The journal of finance , tập 66, số 1, 2011 2

Vol 66 February 2011 No Editor CAMPBELL R HARVEY Duke University Co-Editor JOHN GRAHAM Duke University Associate Editors ANAT R ADMATI Stanford University ANDREW METRICK Yale University ANDREW ANG Columbia University TOBIAS J MOSKOWITZ University of Chicago KERRY BACK Rice University DAVID K MUSTO University of Pennsylvania MALCOLM BAKER Harvard University TERRANCE ODEAN University of California, Berkeley NITTAI K BERGMAN Massachusetts Institute of Technology CHRISTINE A PARLOUR University of California, Berkeley HENDRIK BESSEMBINDER University of Utah ´ L˘ UBOS˘ PASTOR University of Chicago MICHAEL W BRANDT Duke University ALON BRAV Duke University MARKUS K BRUNNERMEIER Princeton University DAVID A CHAPMAN Boston College 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 STEVEN N KAPLAN University of Chicago JONATHAN M KARPOFF University of Washington ARVIND KRISHNAMURTHY Northwestern University MICHAEL LEMMON University of Utah FRANCIS A LONGSTAFF University of California, Los Angeles LASSE H PEDERSEN New York University MITCHELL A PETERSEN Northwestern University MANJU PURI Duke University RAGHURAM RAJAN University of Chicago 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; Tel: +1 781 388 8598 or +1 800 835 6770 (toll free in the USA & Canada) Europe, Middle East and Africa: Email: cs-journals@wiley.com; Tel: +44 (0) 1865 778315 Asia Pacific: Email: cs-journals@wiley.com; Tel: +65 6511 8000 Japan: For Japanese speaking support, email: cs-japan@wiley.com; Tel: +65 6511 8010 or Tel (toll-free): 005 316 50 480 Further Japanese customer support is also available at www.interscience.wiley.com/support Visit our Online Customer Self-Help available in six languages at www.interscience wiley.com/support Permissions to Reprint Materials from the Journal of Finance C 2011 The American Finance Association All rights reserved With the exception of fair dealing for the purposes of research or private study, or criticism or review, no part of this publication may be reproduced, stored or transmitted in any form or by any means without the prior permission in writing from the copyright holder Authorization to photocopy items for internal and personal use is granted by the copyright holder for libraries and other users of the Copyright Clearance Center (CCC), 222 Rosewood Drive, Danvers, MA 01923, USA (www.copyright.com), provided the appropriate fee is paid directly to the CCC This consent does not extend to other kinds of copying, such as copying for general distribution for advertising or promotional purposes, for creating new collective works, or for resale For information regarding reproduction for classroom use, please see the AFA policy statement in the back of this issue Advertising in the Journal For advertising information, please visit the journal’s web site at www.afajof.org or contact the Academic and Science, Advertising Sales Coordinator, at corporatesalesusa@wiley.com 350 Main St Malden, MA 02148 Phone: 781.388.8532, Fax: 781.338.8532 Association Business Those having business with the American Finance Association, or members wishing to volunteer their service or ideas that the association might develop, should contact the Executive Secretary and Treasurer: Prof David Pyle, American Finance Association, University of California, Berkeley—Haas School of Business, 545 Student Services Building, Berkeley, CA 94720-1900 Telephone and Fax: (510) 642-2397; email: pyle@haas.berkeley.edu Volume 66 CONTENTS for FEBRUARY 2011 No ARTICLES Does Algorithmic Trading Improve Liquidity? TERRENCE HENDERSHOTT, CHARLES M JONES, and ALBERT J MENKVELD When Is a Liability Not a Liability? Textual Analysis, Dictionaries, and 10-Ks TIM LOUGHRAN and BILL MCDONALD 35 The Causal Impact of Media in Financial Markets JOSEPH E ENGELBERG and CHRISTOPHER A PARSONS 67 Leverage, Moral Hazard, and Liquidity VIRAL V ACHARYA and S VISWANATHAN 99 Stock Market Liquidity and the Business Cycle RANDI NÆS, JOHANNES A SKJELTORP, and BERNT ARNE ØDEGAARD 139 Asset Pricing with Garbage ALEXI SAVOV 177 Derivative Pricing with Liquidity Risk: Theory and Evidence from the Credit Default Swap Market DION BONGAERTS, FRANK DE JONG, and JOOST DRIESSEN 203 The Decision to Privatize: Finance and Politics I SERDAR DINC and NANDINI GUPTA 241 Why Do Mutual Fund Advisory Contracts Change? Performance, Growth, and Spillover Effects JEROLD B WARNER and JOANNA SHUANG WU 271 Style-Related Comovement: Fundamentals or Labels? BRIAN H BOYER 307 MISCELLANEA 333 THE JOURNAL OF FINANCE • VOL LXVI, NO • FEBRUARY 2011 Does Algorithmic Trading Improve Liquidity? TERRENCE HENDERSHOTT, CHARLES M JONES, and ALBERT J MENKVELD∗ ABSTRACT Algorithmic trading (AT) has increased sharply over the past decade Does it improve market quality, and should it be encouraged? We provide the first analysis of this question The New York Stock Exchange automated quote dissemination in 2003, and we use this change in market structure that increases AT as an exogenous instrument to measure the causal effect of AT on liquidity For large stocks in particular, AT narrows spreads, reduces adverse selection, and reduces trade-related price discovery The findings indicate that AT improves liquidity and enhances the informativeness of quotes TECHNOLOGICAL CHANGE HAS REVOLUTIONIZED the way financial assets are traded Every step of the trading process, from order entry to trading venue to back office, is now highly automated, dramatically reducing the costs incurred by intermediaries By reducing the frictions and costs of trading, technology has the potential to enable more efficient risk sharing, facilitate hedging, improve liquidity, and make prices more efficient This could ultimately reduce firms’ cost of capital Algorithmic trading (AT) is a dramatic example of this far-reaching technological change Many market participants now employ AT, commonly defined as the use of computer algorithms