The turn-of-the-year effect and tax-loss-selling by institutional investors

Journal of Accounting and Economics 57 (2014) 22–42

Contents lists available at ScienceDirect

Journal of Accounting and Economics journal homepage: www.elsevier.com/locate/jae

The turn-of-the-year effect and tax-loss-selling by institutional investors$ Stephanie A. Sikes n The Wharton School, University of Pennsylvania, 1300 Steinberg Hall-Dietrich Hall, 3620 Locust Walk, Philadelphia, PA 19104-6365, USA

a r t i c l e in f o

abstract

Article history: Received 18 March 2012 Received in revised form 14 November 2013 Accepted 17 December 2013 Available online 2 January 2014

Prior studies attribute the turn-of-the-year effect whereby small capitalization stocks earn unusually high returns in early January to tax-loss-selling by individual investors and window-dressing by institutional investors. My results suggest that a significant portion of the effect on turn-of-the-year returns that prior studies attribute to window-dressing is actually attributable to tax-loss-selling by institutional investors. Among small capitalization stocks, I find that institutional investors with strong tax incentives and weak window-dressing incentives realize significantly more losses in the fourth quarter than in the first three quarters of the calendar year, and that their fourth quarter realized losses have a significant impact on turn-of-the-year returns. A one percentage point change in these institutional investors0 fourth quarter realized losses scaled by a firm0 s market capitalization results in an increase of 47 basis points in the firm0 s average daily return over the first three trading days of January, which represents a 46 percent change for the mean firm. & 2013 Elsevier B.V. All rights reserved.

JEL classification: G20 H24 Keywords: Turn-of-the-year effect Tax-loss-selling Institutional investors

1. Introduction Since Rozeff and Kinney (1976) first documented the pattern of abnormally high returns of small capitalization stocks at the beginning of January, commonly referred to as the “turn-of-the-year effect” or the “January effect,” a large number of studies have attempted to determine what causes the unusual pattern. The two most commonly offered explanations are window-dressing by institutional investors and tax-loss-selling by individual investors. The tax-loss-selling hypothesis holds that, prior to year-end, individual investors sell stocks that have declined in value to realize tax losses. According to the window-dressing hypothesis, just prior to year-end, institutional investors buy stocks with positive prior returns (“winners”) and sell stocks with negative prior returns (“losers”) to present attractive year-end portfolio holdings to their clients.

☆ I appreciate the helpful comments and advice of my dissertation committee: Michael Clement (co-chair), John Robinson (co-chair), Jay Hartzell, Li Jin, Lillian Mills, and Laura Starks. In addition, I thank Michelle Hanlon (editor), an anonymous referee, Jerry Auten (discussant), Linda Bamber, Brian Bushee, Jennifer Brown, Jonathan Cohn, Itay Goldstein, John Graham, Steve Huddart, Ross Jennings, Wei Jiang, William Kinney, Bill Mayew, Leslie Robinson (discussant), Pavel Savor, Catherine Schrand, Doug Shackelford, Nate Sharp, Richard Sias, Ryan Wilson, and workshop participants at Duke University, New York University, Penn State University, Southern Methodist University, Stanford University, University of Arizona, University of Georgia, University of Michigan, the University of Texas at Austin, the 2008 American Accounting Association Annual Meeting, and the 2008 National Tax Association Annual Conference on Taxation for their helpful comments. I thank Brian Bushee for sharing his institutional investor classifications, and Vikas Agarwal, Wei Jiang, Yuehua Tang, and Baozhong Yang for sharing their list of hedge funds with me. I appreciate the financial assistance of the Dean0 s Research Fund at The Wharton School. n Tel.: þ1 215 898 7783. E-mail address: [email protected]

0165-4101/$ - see front matter & 2013 Elsevier B.V. All rights reserved. http://dx.doi.org/10.1016/j.jacceco.2013.12.002

S.A. Sikes / Journal of Accounting and Economics 57 (2014) 22–42

23

Although there are many studies of the turn-of-the-year effect, institutional investors0 contribution to the phenomenon is still not fully understood. Prior studies on the turn-of-the-year effect implicitly assume that all institutional investors are tax-insensitive, which is inconsistent with the reality that some institutional investors are tax-sensitive (Jin, 2006). I expect that some institutional investors0 sales of poorly performing stocks at calendar year-end that prior studies attribute to window-dressing are actually tax-motivated, and thus that institutional investors contribute to the turn-of-the-year effect via tax-loss-selling. Clarifying the incentives behind institutional investors0 year-end loss realizations is important because window-dressing is meant to misrepresent an institution0 s holdings over the prior year to investors, and is indicative of an agency problem between institutional investors and their clients, whereas institutional investors execute tax-motivated sales in response to clients0 requests and preferences. I begin my analysis by classifying institutional investor types according to the strength of their tax and window-dressing incentives at calendar year-end. Then I conduct portfolio-level analysis in which I examine the quarterly realized gains and losses of the different institutional investor types. Because the turn-of-the-year effect is a small stock phenomenon, I restrict the sample to NYSE, AMEX, and NASDAQ stocks whose market capitalization at the beginning of a year places them into the bottom quintile when stocks are sorted into quintiles according to the market capitalization of NYSE stocks.1 The sample period is 1987–2010. I find that institutional investors with strong tax incentives and weak window-dressing incentives realize significantly more losses in the fourth quarter than in the first three quarters. Moreover, the difference between realized losses in the fourth quarter relative to the first three quarters is greater for such institutional investors than for institutional investors with strong window-dressing incentives and either no or weaker tax incentives. The results are similar if I do not restrict the sample to only small cap stocks. Next I examine the relation between turn-of-the-year returns of small cap stocks and fourth quarter realized losses in these stocks.2 I measure the turn-of-the-year effect as a stock0 s average return over the first three, five, or ten trading days of January minus its average return over the last three, five, or ten trading days of the previous December (starting with the second to last trading day of December), respectively (Sias and Starks, 1997; Ng and Wang, 2004).3 Consistent with the presence of a turn-of-the-year effect over the sample period, I find that the average daily return over the first three, five and ten trading days of January is 79, 48, and 36 basis points greater than the average daily return over the last three, five, and ten trading days of the previous December, respectively. For each stock-year observation, I sum the fourth quarter realized losses in a stock across all institutional investors of a certain type and then divide the sum by the stock0 s market capitalization. I find that the greater the realized losses in a stock in the fourth quarter by institutional investors with strong tax incentives and weak window-dressing incentives, the greater the stock0 s average daily return in early January and the greater the difference between the stock0 s average daily return in early January and its average daily return at the end of December, using the 3-day, 5-day, and 10-day windows. In terms of economic magnitudes, a one percentage point change in the scaled fourth quarter realized losses of this group of institutional investors results in an increase of 47 basis points in the stock0 s average daily return over the first three trading days of January, which is a 46 percent change for the mean stock. Although fourth quarter realized losses of institutional investors with strong window-dressing incentives also contribute to higher returns in early January, the magnitude of their impact is smaller. For instance, a one percentage point change in scaled fourth quarter realized losses of institutional investors with window-dressing incentives but no tax incentives results in an increase of 27 basis points in the stock0 s average daily return over the first three trading days of January. In summary, the results support my hypothesis that institutional investors contribute to the turn-of-the-year effect via tax-loss-selling and that the impact of institutional investors0 tax-motivated sales on turn-of-the-year returns is economically meaningful. The paper contributes to prior literature on the turn-of-the-year effect by illuminating the incentives behind institutional investors0 year-end sales. The paper proceeds as follows. In Section 2, I discuss prior literature. In Section 3, I describe the tax and window-dressing incentives of institutional investors included in the study, and how these incentives relate to my predictions. In Section 4, I describe the data. In Section 5, I discuss the analysis of quarterly gain and loss realizations. In Section 6, I present the tests of the effect of institutional investors0 fourth quarter realized losses on turn-of-the-year returns. Section 7 provides and summarizes the robustness tests. Section 8 concludes. 2. Prior literature As previously noted, the two leading explanations in the extant literature for the turn-of-the-year effect are tax-lossselling by individual investors and window-dressing by institutional investors. The tax-loss-selling hypothesis holds that just prior to year-end, individual investors sell stocks that have declined in value to realize tax losses (e.g., Rozeff and Kinney, 1976; Dyl, 1977; Givoly and Ovadia, 1983; Keim, 1983; Reinganum, 1983; Ritter, 1988; Poterba and Weisbenner, 2001; Grinblatt and Moskowitz, 2004; among others). According to the window-dressing hypothesis, just prior to year-end, institutional investors buy stocks with positive prior returns (“winners”) and sell stocks with negative prior returns 1

This definition of small cap stocks is consistent with the definition used in other studies of the turn-of-the-year effect (e.g., Ng and Wang, 2004). Although institutional investors realize losses in stocks of all sizes for tax and window-dressing purposes, the resulting abnormal return pattern around the turn-of-the-year is only present among small cap stocks due to their lower liquidity. 3 As explained in Section 6.1, I omit the return on the last day of December to control for fund managers0 last minute purchases of stocks already held in their portfolios, which Carhart et al. (2002) show inflates quarter-end portfolio prices. 2

24

S.A. Sikes / Journal of Accounting and Economics 57 (2014) 22–42

(“losers”) to present attractive year-end portfolio holdings to their clients (e.g., Lakonishok et al., 1991; Musto, 1997; Ng and Wang, 2004; among others).4 Institutional investors have an incentive to window dress if they disclose the return earned on individual stocks in their portfolio, as opposed to just the portfolio0 s overall return, to their clients or plan sponsors at calendar year-end, and if these disclosures affect money inflows and outflows. Selling stocks with negative prior returns is a more common form of window-dressing than is buying stocks with positive prior returns (Lakonishok et al., 1991; He et al., 2004). If a money manager sells a loser, there is no scrutiny of the window-dressing behavior because the stock does not appear in the listing of calendar year-end holdings. On the other hand, if a money manager attempts to window-dress his or her portfolio by buying a winner at calendar year-end after the stock0 s run-up in price, disclosures that include the return earned on each stock within a portfolio reveal that the money manager did not hold the stock during the price rise. Many studies try to identify which explanation, tax-loss-selling by individual investors or window-dressing by institutional investors, is the leading cause of the turn-of-the-year effect. For example, Sias and Starks (1997) compare turn-of-the-year returns for stocks with high institutional investor ownership to those for stocks with low institutional investor ownership. They find that the turn-of-the-year effect is stronger among the latter, which they interpret as evidence supporting tax-loss-selling by individual investors as the primary cause. Poterba and Weisbenner (2001) and Grinblatt and Moskowitz (2004) analyze how returns around the turn-of-the-year vary with changes in tax rules applicable to capital gains, which could affect the timing of individual investors0 loss realizations but not institutional investors0 incentives to window-dress. The results in both papers support tax-loss-selling by individual investors as an explanation for the turn-ofthe-year effect. Two studies (He et al., 2004; Ng and Wang, 2004) utilize data on quarterly institutional holdings to examine whether the fourth quarter trading behavior of institutional investors is consistent with window-dressing. He et al. (2004) find that institutional investors increase sales of poorly performing stocks in the fourth quarter, which they conclude supports the window-dressing hypothesis. They also find that institutional investors that manage funds on behalf of their clients are more likely to sell more loser stocks than winner stocks in the fourth quarter than are internally managed pension funds, colleges, university endowments and foundations. They attribute this finding to greater window-dressing incentives among institutional investors who manage their clients0 funds rather than their own funds because external managers compete with one another for clients. He et al. (2004), however, do not consider that many of the external managers (i.e., banks, insurance companies, and investment advisers whose majority clientele are high net-worth individuals) also have tax incentives. Although He et al. (2004) examine quarterly trading by institutional investors, they do not link the trading behavior to turn-of-the-year returns. Ng and Wang (2004) fill that void. Ng and Wang (2004) find that in the last quarter of the year institutional investors sell more than they buy of poorly performing stocks and buy more than they sell of well performing stocks. They attribute these findings to window-dressing. They extend He et al. (2004) by showing that the sales of poorly performing stocks strengthen the turn-of-the-year effect. Similar to He et al. (2004), Ng and Wang (2004) do not consider the possibility that some of institutional investors0 sales of poorly performing stocks in the fourth quarter are tax-motivated rather than a response to window-dressing incentives. I contribute to prior literature by examining a potential new explanation for the turn-of-the-year effect: tax-loss-selling by institutional investors. Unlike prior studies on the turn-of-the-year effect, I recognize that institutional investors are heterogeneous with respect to tax incentives. Classifying institutional investors according to the strength of their tax and window-dressing incentives allows me to address the research question. I estimate the magnitude of both the effect of institutional investors0 tax-loss-selling and the effect of institutional investors0 window-dressing on turn-of-the-year returns. This quantification is important because to the extent that some of institutional investors0 fourth quarter realized losses are attributable to tax-loss-selling rather than to window-dressing, this will suggest that prior studies overstate the impact of window-dressing on turn-of-the-year returns. Moreover, showing that part of institutional investors0 impact on turn-of-the-year returns is attributable to tax-loss-selling rather than to window-dressing is important because windowdressing is indicative of an agency problem between institutional investors and their clients, whereas institutional investors execute tax-motivated sales in response to clients0 requests and preferences.5 3. Tax incentives and window-dressing incentives of institutional investors I include the following types of institutional investors in this study: investment advisers whose majority (450 percent) clientele are high net-worth individuals; pensions, endowments, and investment advisers whose majority clientele are 4 The tendency for institutional investors to buy winner stocks and sell loser stocks could also be related to momentum strategies, since winner (loser) stocks continue to outperform (underperform) over the subsequent year (Jegadeesh and Titman, 1993). Such a strategy might be economically justified. A potential indication of window-dressing, however, is a significant increase in either selling losers and/or buying winners in the fourth quarter relative to the other three quarters. 5 Starks et al. (2006) find that tax-loss-selling by individual investors in municipal bond closed-end funds is greater if the fund is associated with a brokerage firm. Although the results in Starks et al. (2006) suggest that brokers might play a role in year-end tax-loss-selling by providing tax counseling to individual investors, Starks et al. (2006) only examine trades made by individual investors. A key difference between this paper and Starks et al. (2006) is that unlike the brokers studied by Starks et al. (2006), the institutional investors that I classify as tax-sensitive have complete discretion over the assets under their management. In Starks et al. (2006), the sales are made by individual investors, whereas in this paper, the sales are made by institutional investors.

