The journal of finance , tập 66, số 3, 2011 6

Vol 66 June 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 ALON BRAV Duke University MARKUS K BRUNNERMEIER Princeton University DAVID A CHAPMAN Boston College MIKHAIL CHERNOV London School of Economics JENNIFER S CONRAD University of North Carolina FRANCESCA CORNELLI London Business School BERNARD DUMAS INSEAD DAVID HIRSHLEIFER University of California, Irvine 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 ´ L˘ UBOS˘ PASTOR University of Chicago LASSE H PEDERSEN New York University MITCHELL A PETERSEN Northwestern University MANJU PURI Duke University RAGHURAM RAJAN University of Chicago 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 JUNE 2011 No FELLOW OF THE AMERICAN FINANCE ASSOCIATION FOR 2011 iv ARTICLES The Internal Governance of Firms VIRAL V ACHARYA, STEWART C MYERS, and RAGHURAM G RAJAN 689 Municipal Debt and Marginal Tax Rates: Is There a Tax Premium in Asset Prices? FRANCIS A LONGSTAFF 721 Watch What I Do, Not What I Say: The Unintended Consequences of the Homeland Investment Act DHAMMIKA DHARMAPALA, C FRITZ FOLEY, and KRISTIN J FORBES 753 Financial Distress and the Cross-section of Equity Returns LORENZO GARLAPPI and HONG YAN Are All Inside Directors the Same? Evidence from the External Directorship Market RONALD W MASULIS and SHAWN MOBBS Estimation and Evaluation of Conditional Asset Pricing Models STEFAN NAGEL and KENNETH J SINGLETON The Illiquidity of Corporate Bonds JACK BAO, JUN PAN, and JIANG WANG Intermediated Investment Management NEAL M STOUGHTON, YOUCHANG WU, and JOSEF ZECHNER 789 823 873 911 947 Employee Stock Options and Investment ILONA BABENKO, MICHAEL LEMMON, and YURI TSERLUKEVICH 981 Do Individual Investors Have Asymmetric Information Based on Work Experience? TROND M DØSKELAND and HANS K HVIDE 1011 MISCELLANEA 1043 THE JOURNAL OF FINANCE • VOL LXVI, NO • JUNE 2011 Milton Harris Fellow of the American Finance Association for 2011 Fellow of the American Finance Association for 2011 v Milton Harris Milton Harris is The Chicago Board of Trade Professor of Finance and Economics at the University of Chicago Booth School of Business, a title he has held since 1988 In addition to teaching at Chicago Booth, Harris has held permanent academic appointments at the Kellogg Graduate School of Management and Carnegie Mellon University, and visiting appointments at Stanford University, the University of Haifa in Israel, and Tel Aviv University in Israel After graduating from Rice University in 1968 with a Bachelor’s degree in Mathematics, Harris worked as a mathematician for the U.S Naval Research Laboratory until 1971 In 1973, he earned a Master’s degree in Economics from the University of Chicago and received his Ph.D from the same institution the following year Prof Harris’ research has focused on the economics of information and includes theoretical research on optimal contracts and mechanisms, especially financial contracts His current research is in the area of corporate governance theory Harris’ work was cited in the scientific background document for the 2007 Nobel Prize in Economics He is a Fellow of the American Finance Association and the Econometric Society and a former president of the Western Finance Association and the Society for Financial Studies THE JOURNAL OF FINANCE • VOL LXVI, NO • JUNE 2011 Municipal Debt and Marginal Tax Rates: Is There a Tax Premium in Asset Prices? FRANCIS A LONGSTAFF∗ ABSTRACT We study the marginal tax rate incorporated into short-term municipal rates using municipal swap market data Using an affine model, we identify the marginal tax rate and the credit/liquidity spread in 1-week tax-exempt rates, as well as their associated risk premia The marginal tax rate averages 38.0% and is related to stock, bond, and commodity returns The tax risk premium is negative, consistent with the strong countercyclical nature of after-tax fixed-income cash flows These results demonstrate that tax risk is a systematic asset pricing factor and help resolve the muni-bond puzzle ONE OF THE MOST FUNDAMENTAL issues in financial economics is the question of how taxes affect security values This important topic has been the focus of an extensive literature that now dates back nearly a century Despite the many important contributions in this area, however, there is still much about the effects of taxation on investment values that is not yet fully understood The challenge is particularly evident in studying municipal debt markets Many researchers document that the ratio of municipal bond yields to Treasury or corporate bond yields appears to imply marginal tax rates that are much smaller than would be expected given federal income tax rates This perplexing relation between taxable