughran and Ritter (1995, 2002), among many others, IPOs usually experience temporarily high returns followed by longer-run reversal Moreover, many authors have suggested these two stylized features of IPO returns are related to the behavior of retail investors (Ritter and Welch (2002), Ljungqvist, Nanda, and Singh (2006), and Cook, Kieschnick, and Van Ness (2006)) Because search volume exists prior to the IPO while other trading-based measures not, SVI offers a unique opportunity to empirically study the impact of retail investor attention on IPO returns We find considerable evidence that retail attention measured by search volume is related to IPO first-day returns and subsequent return reversal First, we find that searches related to IPO stocks increase by almost 20% during the IPO week The jump in SVI indicates a surge in public attention consistent with the marketing role of IPOs documented by Demers and Lewellen (2003) When we compare the group of IPOs that experiences large positive ASVI during the week prior to the IPO to the group of IPOs that experiences smaller ASVI, we find that the former group outperforms the latter by 6% during the first day after the IPO and the outperformance is statistically significant We also document significant long-run return reversals among IPO stocks that experience large increases in search prior to their IPOs and large first-day returns after their IPOs These patterns are confirmed using cross-sectional regressions after taking into account a comprehensive list of IPO characteristics, aggregate market sentiment, and an alternative attention measure of media coverage, as discussed in Liu, Sherman, and Zhang (2009) Our results are different, however, from those in Liu, Sherman, and Zhang (2009), who find that increased pre-IPO investor attention as measured by media coverage does not lead to price reversal or underperformance in the long run The difference 1466 The Journal of Finance R in these two paper’s findings highlights the subtleties between news-based and search-based measures of investor attention.3 The rest of the paper is organized as follows Section I describes data sources and how we construct the aggregate Google SVI variable Section II compares our SVI measure to alternative proxies of investor attention and examines additional factors that drive our SVI measure Section III provides direct evidence that SVI captures the attention of retail investors Section IV tests the price pressure hypothesis of Barber and Odean (2008) in various settings Section V concludes I Data and Sample Construction Google Trends provides data on search term frequency dating back to January 2004 For our analysis, we download the weekly Search Volume Index for individual stocks To make the data collection and cleaning task manageable, we focus on stocks in the Russell 3000 index for most of the paper The Russell 3000 index contains the 3,000 largest U.S companies, representing more than 90% of the total U.S equity market capitalization We obtain the membership of the Russell 3000 index directly from Frank Russell and Company To eliminate survivorship bias and the impact of index addition and deletion, we examine all 3,606 stocks ever included in the index during our sampling period from January 2004 to June 2008 As Russell 3000 stocks are relatively large stocks, our results are less likely to be affected by bid-ask bounce To further alleviate market microstructure-related concerns, we exclude stock-week observations for which the market price is less than three dollars when testing the attention-induced price pressure hypothesis Our next empirical choice concerns the identification of a stock in Google A search engine user may search for a stock in Google using either its ticker or company name Identifying search frequencies by company name may be problematic for two reasons First, investors may search the company name for reasons unrelated to investing For example, one may search “Best