S.A. Sikes / Journal of Accounting and Economics 57 (2014) 22–42

25

tax-exempt entities (e.g., pensions, charitable organizations, and state and local governments); investment companies (i.e., mutual fund families) and investment advisers whose majority clientele are mutual funds; banks; insurance companies; and hedge fund management companies. For the sake of brevity, throughout the remainder of the paper, I refer to mutual fund families and investment advisers whose majority clientele are mutual funds as “mutual funds” and to hedge fund management companies as “hedge funds.” In Sections 3.1 and 3.2, I describe the tax incentives and window-dressing incentives of each institutional investor type. In Section 3.3, I explain how these incentives influence my predictions of whether the relation between turn-of-the-year returns and fourth quarter realized losses of the different institutional investor types is attributable to tax-loss-selling, window-dressing, or both.

3.1. Tax incentives The institutional investors that I classify as unambiguously tax-sensitive are investment advisers whose majority clientele are high net-worth individuals. Other papers that classify these investment advisers as tax-sensitive are Jin (2006), Jin and Kothari (2008), and Desai and Jin (2011). Even if an adviser balances realized gains and losses throughout the year, at calendar year-end a client could want to offset gains realized outside of the portfolio managed by his or her adviser. Although the investment advisers have complete discretion over the assets under their management, they are unlikely to fully ignore a client0 s request to harvest tax losses at calendar year-end. In contrast, pensions, endowments, and investment advisers whose majority clientele are tax-exempt entities are unambiguously tax-insensitive as either they or their clients are tax-exempt (Jin 2006). Sensitivity to taxes varies among institutions within each of the remaining institutional investor types (mutual funds, banks, insurance companies, and hedge funds). For example, some mutual funds are tax-sensitive, while other mutual funds are not. As a result, extant literature is not always consistent with the classification of each of these institutional investor types as either tax-sensitive or tax-insensitive. The appendix includes a discussion of prior studies0 classification of mutual funds, banks, insurance companies, and hedge funds as either tax-sensitive or tax-insensitive. Unlike these prior studies, rather than excluding all institutions in each of these institutional investor types or assuming that they are either all taxsensitive or all tax-insensitive, I recognize that there is heterogeneity in the tax-sensitivity of the institutional investors within each of these types and that I cannot identify which of the institutions within these types are tax-sensitive and which are tax-insensitive. As a result, I expect that some, but not all, of the fourth quarter realized losses of mutual funds, banks, insurance companies, and hedge funds are tax-motivated.

3.2. Window-dressing incentives I rely on prior literature in classifying the following institutional investor types as having window-dressing incentives to realize losses at calendar year-end: pensions and endowments as well as investment advisers whose majority clientele are tax-exempt entities; mutual funds; banks; and insurance companies (Lakonishok et al., 1991; O0 Barr and Conley, 1992; He et al., 2004). In contrast, investment advisers whose majority clientele are high net-worth individuals have weak window-dressing incentives. These institutions manage assets on a separate account basis rather than at a fund level. As a result, they are more likely to receive inquiries from clients about their accounts prior to regularly scheduled reporting dates, which weaken any incentive that these advisers have to window-dress their portfolios prior to calendar year-end.6 Hedge fund managers also have weak window-dressing incentives because hedge funds0 disclosures to their investors are more limited than those of other types of institutional investors. Moreover, hedge fund managers often seek confidential treatment of certain holdings through amendments to their original Form 13F, which all institutional investment managers who exercise investment discretion of $100 million or more in Section 13(f) securities must file with the Securities and Exchange Commission (SEC).7 When the SEC grants such confidential treatment, a hedge fund can delay the disclosure, usually up to one year. Two studies find that hedge funds do not seek confidentiality of holdings to window-dress their original Form 13F (i.e., to hide holdings of poorly performing stocks) (Agarwal et al., 2013; Aragon et al., forthcoming). Rather, the stocks granted confidential treatment outperform those reported in the original Form 13F. Panel A of Table 1 summarizes the strength of the tax and window-dressing incentives of the different institutional investor types. 6 The results of two untabulated tests further support that investment advisers whose majority clientele are high net-worth individuals have weak incentives to window dress. First, consistent with fourth quarter tax-loss-selling and inconsistent with window-dressing, the percent of these advisers0 loss realizations that are associated with partial sales, as opposed to complete liquidations, increases in the fourth quarter. Second, inconsistent with fourth quarter window-dressing, I find that tax-sensitive institutions0 purchases of “winner” stocks (i.e., stocks whose cumulative return over the 12 months preceding a quarter is positive) decrease from the first three quarters to the fourth quarter. 7 Institutional investment managers file Form 13F on a quarterly basis and must report holdings of more than 10,000 shares or holdings valued in excess of $200,000.

26

S.A. Sikes / Journal of Accounting and Economics 57 (2014) 22–42

Table 1 Tax incentives and window-dressing incentives of institutional investor types. Panel A summarizes the tax and window-dressing incentives of the different institutional investor types that are included in the study. See Sections 3.1 and 3.2 for a detailed discussion of the tax incentives and window-dressing incentives, respectively. Panel B outlines my predictions of whether the relation between turn-of-the-year returns and fourth quarter realized losses of the various institutional investor types is attributable to tax-loss-selling, windowdressing, or both. See Section 3.3 for a discussion of the predictions. Panel A: Tax and window-dressing incentives of institutional investor types A Weak window-dressing incentives I

Unambiguously tax-sensitive

II

Unambiguously tax-insensitive

III

Heterogenous with respect to tax-sensitivity

B Strong window-dressing incentives

Investment advisers whose majority clientele are high net-worth individual (“tax-sensitive” institutional investors) Pensions, endowments, and investment advisers whose majority clientele are tax-exempt entities (“tax-insensitive” institutional investors) (1) Investment companies (i.e., mutual fund families) and investment advisers whose majority clientele are mutual funds; (2) banks; (3) insurance companies

Hedge funds

Panel B: Mapping of incentives into predictions Cell

Relation between fourth quarter realized losses & turn-of-the-year returns attributable to

AI A III B II B III

Tax-loss-selling Tax-loss-selling (but weaker relation than A I) Window-dressing Tax-loss-selling and window-dressing

3.3. Predictions Panel B of Table 1 outlines how my predictions relate to the combination of each institutional investor type0 s tax incentives and window-dressing incentives. First, I expect that, due to their unambiguous tax-sensitivity and weak windowdressing incentives, investment advisers whose majority clientele are high net-worth individuals contribute to the turn-ofthe-year effect via tax-loss-selling rather than window-dressing. An increase in these institutions0 realized losses in the fourth quarter and a relation between their fourth quarter realized losses and turn-of-the-year returns will support my hypothesis. On the other hand, I expect for pensions, endowments, and investment advisers whose majority clientele are tax-exempt entities to contribute to the turn-of-the-year effect via window-dressing rather than tax-loss-selling because of their unambiguous tax-insensitivity and their strong window-dressing incentives. I will attribute an increase in realized losses in the fourth quarter by these institutions and a relation between their fourth quarter realized losses and turn-of-the-year returns to window-dressing. Because banks, mutual funds, and insurance companies have window-dressing incentives but some of them are also taxsensitive, I am unable to attribute an increase in their realized losses in the fourth quarter and a relation between their fourth quarter realized losses and turn-of-the-year returns exclusively to window-dressing or exclusively to tax-loss-selling. Rather, I will attribute such findings to a combination of tax-loss-selling and window-dressing. I will attribute an increase in hedge funds0 realized losses in the fourth quarter and a relation between their fourth quarter realized losses and turn-of-the-year returns to tax-loss-selling since hedge funds have weak window-dressing incentives and some hedge funds have tax incentives. However, I expect the relation between fourth quarter realized losses and turn-of-the-year returns to be weaker for hedge funds than for investment advisers to high net-worth individuals because not all hedge funds are tax-sensitive (Liang et al., forthcoming).

4. Data I collect data on the client types of investment advisers from the SEC0 s Investment Adviser Public Disclosure (IAPD) database.8 The Form ADV, which SEC-registered investment advisers must file, lists the following ten client types: individuals (other than high net-worth individuals); high net-worth individuals; banking or thrift institutions; investment companies (including mutual funds); pension and profit-sharing plans (other than plan participants); other pooled investment vehicles (e.g., hedge funds); charitable organizations; corporations or other businesses not listed above; state 8

http://www.adviserinfo.sec.gov/IAPD/Content/IapdMain/iapd_SiteMap.aspx.

S.A. Sikes / Journal of Accounting and Economics 57 (2014) 22–42

27

or municipal government entities; and “others” such as non-U.S. government entities.9 Investment advisers must provide the approximate percentage of their clients that are of each type: none; up to ten percent; 11-25 percent; 26-50 percent; 51-75 percent; 76-99 percent; 100 percent. I classify an institutional investor as “tax-sensitive” if it is an investment adviser whose majority (450 percent) clientele are high-net worth individuals (TaxSensitive).10 As explained in Section 3.1, some banks, mutual funds, insurance companies, and hedge funds are also tax-sensitive. I use the label “tax-sensitive” to only describe the type of institutional investor that is unambiguously tax-sensitive and that has weak window-dressing incentives. Thomson Reuters provides a dataset of institutional investors0 quarterly holdings reported on Form 13F for each calendar quarter beginning with the year 1980. Thomson Reuters divides institutional investors into the following five types: banks, insurance companies, investment companies, independent investment advisers, and others (i.e., endowments, foundations, employee stock ownership plans, pensions, etc.). I use Thomson Reuters0 classifications to identity the banks (Bank) and insurance companies (Insurance) used in the study. Beginning in 1998, Thomson Reuters misclassified many investment companies and investment advisers by including them in the “other” category. Thus, I use the classifications in Abarbanell et al. (2003) and Bushee and Goodman (2007) to identify investment companies, corporate (private) pension funds, public pension funds, and university and foundation endowments. For institutions that exist in Thomson Reuters0 13F dataset prior to 1998, these authors use the reliable classifications of institutional investor type from Thomson Reuters before 1998 and apply the same classifications to the institutions in 1998 and afterwards. Then for each new institution that enters the Thomson Reuters 13F dataset in 1998 and afterwards, they attempt to distinguish whether it is an investment company, investment adviser, public pension fund, private pension fund, or endowment and classify it accordingly.11 I combine institutional investors that these authors classify as investment companies with investment advisers whose majority clientele are mutual funds (MutualFund).12 I also combine institutional investors that these authors classify as private pension funds, public pension funds, or endowments with investment advisers whose majority clientele consists of pensions, charitable organizations, and/or state and local governments (TaxInsensitive). As explained in Section 3.1, some banks, mutual funds, insurance companies, and hedge funds are also tax-insensitive. I use the label “tax-insensitive” to only describe the type of institutional investor that is unambiguously tax-insensitive and that has strong window-dressing incentives. The sample of hedge fund management companies (HedgeFund) is the sample used in Agarwal et al. (2013).13 There is some overlap between the hedge fund management companies in the Agarwal et al. (2013) sample and the other institutional investor types included in the study.14 In these cases, I classify the institutional investor as the type with which it overlaps (i.e., tax-sensitive, tax-insensitive, mutual fund, bank, or insurance) rather than as a hedge fund management company. Untabulated robustness tests show that the results reported in the paper are not sensitive to how I treat the overlapping institutions and are also not sensitive to excluding hedge funds entirely from the study.15 I collect institutional investors0 quarterly holdings data reported on Form 13F from Thomson Reuters, stock return and market capitalization data from CRSP, and financial statement variables from Compustat.

9 The Form ADV defines a “high net-worth individual” as “an individual with at least $750,000 managed by [the investment adviser], or whose net worth [the investment adviser] reasonably believes exceeds $1,500,000, or who is a ‘qualified purchaser’ as defined in Section 2(a)(51)(A) of the Investment Company Act of 1940. The net worth of an individual may include assets held jointly with his or her spouse.” 10 The most recently updated Form ADV is available for investment advisers who are currently registered with the SEC or who were registered at some point since 2001. My conversations with investment advisers reveal that their majority clientele0 s type generally does not change over time. To address any concern that the investment advisers that I classify as “tax-sensitive” based on data that I collected in 2006 are not tax-sensitive throughout the sample period, in an untabulated robustness test, I only classify investment advisers with over 75 percent of their clientele comprised of high net-worth individuals as “tax-sensitive.” These investment advisers are even less likely to have specialized in a client type other than high net-worth individuals earlier in the sample period. The results are quantitatively similar to those reported in the paper. 11 These authors google the institutional investor name, click on the “about us” link on the institution0 s webpage, and try to determine the type based on what the institution writes about itself. If they cannot find any information about the institution, they classify it as miscellaneous. 12 In classifying the institutions that enter Thomson Reuters0 13F dataset in 1998 and afterwards, Abarbanell et al. (2003) and Bushee and Goodman (2007) do not distinguish between those that are investment companies and those that are investment advisers. Thus, I only include investment companies that exist in 1998 and afterwards in my MutualFund group if the investment company existed prior to 1998 and was classified as an investment company by Thomson Reuters prior to 1998. This requirement prevents misclassifying investment advisers whose majority clientele are not mutual funds into the MutualFund group. This requirement does not affect the results for the MutualFund group. In an untabulated test, I find the same relation between turn-ofthe-year returns and mutual funds0 fourth quarter realized losses in the first half of the sample period (1987–1998) as I do in the second half (1999–2010). 13 Agarwal et al. (2013) are very careful in classifying institutional investors as hedge fund management companies. They check the business description of each fund/fund management company. I thank them for sharing the data with me. 14 Eighty-seven of the 940 hedge fund management companies in the Agarwal et al. (2013) dataset overlap with the other institutional investor types included in the paper. 15 The institutional investors excluded from this study are institutional investors that Abarbanell et al. (2003) and Bushee and Goodman (2007) classify as “miscellaneous” (e.g., law firms, partnerships, and individual investors who have enough discretion over 13(f) securities that they are required to file a Form 13F) and that are not classified as hedge funds by Agarwal et al. (2013); and investment advisers whose clients are primarily “individuals,” banking and thrift institutions, corporations, other businesses, or “other.” I exclude these institutional investors because their tax and window-dressing incentives are ambiguous. The category “individuals” on the Form ADV includes trusts, estates, 401(k) plans, and IRAs of individuals and their family members. Because of the tax-deferred nature of these investments, I do not classify advisers whose majority clientele is “individuals” as tax-sensitive.