and tax-exempt yields is often termed the muni-bond puzzle.1 This paper presents a new and fundamentally different approach to estimating the marginal tax rate τ t incorporated into tax-exempt municipal debt rates In doing so, we take advantage of an extensive new data set that ∗ Francis A Longstaff is with the UCLA Anderson School and NBER I am very grateful for helpful discussions with Hanno Lustig, Douglas Montague, Eric Neis, Mike Rierson, Derek Schaeffer, and Joel Silva, and for the comments of seminar participants at UCLA I am particularly grateful for the comments and suggestions of the Editor, Campbell Harvey, and of an anonymous referee, and for research assistance provided by Scott Longstaff and Karen Longstaff All errors are my responsibility Key papers discussing the muni-bond puzzle include Trzcinka (1982), Livingston (1982), Arak and Gentner (1983), Stock and Schrems (1984), Ang, Peterson, and Peterson (1985), Buser and Hess (1986), Kochin and Parks (1988), and Green and Oedegaard (1997) A number of papers consider whether the puzzle can be explained by municipal credit risk, including Kidwell and Trzcinka (1982), Skelton (1983), Chalmers (1998), and Neis (2006) In an important paper, Green (1993) develops a simple model that takes into account the asymmetries between the taxation of capital gains and losses as well as the treatment of coupon income and shows that the resulting effect of these tax asymmetries may help explain the muni-bond puzzle 721 722 The Journal of Finance R includes both the yields of 1-week tax-exempt municipal debt as well as the term structure of rates for municipal swaps exchanging this tax-exempt yield for a percentage of the London Interbank Offering Rate (LIBOR) Using these data, we estimate an affine term structure model of the municipal swap curve via maximum likelihood and obtain estimates of both the marginal tax rate and the credit/liquidity spread embedded in municipal yields This new approach has a number of important advantages First, by estimating the marginal tax rate from 1-week municipal yields, our results are free of the types of tax asymmetry or tax trading complications that Green (1993), Constantinides and Ingersoll (1982), and others show may affect yields on longer-term municipal bonds Second, this approach allows us to estimate the market risk premia incorporated into the term structure as compensation to investors for bearing the risk of time variation in the marginal tax rate.2 Thus, we can directly evaluate whether there is a tax premium embedded in asset prices stemming from tax risk Third, our approach allows us to study directly how changes in marginal tax rates are related to financial and macroeconomic shocks The empirical results are very striking We find that the average marginal tax rate during the 2001 to 2009 sample period is 38.0% This value is very close to both the maximum Federal individual income tax rates during the sample period (39.1% during 2001, 38.6% during 2002, and 35.0% during the remainder of the sample period) and the maximum corporate income tax rate of 39.0% during the sample period The estimated marginal tax rate, however, varies substantially over time and ranges from roughly 8% to 55% during the sample period These estimates of the marginal tax rate are also consistent with the higher marginal rates identified by Ang, Bhansali, and Xing (2010) in a recent paper studying the cross-sectional pricing of discount municipal bonds It is important to acknowledge the usual caveat, however, that our results are all conditional on the maintained assumption that our affine model is correctly specified The estimated values of the marginal tax rate are also significantly larger than those obtained by a naive comparison of the short-term tax-exempt rate to the corresponding fully taxable riskless rate For example, the short-term tax-exempt rate has been higher than the riskless rate ever since the Lehman default in September 2008 A naive comparison might interpret this as evidence of a “negative” marginal tax rate Intuitively, the reason our estimates of the marginal tax rate are higher is that we explicitly allow for the possibility of a credit/liquidity spread in short-term tax-exempt municipal yields The empirical results show that there is a substantial credit/liquidity spread in these short-term tax-exempt yields We find that the average value of this spread during the sample period is 56 basis points The estimated spread, Time variation in the marginal tax rate can occur as the marginal investor’s income stream changes and is taxed via the progressive income tax schedule, as the marginal investor changes because of liquidity shocks or other reasons, or as tax laws change and affect the value