to automatically make certain trading decisions, submit orders, and manage those orders after submission From a starting point near zero in the mid-1990s, AT is thought to be responsible for as much as 73 percent of trading volume in the United States in 2009.1 There are many different algorithms, used by many different types of market participants Some hedge funds and broker–dealers supply liquidity ∗ Hendershott is at Haas School of Business, University of California Berkeley Jones is at Columbia Business School Menkveld is at VU University Amsterdam We thank Mark van Achter, Hank Bessembinder, Bruno Biais, Alex Boulatov, Thierry Foucault, Maureen O’Hara, S´ebastien Pouget, Patrik Sandas, Kumar Venkataraman, the NASDAQ Economic Advisory Board, and seminar participants at the University of Amsterdam, Babson College, Bank of Canada, CFTC, HEC Paris, IDEI Toulouse, Southern Methodist University, University of Miami, the 2007 MTS Conference, NYSE, the 2008 NYU-Courant algorithmic trading conference, University of Utah, the 2008 Western Finance Association meetings, and Yale University We thank the NYSE for providing system order data Hendershott gratefully acknowledges support from the National Science Foundation, the Net Institute, the Ewing Marion Kauffman Foundation, and the Lester Center for Entrepreneurship and Innovation at the Haas School at UC Berkeley Menkveld gratefully acknowledges the College van Bestuur of VU University Amsterdam for a VU talent grant See “SEC runs eye over high-speed trading,” Financial Times, July 29, 2009 The 73% is an estimate for high-frequency trading, which, as discussed later, is a subset of AT The Journal of Finance R using algorithms, competing with designated market-makers and other liquidity suppliers (e.g., Jovanovic and Menkveld (2010)) For assets that trade on multiple venues, liquidity demanders often use smart order routers to determine where to send an order (e.g., Foucault and Menkveld (2008)) Statistical arbitrage funds use computers to quickly process large amounts of information contained in the order flow and price moves in various securities, trading at high frequency based on patterns in the data Last but not least, algorithms are used by institutional investors to trade large quantities of stock gradually over time Before AT took hold, a pension fund manager who wanted to buy 30,000 shares of IBM might hire a broker-dealer to search for a counterparty to execute the entire quantity at once in a block trade Alternatively, that institutional investor might have hired a New York Stock Exchange (NYSE) floor broker to go stand at the IBM post and quietly “work” the order, using his judgment and discretion to buy a little bit here and there over the course of the trading day to keep from driving the IBM share price up too far As trading became more electronic, it became easier and cheaper to replicate that floor trader with a computer program doing AT (see Hendershott and Moulton (2009) for evidence on the decline in NYSE floor broker activity) Now virtually every large broker-dealer offers a suite of algorithms to its institutional customers to help them execute orders in a single stock, in pairs of stocks, or in baskets of stocks Algorithms typically determine the timing, price, quantity, and routing of orders, dynamically monitoring market conditions across different securities and trading venues, reducing market impact by optimally and sometimes randomly breaking large orders into smaller pieces, and closely tracking benchmarks such as the volume-weighted average price (VWAP) over the execution interval As they pursue a desired position, these algorithms often use a mix of active and passive strategies, employing both limit orders and marketable orders Thus, at times they function as liquidity demanders, and at times they supply liquidity Some observers use the term AT to refer only to the gradual accumulation or disposition of shares by institutions (e.g., Domowitz and Yegerman (2005)) In this paper, we take a broader view of AT, including in our definition all participants who use algorithms to submit and cancel orders We note that algorithms are also used by quantitative fund managers and others to determine portfolio holdings and formulate trading strategies, but we focus on the execution aspect of algorithms because our data reflect counts of actual orders submitted and cancelled The rise of AT has obvious direct impacts For example, the intense activity generated by algorithms threatens to overwhelm exchanges and market data providers,2 forcing significant upgrades to their infrastructures But researchers, regulators, and policymakers should be keenly interested in the broader implications of this sea change in trading Overall, does AT have See “Dodgy tickers-stock exchanges,” Economist, March 10, 2007 Does Algorithmic Trading Improve Liquidity? salutary effects on market quality, and should it be encouraged? We provide the first empirical analysis of this question As AT has grown rapidly since the mid-1990s, liquidity in world equity markets has also dramatically improved Based on these two coincident trends, it is tempting to conclude that AT is at least partially responsible for the increase in liquidity But it is not at all obvious a priori that AT and liquidity should be positively related If algorithms are cheaper and/or better at supplying liquidity, then AT may result in more competition in liquidity provision, thereby lowering the cost of immediacy However, the effects could go the other way if algorithms are used mainly to demand liquidity Limit order submitters grant a trading option to others, and if algorithms make liquidity demanders better able to identify and pick off an in-the-money trading option, then the cost of providing the trading option increases, in which case spreads must widen to compensate In fact, AT could actually lead to an unproductive arms race, where liquidity