Buy” for online shopping rather than collect financial information about the firm This problem is more severe if the company name has multiple meanings (e.g., “Apple” or “Amazon” ) Second, different investors may search the same firm using several variations of its name For example, American Airlines is given a company name of “AMR Corp.” in CRSP However, investors may search for the company in Google using any one of the following: “AMR Corp,” “ AMR,” “AA,” or “American Airlines.” Searching for a stock using its ticker is less ambiguous If an investor is searching “AAPL” (the ticker for Apple Computer Inc.) in Google, it is likely that she is interested in financial information about the stock of Apple Inc However, there is no inherent inconsistency in these two seemingly different results SVI is likely to capture the attention of less sophisticated retail investors, while pre-IPO media coverage is likely to reflect information demand and attention of institutional investors, as suggested in Liu, Sherman, and Zhang (2009) In Search of Attention 1467 Since we are interested in studying the impact of investor attention on trading and asset pricing, this is precisely the group of people whose attention we would like to capture Since a firm’s ticker is always uniquely assigned, identifying a stock using its ticker also avoids the problem of multiple reference names For these reasons, we choose to identify a stock using its ticker for the majority of our study The only exception is when we examine IPO stocks Because the ticker is not widely available prior to the IPO, we search for the company using its company name We are cautious about using tickers with a generic meaning such as “GPS,” “DNA,” “BABY,” “A,” “ B,” and “ALL.” We manually go through all the Russell stock tickers in our sample and flag such “noisy” tickers These tickers are usually associated with abnormally high SVIs that may have nothing to with attention paid to the stocks with these ticker symbols While we report the results using all tickers to avoid subjectivity in sample construction, we confirm that our results are robust to the exclusion of the “noisy” tickers we identified (about 7% of all Russell 3000 stocks) Panel B of Figure plots the SVI of Apple’s ticker (AAPL) against that of Microsoft (MSFT) Two interesting observations emerge from this figure First, we observe spikes in the SVI of “AAPL” in the beginning of a year These spikes are consistent with increasing public attention coming from (1) the MacWorld conference that is held during the first week of January and (2) awareness of the company after receiving Apple products as holiday gifts Second, SVIs are correlated with but remain different from news coverage These two observations again support our argument that SVI indeed captures investor attention and is different from existing proxies of attention To collect data on all 3,606 stocks in our sample (i.e., all stocks ever included in the Russell 3000 index during our sample period), we employ a web crawling program that inputs each ticker and uses the Google Trends’ option to download the SVI data into a CSV file.4 We this for all stocks in our sample This generates a total of 834,627 firm-week observations Unfortunately, Google Trends does not return a valid SVI for some of our queries If a ticker is rarely searched, Google Trends will return a zero value for that ticker’s SVI.5 Of our 834,627 firm-week observations, 468,413 have a valid SVI For comparison, we also collect two other types of SVI First, we collect SVIs based on company name (Name SVI) We have two independent research assistants report how they would search for each company based on the company To increase the response speed, Google currently calculates SVI from a random subset of the actual historical search data This is why SVIs on the same search term might be slightly different when they are downloaded at different points in time We believe that the impact of such sampling