of tax exemption I am indebted to the referee for these insights Municipal Debt and Marginal Tax Rates 723 however, increased dramatically during the early stages of the subprime credit crisis as monoline municipal bond insurers suffered major credit-related losses and auction failures in the short-term auction rate security markets became widespread.3 To explore how the marginal tax rate evolves over time, we regress changes in the marginal tax rate on a number of variables proxying for changes in investors’ personal income and in the macroeconomic environment We find that the marginal tax rate is significantly positively related to returns on the S&P 500 and U.S Treasury bonds, and significantly negatively related to returns on an index of commodities These results provide intriguing insights into the nature of the marginal investor in the municipal bond markets One of the most surprising empirical results is that the market risk premium for the marginal tax rate is negative in sign In particular, the long-run expected marginal tax rate is 38.2% under the physical measure, but only 27.2% under the risk-neutral pricing measure This implies that the market values a taxable bond coupon payment at a higher value than if there were no tax risk To understand the intuition for this negative risk premium, observe that marginal tax rates are very procyclical because of the progressivity of the Federal income tax system In good states of the economy, taxable income increases and investors move into higher marginal tax brackets, while the opposite is true in bad states of the economy This means that c(1 − τ t ), where c is the coupon on a bond, is actually highly countercyclical Thus, the risk premium for this cash flow can be negative because of its “negative consumption beta.” These results are important for a number of reasons First, they provide clear evidence that taxation has first-order effects on the valuation of securities Second, the marginal tax rate incorporated into the short-term tax-exempt rate makes sense from an economic perspective; the estimated marginal tax rate of 38.0% closely matches the top income tax rate during the sample period Third, these results offer a possible resolution of the long-standing muni-bond puzzle that has perplexed financial researchers for nearly 30 years Fourth, the evidence of a significant negative tax risk premium suggests that the market rationally takes into account the countercyclical nature of after-tax cash flows For example, our results suggest that the negative risk premium may reduce the spread between longer-term Treasury and tax-exempt municipal yields by 50 basis points or more during the sample period Finally, the evidence of a significant tax risk premium in the bond market raises the strong possibility that tax risk is a systematic factor that might affect asset prices in other markets such as the real estate, commodity, and stock markets.4 In an important recent paper, McConnell and Sarreto (2010) study the events in the auction rate markets Other important research on municipal debt markets includes Yawitz, Maloney, and Ederington (1985), Green (1993), Green and Oedegaard (1997), Chalmers (1998), Downing and ¨ Zhang (2004), Nanda and Singh (2004), Green (2007), Green, Hollifield, and Schurhoff (2007a, ¨ 2007b), Green, Li, and Schurhoff (2007), Wang, Wu, and Zhang (2008), and Ang et al (2010) Important papers addressing the impact of taxation on bond prices and trading strategies include Livingston (1979), Constantinides and Ingersoll (1982), Schaefer (1982), Litzenberger and Rolfo 724 The Journal of Finance R The remainder of the paper is organized as follows Section I provides an introduction to the municipal swap market Section II describes the data Section III presents the affine model of the term structure of municipal swap rates Section IV describes the maximum likelihood estimation of the model Section V presents the empirical results Section VI discusses the implications of the results for the muni-bond puzzle Section VII summarizes the results and presents concluding remarks I The Municipal Swap Market In this section, we provide a brief introduction to the municipal swap market Because swaps in this market are tied to the Securities Industry and Financial Markets Association Municipal Swap Index (MSI, formerly known as the Bond Market Association (BMA) index), we first explain how this index is constructed We then describe the various types of municipal swap contracts available in the over-the-counter financial markets A The Municipal Swap Index The MSI is a high-grade market index reflecting the yields on 7-dayresettable tax-exempt variable rate demand obligations (VRDOs) Thus, the MSI is effectively a 1-week