suppliers and liquidity demanders both invest in better algorithms to try to take advantage of the other side, with measured liquidity the unintended victim In this paper, we investigate the empirical relationship between AT and liquidity We use a normalized measure of NYSE electronic message traffic as a proxy for AT This message traffic includes electronic order submissions, cancellations, and trade reports Because we normalize by trading volume, variation in our AT measure is driven mainly by variation in limit order submissions and cancellations This means that, for the most part, our measure is picking up variation in algorithmic liquidity supply This liquidity supply likely comes both from proprietary traders that are making markets algorithmically and from buy-side institutions that are submitting limit orders as part of “slice and dice” algorithms We first examine the growth of AT and the improvements in liquidity over a 5-year period As AT grows, liquidity improves However, while AT and liquidity move in the same direction, it is certainly possible that the relationship is not causal To establish causality we study an important exogenous event that increases the amount of AT in some stocks but not others In particular, we use the start of autoquoting on the NYSE as an instrument for AT Previously, specialists were responsible for manually disseminating the inside quote This was replaced in early 2003 by a new automated quote whenever there was a change to the NYSE limit order book This market structure provides quicker feedback to algorithms and results in more electronic message traffic Because the change was phased in for different stocks at different times, we can take advantage of this nonsynchronicity to cleanly identify causal effects We find that AT does in fact improve liquidity for large-cap stocks Quoted and effective spreads narrow under autoquote The narrower spreads are a result of a sharp decline in adverse selection, or equivalently a decrease in the amount of price discovery associated with trades AT increases the amount of price discovery that occurs without trading, implying that quotes become more informative There are no significant effects for smaller-cap stocks, but our instrument is weaker there, so the problem may be a lack of statistical power The Journal of Finance R Surprisingly, we find that AT increases realized spreads and other measures of liquidity supplier revenues This is surprising because we initially expected that if AT improved liquidity, the mechanism would be competition between liquidity providers However, the evidence clearly indicates that liquidity suppliers are capturing some of the surplus for themselves The most natural explanation is that, at least during the introduction of autoquote, algorithms had market power Over a longer time period liquidity supplier revenues decline, suggesting that any market power was temporary, perhaps because new algorithms require considerable investment and time to build The paper proceeds as follows Section I discusses related literature Section II describes our measures of liquidity and AT and discusses the need for an instrumental variables approach Section III provides a summary of the NYSE’s staggered introduction of autoquote in 2003 Section IV examines the impact of AT on liquidity Section V explores the sources of the liquidity improvement Section VI studies AT’s relation to price discovery via trading and quote updating Section VII discusses and interprets the results, and Section VIII concludes I Related Literature Only a few papers address AT directly For example, Engle et al (2007) use execution data from Morgan Stanley algorithms to study the effects on trading costs of changing algorithm aggressiveness Domowitz and Yegerman (2005) study execution costs for a set of buy-side institutions, comparing results from different algorithm providers Chaboud et al (2009) study AT in the foreign exchange market and focus on its relation to volatility, while Hendershott and Riordan (2009) measure the contributions of AT to price discovery on the Deutsche Boerse Several strands of literature touch related topics Most models take the traditional view that one set of traders provides liquidity via quotes or limit orders and another set of traders initiates a trade to take that liquidity—for either informational or liquidity/hedging reasons Many assume that liquidity suppliers are perfectly competitive, for example, Glosten (1994) Glosten (1989) models a monopolistic liquidity supplier, while Biais et al (2000) model competing liquidity suppliers and find that their rents decline as the number increases Our initial expectation is that AT facilitates the entry of additional liquidity suppliers, increasing competition The development and adoption of AT also involves strategic considerations While algorithms have low marginal costs, there may be substantial development costs, and it may be costly to optimize the algorithms’ parameters for each security The need to recover these costs should lead to the adoption of AT at times and in securities where the returns to adoption are highest (see Reinganum (1989) for a review of innovation and technology adoption) As we discuss briefly in the introduction, liquidity supply involves posting firm commitments to trade These standing orders provide free trading options to other traders Using standard option pricing techniques, Copeland Does Algorithmic Trading Improve Liquidity? and Galai (1983) value the cost of the option granted by liquidity suppliers Foucault et al (2003) study the equilibrium level of effort that liquidity suppliers should expend in monitoring the market to reduce this option’s cost Black (1995) proposes a new limit order type that is indexed to the overall market to minimize picking-off risk Algorithms can efficiently implement this kind of monitoring and adjustment of limit orders.3 If AT reduces the cost of