28

S.A. Sikes / Journal of Accounting and Economics 57 (2014) 22–42

5. Quarterly gain and loss realizations I begin with portfolio-level analysis in which I estimate the proportion of an institutional investor0 s losses that it realizes each quarter (PLR) and the proportion of its gains that it realizes each quarter (PGR). I then compare PLR and PGR ratios for the fourth quarter of the calendar year to the average of these ratios over the first three quarters of the year. In constructing the ratios, I follow Odean (1998) and Barber and Odean (2003), who use these ratios to study retail investors0 tendency to realize gains as opposed to losses. I use quarterly holdings data and monthly stock price data to estimate the realized and unrealized capital gains and losses for each institutional investor j-stock i pair each quarter. Estimating unrealized and realized gains and losses at the institution-stock-quarter level is an improvement over prior studies that assume that if a stock0 s return over the prior one or two years is positive (negative), then all investors have an unrealized gain (loss) in the stock (e.g., Lakonishok et al., 1991; He et al., 2004; Ng and Wang, 2004). In other words, my estimation allows each institution to have a unique unrealized or realized gain or loss in a stock, as opposed to assuming that all institutions have the exact same unrealized or realized gain or loss in a stock. I assume that an increase in the number of shares of stock i that institutional investor j holds from the end of quarter q 1 to the end of quarter q reflects a purchase of that many shares of stock i in quarter q. I estimate the purchase price as the average of the three month-end prices of stock i in quarter q, which becomes institutional investor j0 s tax basis for these shares.16 Likewise, I assume that a decrease in shares of stock i that institutional investor j holds from the end of quarter q 1 to the end of quarter q reflects a sale of that many shares in quarter q. I estimate the sales price as the average of the three month-end prices of stock i in quarter q. I adjust stock prices and the quarterly holdings data for stock splits. If the institutional investor owns multiple lots of the same stock that were purchased at different prices, then I assume that the institutional investor uses highest-in first-out (HIFO) in calculating realized gains/losses on sales.17 In estimating institutional investor j0 s unrealized gain or loss in stock i for quarter q, I use a hypothetical sales price equal to the average of the three month-end prices of stock i in quarter q. I sum institutional investor j0 s realized losses in quarter q and unrealized losses in quarter q across all stocks that institutional investor j holds at the beginning of quarter q. I divide the sum of institutional investor j0 s realized losses in quarter q by the sum of institutional investor j0 s realized and unrealized losses (i.e., total losses) in quarter q. I calculate an analogous ratio of institutional investor j0 s realized gains in quarter q to the sum of its realized and unrealized gains in quarter q. Scaling realized losses and gains, respectively, by the total amount of losses and gains that the institutional investor could realize during the quarter controls for an institution0 s prior performance. In other words, it controls for the possibility that an institution realizes more losses than other institutions in a particular quarter because it has more losses to realize. In summary, the PLR and PGR ratios are as follows18: Proportion of Losses Realized ðPLRÞj;q ¼

Realized Lossesj;q Realized Lossesj;q þ Unrealized Lossesj;q

Proportion of Gains Realized ðPGRÞj;q ¼

Realized Gainsj;q Realized Gainsj;q þUnrealized Gainsj;q

After I calculate institutional investor j0 s PLR and PGR ratios for every quarter, for each institutional investor type I calculate the average of each ratio across all institutional investors for each of the 96 unique quarters in the sample period (1987–2010). Then for each institutional investor type, I calculate the mean PLR ratio and the mean PGR ratio for the first quarter of the year across the 24 years of the sample period. I do the same for the second, third, and fourth quarters. These ratios are reported in the first four columns of Panels A through D of Table 2. For each of the 24 years in the sample period, I also calculate the difference between an institutional investor type0 s average fourth quarter PLR ratio and the type0 s average PLR ratio over the first three quarters. I then average the 24 annual differences for each type to get the mean PLR difference ratio for each of the six institutional investor types. I do the same calculation for the PGR ratio. The means, along with their statistical significance based on a two-tailed t-test, are reported in the last column of Panels A through D of Table 2. Although the turn-of-the-year effect is a small stock phenomenon, I conduct the portfolio-level analysis first using stocks of all sizes. If institutional investors realize more losses in the fourth quarter in response to tax or window-dressing incentives, it is reasonable to expect that they would do so across all stocks, not just small cap stocks. The results for all stocks are presented in Panels A and B of Table 2. Panel A shows that in the fourth quarter tax-sensitive institutional investors realize 25.6 percent of their total losses. This is 3.5 percentage points more than in the first three quarters. The 16 To determine the tax basis of shares held, I conduct this computation using quarterly holdings data all the way back to 1980, which is the first year that Form 13F reports are available on Thomson Reuters. 17 Under U.S. tax law, an institution can designate the lot of stocks to be sold. With highest-in, first-out, an institution sells shares that it purchased at the highest price first in order to minimize capital gains or maximize capital losses. Prior studies measure unrealized and realized gains and losses similarly (Huddart and Narayanan, 2002; Jin, 2006). 18 The labels “Unrealized Losses” and “Unrealized Gains” in the ratios refer to what Odean (1998) and Barber and Odean (2003) label “Paper Losses” and “Paper Gains,” respectively.

S.A. Sikes / Journal of Accounting and Economics 57 (2014) 22–42

29

Table 2 Analysis of quarterly gain and loss realizations. This table reports the results of portfolio-level analysis of the proportion of losses realized (PLR¼Realized Loss/(Realized Lossþ Unrealized Loss)) each quarter (Panels A and C) and of the proportion of gains realized (PGR¼ Realized Gain/(Realized Gainþ Unrealized Gain)) each quarter (Panels B and D). The mean quarterly ratios are reported in the first four columns. The last column reports the difference between the fourth quarter ratio and the average of the ratio over the first three quarters. See Section 5 for an explanation of how the ratios, means, and difference are calculated. The six different institutional investor types (TaxSensitive, TaxInsensitive, MutualFund, Bank, Insurance, and HedgeFund) are defined in Section 4. The sample period is 1987–2010. Ratios in Panels A and B are calculated using NYSE, AMEX, and NASDAQ stocks of all sizes. Ratios in Panels C and D include NYSE, AMEX, and NASDAQ stocks whose market capitalization at the beginning of the year places them into the bottom quintile when stocks are sorted into quintiles according to the market capitalization of NYSE stocks. nnn, nn, and n denote that the difference is statistically significant at the 1%, 5%, and 10% level, respectively, using a two-tailed test. Panel A: Proportion of losses realized (PLR), all stocks

TaxSensitive TaxInsensitive MutualFund Bank Insurance HedgeFund

Q1

Q2

Q3

Q4

Q4  Average(Q1–Q3)

0.2277 0.2405 0.2446 0.1644 0.1831 0.3554

0.2111 0.2322 0.2347 0.1605 0.1720 0.3440

0.2244 0.2344 0.2492 0.1596 0.1785 0.3469

0.2561 0.2392 0.2499 0.1850 0.2015 0.3748

0.0351nnn 0.0036 0.0071n 0.0235nnn 0.0237nnn 0.0261nnn

Panel B: Proportion of gains realized (PGR), all stocks

TaxSensitive TaxInsensitive MutualFund Bank Insurance HedgeFund

Q1

Q2

Q3

Q4

Q4 – Average(Q1–Q3)

0.1465 0.1547 0.1487 0.0719 0.1122 0.2968

0.1222 0.1440 0.1323 0.0659 0.1072 0.2842

0.1336 0.1427 0.1405 0.0624 0.0977 0.2793

0.1253 0.1473 0.1418 0.0702 0.1103 0.2862

 0.0087 0.0002 0.0014 0.0035 0.0046  0.0006

Panel C: Proportion of losses realized (PLR), small stocks

TaxSensitive TaxInsensitive MutualFund Bank Insurance HedgeFund

Q1

Q2

Q3

Q4

Q4  Average(Q1–Q3)

0.2354 0.2742 0.2832 0.1818 0.2300 0.2864

0.2128 0.2659 0.2605 0.1788 0.2200 0.2819

0.2181 0.2479 0.2555 0.1729 0.1991 0.2718

0.2724 0.2491 0.2658 0.1977 0.2232 0.3061

0.0503nnn  0.0135n  0.0006 0.0198nnn 0.0069 0.0261nn

Panel D: Proportion of gains realized (PGR), small stocks

TaxSensitive TaxInsensitive MutualFund Bank Insurance HedgeFund

Q1

Q2

Q3

Q4

Q4  Average(Q1–Q3)

0.1847 0.2085 0.1843 0.1501 0.1895 0.2773

0.1722 0.2001 0.1809 0.1579 0.1851 0.2716

0.1904 0.2093 0.1806 0.1455 0.1832 0.2696

0.1891 0.2147 0.2016 0.1577 0.1792 0.3102

0.0067 0.0087 0.0197nn 0.0065  0.0067 0.0373nn

difference is statistically significant at the one percent level, and is larger than the difference for each of the other institutional investor types. Hedge funds, which also have weak window-dressing incentives and some of which are tax-sensitive, realize 37.5 percent of their total losses in the fourth quarter, which is 2.61 percentage points more than in the first three quarters (p o0.01). Banks and insurance companies also realize significantly more losses as a percentage of their total losses in the fourth quarter (banks: 2.35 percentage points; insurance companies: 2.37 percentage points) As explained in Section 3.1, in addition to having window-dressing incentives, some banks and insurance companies are tax-sensitive. Mutual funds also realize significantly more losses as a percentage of their total losses in the fourth quarter. However, the magnitude of the difference for mutual funds is smaller (0.71 percentage points) and its statistical significance is lower (po0.10). In addition to having window-dressing incentives, mutual funds could have tax incentives to realize losses. The smaller difference for mutual funds is consistent with the finding in Gibson et al. (2000) that mutual funds spread out their tax motivated sales several months prior to their October 31 tax year-end to reduce price pressure when they hold larger positions in stocks. Interestingly, the only institutional investor type that does not realize significantly more losses as a percentage of its total losses in the fourth quarter is tax-insensitive institutions, which is the only institutional investor type that has strong

30

S.A. Sikes / Journal of Accounting and Economics 57 (2014) 22–42

window-dressing incentives and is unambiguously tax-insensitive. Taken as a whole, the results in Panel A suggest that tax incentives are the primary motivation for institutional investors0 fourth quarter loss realizations. Panel B reports the results for the PGR ratios calculated using all stocks. For all six institutional investor types, there is no significant difference between their PGR ratio in the fourth quarter relative to the first three quarters. Thus, the increase in realized losses in the fourth quarter documented in Panel A is not due to a general increase in total sales of stocks in the fourth quarter. Panels C and D present the results using the sample of small cap stocks. Consistent with the results in Panel A, Panel C shows that tax-sensitive institutional investors realize significantly more losses as a percentage of their total losses in the fourth quarter than in the first three quarters of the year (p o0.01). The magnitude of the difference (5 percentage points) is even larger than in Panel A, and continues to exceed the magnitude of the difference for the other institutional investor types. The only other types to realize significantly more losses as a percentage of their total losses in the fourth quarter are hedge funds (difference ¼2.61 percentage points, po0.05) and banks (difference ¼1.98 percentage points, p o0.01). Realized losses as a percentage of total losses are 1.4 percentage points lower in the fourth quarter for tax-insensitive institutions, which are the only institutions with window-dressing incentives but absolutely no tax incentive to realize losses at calendar year-end, and the difference is statistically significant at the ten percent level. Panel D shows that among small cap stocks, only mutual funds and hedge funds realize significantly more gains as a percentage of their total gains in the fourth quarter (p o0.05). The differences for the other four types are statistically insignificant. Similar to the results using all stocks in Panel B, the insignificant differences in Panel D for investment advisers whose clients are primarily high net-worth individuals and for banks suggest that the increase in their realized losses in the fourth quarter documented in Panel C is not due to a general increase in their total sales of stocks in the fourth quarter. In summary, using either all stocks or small cap stocks only, the results in Table 2 suggest that institutional investors0 fourth quarter loss realizations are motivated more by tax incentives than by window-dressing incentives. The difference between realized losses in the fourth quarter relative to the first three quarters is greatest for tax-sensitive institutional investors. Moreover, institutional investors that are unambiguously tax-insensitive and that have strong window-dressing incentives are the only institutions that do not realize significantly more losses in the fourth quarter when the analysis is conducted using all stocks. They are also the only institutions that realize significantly fewer losses in the fourth quarter when the analysis is conducted using the sample of small cap stocks only.