tax-exempt rate The index is produced by Municipal Market Data, which maintains an extensive database containing information for more than 15,000 active VRDOs Municipal Market Data is a subsidiary of Thompson Financial Services.5 VRDOs are long-term tax-exempt floating rate notes issued by municipalities Typically, the floating rate on the notes is reset at a weekly frequency, although both shorter and longer frequencies occur in the markets Although the maturities of VRDOs are often 30 to 40 years, they are effectively shorterterm securities because they can be put back or tendered to the investment dealer or remarketing agent on a schedule coinciding with the weekly yield reset The remarketing agent, which is often the financial institution that originally issued the VRDO for the municipality, has two ongoing roles First, the remarketing agent functions as a broker in that if VRDOs are tendered at the weekly yield reset, the remarketing agent attempts to find a buyer for the tendered VRDOs Second, as part of this process, the remarketing agent sets the weekly yield to whatever level is required for the market to clear the tendered VRDOs (and which may also incorporate market information about market clearing rates for similar VRDO issues) In this respect, VRDOs have a number of features in common with auction rate securities, which also reset (1984), Jordan (1984), Dybvig and Ross (1986), Dammon and Green (1987), Graham (2003), and Dammon, Spatt, and Zhang (2004) This section is based on the description of the market provided by the Securities Industry and Financial Markets Association (www.sifma.org/capital markets/swapindex.shtml) Municipal Debt and Marginal Tax Rates 725 frequently via a market clearing mechanism Note, however, that the weekly reset for a VRDO is determined by the remarketing agent while the weekly reset for an auction rate note is determined via a constrained Dutch auction (which may fail in that the maximum allowable yield is below the rate needed to clear the market) VRDOs are typically issued at par When they are put back to the remarketing agent, an investor receives par plus accrued interest Criscuolo and Faloon (2007) estimate that 70% of VRDOs are held by money market funds, 15% by corporations, 7% by bond funds, and 8% by trust departments Thus, the marginal tax rate applied to interest received by a VRDO investor is likely to reflect that of an individual However, it is also possible that the marginal tax rate could reflect a marginal corporate tax rate or the marginal rate faced by a taxable trust The VRDO market presents a large and rapidly growing segment of the $2.6 trillion municipal debt market In particular, the Securities Industry and Financial Markets Association reports that $63.3 billion of variable rate municipal bond obligations were issued during 2007, $109.2 billion were issued during 2008, and $32.0 billion were issued through October of 2009 There are a number of criteria that a VRDO must satisfy for its yield to be included in the MSI First, the VRDO must have a weekly reset, effective on Wednesday Second, the VRDO must not be subject to alternative minimum tax Third, the VRDO must have an outstanding amount of at least $10 million Fourth, the VRDO must have the highest short-term rating, which is VMIG1 by Moody’s or A-1+ by Standard and Poor’s Historically, a municipal issuer of VRDOs would need to obtain some sort of credit enhancement (such as a letter of credit from a highly rated bank) to obtain the highest short-term rating.6 Fifth, the VRDO must pay interest on a monthly basis Finally, only one quote per obligor per remarketing agent can be included in the MSI The MSI can include issues from any state The MSI is calculated weekly on Wednesday and officially released on Thursday.7 The underlying data for the index come from Municipal Market Data’s Variable Rate Demand Note Network This network collects market data from over 80 remarketing agents who download daily rate change information to Municipal Market Data’s network The actual number of VRDOs included in the weekly index fluctuates, but is estimated to include roughly 650 issues in any given week B The Municipal Swap Market The primary type of municipal swap contract available in the financial markets is the percentage-of-LIBOR contract This contract is very similar to a For a discussion of the role of credit enhancement in VRDO issuance, see Criscuolo and Faloon (2007) Market participants, however, are easily able to infer the index value by the end of Wednesday because the VRDO resets are posted throughout the day and remarketing agents provide transparency Table VIII 2,820,955 0.236 Obs R2 2,820,955 0.237 Yes No −30.9 (−0.5) −14.1 (−1.4) −4.8 (−0.5) −13.8 (−1.4) Yes