the free trading option implicit in limit orders, then measures of adverse selection depend on AT If some users of AT are better at avoiding being picked off, they can impose adverse selection costs on other liquidity suppliers as in Rock (1990) and even drive out other liquidity suppliers AT may also be used by traders who are trying to passively accumulate or liquidate a large position.4 There are optimal dynamic execution strategies for such traders For example, Bertsimas and Lo (1998) find that, in the presence of temporary price impacts and a trade completion deadline, orders are optimally broken into pieces so as to minimize cost.5 Many brokers incorporate such considerations into the AT products that they sell to their clients In addition, algorithms monitor the state of the limit order book to dynamically adjust their trading strategies, for example, when to take and offer liquidity (Foucault et al (2010)) II Data We start by characterizing the time-series evolution of AT and liquidity for a sample of NYSE stocks over the years from February 2001 through December 2005 We limit attention to the post-decimalization regime because the change to a one-penny minimum tick was a structural break that substantially altered the entire trading landscape, including liquidity metrics and order submission strategies We end in 2005 because substantial NYSE market structure changes occur shortly thereafter We start with a sample of all NYSE common stocks that can be matched in both the Trades and Quotes (TAQ) and Center for Research in Security Prices CRSP databases To maintain a balanced panel, we retain those stocks that are present throughout the whole sample period Stocks with an average share Rosu (2009) develops a model that implicitly recognizes these technological advances and simply assumes that limit orders can be constantly adjusted Consistent with AT, Hasbrouck and Saar (2009) find that by 2004 a large number of limit orders are cancelled within two seconds on the INET trading platform Keim and Madhavan (1995) and Chan and Lakonishok (1995) study institutional orders that are broken up Almgren and Chriss (2000) extend this optimization problem by considering the risk that arises from breaking up orders and slowly executing them Obizhaeva and Wang (2005) optimize assuming that liquidity does not replenish immediately after it is taken but only gradually over time For each component of a larger transaction, a trader or algorithm must choose the type and aggressiveness of the order Cohen et al (1981) and Harris (1998) focus on the simplest static choice: market order versus limit order However, a limit price must be chosen, and the problem is dynamic; Goettler et al (2009) model both aspects The Journal of Finance R price of less than $5 are removed from the sample, as are stocks with an average share price of more than $1,000 The resulting sample consists of monthly observations for 943 common stocks The balanced panel eliminates compositional changes in the sample over time, which could induce some survivorship effects if disappearing stocks are less liquid This could overstate time-series improvements in liquidity, although the same liquidity patterns are present without a survivorship requirement (see Comerton-Forde et al (2010)) Stocks are sorted into quintiles based on market capitalization Quintile refers to large-cap stocks and quintile corresponds to small-cap stocks All variables used in the analysis are 99.9 % winsorized: values smaller than the 0.05% quantile are set equal to that quantile, and values larger than the 99.95% quantile are set equal to that quantile A Proxies for AT We cannot directly observe whether a particular order is generated by a computer algorithm For cost and speed reasons, most algorithms not rely on human intermediaries but instead generate orders that are sent electronically to a trading venue Thus, we use the rate of electronic message traffic as a proxy for the amount of AT taking place.6 This proxy is commonly used by market participants, including consultants Aite Group and Tabb Group, as well as exchanges and other market venues.7 For example, in discussing market venue capacity limits following an episode of heavy trading volume in February 2007, a Securities Industry News report quotes NASDAQ senior vice president of transaction services, Brian Hyndman, who noted that exchanges have dealt with massive increases in message traffic over the past to years, coinciding with algorithmic growth: “It used to be one-to-one,” Hyndman said “Then you’d see a customer send ten orders that would result in only one execution That’s because the black box would cancel a lot of the orders We’ve seen that rise from 20- to 30- to 50-to-one The amount of orders in the marketplace increased exponentially.”8 In the case of the NYSE, electronic message traffic includes order submissions, cancellations, and trade reports that are handled by the NYSE’s SuperDOT system and captured in the NYSE’s System Order Data (SOD) database The electronic message traffic measure for the NYSE excludes all specialist quoting, as well as all orders that are sent manually to the floor and are handled by a floor broker See Biais and Weill (2009) for theoretical evidence on how AT relates to message traffic See, e.g., Jonathan Keehner, “Massive surge in quotes, electronic messages may paralyse US market,” http://www.livemint.com/2007/06/14005055/Massive-surge-in-quotes-elect.html, June 14, 2007 See Shane Kite, “Reacting to market break, NYSE and NASDAQ act on capacity,” Securities Industry News, March 12, 2007 Does Algorithmic Trading Improve Liquidity? As suggested by the quote above, an important issue is whether and how to normalize the message traffic numbers The top half of Figure shows the evolution of message traffic over time We focus on the largest-cap quintile of stocks, as they constitute the vast bulk of stock market capitalization and