error is small for our study and should bias against finding significant results When we download the SVIs several times and compute their correlation, we find the correlations are usually above 97% In addition, we also find that if we restrict our analysis to a subset of SVIs for which the sampling error standard deviation reported by Google Trends is low, we get stronger results The truncation issue almost certainly works against us as we analyze price pressure in this paper As our empirical results suggest, price pressure is typically stronger among small stocks These are precisely the set of stocks that, on average, will have less search and be removed from the sample due to Google’s truncation Motivating Innovation 1853 I now argue that some incentive compatibility constraints are redundant If (i, j) = (1, 1), then it follows from IC 110 and IC i j that IC i j are redundant If (i, k) = (1, 1), then it follows from IC 101 and IC i1k that IC i0k are redundant If j ik = 221 and either i = 2, j = 2, or k = 2, then it follows from c2 /c1 ≥ (E[p2 ] − p0 )/(p1 − p0 ) that IC i j is redundant Rewriting the incentive compatibility k constraints that are not redundant, we have: ( p1 − p0 )w SS ≥ c1 , IC 101 ( p1 − p0 )w F S ≥ c1 , IC 110 ( p1 − p0 )w S + p12 − p0 p1 w SS − p12 − p0 p1 w F S ≥ c1 , IC 011 and ( p1 − E[ p2 ])w S + p12 − E[ p2 ]E[ p2 |S, 2] w SS − p12 − E[ p2 ] p1 w F S ≥ c1 − c2 +E[ p2 ](c1 − c2 ) (IC 221 ) I now show that IC 101 and IC 110 are binding If that is not the case, then either ≡ w SS − c1 >0 p1 − p0 ≡ w SF − c1 > p1 − p0 or Let w be the same as w except that w SS = wSS − , wS = wS + p1 , w FS = wFS − , and wF = wF + (1 − p1 ) The contract w satisfies the above constraints, W(w , 111 ) = W(w, 111 ), and w pays the agent earlier than w The incentive compatibility constraints IC 221 and IC 011 then become ( p1 − p0 )w S ≥ c1 ( p1 −E[ p2 ])w S + E[ p2 ](E[ p2 |S, 2] − p1 ) IC 011 c1 p1 − p0 ≥ c1 − c2 + E[ p2 ](c1 − c2 ) IC 221 If c2 /c1 ≥ β , then IC 011 is binding Otherwise, IC 221 is binding Proof of Proposition 2: The optimal contract w that implements action plan 221 satisfies the following incentive compatibility constraints: (E[ p2 ] −E[ pi ]) VS w, 221 − VF w, 221 + E[ pi ](E[ p2 |S, 2] − E[ p j |S, i])(w SS − w SF ) + (1 − E[ pi ])( p1 − E[ pk|F, i])(w F S − w F F ) ≥ (c2 + E[ p2 ]c2 + (1 − E[ p2 ])c1 ) − (ci + E[ pi ]c j + (1 − E[ pi ])ck) IC i j k 1854 The Journal of Finance R First, I show that wS = wSF = wFF = Suppose wS > Let w be the S − There exists an > same as w except that w S = 0, w SS = w SS + E[ pw2 |S,2] such that the contract w satisfies all IC i j and W(w , 221 ) < W(w, 221 ) Now k suppose wSF > Let the contract w be the same as w except that w SF = and p2 |S,2] w SF − There exists an > such that the contract w w SS = w SS + 1−E[ E[ p2 |S,2] satisfies all IC i j and W(w , 221 ) < W(w, 221 ) Finally, suppose wFF > If the k contract w is the same as w, except that w FF = 0, and wF = wF + (1 − p1 )wFF , then all IC i j are still satisfied, W(w , 221 ) = W(w, 221 ), and the contract w k pays the agent earlier than w If follows from IC 220 and IC ij that IC ij and IC ij are redundant From IC 220 , we have that w F S ≥ p1c−1 p0 and IC ij implies IC ij Since c2 ≥ E[pp12−]−p0p0 c1 , IC ij implies IC ij Rewriting the incentive compatibility constraints that are not redundant, we have: ( p1 − p0 )(w F S − w F F ) ≥ c1 , IC 220 (E[ p2 ]E[ p2 |S, 2] − p1 E[ p j ])w SS + ( p1 − E[ p2 ])w F + ((1 − E[ p2 ]) p1 − (1 − p1 ) p0 )w F S ≥ (c2 + E[ p2 ]c2 + (1 − E[ p2 ])c1 ) − (c1 + p1 c j ), IC j (E[ p2 ]E[ p2 |S, 2] − p0 E[ p j ])w SS − (E[ p2 ] − p0 )w F + ((1 − E[ p2 ]) p1 − (1 − p0 ) p0 )w F S ≥ (c2 + E[ p2 ]c2 + (1 − E[ p2 ])c1 ) − p0 c j , IC j and (E[ p2 |S, 2] − E[ p j ])w SS ≥ c2 − c j IC j The incentive compatibility constraint IC 220 is binding and w F S = p1c−1 p0 Suppose w F S > p1c−1 p0 If the contract w is the same as w, except that w F S = p1c−1 p0 , and w F = wF + (1 − p1 )(wFS − w