No −30.3 (−0.5) All (2) All Risk-adjusted? Extra socio? Constant Industry experience ln (Trades+1) Excess weight Local Portfolio diversification Buy Trades (1) −16.2 (−1.5) −25.2∗∗ (−2.1) 877,817 0.240 680,704 0.245 Yes No −10.0 (−0.2) Long educ # stocks > Yes No −31.0 (−0.6) (4) (3) 1,160,859 0.266 Yes No −55.0 (−0.7) −18.8 (−1.6) Local (5) Returns 2,805,223 0.244 Yes No −10.0 (−0.2) −47.7 (−1.2) −13.7 (−1.4) All (6) 2,820,955 0.238 Yes No −26.8 (−0.5) −16.5 (−1.3) −19.0∗ (−1.9) All (7) 2,805,223 0.247 Yes No −9.8 (−0.2) −19.0∗ (−1.8) 7.0 (0.5) −45.4 (−1.2) 7.1 (0.2) −16.2 (−1.0) All (8) 2,767,311 0.248 −18.4∗ (−1.9) −6.7 (−0.5) −38.3 (−1.0) −25.6 (−0.6) −23.5 (−1.4) 58.8 (1.2) Yes Yes −93.1 (−1.5) All (9) The table shows the coefficient estimates and t-values (in parentheses) from pooled OLS (trade-weighted) regressions with Driscoll-Kray standard errors The table reports monthly returns in basis points The regressions include all expertise buys and sells The portfolio formation period is 12 months preceding month t In regression (3), only trades performed by investors holding more than five stocks are included In regression (4), only trades performed by investors with at least 16 years of education are included A trade is defined as local if it is a trade in a company headquartered within 100 km of the individual In regression (5), only local trades are included Excess weight is measured by wi corr , defined in equation (1) The individuals are grouped into three groups based on how large the excess weight in expertise stocks is All regressions are risk-adjusted with the same factors as in equation (2) Extra socio are the variables included in Table V Variable definitions can be found in Table II t-statistics are reported in parentheses ∗∗∗ , ∗∗ , and ∗ denotes significance at the 1%, 5%, and 10% level, two-sided test Multivariate Calendar Returns 1034 The Journal of Finance R Asymmetric Information Based on Work Experience? 1035 to small stocks (which expertise buys disproportionally are made in) having relatively high returns in our sample We calculate the 1-week index returns over our sample period by giving each stock and each date equal weights The returns equal 30 basis points This figure is still around 15 basis points below the returns of the fictitious buys The difference of 15 basis points is due to relatively high returns of small stocks on dates when expertise trades were intensive, that is, to a timing effect specific to small stocks To illustrate this point, when, within each year, we skip the days with above-median expertise buy activity from the returns calculations, the fictitious expertise buy first-week returns drop to 32 basis points Although small stocks experience relatively high returns following dates with high expertise buy activity (a similar finding is reported by Gervais, Kaniel, and Mingelgrin (2001) and Barber, Odean, and Zhu (2009) using U.S data), the individual investors are not able to pick the small stocks that drive the market up through their expertise investments We conclude that there is no evidence suggesting that individual investors can make a profit from their industry expertise In fact, we find that individual investors realize abnormally low short-term returns from trading on such expertise In the calendar-time portfolio approach analysis, the estimated 4-month return using a 4-month build-up period equals −44 basis points, while the average 4-month return in the control-firm analysis equals 59 basis points This difference of 103 basis points, which corresponds to approximately 3% annual returns, seems puzzling The explanation is quite straightforward In the control-firm analysis, we use the return from the end of the actual trade date, while in the calendar-time portfolio analysis, we build portfolio up over a period and use the returns of the following month The sample period that the two methods cover is therefore not exactly the same; compared to the calendar-time portfolio approach using a 4-month build-up period, the control-firm analysis has extra months with return data in the start of the sample period and extra months at the end The reason the returns of the control-firm analysis are higher than the returns of the calendar-time portfolio analysis is that the stock market experienced an appreciation both at the start and at the end of the period covered by the data set The average equal-weighted monthly return of the OSE was 322 basis points during the months from January to April 1996 and from February to April 2006.10 V Further Analysis Although expertise investments perform poorly overall, it is conceivable that subgroups of individuals can obtain positive abnormal returns through such 10 We also run the calendar-time