trading activity Immediately after decimalization at the start of 2001, the average large-cap stock sees about 35 messages per minute during the trading day There are a few bumps along the way, but by the end of 2005 there are an average of about 250 messages per minute (more than messages per second) for these same large-cap stocks We could, of course, simply use the raw message traffic numbers, but there has been an increase in trading volume over the same interval, and without normalization a raw message traffic measure may just capture the increase in trading rather than the change in the nature of trading Therefore, for each stock each month we calculate our AT proxy, algo tradit , as the number of electronic messages per $100 of trading volume.9 The normalized measure still rises rapidly over the 5-year sample period, while measures of market liquidity such as proportional spreads have declined sharply but appear to asymptote near the end of the sample period (see, e.g., the average quoted spreads in the top half of Figure below), which occurs as more and more stocks are quoted with the minimum spread of $0.01 The time-series evolution of algo tradit is displayed in the bottom half of Figure For the largest-cap quintile, there is about $7,000 of trading volume per electronic message at the beginning of the sample in 2001, decreasing dramatically to about $1,100 of trading volume per electronic message by the end of 2005 Over time, smaller-cap stocks display similar time-series patterns It is worth noting that our AT proxies may also capture changes in trading strategies For example, messages and algo tradit will increase if the same market participants use algorithms but modify their trading or execution strategies so that those algorithms submit and cancel orders more often Similarly, the measure will increase if existing algorithms are modified to “slice and dice” large orders into smaller pieces This is useful, as we want to capture increases in the intensity of order submissions and cancellations by existing algorithmic traders, as well as the increase in the fraction of market participants employing algorithms in trading B Liquidity Measures We measure liquidity using quoted half-spreads, effective half-spreads, 5minute and 30-minute realized spreads, and 5-minute and 30-minute price impacts, all of which are measured as share-weighted averages and expressed in basis points as a proportion of the prevailing midpoint The effective spread is the difference between the midpoint of the bid and ask quotes and the actual Our results are virtually the same when we normalize by the number of trades or use raw message traffic numbers (see Table IA.4 in the Internet Appendix, available online in the “Supplements and Datasets” section at http://www.afajof.org/supplements.asp) The results are also the same when we use the number of cancellations rather than the number of messages to construct the AT measure γ¯G t-stat Block p γ¯V t-stat Block p 0.721 0.480 −0.203 −(2.53)∗∗∗ (0.01)∗∗∗ 0.065 (0.57) (0.27) 0.612 0.356 PE PE −0.223 −(3.20)∗∗∗ (0.00)∗∗∗ 0.144 (1.57)∗ (0.08)∗ Index-Balancers n = 167 All Switchers n = 507 Test Sample 1992–2004 Table III—Continued −0.168 −(1.47)∗ (0.06)∗ 0.063 (0.38) (0.33) 0.887 0.445 PE All Switchers n = 198 −0.176 −(1.82)∗∗ (0.02)∗∗ 0.317 (1.62)∗ (0.04)∗∗ 0.556 0.195 PE Index-Balancers n = 37 High Turnover Sample 1998–2002 −0.050 −(0.76) (0.22) 0.117 (1.59)∗ (0.06)∗ 0.393 0.527 PE All Switchers n = 445 0.250 (2.58) (0.99) −0.317 −(2.99) (1.00) 0.632 0.311 PE Index-Balancers n = 163 Control Sample 1981–1991 Panel B: Stocks That Switch from the Growth Index to the Value Index −0.172 −(1.80)∗∗ (0.032)∗∗ 0.028 (0.24) (0.42) All −0.453 −(3.59)∗∗∗ (0.00)∗∗∗ 0.382 (2.45)∗∗∗ (0.01)∗∗∗ IB Test-Control −0.118 −(0.89) (0.15) −0.053 −(0.29) (0.60) All IB −0.427 −(3.11)∗∗∗ (0.00)∗∗∗ 0.634 (2.85)∗∗∗ (0.00)∗∗∗ HT-Control Difference-in-Difference Statistics Style-Related Comovement 325 326 The Journal of Finance R on the S&P/Barra labels are particularly active in trading following strong performance in the Growth index B Mutual Fund Holdings In this section, I investigate whether the S&P/Barra labels influence the holdings of mutual fund managers Funds that tend to hold value (growth) stocks should naturally increase their holdings, relative to others, of stocks after their BM ratios increase (decrease) Hence, value style funds should naturally increase their relative holdings of Growth index balancers as their BM ratios increase, and growth style funds should naturally increase their relative holdings of Value index balancers as their BM ratios decline I investigate the relative holdings of value and growth style funds around index rebalancing dates, and find that growth style funds during the test sample actually decrease their relative holdings of Value index balancers despite their declining BM ratios During the control sample, however, I find the opposite result: growth style funds increase their relative holdings of Value index balancers when these stocks switch indices The difference in results across samples is statistically significant, suggesting that S&P/Barra labels influence the portfolio allocation decisions of mutual fund managers I use quarterly data provided by CDA/Spectrum for mutual funds classified as “aggressive growth,” “growth,” or “growth and income.” The data begin June 1981 and end December 2004 For each event month, I estimate the following two cross-sectional regressions across funds, i, HiG = θG0 + θG1 Vi + θG2 Si + θG3 Mi + eiG , (8a) HiV = θV + θV Vi + θV Si + θV Mi + eiV , (8b) where HiG (HiV ) denotes the average change in holdings for fund i of stocks that switch from Value to Growth (Growth to Value), and Vi , Si , and Mi represent the value score, size score, and fund size for fund i, respectively I first explain the details behind the variables of these two regressions and then discuss the insights gained