6. Tests of turn-of-the-year effect The analysis of quarterly loss realizations discussed in the previous section shows that institutions with strong tax incentives and weak window-dressing incentives increase their realized losses in the fourth quarter relative to the prior three quarters more so than institutional investors with strong window-dressing incentives and either no or weaker tax incentives. In this section, I test whether the realized losses of the institutions with strong tax incentives and weak window-dressing incentives affect turn-of-the-year returns. If they do, this will suggest that institutional investors contribute to the turn-of-theyear effect via tax-loss-selling. I also compare the effect of realized losses by institutions with strong tax incentives and weak window-dressing incentives on turn-of-the-year returns to the effect of realized losses by institutional investors with strong window-dressing incentives and either no or weaker tax incentives. I do so to get a sense of the importance of tax-loss-selling by institutional investors as an explanation for the turn-of-the-year effect vis-à-vis window-dressing.

6.1. Empirical model I examine the effect of fourth quarter realized losses by each institutional investor type on a stock0 s average daily return over the first X trading days of the following January (Return_JanX), and on the difference between a stock0 s average daily return over the first X trading days of the following January and the stock0 s average daily return over the last X trading days of December (Return_DiffX), where X equals either 3, 5, or 10. In calculating the average daily return over the last X days of December, I omit the last day of December in order to control for “leaning for the tape” behavior described in Carhart et al. (2002) (i.e., Return_Diff3 equals Return_Jan3 minus the average daily return over the period beginning with the fourth to last trading day and ending with the second to last trading day of December). Carhart et al. (2002) document that equity mutual fund prices are abnormally high on the last day of quarters, especially the fourth quarter and especially among small cap funds. Carhart et al. (2002) attribute this finding to fund managers inflating quarter-end portfolio prices with last minute purchases of stocks already held.19 I estimate the following ordinary least squares regression for six different dependent 19 Although “leaning for the tape” is a form of window-dressing, it has the opposite effect on returns than does selling losers, either for windowdressing or tax purposes. If equity mutual funds indeed “lean for the tape” on the last day of December, this mitigates the downward price pressure on stocks at calendar year-end that is related to selling losers. Moreover, unrelated to “leaning for the tape,” prior studies document that the shift from sellerinitiated transactions closing at their bid prices to buyer-initiated transactions closing at their ask prices begins to occur on the last day of December (Roll, 1983), which is why other papers (e.g., Ritter, 1988) also exclude the last day of December from their tests of the turn-of-the-year effect. The results reported in the paper are quantitatively similar if I include the last day of December in computing returns.

S.A. Sikes / Journal of Accounting and Economics 57 (2014) 22–42

31

variables (Return_Jan3, Return_Diff3, Return_Jan5, Return_Diff5, Return_Jan10, or Return_Diff10): DepVar ¼ β0 þ β1 RLTaxSensitiveit þ β2 RLTaxInsensitiveit þβ3 RLMutualFundit þβ4 RLBankit þ β5 RLInsuranceit þ β6 RLHedgeFundit þ β7 %TaxSensitiveit þβ8 %TaxInsensitiveit þ β9 %MutualFundit þβ10 %Bankit þ β11 %Insuranceit þ β12 %HedgeFundit þ β13 ReturnX it þβ14 Book=Market it þ ε:

ð1Þ

The sample period is 1987–2010, where December is referred to as year t and January is referred to as year tþ1 (i.e., January returns are collected for years 1988–2011). All independent variables are measured in year t. I remove subscripts from the variables throughout the remainder of the paper. Because the turn-of-the-year effect is a small cap stock phenomenon, the sample only includes small cap stocks. The primary independent variable of interest is RLTaxSensitive, which equals the absolute value of the dollar amount of losses that tax-sensitive institutional investors realize in stock i during quarter four, scaled by stock i0 s market capitalization. The variables RLTaxInsensitive, RLMutualFund, RLBank, RLInsurance, and RLHedgeFund are defined analogously. I predict that at calendar year-end tax-sensitive institutional investors sell stocks in which they have unrealized losses to generate tax losses for their clients, driving down prices of the affected stocks in the process. Immediately following the turn of the year, selling pressure ceases and prices return to their equilibrium, resulting in abnormally high returns in early January. Thus, I expect a positive coefficient on RLTaxSensitive for each of the six dependent variables. The effect of these tax-motivated sales on turn-of-the-year returns should be magnified among small cap stocks due to their lower liquidity, which is consistent with the turn-of-the-year effect being a small cap stock phenomenon. I control for the percent of outstanding shares of stock i owned by each of the six institutional investor types at the end of the third quarter (%TaxSensitive, %TaxInsensitive, %MutualFund, %Bank, %Insurance, and %HedgeFund). I require positive total institutional investor ownership at the end of the third quarter for a stock to enter the sample; however, I do not require for all six institutional investor types to own shares in each stock. Thus, if a particular institutional investor type does not own shares in stock i, then the percent ownership variable for the particular institutional investor type for stock i equals zero. Sias and Starks (1997) find that the average return in both late December and early January is higher for poorer performing stocks. Thus, I control for prior performance by including stock i0 s return from the first trading day of year t to the X to last trading day of year t, where X equals either 3, 5, or 10 (ReturnX). I also control for firm i0 s book-to-market ratio (Book/Market). With the exception of Return_JanX, Return_DiffX, and ReturnX, all variables are winsorized at the 1st and 99th percentiles. I cluster the standard errors by firm and year (Petersen, 2009; Gow et al., 2010).20 6.2. Descriptive statistics I first document that there is indeed a turn-of-the-year effect over the sample period for the sample of small cap stocks. For each year of the sample period, I calculate the mean Return_JanX and mean Return_DiffX, and test whether the means are significantly different from zero. Columns (1)–(2), (3)–(4), and (5)–(6) in Table 3 report the results for the 3-day, 5-day, and 10-day windows, respectively. Return_DiffX is positive and statistically significant in 20, 20, and 18 years of the 24-year sample period when the 3-day, 5-day, and 10-day window is used, respectively. Table 4 reports descriptive statistics for the variables in Eq. (1). The mean (median) Return_Jan3, Return_Jan5, and Return_Jan10 across the entire sample period equal 101 (25), 79 (25), and 55 (20) basis points per day, respectively. The mean (median) Return_Diff3, Return_Diff5, and Return_Diff10 equal 79 (25), 48 (16), and 36 (14) basis points per day, respectively. Consistent with the existence of a turn-of-the-year effect over the sample period, untabulated tests show that all three differences (Return_Diff3, Return_Diff5, Return_Diff10) are positive and statistically significant at the one percent level. Moreover, consistent with the turn-of-the-year effect being a small cap stock phenomenon, untabulated tests show that the three differences are negative and significant at the one percent level for larger stocks (i.e., NYSE, AMEX, and NASDAQ stocks whose market capitalization at the beginning of a year places them into the top four quintiles when stocks are sorted into quintiles according to the market capitalization of NYSE stocks). The one exception is that Return_Diff3 is positive and statistically significant at the one percent level for the second to smallest size quintile. The mean (median) percent of outstanding shares owned by tax-sensitive institutional investors (%TaxSensitive) equals 1.37 percent (0.00 percent). The mean (median) fourth quarter realized losses of tax-sensitive institutional investors scaled by market capitalization (RLTaxSensitive) equal 0.06 percent (0.00 percent). These are lower bound estimates of actual ownership and realized losses by tax-sensitive institutional investors. Thus, the economic magnitude of the effect of institutional investors0 tax-loss-selling on turn-of-the-year returns is likely larger than what is reported in the paper. Institutional investors file Form 13F on a consolidated basis. In some cases, multiple Forms ADV are associated with one institutional investor0 s consolidated Form 13F. In these cases, I have no way of knowing the majority clientele that is associated with the holdings reported on the Form 13F. Thus, I only include an investment adviser whose majority clientele are high net-worth individuals if its name on its Form ADV exactly matches its name on its Form 13F.21 The mean (median) % TaxInsensitive, %MutualFund, %Bank, %Insurance, and %HedgeFund equal 1.4 percent (0.05 percent), 5.26 percent (2.44 20 In this paragraph and in a few other instances throughout the paper, I use the terms “firm” and “stock” interchangeably. The reference to a “firm” is to a stock, not an institutional investor. 21 The same is true of the investment advisers whose majority clientele are mutual funds or tax-exempt entities.

32

S.A. Sikes / Journal of Accounting and Economics 57 (2014) 22–42

Table 3 Turn-of-the-year effect by year. This table documents the existence of a turn-of-the-year effect over the sample period (1987–2010). The sample includes NYSE, AMEX, and NASDAQ stocks whose market capitalization at the beginning of the year places them into the bottom quintile when stocks are sorted into quintiles according to the market capitalization of NYSE stocks. Return_JanX equals the mean daily raw return averaged over the first X trading days of year tþ 1. Return_DiffX equals the mean difference between the daily raw return averaged over the first X trading days of year t þ1 and the daily raw return averaged over the last X trading days of year t, ending with the second to last rather than last trading day of December. X ¼ 3 in columns (1)–(2), 5 in columns (3)–(4), and 10 in columns (5)–(6). nnn nn , , and n denote whether the mean values reported are statistically significant at the 1%, 5%, and 10% level, respectively, using a two-tailed test. Year

(1) Return_Jan3

(2) Return_Diff3

(3) Return_Jan5

(4) Return_Diff5

(5) Return_Jan10

(6) Return_Diff10

1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010

0.0240nnn 0.0075nnn 0.0082nnn 0.0080nnn 0.0145nnn 0.0106nnn 0.0084nnn 0.0070nnn 0.0063nnn 0.0096nnn 0.0078nnn 0.0140nnn 0.0045nnn 0.0273nnn 0.0140nnn 0.0100nnn 0.0105nnn  0.0084nnn 0.0059nnn  0.0008n  0.0061nnn 0.0395nnn 0.0098nnn 0.0081nnn

0.0298nnn 0.0059nnn 0.0084nnn 0.0089nnn 0.0052nnn 0.0084nnn 0.0046nnn 0.0083nnn 0.0063nnn 0.0104nnn 0.0063nnn 0.0098nnn  0.0058nnn 0.0264nnn 0.0058nnn 0.0111nnn 0.0039nnn  0.0161nnn 0.0062nnn  0.0042nnn  0.0051nnn 0.0406nnn 0.0073nnn 0.0051nnn

0.0141nnn 0.0066nnn 0.0058nnn 0.0026nnn 0.0135nnn 0.0079nnn 0.0074nnn 0.0061nnn 0.0065nnn 0.0090nnn 0.0046nnn 0.0119nnn 0.0078nnn 0.0175nnn 0.0111nnn 0.0068nnn 0.0103nnn  0.0047nnn 0.0067nnn 0.0002  0.0067nnn 0.0249nnn 0.0109nnn 0.0051nnn

0.0128nnn 0.0037nnn 0.0035nnn 0.0025nnn 0.0055nnn 0.0039nnn 0.0040nnn 0.0053nnn 0.0034nnn 0.0100nnn 0.0034nnn 0.0064nnn 0.0003 0.0156nnn 0.0047nnn 0.0068nnn 0.0045nnn  0.0113nnn 0.0050nnn  0.0026nnn  0.0090nnn 0.0255nnn 0.0064nnn 0.0014nn

0.0073nnn 0.0043nnn 0.0008nnn 0.0006n 0.0128nnn 0.0070nnn 0.0055nnn 0.0048nnn 0.0023nnn 0.0068nnn 0.0014nnn 0.0087nnn 0.0076nnn 0.0182nnn 0.0062nnn 0.0066nnn 0.0078nnn  0.0024nnn 0.0050nnn 0.0022nnn  0.0049nnn 0.0074nnn 0.0066nnn 0.0060nnn

0.0029nnn 0.0021nnn 0.0003  0.0003 0.0098nnn 0.0036nnn 0.0039nnn 0.0032nnn 0.0003 0.0070nnn 0.0023nnn 0.0061nnn 0.0029nnn 0.0224nnn 0.0001 0.0082nnn 0.0043nnn  0.0067nnn 0.0046nnn 0.0008nn  0.0050nnn 0.0055nnn 0.0030nnn 0.0011nn

Table 4 Descriptive statistics for tests of turn-of-the-year effect. This table reports the descriptive statistics for the variables in Eq. (1) calculated over the sample period (1987–2010). The sample includes NYSE, AMEX, and NASDAQ stocks whose market capitalization at the beginning of the year places them into the bottom quintile when stocks are sorted into quintiles according to the market capitalization of NYSE stocks. See Section 6.1 for variable definitions. With the exception of Return_JanX, Return_DiffX, and ReturnX, where X ¼ 3, 5, or 10, all variables are winsorized at the 1st and 99th percentiles. All of the statistics are reported in decimal format. N ¼ 63,886. Variable

Mean

Std. dev.