FS ), then all IC ij are still satisfied, W(w , 221 ) = W(w, 221 ), and the contract w pays the agent earlier than w On the other hand, the incentive compatibility constraints IC 121 , IC 101 , IC 211 , and IC 201 are redundant If c2 ≥ c1 , IC 111 implies IC 121 , and if c2 < c1 , IC 121 is trivially satisfied Also, IC 011 and IC 012 imply IC 101 Moreover, IC 021 implies p1 −E[ p2 ] IC 011 implies IC 211 IC 201 Finally, IC 111 + E[ p2 ]− p0 −c2 If c2 /c1 ≥ β , then one can show that w SS ≥ p1c−1 p0 ≥ p1c−E[ Therefore, IC 011 p2 ] implies IC 001 and IC 021 Either wF > and IC 111 and IC 011 are binding, or wF = and IC 111 is binding When IC 111 and IC 011 are binding, the contract Motivating Innovation 1855 is always feasible Comparing the promised wages in each of the two possible contracts one can show that when E[ p2 ]E[ p2 |S, 2] − E[ p2 ] ≥ , − p1 p12 the former contract is less costly for the principal than the latter contract Otherwise, the latter contract is less costly If c2 /c1 < β , then the candidate for the optimal contract is such that IC 0j 0j1 and IC 220 are binding, wSS = w SS , and wF = 0, where j ∈ arg max j˜ ∈{0,1} 0j˜ w SS ≡ (1 + E[ p2 ])c2 − p0 cj˜ (E[ p2 ]E[ p2 |S, 2] − p0 E[ pj˜ ]) c1 (E[ p2 ] − p0 ) p0 p1 − p0 + (E[ p2 ]E[ p2 |S, 2] − p0 E[ pj˜ ]) I first prove that the candidate contract is feasible For that it suffices to show that IC 111 is satisfied If E[p2 ]E[p2 |S, 2] ≥ p21 , then IC 011 implies IC 111 If E[p2 ]E[p2 |S, 2] < p21 , c1 (E[ p2 ] − p0 ) p0 ])β c − p c (1 + E[ p p1 − p0 2 1 j1 + w SS < (E[ p2 ]E[ p2 |S, 2] − p0 p1 ) (E[ p2 ]E[ p2 |S, 2] − p0 p1 ) c1 ( p1 − E[ p2 ]) p0 (1 + E[ p2 ])β2 c1 − (1 + p1 )c1 p1 − p0 − = (E[ p2 ]E[ p2 |S, 2] − p0 p1 ) E[ p2 ]E[ p2 |S, 2] − p12 c1 ( p1 − E[ p2 ]) p0 (1 + E[ p2 ])c2 − (1 + p1 )c1 p1 − p0 < − (E[ p2 ]E[ p2 |S, 2] − p0 p1 ) E[ p2 ]E[ p2 |S, 2] − p12 0j1 In addition, IC 0j is not satisfied for any wSS < w SS Therefore, it is impossible to improve on the candidate contract Proof of Proposition 3: The proof of the proposition follows from the fact that IC 101 and IC 110 are binding under the optimal long-term contract Proof of Proposition 4: To implement 221 , the following incentive compatibility constraints must be satisfied: (E[ p2 ] −E[ pi ]) VS w, 221 − VF w, 221 + E[ pi ](E[ p2 |S, 2] − E[ p j |S, i])(w SS − w SF ) + (1 − E[ pi ])( p1 − E[ pk|F, i])(w F S − w F F ) ≥ (c2 + E[ p2 ]c2 + (1 − E[ p2 ])c1 ) − (ci + E[ pi ]c j + (1 − E[ pi ])ck) (IC i j ) k Moreover, for the contract to be renegotiation-proof, we must have j, k ∈ I such that IC 2j and IC 22k bind 1856 If c2 ≥ The Journal of Finance R E[ p2 |S,2]− p0 c1 , p1 − p0 from IC 211 we have that w SS = c2 − c1 , E[ p2 |S, 2] − p1 w SF = This contradicts IC 111 + p1 −E[ p2 ] IC E[ p2 ]− p0 011 Therefore, 221 is not implementable with a sequence of short-term contracts if c2 ≥ from IC 01 we have that w SS = E[ p2 |S,2]− p0 c1 If c2 p1 − p0 < E[ p2 |S,2]− p0 c1 , p1 − p0 c2 , E[ p2 |S, 2] − p0 w SF = From IC 2k , k ∈ {0, 2} we have that wF S = c1 , p1 − p0 w F F = Using the above equations, we can rewrite the following incentive compatibility constraints: wS ≥ wS ≥ c1 c2 (1 − p0 ) + p0 , E[ p2 ] − p0 p1 − p0 (IC 021 ) c2 (E[ p2 |S, 2] − p0 (1 − ( p1 − E[ p2 ]))) (E[ p2 |S, 2] − p0 )(E[ p2 ] − p0 ) c1 p0 (E[ p2 |S, 2] − p0 ) c1 + p0 , (E[ p2 |S, 2] − p0 )(E[ p2 ] − p0 ) p1 − p0 (IC 011 ) c2 E[ p2 |S, 2] − p0 (1 + E[ p2 ]) + p02 c1 + p0 (E[ p2 |S, 2] − p0 )(E[ p2 ] − p0 ) p1 − p0 (IC 00k ) − and wS ≥ p0 It is easy to show that given c2 < E[ p2p|S,2]− c1 , IC 001 implies IC 011 and IC 021 − p0 Therefore, from IC 001 , our candidate for wS is wS = c1 c2 p0 c2 − + p0 E[ p2 ] − p0 E[ p2 |S, 2] − p0 p1 − p0 Motivating Innovation 1857 It can be shown that the candidate contract satisfies all other incentive compatibility constraints if and only if c2 < (E[ p2 |S, 2] − p0 )(1 + p1 ) E[ p2 |S, 2] − p0 ( p1 − p0 ) + p1 E[ p2 ] − p0 c1 In this case, the sequence of short-term contracts derived above is