portfolio analysis using a one-month build-up period For this time horizon, the different periods covered by the two methods is less of an issue, as the control-firm analysis has just extra month in the start of the sample The estimated 1-month return using a 1-month build-up period in the calendar-time portfolio approach (not reported) equals 30 basis points, while the average 1-month return in the control-firm analysis equals 43 basis points 1036 The Journal of Finance R investments In Section V.A, we analyze whether highly educated investors can obtain positive abnormal returns In Section V.B, we focus on investors that live close to the headquarters of a listed company, similar to Ivkovic and Weisbenner (2005) Finally, in Section V.C, we consider alternative behavioral interpretations of our findings We report the results from the calendar-time portfolio approach using a 12-month portfolio formation period When we run the same regressions for different formation periods, we obtain the same conclusions On a methodological note, the traditional calendar-time portfolio approach allows only a single binary investor characteristic (e.g., gender) to be incorporated in the analysis We apply a method recently developed by Hoechle, Schmid, and Zimmermann (2009) to embed the calendar-time portfolio approach in a multivariate regression framework This allows us to include several, as well as continuous, investor characteristics (e.g., gender and income level).11 For example, in regression (2) in Table VIII, we find that the risk-adjusted returns of expertise buys not outperform the risk-adjusted returns of expertise sells even if we control for the number of stocks in investors’ portfolio In regression (3), we only investigate trades performed by investors owning more than five stocks We find that the risk-adjusted returns of expertise buys significantly underperform the risk-adjusted returns of expertise sells Generally, we find that t-values increase if we include more variables in the regression We find significantly negative abnormal returns of expertise buys in several specifications A Expertise and Education Level So far, we define professional proximity through the current workplace of an investor Formal competence obtained through education could also play a role in producing excess returns Most interestingly, work experience and education could be complements in the production of value-relevant information To investigate this possibility, we analyze whether expertise investments are associated with abnormal returns for individuals with more than 16 years of education (so that we include individuals with an M.Sc or a Ph.D.) Our conclusions are very similar to those in the main analysis In regression (4) in Table VIII, we find that the difference between the risk-adjusted expertise buy and the risk-adjusted expertise sell portfolios is an insignificant −16 basis points monthly The Internet Appendix reports the results of the control-firm analysis The expertise buy returns significantly underperform the expertise sell returns 11 The generalized calendar-time portfolio method of Hoechle, Schmid, and Zimmermann (2009) is achieved by pooled linear regression with Driscoll and Kraay (1998) standard errors and extended with additional investor-specific variables (see equation (5) in Hoechle, Schmid, and Zimmermann (2009)) This model is similar to the structure of Ferson and Schadt’s (1996) conditional performance measurement model In regression (1) in Table VIII, we reproduce the risk-adjusted result from the traditional calendar-time portfolio method (see Table VI, Panel C, 1-year formation period) Asymmetric Information Based on Work Experience? 1037 We also test whether more experience within an industry can be associated with abnormal returns Toward this end, we include industry experience as a control variable in the calendar-time analysis, where industry experience is defined as the fraction of the last years in which an individual was employed in the industry The results are reported in regression (9), where industry experience turns out to be positive but insignificant The difference between expertise buys and sells continues to be negative B Local Investments It could be the case that the combination of professional and geographical proximity can lead to value-relevant information and in turn abnormal returns As in Section II.E, we define an investment to be local if the company has its headquarter within 100 kilometers of the individual’s residence Our conclusions are very similar to those in the main analysis—all our point estimates of abnormal expertise buy returns are negative, and significantly so in the control-firm analysis Regression (5) in Table VIII shows that