5th Pctile

25th Pctile

Median

75th Pctile

95th Pctile

Return_Jan3 Return_Diff3 Return_Jan5 Return_Diff5 Return_Jan10 Return_Diff10 RLTaxSensitive RLTaxInsensitive RLMutualFund RLBank RLInsurance RLHedgeFund %TaxSensitive %TaxInsensitive %MutualFund %Bank %Insurance %HedgeFund Book/Market Return3 Return5 Return10

0.0101 0.0079 0.0079 0.0048 0.0055 0.0036 0.0006 0.0007 0.0034 0.0015 0.0004 0.0007 0.0137 0.0141 0.0526 0.0278 0.0079 0.0171 0.8147 0.1849 0.1760 0.1788

0.0381 0.0536 0.0277 0.0386 0.0189 0.0263 0.0039 0.0049 0.0187 0.0081 0.0029 0.0050 0.0329 0.0337 0.0721 0.0449 0.0227 0.0439 0.8467 1.2299 1.1886 1.1196

 0.0316  0.0574  0.0223  0.0436  0.0148  0.0291 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0250  0.7262  0.7250  0.7121

 0.0069  0.0129  0.0048  0.0103  0.0031  0.0072 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0026 0.0000 0.0000 0.3362  0.3258  0.3306  0.3178

0.0025 0.0025 0.0025 0.0016 0.0020 0.0014 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0005 0.0244 0.0117 0.0000 0.0000 0.6614 0.0000  0.0025 0.0000

0.0196 0.0243 0.0151 0.0174 0.0105 0.0123 0.0000 0.0000 0.0000 0.0002 0.0000 0.0000 0.0093 0.0083 0.0770 0.0321 0.0040 0.0070 1.0901 0.3738 0.3659 0.3662

0.0704 0.0887 0.0526 0.0621 0.0352 0.0428 0.0023 0.0016 0.0152 0.0070 0.0010 0.0021 0.0796 0.0819 0.1982 0.1127 0.0416 0.1027 2.2490 1.6111 1.5843 1.5771

S.A. Sikes / Journal of Accounting and Economics 57 (2014) 22–42

33

Table 5 Test of Turn-of-the-Year Effect. Panel A: Estimation of Eq. (1) only including tax-sensitive and tax-insensitive institutional investors. The dependent variable is denoted in the column heading. The sample period is 1987–2010. The sample includes NYSE, AMEX, and NASDAQ stocks whose market capitalization at the beginning of the year places them into the bottom quintile when stocks are sorted into quintiles according to the market capitalization of NYSE stocks. See Section 6.1 for variable definitions. t-Statistics, calculated using robust standard errors clustered by firm and year, are presented in parentheses below the coefficient estimates. nnn and nn indicate significance of the t-statistic at the 1% and 5% level, respectively, using a two-tailed test. Variable

(1) Return_Jan3

(2) Return_Diff3

(3) Return_Jan5

(4) Return_Diff5

(5) Return_Jan10

(6) Return_Diff10

RLTaxSensitive

0.6600nnn (3.644) 0.4387nnn (3.965)  0.0522nnn (  3.964)  0.0474nnn (  5.326) 0.0027nnn (2.804)  0.0024nn (  2.228)

0.7188nnn (3.486) 0.4114nnn (3.448)  0.0547nnn (  3.773)  0.0625nnn (  5.768) 0.0035nnn (3.295)  0.0028nn (  2.368)

0.4465nnn (4.109) 0.3013nnn (4.733)  0.0416nnn (  4.292)  0.0370nnn (  6.112) 0.0019nnn (2.689)

0.4892nnn (3.708) 0.2485nnn (3.219)  0.0398nnn (  3.721)  0.0511nnn (  6.628) 0.0029nnn (3.612)

0.2068nnn (3.793) 0.1864nnn (5.314)  0.0321nnn (  4.483)  0.0273nnn (  6.765) 0.0011nnn (2.898)

0.2282nnn (3.525) 0.1314nnn (2.595)  0.0341nnn (  4.290)  0.0358nnn (  7.225) 0.0019nnn (3.580)

 0.0015nn (  1.965)

 0.0017nn (  2.145)

 0.0013nn (  2.169) 0.0031nnn (2.740) 63,886 0.013

RLTaxInsensitive %TaxSensitive %TaxInsensitive Book/Market Return3 Return5

Intercept

0.0089nnn (5.707)

0.0065nnn (3.000)

0.0072nnn (6.709)

0.0035nnn (2.816)

 0.0008 (  1.602) 0.0053nnn (5.512)

Observations R-squared

63,886 0.023

63,886 0.015

63,886 0.019

63,886 0.014

63,886 0.015

Return10

Panel B: Estimation of Eq. (1) including all institutional investor types. This table reports the results of estimating Eq. (1), where the dependent variable is denoted in the column heading. The sample period is 1987–2010. The sample includes NYSE, AMEX, and NASDAQ stocks whose market capitalization at the beginning of the year places them into the bottom quintile when stocks are sorted into quintiles according to the market capitalization of NYSE stocks. See Section 6.1 for variable definitions. t-Statistics, calculated using robust standard errors clustered by firm and year, are presented in parentheses below the coefficient estimates. nnn, nn, and n indicate significance of the t-statistic at the 1%, 5%, and 10% level, respectively, using a two-tailed test. Variable

(1) Return_Jan3

(2) Return_Diff3

(3) Return_Jan5

(4) Return_Diff5

(5) Return_Jan10

(6) Return_Diff10

RLTaxSensitive

0.4677nnn (3.760) 0.2713nnn (2.999) 0.1572nnn (4.550) 0.1492nnn (2.579) 0.3921nnn (2.881) 0.0138 (0.116)  0.0308nnn (  3.116)  0.0201nnn (  2.617)  0.0294nnn (  6.485)  0.0270nnn (  3.340)  0.0100 (  0.928)  0.0244 (  0.949) 0.0025nnn (2.817)  0.0021nn (  2.140)

0.5222nnn (3.447) 0.2372nn (2.036) 0.1734nnn (4.558) 0.1379n (1.744) 0.5097nnn (4.005)  0.1013 (  0.769)  0.0303nnn (  2.728)  0.0312nnn (  3.300)  0.0345nnn (  5.803)  0.0225nn (  2.051)  0.0229n (  1.710)  0.0306 (  1.183) 0.0031nnn (3.315)  0.0025nn (  2.317)

0.3052nnn (4.676) 0.1772nnn (3.653) 0.1183nnn (3.791) 0.1195nnn (3.671) 0.2689nnn (3.998)  0.0159 (  0.195)  0.0240nnn (  3.338)  0.0138nnn (  3.143)  0.0240nnn (  5.881)  0.0224nnn (  4.539)  0.0139nn (  2.022)  0.0194 (  1.129) 0.0017nnn (2.667)

0.3555nnn (3.866) 0.1297n (1.862) 0.1235nnn (3.571) 0.0958nn (2.561) 0.3200nnn (5.103)  0.0893 (  0.900)  0.0212nnn (  2.748)  0.0266nnn (  4.336)  0.0263nnn (  5.488)  0.0173nnn (  2.922)  0.0228nnn (  2.675)  0.0229 (  1.285) 0.0027nnn (3.596)

0.1199nnn (3.358) 0.1077nnn (3.337) 0.0671nnn (4.898) 0.0804nn (2.465) 0.1654nnn (3.066) 0.0312 (0.649)  0.0195nnn (  3.765)  0.0096nnn (  2.932)  0.0144nnn (  5.016)  0.0228nnn (  4.646)  0.0102nn (  2.378)  0.0171nn (  2.145) 0.0010nnn (2.807)

0.1369nnn (3.444) 0.0457 (1.276) 0.0661nn (2.458) 0.1009nnn (4.410) 0.2082nnn (2.770) 0.0088 (0.150)  0.0191nnn (  3.531)  0.0145nnn (  4.553)  0.0186nnn (  5.951)  0.0240nnn (  3.416)  0.0155nnn (  2.699)  0.0192nnn (  2.626) 0.0017nnn (3.496)

 0.0012n (  1.826)

 0.0014nn (  2.052)  0.0007 (  1.400)

 0.0011nn (  2.063)

RLTaxInsensitive RLMutualFund RLBank RLInsurance RLHedgeFund %TaxSensitive %TaxInsensitive %MutualFund %Bank %Insurance %HedgeFund Book/Market Return3 Return5 Return10

34

S.A. Sikes / Journal of Accounting and Economics 57 (2014) 22–42

Table 5 (continued ) Panel B: Estimation of Eq. (1) including all institutional investor types. This table reports the results of estimating Eq. (1), where the dependent variable is denoted in the column heading. The sample period is 1987–2010. The sample includes NYSE, AMEX, and NASDAQ stocks whose market capitalization at the beginning of the year places them into the bottom quintile when stocks are sorted into quintiles according to the market capitalization of NYSE stocks. See Section 6.1 for variable definitions. t-Statistics, calculated using robust standard errors clustered by firm and year, are presented in parentheses below the coefficient estimates. nnn, nn, and n indicate significance of the t-statistic at the 1%, 5%, and 10% level, respectively, using a two-tailed test. Variable

(1) Return_Jan3

(2) Return_Diff3

(3) Return_Jan5

(4) Return_Diff5

(5) Return_Jan10

(6) Return_Diff10

Intercept

0.0105nnn (6.984)

0.0083nnn (3.887)

0.0086nnn (8.243)

0.0050nnn (4.029)

0.0064nnn (6.205)

0.0044nnn (3.457)

Observations R-squared

63,886 0.035

63,886 0.023

63,886 0.034

63,886 0.022

63,886 0.029

63,886 0.023

percent), 2.78 percent (1.17 percent), 0.79 percent (0.00 percent), and 1.71 percent (0.00 percent), respectively. The mean (median) RLTaxInsensitive, RLMutualFund, RLBank, RLInsurance and RLHedgeFund equal 0.07 percent (0.00 percent), 0.34 percent (0.00 percent), 0.15 percent (0.00 percent), 0.04 percent (0.00 percent) and 0.07 percent (0.00 percent), respectively. 6.3. Regression results I first estimate Eq. (1) only including the realized loss and percent ownership variables for the institutional investors that are unambiguously tax-sensitive and that have weak window-dressing incentives and for the institutional investors that are unambiguously tax-insensitive and that have strong window-dressing incentives, along with control variables. Panel A of Table 5 reports the results of estimating Eq. (1) by firm-year with robust standard errors clustered by firm and year. The coefficients on RLTaxSensitive and RLTaxInsensitive are positive and significant at the one percent level in all columns, consistent with each group0 s fourth quarter realized losses having a significant impact on turn-of-the-year returns. Untabulated results of F tests of the difference between the coefficients on RLTaxSensitive and RLTaxInsensitive show that the coefficient on RLTaxSensitive is significantly greater than the coefficient on RLTaxInsensitive in all columns, except when Return_Jan10 or Return_Diff10 is the dependent variable. The difference between the two is significant at the five percent level in columns (1)–(3), and at the one percent level in column (4). The larger coefficient on RLTaxSensitive than on RLTaxInsensitive is consistent with the economic magnitude of the effect of tax-loss-selling by institutional investors on turn-of-the-year returns being greater than the effect of window-dressing by institutional investors on turn-of-the-year returns. A more in-depth discussion of the economic magnitude of the results follows the discussion of the results in Panel B. Next I incorporate the realized loss and percent ownership variables for the other institutional investor types. Panel B of Table 5 presents the results. Consistent with my hypothesis that institutional investors contribute to the turn-of-the-year effect via tax-loss-selling, the coefficient on RLTaxSensitive is positive and statistically significant at the one percent level in all six columns. The coefficient on RLTaxInsensitive remains positive and statistically significant at the one percent level in columns (1), (3), and (5). However, its statistical significance falls to the five percent level in column (2), and to the ten percent level in column (4), and it is insignificant in column (6). The coefficient on RLInsurance is positive and significant at the one percent level in all six columns. The coefficient on RLMutualFund is positive and significant at the one percent level in the first five columns and at the five percent level in column (6). The coefficient on RLBank is positive and significant in all six columns, with significance ranging from the ten percent to the one percent level. Because some insurance companies, mutual funds, and banks are tax-sensitive and they also have window-dressing incentives, the relation between their fourth quarter realized losses and turn-of-the-year returns is likely a result of both tax-loss-selling and window-dressing. Although the results in Panel C of Table 2 show that hedge funds realize significantly more losses as a percentage of their total losses in small cap stocks in the fourth quarter relative to the first three quarters, the coefficient on RLHedgeFund is insignificant in all six columns. It is possible that hedge funds do not wait until the very end of the calendar year to conduct their tax-motivated sales, which could explain why I do not find that hedge funds0 fourth quarter realized losses impact turn-of-the-year returns. The negative and significant coefficients on Return3, Return5, and Return10 are consistent with a stronger turn-of-theyear effect among underperforming stocks. I do not include a control for a stock0 s market capitalization in Eq. (1) because I only estimate the equation across small cap stocks. In untabulated tests, I estimate Eq. (1) and include the natural logarithm of stock i0 s market capitalization (LnCap) as an additional control variable. The coefficient on LnCap is negative and significant in all of the estimations, consistent with a stronger turn-of-the-year effect among smaller stocks, and the results for all other variables are quantitatively similar to those reported in Panel B of Table 5.22 22 Because I collected investment advisers0 clientele data in 2006 for all investment advisers that were registered with the SEC between 2001 and 2006, some tax-sensitive investment advisers that existed in the earlier part of the sample period could be missing from my sample. Moreover, as I explain