from estimation I define holdings for a given fund of a given stock as the fraction of total shares outstanding held The change in holdings for a given fund of a given stock around a given event month is equal to holdings observed at the end of the event month, time t1 , minus holdings at time t0 For funds that report holdings semiannually, t0 is months prior to t1 , and for funds that report quarterly, t0 is months prior to t1 For each event month, I compute a simple average of this change across stocks that switch from Value to Growth (Growth to Value) to obtain HiG (HiV ) for fund i The SEC requires all funds to report holdings biannually However, Thompson Financial investigates fund prospectuses and contacts mutual fund management companies to increase reporting frequency Style-Related Comovement 327 Fund size, Mi , is the log of total equity holdings reported at time t0 for fund i I measure size score, Si , and value score, Vi , using the method of Kacperczyk, Sialm, and Zheng (2005) In particular, I assign each stock traded on a major U.S exchange into a size quintile and BM quintile (independently), where size and BM are measured as of time t0 Size score, Si , is the value-weighted average size quintile across stocks held by fund i observed at time t0 , where value weights are measured relative to total holdings of fund i Likewise, value score, Vi , is the value-weighted average BM quintile across stocks held by fund i observed at time t0 After estimating the two cross-sectional regressions identified earlier for each event month, I average parameter estimates across event months, as in Fama and MacBeth (1973) To obtain standard errors, I use the estimated variances from the cross-sectional regressions and calculate the variance of the average as the sum of the variances divided by N , where N is the number of event months I then take the square-root to obtain the standard error To be included in the analysis for a given event month, a fund must appear in the CDA/ Spectrum data at both t0 and t1 At these two points in time, many such funds report no holdings of stocks that switch among the S&P/Barra indices during the corresponding event month If at time t0 a fund reports no holdings of a given stock that switches indices during the corresponding event month, I exclude this fund-stock pair from the analysis for that event month On the other hand, if at time t1 a fund reports no holdings of a given stock that switches indices during the corresponding event month, I assume the fund holds zero shares of this stock at time t1 I report time-series averages of cross-sectional summary statistics for the mutual fund data in Table IV Panel A is for the test sample while Panel B is for the control sample Results indicate that those funds that pass the screens described earlier are primarily large-cap funds, with average size scores in both periods near 5.0 These funds also tilt somewhat towards growth stocks, with value scores in the range of 2.05 to 2.68 A value score of 3.0 implies that the fund is following a neutral value-growth strategy Value style funds should naturally increase their holdings, relative to others, of stocks after their BM ratios increase However, if the S&P/Barra labels influence fund holdings, then value style funds should also increase their relative holdings of Value index balancers after their BM ratios decrease Applying a similar line of reasoning to growth style funds, this implies that using index balancers to create HiG and HiV , the average value of θG1 , θ G1 , should be negative, and the average value of θV , θ V , should be positive for the test sample That is, an increase in value score should be associated with lower relative holdings of Growth index balancers (despite their increasing BM ratios) and higher relative holdings of Value index balancers (despite their declining BM ratios) when these stocks switch indices I therefore formally test if θ G1 is negative during the test sample and less than that when estimated over the control sample using index balancers I also formally test if θ V is positive during the test sample and greater than that when estimated over the control sample using this same set of stocks 328 The Journal of Finance R Table IV Summary Statistics on Mutual Funds This table presents summary statistics for the mutual fund panel data The data are quarterly mutual fund holdings from the CDA/Spectrum database provided by Thompson Financial The data begin June 1981 and end December 2004 I use mutual funds classified as “aggressive growth,” “growth,” or “growth and income.” For a given event month in which the S&P/Barra indices are rebalanced, I select all funds that hold a nonzero position at date t0 in stocks that switch among the S&P/Barra Value and Growth indices For funds that report holdings semiannually, t0 is months before the end of the corresponding event month, while t0 is months before the end of the corresponding event month for funds that report quarterly For each fund for the given event month, I calculate five variables: (1) average change in holdings of stocks that switch to the Growth index, HiG , (2) average change in holdings of stocks that switch to the Value index, HiV , (3) fund size, (4) value score, and (5) size score The change in holdings of a stock for the given event month is defined as the fraction of shares outstanding held by the fund at t1 minus the fraction of shares held at t0 , where t1 is the end of the event month Both HiG and HiV are scaled by 100 If a fund reports no holdings of a given stock at t1 , I assume the fund holds zero shares of this stock at t1 Value score, size score, and fund size are all observable at t0 Value score and size score are measures of fund style that range on a continuum from to 5, measured using the method of Kacperczyk, Sialm, and Zheng (2005) Fund size is log equity holdings reported at time t0 For each event month, I calculate