the optimal sequence of short-term contracts Proof of Proposition 5: The proof is similar to the proof of Proposition Proof of Corollary 1: The proof follows from comparing the costs of implementing exploration and exploration with termination from the contracts derived in Propositions and If c2 /c1 > max (κ m , κ e )β + (1 − max (κ m , κ e ))β , then W(w2 , 221 ) − W(w5 , 22t ) > (1 − E[ p2 ]) p1 α2 and there is inefficient continuation with exploration If c2 /c1 < max (κ m , κ e )β + (1 − max (κ m , κ e ))β , then W(w2 , 221 ) − W(w5 , 22t ) < (1 − E[ p2 ]) p1 α2 and there is inefficient termination with 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What Drives Demand for Financial Services in Emerging Markets?” Harvard Business School, Harvard University, and the World Bank David Backus, Mikhail Chernov, and Ian Martin, “Disasters Implied by Equity Index Options,” New York University, London School of Economics, and Stanford University Robert Battalio and Paul Schultz, “Regulatory Uncertainty and Market Liquidity: The 2008 Short Sale Ban’s Impact on Equity Option Markets,” University of Notre Dame Mark Abrahamson, Tim Jenkinson, and Howard Jones, “Why Don’t U.S Issuers Demand European Fees for IPOs?” Oxford University Radhakrishnan Gopalan, Vikram Nanda, and Vijay Yerramilli, “Does Poor Performance Damage the Reputation of Financial Intermediaries? Evidence from the Loan Syndication Market,” Washington University in St Louis, Georgia Institute of Technology, and University of Houston Mark Grinblatt, Matti Keloharju, and Juhani Linnainmaa, “IQ and Stock Market Participation,” University of California at Los Angeles, Aalto University, and University of Chicago 1861 1862 The Journal of Finance R Tim Bollerslev and Viktor Todorov, “Tails, Fears, and Risk Premia,” Duke University and Northwestern University Philipp Karl Illeditsch, “Ambiguous Information, Portfolio Inertia, and Excess Volatility,” University of Pennsylvania THE JOURNAL OF FINANCE • VOL LXVI, NO • OCTOBER 2011 ANNOUNCEMENTS New Editor Appointed: The Editor Search Committee has recommended and the AFA Board has unanimously and enthusiastically approved the appointment of Kenneth Singleton as the Editor of The Journal of Finance beginning in July 1, 2012 Ken has accepted this post and has appointed Bruno Biais from Toulouse and Michael Roberts from Wharton as co-editors The Association is indebted to the Editorial Search Committee (Kerry Back, Douglas Diamond, Richard Green (Chair), Francis Longstaff, Monika Piazzesi, Jay Ritter, and Jeremy Stein) for their efforts in assembling this excellent editorial team It is also indebted to the current editorial team of Campbell Harvey and John Graham and their Associate Editors for maintaining and enhancing the standing of The Journal of Finance as a scholarly journal Annual Meeting: The Seventy Second Annual Meeting will be held in Chicago, Il, January 6–8, 2012 with Sheridan Titman as Program Chair The AFA sessions will be held in the Swissotel Submissions closed March 11, 2011 Worldwide Directory of Finance Faculty: The AFA and the Department of Finance at Ohio State University have entered into a joint venture to maintain and enhance the finance faculty directory held on the OSU web site At present, information on over 3,000 finance professors and professionals is available in the directory An effort is 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For contributions to collective works: Grossman, Sanford J., and Oliver D Hart, 1982, Corporate financial structure and managerial incentives, in John J McCall, ed.: The Economics of Information and Uncertainty (University of Chicago Press, Chicago, III.) For periodicals: Jensen, Michael C., and William H Meckling, 1976, Theory of the firm: Managerial behavior, agency costs and ownership structure, Journal of Financial Economics 3, 305–360 [...]