S.A. Sikes / Journal of Accounting and Economics 57 (2014) 22–42

35

Focusing on column (1) of Panel B of Table 5, the magnitude of the coefficients on the realized loss variables suggests that taxsensitive institutional investors0 fourth quarter realized losses have a greater impact on turn-of-the-year returns than do realized losses of any of the other institutional investor types. A one percentage point change in tax-sensitive institutional investors0 scaled fourth quarter realized losses in a stock results in an increase of 47 basis points in a stock0 s average daily return over the first three trading days of January, which is a 46 percent change for the mean firm.23 The institutional investor type with the next largest impact is insurance companies. A one percentage point change in scaled fourth quarter realized losses of insurance companies results in an increase of 39 basis points in a stock0 s average daily return over the first three trading days of January. A one percentage point change in scaled fourth quarter realized losses of tax-insensitive institutions, mutual funds, and banks results in an increase of 27, 16 and 15 basis points, respectively, in a stock0 s average daily return over the first three days of January. Focusing on column (2) of Panel B of Table 5, tax-sensitive institutional investors also have the greatest impact on the difference between early January and late December returns. A one percentage point change in scaled fourth quarter realized losses of tax-sensitive institutional investors results in an increase of 52 basis points in the difference between a stock0 s average return over the first three trading days in January and its average return over the last three trading days in December, which represents a 66 percent change for the mean firm.24,25 One can also describe the economic magnitude of the results by analyzing the effects of a one standard deviation increase in the scaled realized losses of the various institutional investor types. A one standard deviation increase in tax-sensitive institutional investors0 scaled fourth quarter realized losses in a stock results in an 18 basis point per day increase on average in the stock0 s return over the first three trading days of the following January, which is an 18 percent change for the mean stock.26 The economic magnitude of the effect of tax-sensitive institutional investors0 scaled fourth quarter realized losses on a stock0 s average daily return over the first three trading days of January is greater than that of tax-insensitive institutions (one standard deviation increase in RLTaxInsensitive results in a 13 basis point per day increase on average), banks (one standard deviation increase in RLBank results in an 12 basis point per day increase on average), and insurance companies (one standard deviation increase in RLInsurance results in an 11 basis point per day increase on average). Mutual funds are the only institutional investor type whose fourth quarter realized losses have a greater impact on returns over the first three trading days in January than do the realized losses of tax-sensitive institutional investors when impact is analyzed using one standard deviation changes in the realized loss variables. However, this is due to the fact that mutual funds own a larger percentage of shares of the sample firms on average than do any of the other institutional investor types, as illustrated in Table 4. A one standard deviation increase in mutual funds0 scaled fourth quarter realized losses in a stock results in a 29 basis point per day increase on average in the daily return over the first three trading days of the following January, which is a 29 percent change for the mean stock. Although prior literature describes mutual funds as having window-dressing incentives (O0 Barr and Conley, 1992; He et al., 2004), prior literature also suggests that mutual funds manage their tax positions throughout the year, and not just immediately preceding their October 31 tax year-end (e.g., Gibson et al., 2000; Huddart and Narayanan, 2002; Sialm and Starks, 2012). Similar patterns emerge from the comparison of the economic magnitude of the effect of fourth quarter realized losses on the difference between the average daily return at the beginning of January and the average daily return at the end of December. A one standard deviation increase in tax-sensitive institutional investors0 scaled fourth quarter realized losses in a stock results in a 20 basis point increase on average in the difference between the average daily return over the first three trading days of the following January and the average daily return over the last three trading days of December, which represents a 26 percent change for the mean stock. The effect is larger than that of the other institutional investor types, with the exception of mutual funds.27,28

(footnote continued) in footnote 12, I do not include investment companies that enter the Thomson Reuters 13F dataset beginning in 1998 because I have no way of identifying which new institutions are investment companies. To ensure that neither of these choices affects the results, in untabulated tests, I estimate Eq. (1) separately over the first half (1987–1998) and second half (1999–2010) of the sample period. The results for both halves are quantitatively similar to those reported in Panel B of Table 5. 23 The 46 percent equals the increase of 47 basis points divided by the mean of Return_Jan3 (0.0101). 24 The coefficient on RLTaxSensitive also exceeds the coefficients on the other realized loss variables in columns (3) and (4), but is smaller than the coefficient on RLInsurance in columns (5) and (6). 25 I calculate the dependent variables in Eq. (1) using raw returns. The results are robust to using market-adjusted returns instead. When I use marketadjusted returns, the magnitude of the coefficient on RLTaxSensitive continues to exceed the coefficients on the realized loss variables for the other institutional investor types, with the exception that the coefficient on RLInsurance is slightly larger when the dependent variable is Return_Jan5, Return_Diff5, or Return_Jan10. 26 The 18 basis points per day equals the standard deviation of RLTaxSensitive (0.0039) multiplied by the coefficient estimate on RLTaxSensitive (0.4677) when Return_Jan3 is the dependent variable. The 18 percent equals 18 basis points per day divided by the mean value of Return_Jan3 (0.0101). 27 A one standard deviation increase in mutual funds0 scaled fourth quarter realized losses in a stock results in a 32 basis point increase on average in the difference between the average daily return over the first three trading days of the following January and the average daily return over the last three trading days of December, which is a 41 percent change for the mean stock. 28 Similar patterns emerge when analyzing the average daily returns over the first five trading days of January or the difference between the average daily return over the first five trading days of January and the average daily return over the last five trading days of December. When the window is extended to ten days, the impact of realized losses of banks and of insurance companies on turn-of-the-year returns also exceeds the impact of realized losses of tax-sensitive institutions when impact is analyzed using one standard deviation changes in the realized loss variables. The difference between the impact of tax-sensitive institutional investors0 realized losses and those of insurance companies is very small, and the difference between the impact of taxsensitive institutional investors0 realized losses and those of banks is attributable to banks owning the second largest percentage of shares of sample firms.

36

S.A. Sikes / Journal of Accounting and Economics 57 (2014) 22–42

Table 6 Robustness tests addressing tax-loss-selling by individual investors. Panel A: Estimation of Eq. (1) for stocks with less than 25% ownership by individual investors. The dependent variable is denoted in the column heading. The sample period is 1987–2010. The sample includes NYSE, AMEX, and NASDAQ stocks whose market capitalization at the beginning of the year places them into the bottom quintile when stocks are sorted into quintiles according to the market capitalization of NYSE stocks. See Section 6.1 for variable definitions. t-Statistics, calculated using robust standard errors clustered by firm and year, are presented in parentheses below the coefficient estimates. nnn nn , , and n indicate significance of the t-statistic at the 1%, 5%, and 10% level, respectively, using a two-tailed test. Variable

(1) Return_Jan3

(2) Return_Diff3

(3) Return_Jan5

(4) Return_Diff5

(5) Return_Jan10

(6) Return_Diff10

RLTaxSensitive

0.6103nnn (6.214) 0.3492nn (2.370) 0.1038nnn (4.564) 0.0555 (1.200) 0.1208 (0.721)  0.2378 (  1.214)  0.0259n (  1.817)  0.0112 (  0.955)  0.0121nnn (  2.730)  0.0148n (  1.786)  0.0087 (  0.458)  0.0113 (  0.493) 0.0049n (1.882) 0.0005 (0.510)

0.5243nnn (4.884) 0.3655nnn (3.043) 0.1004nnn (2.965) 0.0949nnn (3.641) 0.0473 (0.436)  0.1802 (  1.207)  0.0193 (  1.222)  0.0147 (  1.199)  0.0147nnn (  2.994)  0.0156 (  1.418)  0.0242 (  1.065)  0.0142 (  0.829) 0.0061nn (2.171) 0.0008 (1.116)

0.2862nnn (5.537) 0.2614nnn (3.107) 0.0521nnn (2.923)  0.0023 (  0.058) 0.1870nn (1.997)  0.1390 (  1.263)  0.0148 (  1.219)  0.0111 (  1.637)  0.0087nnn (  2.634)  0.0051 (  0.813)  0.0105 (  0.810)  0.0067 (  0.483) 0.0043nnn (2.921)

0.2566nn (2.082) 0.3677nnn (2.923) 0.0569n (1.768) 0.0337 (0.990) 0.0382 (0.481)  0.1277 (  1.043)  0.0065 (  0.435)  0.0095 (  1.190)  0.0117nnn (  3.687)  0.0079 (  1.014)  0.0148 (  1.013)  0.0105 (  0.792) 0.0050nn (2.292)

0.1077nnn (3.196) 0.0410 (1.470) 0.0052 (0.395)  0.0039 (  0.091) 0.0535 (0.989) 0.0715 (0.968)  0.0096 (  1.193)  0.0079nnn (  2.885)  0.0062nn (  2.542)  0.0110nnn (  3.167)  0.0063 (  0.938)  0.0098 (  1.554) 0.0013 (1.485)

0.0480 (0.630) 0.0310 (0.556) 0.0020 (0.094) 0.0324 (1.100) 0.0043 (0.071) 0.1719n (1.683)  0.0064 (  0.731)  0.0046 (  1.182)  0.0088nnn (  3.597)  0.0119nnn (  3.157)  0.0089 (  1.083)  0.0090 (  1.546) 0.0016 (1.363)

0.0011nn (2.335)

0.0009n (1.744) 0.0005 (1.046) 0.0005 (0.376) 1,622 0.028

RLTaxInsensitive RLMutualFund RLBank RLInsurance RLHedgeFund %TaxSensitive %TaxInsensitive %MutualFund %Bank %Insurance %HedgeFund Book/Market Return3 Return5

Intercept

0.0063nnn (3.103)

0.0025 (0.837)

0.0023 (1.401)

 0.0014 (  0.758)

0.0010nn (2.490) 0.0035nnn (2.903)

Observations R-squared

1,622 0.112

1,622 0.075

1,622 0.086

1,622 0.060

1,622 0.038

Return10

Panel B: Estimation of Eq. (1) for stocks of which individual investors are net buyers. The dependent variable is denoted in the column heading. The sample period is 1987–2010. The sample includes NYSE, AMEX, and NASDAQ stocks whose market capitalization at the beginning of the year places them into the bottom quintile when stocks are sorted into quintiles according to the market capitalization of NYSE stocks. See Section 6.1 for variable definitions. t-Statistics, calculated using robust standard errors clustered by firm and year, are presented in parentheses below the coefficient estimates. nnn, nn, and n indicate significance of the t-statistic at the 1%, 5%, and 10% level, respectively, using a two-tailed test. Variable

(1) Return_Jan3

(2) Return_Diff3

(3) Return_Jan5

(4) Return_Diff5

(5) Return_Jan10

(6) Return_Diff10

RLTaxSensitive

0.4507nnn (2.995) 0.2769nnn (2.798) 0.1508nnn (4.558) 0.1231nn (2.419) 0.3324nnn (2.595)  0.0410 (  0.286)  0.0323nnn (  3.063)  0.0241nn (  2.473)  0.0325nnn

0.5373nnn (3.354) 0.2393n (1.857) 0.1618nnn (4.366) 0.1073 (1.506) 0.4579nnn (3.109)  0.1295 (  0.814)  0.0367nnn (  3.079)  0.0333nnn (  2.689)  0.0368nnn

0.2870nnn (3.203) 0.1754nnn (3.569) 0.1172nnn (3.851) 0.0999nnn (3.617) 0.2439nnn (3.587)  0.0592 (  0.617)  0.0265nnn (  3.459)  0.0131nn (  2.457)  0.0271nnn

0.3535nnn (3.183) 0.1310n (1.832) 0.1190nnn (3.460) 0.0715nn (2.081) 0.3091nnn (3.333)  0.1208 (  1.018)  0.0240nnn (  2.782)  0.0266nnn (  4.102)  0.0303nnn

0.1175nn (2.556) 0.1118nnn (2.825) 0.0680nnn (5.200) 0.0624nn (2.219) 0.1534nnn (2.826) 0.0135 (0.254)  0.0236nnn (  4.552)  0.0099nn (  2.257)  0.0165nnn

0.1334nn (2.361) 0.0396 (0.876) 0.0606nn (2.166) 0.0831nnn (3.806) 0.2788nnn (4.345) 0.0107 (0.155)  0.0238nnn (  3.797)  0.0136nnn (  3.271)  0.0219nnn

RLTaxInsensitive RLMutualFund RLBank RLInsurance RLHedgeFund %TaxSensitive %TaxInsensitive %MutualFund

S.A. Sikes / Journal of Accounting and Economics 57 (2014) 22–42

37

Table 6 (continued ) Panel B: Estimation of Eq. (1) for stocks of which individual investors are net buyers. The dependent variable is denoted in the column heading. The sample period is 1987–2010. The sample includes NYSE, AMEX, and NASDAQ stocks whose market capitalization at the beginning of the year places them into the bottom quintile when stocks are sorted into quintiles according to the market capitalization of NYSE stocks. See Section 6.1 for variable definitions. t-Statistics, calculated using robust standard errors clustered by firm and year, are presented in parentheses below the coefficient estimates. nnn, nn, and n indicate significance of the t-statistic at the 1%, 5%, and 10% level, respectively, using a two-tailed test. Variable

%Bank %Insurance %HedgeFund Book/Market Return3

(1) Return_Jan3

(2) Return_Diff3

(3) Return_Jan5

(4) Return_Diff5

(5) Return_Jan10

(6) Return_Diff10

(  5.822)  0.0296nnn (  3.002)  0.0122 (  0.833)  0.0094 (  0.282) 0.0024nn (2.240)  0.0033nn (  2.308)

(  4.356)  0.0223 (  1.635)  0.0390nn (  2.381)  0.0145 (  0.438) 0.0035nnn (3.375)  0.0035nn (  2.282)

(  4.934)  0.0217nnn (  3.617)  0.0178n (  1.819)  0.0113 (  0.531) 0.0014n (1.907)

(  4.445)  0.0131n (  1.841)  0.0338nnn (  2.940)  0.0140 (  0.618) 0.0027nnn (3.419)

(  4.518)  0.0235nnn (  3.927)  0.0134nn (  2.044)  0.0161nn (  1.998) 0.0007 (1.531)

(  5.172)  0.0244nnn (  2.815)  0.0281nnn (  3.280)  0.0207nnn (  2.930) 0.0016nnn (2.764)

 0.0020nn (  1.971)

 0.0020n (  1.878)

 0.0015n (  1.782) 0.0059nnn (3.478) 31,419 0.025

Return5

Intercept

0.0124nnn (7.168)

0.0098nnn (4.011)

0.0103nnn (8.899)

0.0064nnn (4.394)

 0.0011 (  1.645) 0.0078nnn (6.363)