cross-sectional means and standard deviations across funds Averages of these statistics across event months are reported in this table for the test sample in Panel A and for the control sample in Panel B The bottom line of each panel reports the average number of funds per event month For consistency with other tables, the control sample excludes the crash of October 1987 Panel A: 1992–2004 (Test) Funds Holding Stocks That Switch from Value to Growth Value Score Size Score Fund Size Mean SD 2.06 4.92 0.45 0.16 Avg # Funds = 801.7 18.99 1.94 Funds Holding Stocks That Switch from Growth to Value HiG −0.015 0.11 Value Score Size Score Fund Size 2.05 4.91 0.45 0.16 Avg # Funds = 941.8 18.91 1.95 HiV −0.017 0.12 Panel B: 1981–1991 (Control) Funds Holding Stocks That Switch from Value to Growth Value Score Size Score Fund Size Mean SD 2.68 4.88 0.53 0.18 Avg # Funds = 256.9 18.23 1.64 Funds Holding Stocks That Switch from Value to Growth HiG −0.03 0.12 Value Score Size Score Fund Size 2.66 4.88 0.53 0.17 Avg # Funds = 302.21 18.13 1.67 HiV −0.03 0.13 Table V presents estimates of average coefficients across event months for the two cross-sectional regressions given in (8a) and (8b) Panel A of Table V gives results for index balancers, the focus of the analysis, while Panel B provides results for all switchers The results of Panel A suggest that the S&P/Barra labels influence fund holdings For the test sample, the estimate of θ V is significantly positive, whereas for the control sample it is significantly negative During the test sample, funds with lower size scores (growth style Style-Related Comovement 329 Table V Index Reclassifications and Mutual Fund Holdings Using the data summarized in Table IV, the following two cross-sectional regressions are estimated for each event month: HiG = θG0 + θG1 Vi + θG2 Si + θG3 Mi + eiG HiV = θV + θV Vi + θV Si + θV Mi + eiV , where HiG ( HiV ) is the average change in holdings for fund i of stocks that switch to the Growth (Value) index around the corresponding event month, and Vi , Si , and Mi are the value score, size score, and fund size, respectively, for fund i I aggregate results across event months by averaging parameter estimates, similar to the approach of Fama and MacBeth (1973) Value score, size score, and fund size are all observable at time t0 , where t0 is months (3 months) before the end of the corresponding event month for funds that report holdings semiannually (quarterly) Changes in stock holdings are measured from time t0 to the end of the corresponding event month For further details on these variables, see the caption for Table IV Panel A provides results for index-balancers, while Panel B provides results for all switchers Significance of the one-tailed tests described in the paper is marked as in previous tables Value to Growth θ¯G0 θ¯G1 θ¯G2 0.152 (0.84) −0.003 −(0.35) Growth to Value θ¯G3 θ¯V θ¯V θ¯V θ¯V Panel A: Index Balancers 1992–2004 (Test) t-statistic 1981–1991 (Control) t-statistic 0.010 (0.28) −0.011 −(6.56) 0.470 −0.009 −0.039 (2.09) −(0.76) −(0.83) Test-Control 0.006 t-statistic (0.37) −0.017 −(3.63) 0.080 (0.77) 0.017∗∗∗ 0.010 (2.99) (0.50) −0.114 −0.030 −(0.20) −(1.33) Test-Control 0.047∗∗ t-statistic (2.00) −0.009 −(9.34) 0.016 (0.17) 0.006 (0.31) Panel B: All Switchers 1992–2004 (Test) t-statistic 0.054 (1.52) −0.007 −(3.29) 0.023 (3.40) −0.009 −(18.15) 0.025 (0.76) 0.023 (9.91) 0.022 (3.50) −0.011 −(19.19) 1981–1991 (Control) t-statistic 0.223 (3.90) −0.012 −0.003 −(3.19) −(0.31) −0.011 −(8.97) −0.025 −(0.43) 0.036 (9.86) 0.014 (1.27) −0.009 −(7.41) funds) decrease their relative holdings of Value index balancers despite their declining BM ratios In contrast, during the control sample growth style funds naturally increase their relative holdings of these stocks as their BM ratios decline The difference in θ V across the test and control samples is 0.047 with a t-statistic of 2.00 These findings suggest that the S&P/Barra labels influence the stock holdings of mutual fund managers I not find similar results for Growth index balancers on the left of Table V In Table IV, the average crosssectional standard deviation of value score is 0.45 during the test sample, and in Table V the estimate of θ V in Panel A for the test sample is 0.017 This implies that a one-standard-deviation increase in value score is associated with an increase in HiV of 0.008% for a single fund In contrast, during the control sample a one-standard-deviation increase in value score is associated with a decrease in HiV of 0.016% for a single fund 330 The Journal of Finance R In Panel B, which provides results for all switchers, estimates of θ G1 are significantly negative, and estimates of θ V are significantly positive for both the test and control samples Since the BM ratios of many stocks that switch to the Growth (Value) index decrease (increase) before switching, these results merely suggest that growth style funds increase their relative holdings of stocks after their BM ratios decrease and that value style funds increase their relative holdings of stocks after their BM ratios increase.9 I investigate whether results are sensitive to the assumption that a fund holds zero shares of a given stock at the end of the event month if it does not report any holdings of this stock In the CDA/Spectrum data, a fund may not report any holdings for a given stock if it holds less than 10,000 shares or less than $200,000 in the stock I therefore conduct the analysis assuming a fund holds S shares of the stock if it does not report any holdings at the end of the event month, where S equals 10,000, or S equals 200,000 divided by the price of the stock at the end of the event month, depending on which is larger In this analysis, I find similar results I also determine whether results are robust to adjusting standard errors for autocorrelation In particular, I follow Fama and French (2002), Chakravarty, Gulen, and Mayhew (2004), and Petersen (2007) and multiply standard errors by the square root of (1 + φ)/(1 − φ), where φ is the first-order autocorrelation coefficient of the estimated parameter The autocorrelation coefficients for θG1 and θV in my sample are negative, however, implying that this adjustment actually shrinks standard errors I therefore calculate standard errors assuming independence to be conservative IV Conclusion S&P/Barra divide stocks in the S&P 500 into two mutually exclusive Value and Growth indices based on simple mechanical rules Investors can then trade all stocks within each index as two distinct asset categories without scrutinizing individual assets Although traded together as a group, the fundamental values of assets within the same index may only be loosely correlated Because the mechanical rules by which stocks are assigned to each index are very simple and somewhat arbitrary, I am able to identify the effect of trading unrelated to fundamentals on prices I find that these index labels induce excess covariation in returns, contrary to traditional market efficiency, through the trading activity of investors Specifically, because S&P/Barra require that the market cap of the Value index equal that of the Growth index, stocks that I call index balancers switch to the Value index after their BM ratios decrease and to the Growth index after their BM ratios increase Further, because the mechanical rules are so simple, In Table V, both θ G3 and θ V tend to be negative and highly significant This finding arises because, first, larger funds trade more in absolute terms, and, second, the funds in my analysis are net sellers of S&P/Barra switchers on average because I condition my sample to funds that initially hold a long position in these stocks Style-Related Comovement 331 Barra backdated the constituent data to 1981, thus providing a control sample The indices were first created in 1992 Using post-1992 data I find that both returns and turnover of index balancers begin to covary more strongly with the index they join and less with the index they leave after they switch These changes in comovement are not only statistically significant from zero but are statistically different from similar results estimated using the control sample I also find some evidence that active fund managers are among the investors that use the S&P/Barra labels to allocate capital regardless of fundamentals, perhaps motivated by a desire to cling to their benchmark index Stocks in the S&P 500 are among the most liquid and closely watched by analysts This paper shows that arbitrary, economically meaningless labels cause the prices of these stocks to diverge from fundamental value through the trading activity of style investors who use these labels for capital allocation decisions REFERENCES Baker, Malcolm, and Jeremy C Stein, 2004, Market liquidity as a sentiment indicator, Journal of Financial Markets 7, 271–299 Barberis, Nicholas, and Andrei Shleifer, 2003, Style investing, Journal of Financial Economics 68, 161–199 Barberis, Nicholas, Andrei Shleifer, and Jeffrey Wurgler, 2005, Comovement, Journal of Financial Economics 75, 283–317 Boyer, Brian H., and Lu Zheng, 2009, Investor flows and stock market returns, Journal of Empirical Finance 16, 87–100 Campbell, John Y., Sanford J Grossman, and Jiang Wang, 1993, Trading volume and serial correlation in stock returns, Quarterly Journal of Economics 108, 905–939 Carhart, Mark M., 1997, On persistence in mutual fund performance, Journal of Finance 52, 57–82 Chakravarty, Sugato, Huseyin Gulen, and Stewart Mayhew, 2004, Informed trading in stock and option markets, Journal of Finance 59, 1235–1257 Denis, Diane K., John J McConnell, Alexei V Ovtchinnikov, and Yun 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Using Strategic Surveys to Separate Public Care Aversion from Bequest Motives,” Vanguard, New York University, New York University, and New York University Xavier Giroud and Holger M Mueller, “Corporate Governance, Product Market Competition, and Equity Prices,” New York University Andy Puckett and Xuemin (Sterling) Yan, “The Interim Trading Skills of Institutional Investors,” University of Tennessee and University of Missouri Amil Dasgupta, Andrea Prat, and Michela Verardo, “Institutional Trade Persistence and Long-term Equity Returns,” London School of Economics Bo Becker, Zoran Ivkovi´c, and Scott Weisbenner, “Local Dividend Clienteles,” Harvard University, Michigan State University, and University of Illinois at Urbana-Champaign 333 THE JOURNAL OF FINANCE • VOL LXVI, NO • FEBRUARY 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 See the Call for Papers in this issue Submissions close March 11, 2011 Editor Search: Campbell Harvey’s term as Editor of The Journal of Finance will end in June of 2012 The Executive Committee of the AFA has appointed a search committee consisting of Rick Green (chair, Carnegie Mellon), Kerry Back (Rice), Doug Diamond (Chicago), Francis Longstaff (UCLA), Monica Piazzesi (Stanford), Jay Ritter (Florida), and Jeremy Stein (Harvard) The committee welcomes suggestions, advice, and nominations from the membership Please contact Rick Green (rcgreen@cmu.edu) 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 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end of the paper References to publications in the text should appear as follows: “Jensen and Meckling (1976) report that ” At the end of the manuscript (before tables and figures), the complete list of references should be listed as follows: For monographs: Fama, Eugene F., and Merton H Miller, 1972, The Theory of Finance (Dryden Press, Hinsdale, III.) 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 [...]... trading, liquidity, and the role of the monopolist specialist, Journal of Business 6 2, 21 1 23 5 Glosten, Lawrence R ., 199 4, Is the electronic limit order book inevitable? 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