local expertise buys realize insignificantly lower risk-adjusted returns than local expertise sells The local expertise buys yield lower risk-adjusted returns than local expertise sells by about 19 basis points monthly or about 2% annually The Internet Appendix reports the results of the control-firm analysis The expertise buy returns are significantly less than the expertise sell returns The results not suggest that geographical and professional proximity are complements in providing value-relevant information C Which Behavioral Bias Is Driving Our Results? We have shown that, in spite of their poor hedging properties, individuals extensively trade and hold professionally close stocks They invest in assets that are more risky and, according to standard theory, should therefore obtain a higher return Yet, we find that professionally close investments yield negative abnormal returns, statistically significantly so in the majority of specifications It is difficult to reconcile this result with rational behavior In this section, we discuss two alternative behavioral explanations for our results: overconfidence and familiarity Overconfidence means that investors overestimate the precision of their information about future returns of financial securities In Odean (1998), such miscalibration leads to heterogeneity in investor opinion, which in turn causes them to trade.12 It seems reasonable that the accuracy of information about industry prospects might be overrated by professionally related investors, which would lead them to trade excessively Hence, excess trading in expertise stocks is consistent with investor overconfidence 12 This accords with Heath and Tversky (1991), who state that people are more willing to bet on their own judgments when they feel skillful or knowledgeable Using survey data, Graham, Harvey, and Huang (2009) find that investors who self-report being more competent about investment products tend to trade more frequently than investors who feel less competent 1038 The Journal of Finance R Alternatively, individuals might prefer professionally close stocks because they are more familiar with, or simply aware of, these stocks from interaction at the workplace For example, Huberman (2001) shows that shareholders of a Regional Bell Operating Company tend to live in the area that it serves, and Lee, Liu, and Zhu (2008) show that a high fraction of employees in Taiwan voluntarily holds stocks in their own company The familiarity hypothesis is also consistent with individuals choosing stocks that are in the same industry Our findings are therefore consistent with both familiarity and overconfidence being the behavioral driver behind our results; distinguishing between these two explanations is beyond the scope of this study One finding that seems more consistent with overconfidence is that investors make negative short-run abnormal returns on expertise investments We are not aware of why this should be a prediction of familiarity-based theory Although we not know of formal models of overconfidence that contain this feature, it does not seem unreasonable that professional investors would be able to exploit miscalibration of individual investors’ beliefs in order to make a profit at their expense VI Conclusion A large literature considers how asymmetric information affects the pricing of financial assets, but little is known about the sources of asymmetric information Individuals spend much of their time building and maintaining their professional career, and thus they gain a considerable amount of industry-specific experience Accordingly, we conjecture that professional proximity is a route through which individuals can obtain a comparative advantage in acquiring value-relevant information and hence realize abnormal stock market returns Professionally close investments is a particularly fitting environment to detect abnormal returns following conventional portfolio theory, since investors should invest in professionally close investments only if they are informed To test whether industry expertise is associated with asymmetric information, we use an exceptionally detailed data set from Norway that combines individual-level sociodemographic data over a 20-year period and common stock transaction data over a 10-year period Although professionally close stocks are more risky, we find no evidence that professional proximity is associated with abnormally high investment returns At the longer horizons, all point estimates of abnormal returns are negative, and in some cases statistically significant In the short run, all point estimates of abnormal returns are negative and statistically significant These findings provide clear evidence of a behavioral bias in individuals’ investment choices Overconfidence seems to be the most likely explanation for why individuals trade in professionally close stocks Our results contribute to an ongoing debate on whether individual investors are able to acquire and profit from asymmetric information about future stock returns The lack of any evidence of abnormal returns for a very plausible candidate suggests that individual investors are not able to profit from asymmetric information This result might seem at odds with recent findings by Coval, Hirshleifer, and Shumway (2005), Barber, Odean, and Zhu (2009), Asymmetric Information Based on Work Experience? 