Observations R-squared

31,419 0.042

31,419 0.027

31,419 0.039

31,419 0.025

31,419 0.032

Return10

Panel C: Estimation of Eq. (1) controlling for sales made by individual investors. The dependent variable is denoted in the column heading. The sample period is 1987–2010. The sample includes NYSE, AMEX, and NASDAQ stocks whose market capitalization at the beginning of the year places them into the bottom quintile when stocks are sorted into quintiles according to the market capitalization of NYSE stocks. The variable Sales_Indiv equals the number of shares of stock i sold by individual investors in the fourth quarter scaled by stock i0 s total shares outstanding at the end of the third quarter. See Section 6.1 for other variable definitions. t-Statistics, calculated using robust standard errors clustered by firm and year, are presented in parentheses below the coefficient estimates. nnn , nn , and n indicate significance of the t-statistic at the 1%, 5%, and 10% level, respectively, using a two-tailed test. Variable

(1) Return_Jan3

(2) Return_Diff3

(3) Return_Jan5

(4) Return_Diff5

(5) Return_Jan10

(6) Return_Diff10

RLTaxSensitive

0.4668nnn (3.748) 0.2698nnn (3.002) 0.1569nnn (4.549) 0.1492nnn (2.584) 0.3903nnn (2.876) 0.0126 (0.106)  0.0308nnn (  3.121)  0.0199nnn (  2.604)  0.0295nnn (  6.507)  0.0268nnn (  3.305)  0.0096 (  0.900)  0.0243 (  0.945) 0.0025nnn (2.814)  0.0021nn (  2.133)

0.5212nnn (3.437) 0.2356nn (2.032) 0.1730nnn (4.555) 0.1379n (1.741) 0.5077nnn (4.005)  0.1027 (  0.777)  0.0303nnn (  2.732)  0.0309nnn (  3.306)  0.0347nnn (  5.820)  0.0223nn (  2.027)  0.0226n (  1.679)  0.0305 (  1.179) 0.0032nnn (3.311)  0.0025nn (  2.311)

0.3046nnn (4.663) 0.1762nnn (3.657) 0.1180nnn (3.790) 0.1194nnn (3.690) 0.2676nnn (4.001)  0.0167 (  0.204)  0.0240nnn (  3.339)  0.0136nnn (  3.112)  0.0241nnn (  5.882)  0.0223nnn (  4.500)  0.0137nn (  1.994)  0.0193 (  1.125) 0.0017nnn (2.663)

0.3550nnn (3.858) 0.1290n (1.863) 0.1233nnn (3.567) 0.0958nn (2.559) 0.3190nnn (5.116)  0.0899 (  0.904)  0.0212nnn (  2.750)  0.0264nnn (  4.351)  0.0263nnn (  5.520)  0.0172nnn (  2.888)  0.0226nnn (  2.657)  0.0229 (  1.281) 0.0027nnn (3.593)

0.1197nnn (3.349) 0.1073nnn (3.313) 0.0670nnn (4.894) 0.0804nn (2.472) 0.1649nnn (3.061) 0.0308 (0.641)  0.0195nnn (  3.759)  0.0095nnn (  2.900)  0.0145nnn (  5.028)  0.0228nnn (  4.620)  0.0101nn (  2.345)  0.0171nn (  2.142) 0.0010nnn (2.804)

0.1366nnn (3.428) 0.0452 (1.257) 0.0659nn (2.453) 0.1009nnn (4.420) 0.2076nnn (2.760) 0.0084 (0.143)  0.0191nnn (  3.525)  0.0144nnn (  4.531)  0.0186nnn (  5.963)  0.0239nnn (  3.395)  0.0154nnn (  2.662)  0.0192nnn (  2.621) 0.0017nnn (3.493)

 0.0012n (  1.816)

 0.0014nn (  2.043)  0.0007

 0.0011nn

RLTaxInsensitive RLMutualFund RLBank RLInsurance RLHedgeFund %TaxSensitive %TaxInsensitive %MutualFund %Bank %Insurance %HedgeFund Book/Market Return3 Return5 Return10

38

S.A. Sikes / Journal of Accounting and Economics 57 (2014) 22–42

Table 6 (continued ) Panel C: Estimation of Eq. (1) controlling for sales made by individual investors. The dependent variable is denoted in the column heading. The sample period is 1987–2010. The sample includes NYSE, AMEX, and NASDAQ stocks whose market capitalization at the beginning of the year places them into the bottom quintile when stocks are sorted into quintiles according to the market capitalization of NYSE stocks. The variable Sales_Indiv equals the number of shares of stock i sold by individual investors in the fourth quarter scaled by stock i0 s total shares outstanding at the end of the third quarter. See Section 6.1 for other variable definitions. t-Statistics, calculated using robust standard errors clustered by firm and year, are presented in parentheses below the coefficient estimates. nnn , nn , and n indicate significance of the t-statistic at the 1%, 5%, and 10% level, respectively, using a two-tailed test. Variable

(1) Return_Jan3

(2) Return_Diff3

(3) Return_Jan5

(4) Return_Diff5

(5) Return_Jan10

(6) Return_Diff10

Sales_Indiv

0.0060n (1.715) 0.0107nnn (7.195)

0.0067 (1.547) 0.0085nnn (4.000)

0.0042n (1.714) 0.0087nnn (8.394)

0.0031 (0.929) 0.0051nnn (4.199)

(  1.391) 0.0017 (1.184) 0.0064nnn (6.237)

(  2.051) 0.0021 (1.217) 0.0044nnn (3.515)

63,886 0.035

63,886 0.023

63,886 0.034

63,886 0.022

63,886 0.029

63,886 0.023

Intercept Observations R-squared

In summary, the results in Table 5 support my hypothesis that institutional investors contribute to the turn-of-the-year effect via tax-loss-selling. In addition, the economic magnitude of the impact of institutional investors0 tax-loss-selling on turn-of-the-year returns is just as large as, and in most cases larger than, that of institutional investors0 window-dressing. The results contribute to the literature on the turn-of-the-year effect by shedding light on the incentives behind institutional investors0 year-end sales that significantly impact turn-of-the-year returns. The results suggest that there is less of an agency problem between institutional investors and their clients around the turn-of-the-year than suggested by prior studies that only consider institutional investors0 window-dressing incentives.

7. Robustness tests 7.1. Tax-loss-selling by individual investors It is possible that tax-loss-selling by tax-sensitive institutional investors is correlated with tax-loss-selling by individual investors. If this is the case, then the results in Table 5 could suffer from an omitted variable problem. Thus, I conduct a series of robustness tests to control for the effect of tax-loss-selling by individual investors on turn-of-the-year returns. First, I estimate Eq. (1) only across stocks with less than 25 percent individual investor ownership. I estimate individual investor ownership as one minus the percent of outstanding shares owned by institutional investors (Ayers et al., 2003; Dhaliwal et al., 2003). Panel A of Table 6 reports the results. The number of observations declines from 63,886 in Panel B of Table 5 to 1,622. Despite lower power due to fewer observations, the coefficient on RLTaxSensitive remains positive and significant at the one percent level when Return_Jan3, Return_Diff3, Return_Jan5, or Return_Jan10 is the dependent variable, and at the five percent level when Return_Diff5 is the dependent variable. The magnitude of the coefficient on RLTaxSensitive also exceeds the magnitude of the coefficients on the realized loss variables of the other institutional investor types, with the exception of RLTaxInsensitive in column (4) and RLHedgeFund in column (6). Next, I estimate Eq. (1) only across stocks in which individual investors are net buyers of the stock in the fourth quarter. I estimate the change in ownership by individual investors as [(# of shares outstanding of stock i  # of shares of stock i owned by all institutional investors)Qtr4  (# of shares outstanding of stock i # of shares of stock i owned by all institutional investors)Qtr3]. Although there still could be some tax-loss-selling by individual investors in these stocks, I expect for taxloss-selling by individual investors to be lower in these stocks than in stocks in which individual investors are net sellers of the stock. Panel B of Table 6 reports the results. The number of observations falls from 63,886 in Panel B of Table 5 to 31,419. The coefficient on RLTaxSensitive remains positive and significant at the one percent level in columns (1)–(4) and at the five percent level in columns (5)–(6). The magnitude of the coefficient on RLTaxSensitive also continues to exceed the magnitude of the coefficients on the realized loss variables of the other institutional investor types, with the exception of RLInsurance in columns (5) and (6). In the third test, I include a proxy for sales made by individual investors in the fourth quarter. Unfortunately, there is no available proxy for individual investors0 realized losses because there is no publicly available data on specific individual investor0 s holdings, similar to the 13F data for institutional investors, from which I could infer purchases and sales and also an individual investor0 s basis in the shares that he or she owns. Because I cannot estimate individual investors0 realized losses, instead I estimate the aggregate number of shares of stock i sold by individual investors in the fourth quarter. I estimate the change in ownership by individual investors as described in the previous paragraph. A positive change suggests that individual investors are net buyers, as opposed to net sellers, of a stock in the fourth quarter. Thus, if the change is greater than zero, I set it equal to zero. The variable Sales_Indiv equals the number of shares of stock i sold by individual

S.A. Sikes / Journal of Accounting and Economics 57 (2014) 22–42

39

Table 7 Robustness tests excluding internally managed pensions and endowments. Estimation of Eq. (1) excluding internally managed pensions and endowments from tax-insensitive type. The dependent variable is denoted in the column heading. RLTaxInsensitive equals fourth quarter realized losses in stock i by investment advisers whose majority clientele are tax-exempt entities (pensions, endowments, state & local governments, and charitable organizations), scaled by the market capitalization of stock i. %TaxInsensitive equals the percent of stock i0 s outstanding shares owned by these same investment advisers at the end of the third quarter. See Section 6.1 for definitions of other variables. The sample period is 1987–2010. The sample includes NYSE, AMEX, and NASDAQ stocks whose market capitalization at the beginning of the year places them into the bottom quintile when stocks are sorted into quintiles according to the market capitalization of NYSE stocks. t-Statistics, calculated using robust standard errors clustered by firm and year, are presented in parentheses below the coefficient estimates. nnn, nn, and n indicate significance of the t-statistic at the 1%, 5%, and 10% level, respectively, using a two-tailed test. Variable

(1) Return_Jan3

(2) Return_Diff3

(3) Return_Jan5

(4) Return_Diff5

(5) Return_Jan10

(6) Return_Diff10

RLTaxSensitive

0.4749nnn (3.765) 0.4112nnn (2.591) 0.1581nnn (4.624) 0.1558nnn (2.621) 0.4071nnn (3.092) 0.0111 (0.093)  0.0306nnn (  3.096)  0.0397nnn (  3.461)  0.0289nnn (  6.441)  0.0266nnn (  3.279)  0.0094 (  0.895)  0.0242 (  0.945) 0.0025nnn (2.834)  0.0021nn (  2.136)

0.5279nnn (3.406) 0.3183n (1.839) 0.1744nnn (4.572) 0.1443n (1.801) 0.5259nnn (4.256)  0.1030 (  0.779)  0.0301nnn (  2.702)  0.0541nnn (  3.902)  0.0340nnn (  5.785)  0.0223nn (  2.053)  0.0227n (  1.718)  0.0304 (  1.177) 0.0032nnn (3.334)  0.0025nn (  2.316)

0.3104nnn (4.664) 0.2534nnn (3.483) 0.1190nnn (3.843) 0.1242nnn (3.744) 0.2799nnn (4.388)  0.0172 (  0.209)  0.0238nnn (  3.305)  0.0295nnn (  4.083)  0.0234nnn (  5.885)  0.0220nnn (  4.501)  0.0134nn (  1.979)  0.0192 (  1.115) 0.0017nnn (2.681)

0.3566nnn (3.818) 0.1782 (1.602) 0.1236nnn (3.579) 0.0988nnn (2.602) 0.3286nnn (5.303)  0.0905 (  0.904)  0.0209nnn (  2.697)  0.0477nnn (  4.856)  0.0257nnn (  5.422)  0.0170nnn (  2.976)  0.0224nnn (  2.676)  0.0226 (  1.265) 0.0027nnn (3.618)

0.1257nnn (3.730) 0.0916nnn (3.064) 0.0684nnn (5.107) 0.0850nnn (2.736) 0.1769nnn (3.686) 0.0320 (0.666)  0.0193nnn (  3.765)  0.0201nnn (  5.410)  0.0140nnn (  4.939)  0.0225nnn (  4.667)  0.0099nn (  2.328)  0.0169nn (  2.106) 0.0010nnn (2.832)

0.1363nnn (3.554) 0.0629 (1.239) 0.0659nn (2.409) 0.1018nnn (4.482) 0.2113nnn (2.753) 0.0082 (0.141)  0.0189nnn (  3.509)  0.0276nnn (  5.819)  0.0181nnn (  5.951)  0.0237nnn (  3.469)  0.0152nnn (  2.673)  0.0190nnn (  2.585) 0.0017nnn (3.528)

 0.0012n (  1.819)

 0.0014nn (  2.046)

 0.0011nn (  2.053) 0.0044nnn (3.439) 63,886 0.023

RLTaxInsensitive RLMutualFund RLBank RLInsurance RLHedgeFund %TaxSensitive %TaxInsensitive %MutualFund %Bank %Insurance %HedgeFund Book/Market Return3 Return5

Intercept

0.0105nnn (6.990)

0.0083nnn (3.884)

0.0086nnn (8.244)

0.0050nnn (4.005)

 0.0007 (  1.399) 0.0064nnn (6.187)

Observations R-squared

63,886 0.035

63,886 0.023

63,886 0.034

63,886 0.022

63,886 0.029

Return10

investors in the fourth quarter scaled by stock i0 s total shares outstanding at the end of the third quarter. The results are presented in Panel C of Table 6. The coefficient on RLTaxSensitive only decreases very slightly when I include Sales_Indiv in the estimation. The coefficient on Sales_Indiv is positive and significant at the ten percent level in columns (1) and (3), consistent with fourth quarter sales by individual investors contributing to the turn-of-the-year effect. The results of the three robustness tests discussed above suggest that tax-loss-selling by individual investors is not driving the relation between turn-of-the-year returns and tax-sensitive institutional investors0 fourth quarter realized losses documented in Table 5.