... liquidity The information contained in the Dash-5 reports includes number of shares traded, number of orders received, and various dimensions of execution quality by order size and stock Specifically, the monthly Dash-5 reports disaggregate the trading statistics into four categories: (1) 100 to 499 shares, (2) 500 to 1,9 99 shares, (3) 2,0 00 to 4,9 99 shares, and (4) 5,0 00 to 9,9 99 shares The Dash-5... Order (5) 1478 The Journal of Finance R In Search of Attention 1479 value of equity is from the latest available accounting statement and the market value of equity is the month-end close price times the number of shares outstanding at the end of month (t − 1); the percentage of stocks held by all S34-filing institutional shareholders at the end of quarter (Q − 1); the standard deviation of the individual... match the first available market capitalization of the IPO with the immediate past June’s NYSE market capitalization quintile break point, and then match the IPO’s book-to-market equity ratio with the portfolio of stocks of the closest book-to-market equity quintile within the matched size quintile The book value of the IPO is the first available book value of equity immediately after the IPO, and the. .. Panel A plots the crosssectional mean and median of the search volume index (SVI; in logarithm) around the week of the IPO Panel B plots the cross-sectional mean and median of the ASVI around the week of the IPO Week 0 is the week of the IPO The sample period is from January 2004 to December 2007 There are 185 IPOs with valid SVI in this sample In Search of Attention 1489 Figure 3 Pre-IPO ASVI, average... issued in the past 5 years) returns (Panel C) during the 4 to 12 months after the IPO The dependent variable in Panel A is the individual IPO’s cumulative return during the [w+ 5, w+52] week window after the IPO, where week w is the week the company went public The dependent variable in Panel B is the individual IPO’s cumulative return during the [w+ 5, w+52] week window after the IPO adjusted by the corresponding... between 1 day after the filing date and 1 day before the IPO date, normalized by the number of days between the filing date and the IPO date Both Cook, Kieschnick, and Van Ness (2006) and Liu, Sherman, and Zhang (2009) show that this alternative measure of attention also predicts first-day IPO return, though they differ in their interpretation of the effect of pre-IPO media coverage The second variable... Price Revision, defined as the ratio of the offering price divided by the median of the filing price As suggested by Hanley (1993 ), a larger revision of the offering price is also associated with a higher first-day return Finally, it is well known that IPOs come in waves 14 From the SDC new issues database, we can identify 571 common share IPOs traded initially on NYSE, Amex, or NASDAQ There are two... on Chunky News Dummy, which measures whether there is a news event in the current week The weak predictive power is not due to the use of a dummy variable In fact, if we replace the dummy news 12 These additional results are reported in the Internet Appendix, available online in the “Supplements and Datasets” section at http://www.afajof.org/supplements.asp 1482 The Journal of Finance R Table VI ASVI... in the sample Only IPOs with the first available CRSP closing price less than or equal to 5 days from the IPO date are retained Standard errors (in parentheses) are clustered by the offering year and quarter ∗ , ∗∗ , and ∗∗∗ represent significance at the 10 %, 5 %, and 1% level, respectively Pre-IPO Abnormal Search Volume (ASVI) and IPO First-Day Return In Search of Attention 1491 1492 The Journal of Finance. .. Next, we examine the relation between increased attention prior to the IPO and the first-day IPO return Panel A of Figure 3 summarizes the main results Consistent with the attention-induced price pressure hypothesis, the set of IPOs with low ASVI during the week prior to the IPO has first-day average returns of 10. 90% while the set of IPOs with high ASVI has much higher first-day average returns of