1039 Ivkovic, Sialm, Weisbenner (2008), and Ivkovic and Weisbenner (2005) Arguably, our results only differ at a superficial level: only Barber, Odean, and Zhu (2009) find that a group of individual investors consistently beats the market (when transaction costs are accounted for), and then only for a small number of investors Another take-away of our results is to provide guidance to individual investors themselves Conventional portfolio theory recommends that investors shy away from professionally close stocks unless they have superior information, since such stocks carry extra risk We find that investors have a preference for professionally close stocks even if such holdings generate negative abnormal returns It thus seems that individual investors themselves are not aware of their poor investment choices Advice to avoid professionally close investments and investment products tailored to hedge against variations in labor income could provide an economic gain Our paper suggests several directions for future research First, while several studies show how the media (or the stock market itself) could attract the attention of investors (e.g., Barber and Odean (2008)), our work emphasizes the importance of communication in the workplace as a vehicle for attracting attention Future work could examine how the workplace interacts with other channels of communication in affecting investor choices In addition, while a zero abnormal return from expertise investments is what we would expect from investor overconfidence, it is puzzling that expertise investments make a negative abnormal return, particularly in the short run To our knowledge, no existing theory of overconfidence, such as Daniel, Hirshleifer, and Subrahmanyam (1998) or Gervais and Odean (2001), can explain this finding Future theoretical work could gain from investigating this question REFERENCES Barber, Brad M., Yi-Tsung Lee, Yu-Jane Liu, and Terrance Odean, 2005, Do individual day traders make money? 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Empirical Evidence,” University of Chicago, Concordia University, University of Konstanz, and Concordia University THE JOURNAL OF FINANCE • VOL LXVI, NO • JUNE 2011 ANNOUNCEMENTS 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 being made to include all AFA members on this list and members are encouraged to provide information to the directory manager A link to the directory is available on the homepage or you can go directly to http://www.cob.ohio-state.edu/fin/findir/ Other Announcements Please go to our web site, www.afajof.org, for announcements regarding meetings, conferences, and research support 1045 AMERICAN FINANCE ASSOCIATION Publisher of the Journal of Finance Prof David H Pyle Executive Secretary and Treasurer February 2011 To Those Seeking Permissions for Academic Classroom Use: Permission is granted to reproduce articles for classroom use by accredited, notfor-profit colleges and universities or their appointed agents without charge for: r classes of a faculty member who is a subscriber to The Journal of Finance r classes at a college or university with a library subscription to The Journal of Finance Articles also may be distributed for classroom use in electronic (pdf) form if they are stored on a password-protected web site at said institution or its agent Non-subscribers seeking to reproduce articles should contact Wiley-Blackwell Publishing Company (jrights@wiley.com) regarding permission This form is valid through February 1, 2012 University of 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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 [...]... tax-exempt bonds, Journal of Finance 6 5, 565 60 1 Ang, James, David Peterson, and Pamela Peterson, 198 5, Marginal tax rates: Evidence from nontaxable corporate bonds: A note, Journal of Finance 4 0, 327–332 Arak, M ., and K Guentner, 198 3, The market for tax-exempt issues: Why are the yields so high? National Tax Journal 3 6, 145– 161 Buser, Stephen A ., and Patrick J Hess, 198 6, Empirical determinants of the relative... 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