7.2. Externally versus internally managed pensions and endowments According to O0 Barr and Conley (1992), internally managed pension funds, foundations, and university endowments do not have strong window-dressing incentives. To make sure that including these institutions in the main tests does not weaken the results for the TaxInsensitive institutions, I remove internally managed pensions and endowments (i.e., institutional investors that Abarbanell et al. (2003) and Bushee and Goodman (2007) classify as private pensions, corporate pensions, foundations or endowments) from the study. In this robustness test, TaxInsensitive only includes investment advisers whose majority clientele are tax-exempt entities (i.e., externally managed pensions and endowments).

40

S.A. Sikes / Journal of Accounting and Economics 57 (2014) 22–42

Similar to the results in Table 2 for both internally and externally managed pensions and endowments, in an untabulated test I find no difference between realized losses in the fourth quarter relative to the first three quarters or between the realized gains in the fourth quarter relative to the first three quarters for externally managed pensions and endowments.29 Table 7 reports the results of the estimation of Eq. (1) with TaxInsensitive only including externally managed pensions and endowments. The results in Table 7 are similar to those in Panel B of Table 5, with the exception that the coefficient on RLTaxInsensitive is slightly larger in Table 7, but the statistical significance of the coefficient on RLTaxInsensitive declines from the five percent to the ten percent level in column (2) and is no longer significant in column (4). These results do not change any of the inferences from the comparisons across institutional investor types discussed in earlier sections. The results still suggest that institutional investors impact turn-of-the-year returns via tax-loss-selling and window-dressing. Moreover, the magnitude of the effect of realized losses of unambiguously tax-sensitive institutions with weak window-dressing incentives on turn-of-the-year returns still exceeds the magnitude of the effect of realized losses of unambiguously taxinsensitive institutions with strong window-dressing incentives.

8. Conclusion This paper provides evidence supporting a previously unexplored explanation for the turn-of-the-year effect: tax-lossselling by institutional investors. I identify institutional investors that are unambiguously tax-sensitive and that have weak window-dressing incentives: investment advisers whose majority clientele are high net-worth individuals. These taxsensitive institutional investors realize significantly more losses as a percentage of their total losses in the fourth quarter than in the first three quarters. In addition, I find that the more losses that these tax-sensitive institutional investors realize in a stock in the fourth quarter, the greater the stock0 s return at the beginning of the following year and the greater the difference between the stock0 s return at the beginning of the following year and its return at the end of the current year. The effect is economically significant. A one percentage point change in fourth quarter realized losses of institutional investors who are unambiguously tax-sensitive and have weak window-dressing incentives scaled by a firm0 s market capitalization results in an increase of 47 basis points in the stock0 s average daily return over the first three days of January, and an increase of 52 basis points in the difference between the average return over the first three trading days of January and the average return over the last three trading days of December. The former represents a 46 percent change for the mean firm, and the latter represents a 66 percent change for the mean firm. I also show that fourth quarter realized losses of institutional investors with strong window-dressing incentives and either no or weaker tax incentives have an impact on turn-of-the-year returns. However, the economic magnitude of the effect on turn-of-the-year returns of a fixed change in fourth quarter realized losses is greater for unambiguously taxsensitive institutions with weak window-dressing incentives than for institutions with strong window-dressing incentives and either no or weaker tax incentives. Although the turn-of-the-year effect has been well studied over the past several decades, the phenomenon continues to exist and a better understanding of how institutional investors contribute to it is needed. Extant literature only considers the impact that institutional investors have on turn-of-the-year returns via window-dressing. I show that tax-loss-selling by institutional investors contributes to the phenomenon just as much as window-dressing. Institutional investors0 tax-lossselling is potentially in clients0 best interests or is at least executed in response to clients0 requests. On the other hand, window-dressing by institutional investors is meant to misrepresent an institution0 s holdings over the prior year to investors. Thus, the results in this paper show that there is less of an agency problem between institutional investors and their clients around the turn-of-the-year than is suggested by prior studies that only consider institutional investors0 window-dressing incentives. Appendix A. Prior literature0 s classification of mutual fund families, banks, insurance companies, and hedge fund management companies as tax-sensitive or tax-insensitive In Section 3.1, I explain that extant literature is inconsistent in its classification of investment companies (i.e., mutual fund families), banks, insurance companies, and hedge fund management companies as either tax-sensitive or taxinsensitive. The inconsistent treatment is due to the fact that there is heterogeneity with respect to tax-sensitivity among institutional investors within each of these institutional investor types, and the Form 13F does not provide the data necessary to identify which institutions within each of these types are tax-sensitive. In this Appendix A, I summarize the classifications used in prior studies. Banks and mutual fund families typically have many separate accounts and funds, respectively, under one umbrella. For some banks and mutual fund families, the tax-sensitivity of the clients of the separate accounts and funds varies within one particular bank or mutual fund family (Sialm and Starks, 2012). Yet the Form 13F only reports aggregate holdings data for the entire bank or mutual fund family. 29 I find that externally managed pensions and endowments realize fewer losses in small cap stocks in the fourth quarter than in the first three quarters, consistent with the results in Panel C of Table 2. However, the difference is no longer statistically significant.

S.A. Sikes / Journal of Accounting and Economics 57 (2014) 22–42

41

Several papers classify all investment companies (i.e., mutual fund families) as tax-sensitive (Strickland, 1996; Grinstein and Michaely, 2005; Moser, 2007; Moser and Puckett, 2009; Sialm and Starks, 2012; Chyz and Li, 2012). In contrast, Jin (2006) excludes investment companies from his study recognizing that they are heterogeneous with respect to taxsensitivity and that he cannot identify which are tax-sensitive. Jin (2006) and Chyz and Li (2012), both of which focus on sensitivity to capital gains taxes, recognize that banks are heterogeneous with respect to tax-sensitivity and exclude banks altogether from their studies due to their inability to identify which banks are tax-sensitive. On the other hand, papers that focus on tax-sensitivity with respect to dividend income treat banks as tax-insensitive, arguing that many banks are corporations and thus receive a dividend received deduction (Strickland, 1996; Grinstein and Michaely, 2005; Moser, 2007; Moser and Puckett, 2009). Prior studies are also inconsistent in their treatment of insurance companies. Chyz and Li (2012) classify insurance companies as tax-insensitive, but other studies (Strickland, 1996; Grinstein and Michaely, 2005; Jin, 2006; Moser, 2007; Moser and Puckett, 2009) classify insurance companies as tax-sensitive. Jin (2006) outlines the following caveats to his classification of insurance companies as tax-sensitive. First, life insurance and property insurance companies have different tax treatments and could have different sensitivities to capital gains taxes. Second, insurance companies invest large amounts of money in their own accounts, on which they pay taxes, but they also invest on behalf of their clients through potentially tax-advantaged accounts. Tax-sensitivity also varies among hedge fund management companies. Jin (2006) classifies hedge fund management companies as tax-sensitive and notes that in addition to hedge funds having taxable investors, hedge fund managers likely care about taxes for personal reasons since they often invest a significant portion of their wealth in the funds they manage for incentive reasons. However, Liang et al. (forthcoming) explain that not all hedge fund investors are taxable. The Form 13F is filed at the hedge fund management company (i.e., fund sponsor) level, which is one level above the hedge fund itself. A hedge fund management company could have multiple hedge funds under its umbrella, some of which could be taxsensitive and others of which could be tax-insensitive. Similar to the challenge with banks and mutual fund families discussed above, the Form 13F only reports aggregate holdings data for the entire hedge fund management company. References Abarbanell, J.S., Bushee, B.J., Raedy, J.S., 2003. Institutional investor preferences and price pressure: the case of corporate spin-offs. Journal of Business 76 (2), 233–261. Agarwal, V., Jiang, W., Tang, W., Yang, B., 2013. Uncovering hedge fund skill from the portfolio holdings they hide. Journal of Finance 68 (2), 739–783. Aragon, G.O., Hertzel, B., Shi, Z., Why do hedge funds avoid disclosure? Evidence from confidential 13F filings. Journal of Financial and Quantitative Analysis (forthcoming). Ayers, B., Lefanowicz, C., Robinson, J., 2003. Shareholder taxes in acquisition premiums: the effect of capital gains taxation. Journal of Finance 58 (6), 2783–2801. Barber, B.M., Odean, T., 2003. Are individual investors tax savvy? Evidence from retail and discount brokerage accounts. Journal of Public Economics 88, 419–442. Bushee, B.J., Goodman, T.H., 2007. Which institutional investors trade based on private information about earnings and returns? Journal of Accounting Research 45 (2), 289–322. Carhart, M.M., Kaniel, R., Musto, D., Reed, A., 2002. Leaning for the tape: evidence of gaming behavior in equity mutual funds. Journal of Finance 57 (2), 661–693. Chyz, J.A., Li, O.Z., 2012. Do tax sensitive investors liquidate appreciated shares after a capital gains tax rate reduction? National Tax Journal 65 (3), 595–628. Desai, M., Jin, L., 2011. Institutional tax clienteles and payout policy. Journal of Financial Economics 100 (1), 68–84. Dhaliwal, D., Li, O., Trezevant, R., 2003. Is a dividend tax penalty incorporated into the return on a firm0 s common stock? Journal of Accounting and Economics 35, 155–178. Dyl, E., 1977. Capital gains taxation and year-end stock market behavior. Journal of Finance 32, 165–175. Gibson, S., Safieddine, A., Titman, S., 2000. Tax-motivated trading and price pressure: an analysis of mutual fund holdings. Journal of Financial and Quantitative Analysis 35 (3), 369–386. Givoly, D., Ovadia, A., 1983. Year-end induced sales and stock market seasonality. Journal of Finance 38, 171–185. Gow, I., Ormazabal, G., Taylor, D., 2010. Correcting for cross-sectional and time-series dependence in accounting research. The Accounting Review 85 (3), 483–512. Grinblatt, M., Moskowitz, T., 2004. Predicting stock movements from past returns: the role of consistency and tax-loss-selling. Journal of Financial Economics 71, 541–579. Grinstein, Y., Michaely, R., 2005. Institutional holdings and payout policy. Journal of Finance 60 (3), 1389–1426. He, J., Ng, L., Wang, Q., 2004. Quarterly trading patterns of financial institutions. Journal of Business 77 (3), 493–509. Huddart, S., Narayanan, V.G., 2002. An empirical examination of tax factors and mutual funds0 stock sale decisions. Review of Accounting Studies 7, 319–342. Jegadeesh, N., Titman, S., 1993. Returns to buying winners and selling losers. Journal of Finance 48 (1), 65–91. Jin, L., 2006. Capital gains tax overhang and price pressure. Journal of Finance 61 (3), 1399–1430. Jin, L., Kothari, S.P., 2008. Effect of personal taxes on managers0 decisions to sell their stock. Journal of Accounting and Economics 46, 23–46. Keim, D., 1983. Size-related anomalies and stock return seasonality: further empirical evidence. Journal of Financial Economics 12, 13–32. Lakonishok, J., Shleifer, A., Thaler, R., Vishny, R., 1991. Window dressing by pension fund managers. American Economic Review 81 (2), 227–231. Liang, B., Aragon, G.O., Park, H., Onshore and offshore hedge funds: are they twins? Management Science (forthcoming). Moser, W.J., 2007. The effect of shareholder taxes on corporate payout choice. Journal of Financial and Quantitative Analysis 42 (4), 991–1020. Moser, W.J., Puckett, A., 2009. Dividend tax clienteles: evidence from tax law changes. Journal of American Taxation Association 31 (1), 1–22. Musto, D., 1997. Portfolio disclosures and year-end price shifts. Journal of Finance 52 (4), 1563–1588. Ng, L., Wang, Q., 2004. Institutional trading and the turn-of-the-year effect. Journal of Financial Economics 74, 343–366. O0 Barr, W., Conley, J., 1992. Fortune and Folly: The wealth and power of institutional Investing. Business One Irwin, Homewood, IL. Odean, T., 1998. Are investors reluctant to realize their losses? Journal of Finance 53 (5), 1775–1798. Petersen, M., 2009. Estimating standard errors in finance panel data sets: comparing approaches. Review of Financial Studies 22, 435–480. Poterba, J., Weisbenner, S., 2001. Capital gains tax rules, tax loss trading, and turn-of-the-year returns. Journal of Finance 56, 353–367. Reinganum, M., 1983. The anomalous stock market behavior of small firms in January. Journal of Financial Economics 12, 89–104.

42

S.A. Sikes / Journal of Accounting and Economics 57 (2014) 22–42

Ritter, J., 1988. The buying and selling behavior of individual investors at the turn of the year. Journal of Finance 43, 701–717. Roll, R., 1983. Vas is das? The turn-of-the-year effect and the return premia of small firms. Journal of Portfolio Management 9, 18–28. Rozeff, M., Kinney, W., 1976. Capital market seasonality: the case of stock returns. Journal of Financial Economics 3, 379–402. Sias, R., Starks, L., 1997. Institutions and individuals at the turn-of the-year. Journal of Finance 52 (4), 1543–1562. Sialm, C., Starks, L., 2012. Mutual fund tax clienteles. Journal of Finance 67 (4), 1397–1422. Starks, L., Yong, L., Zheng, L., 2006. Tax-loss selling and the turn-of-the-year effect: evidence from municipal bond closed-end funds. Journal of Finance 61 (6), 3049–3067. Strickland, D., 1996. Determinants of institutional ownership: Implications for dividend clienteles. Working paper, Arizona State University.