Ex-Dividend Profitability and Institutional Trading Skill* Tyler R. Henry Miami University, Ohio henrytr3@miamioh.edu Jennifer L. Koski University of Washington jkoski@u.washington.edu March 17, 2014 Abstract Previous research documents that positive abnormal ex-day returns persist despite the tax neutral treatment of dividends and relatively low transaction costs in recent years. We use Abel Noser Solutions trading data to examine whether institutions exploit these returns. Results show significant abnormal institutional volume that is positively related to yield and negatively related to idiosyncratic risk, as predicted by the exdividend literature. Results also show that abnormal returns averaged across all exdays disappear once we account for actual execution prices and transaction costs. However, institutions concentrate their trading around certain ex-dates, and earn higher profits around these events. Institutional profitability is also cross-sectionally related to trade execution skill, further evidence that institutional trading skill is persistent. Some institutions earn significant ex-day profits by targeting certain ex-day events and by executing trades at prices that are favorable relative to the market. * Tyler Henry is the Jellinek Assistant Professor of Finance, Farmer School of Business, Miami University, Oxford, OH 45056, (513) 529-2272. Jennifer Koski is Associate Professor of Finance, Foster School of Business, University of Washington, Seattle, WA 98195, (206) 543-7975.
Ex-Dividend Profitability and Institutional Trading Skill Abstract Previous research documents that positive abnormal ex-day returns persist despite the tax neutral treatment of dividends and relatively low transaction costs in recent years. We use Abel Noser Solutions trading data to examine whether institutions exploit these returns. Results show significant abnormal institutional volume that is positively related to yield and negatively related to idiosyncratic risk, as predicted by the exdividend literature. Results also show that abnormal returns averaged across all exdays disappear once we account for actual execution prices and transaction costs. However, institutions concentrate their trading around certain ex-dates, and earn higher profits around these events. Institutional profitability is also cross-sectionally related to trade execution skill, further evidence that institutional trading skill is persistent. Some institutions earn significant ex-day profits by targeting certain ex-day events and by executing trades at prices that are favorable relative to the market.
1. Introduction Ex-dividend prices decline on average by an amount less than the dividend, generating positive pre-tax ex-day returns. In theory, short-term traders with equal tax rates on dividends and capital gains should trade to profit from abnormal ex-day returns [Kalay (1982)]. Short-term trading will eliminate these returns up to the marginal cost of trading around the ex-day [Karpoff and Walkling (1990)]. Recent research shows that significant abnormal ex-day returns persist after decimalization (which reduced transaction costs), and despite 2003 tax law changes that equalized the tax rates on dividend and capital gains income. The persistence of abnormal ex-day returns is surprising given the tax rates and relatively low transaction costs in recent years. Are short-term traders trading to capture these returns? In this study, we use the Abel Noser Solutions institutional trading data from 1999 through 2007 to examine this issue. The database is unique in that it includes transactions-level purchases and sales with associated trading costs for two specific types of institutional traders. As we discuss more fully below, the institutions in our sample include tax neutral institutions which should be able to transact at very low cost. These are exactly the sort of traders who should be trading to exploit any abnormal ex-day returns. With these data, we study two closely related research questions. First, do institutional traders earn abnormal profits from ex-day pricing? Second, is this ability cross-sectionally related to other measures of institutional trade execution skill? We begin by reporting ex-day returns that are significantly positive and similar in magnitude to those documented previously in the literature during more recent sample periods. However, positive returns averaged across all ex-days disappear once we
account for actual execution prices and transaction costs. This result is not surprising, because although transaction costs in recent years are small, abnormal ex-day returns are even smaller. We also show significant abnormal institutional volume during the exdividend period. Consistent with our assertion that these institutions should execute dividend trading strategies, abnormal institutional volume is almost double overall abnormal volume as measured using CRSP data. Results show that trading volume for the institutions in our sample varies positively with dividend yield and negatively with idiosyncratic risk, as expected based on the ex-dividend literature. Although returns averaged across ex-days are no longer significantly positive once we incorporate transaction costs, institutions may still be able to profit by trading during ex-dividend periods, for several reasons. Average ex-day returns make no allowance for whether an institution has a net long or short position over the ex-day. Institutions may target certain ex-days and realize profitable dividend capture strategies for a subset of ex-day events. Finally, Anand et al. (2012) note that one aspect of institutional trading skill is the ability to time trades. Profitable dividend capture strategies may include trades that are executed over a window surrounding the exdividend day. None of these aspects of trading is captured by average ex-day returns. We test whether institutions are able to earn positive profits after transaction costs from dividend capture trading. To estimate profitability, we compare total cash outflows and inflows around the ex-dividend day using actual transaction prices after all commissions and related trading costs. When we calculate abnormal returns averaged across institutions (or more specifically, across client/manager pairs), institutional profits 2
to long positions are significantly positive even after incorporating all trading costs. 1 We show specifically that institutions concentrate their trading around certain ex-days, and ex-day events with higher institutional buying intensity are associated with higher profits. Cross-sectionally, institutional profitability is related to institutional trade execution skill [Anand et al. (2012)]. 2 Institutions who demonstrate prior trading skill are more able to implement profitable dividend capture strategies. Our overall conclusion is that institutional profitability results when skilled institutions target certain ex-day events and execute trades at prices that are favorable relative to the market. Our results contribute to the literature on the role of execution costs for institutional investment performance. Perold (1988) shows that traders may be unable to exploit stock selection skill due to an implementation shortfall, the performance difference between a paper portfolio and a real portfolio. A key component of implementation shortfall is execution cost. Previous research has shown that institutions trade strategically to minimize their execution costs, which are economically significant [Chan and Lakonishok (1995); Keim and Madhavan (1997)]. Conrad, Johnson, and Wahal (2001) link weak performance by institutional traders to poor trade execution. Finally, Anand et al. (2012) document that institutional trading desks add value to portfolio performance through the trade implementation process, and this trading-desk skill is persistent. We show that some institutions are able to earn positive profits from ex-dividend trading, and a major determinant of these profits is institutional trade execution skill. Furthermore, the magnitude of dividend capture profitability is 1 For this calculation, each observation is the collection of trades executed by a particular money management firm on behalf of a particular client during an individual ex-dividend event window. See Section 5, below, for more details. 2 We emphasize that trade execution skill is distinct from stock selection skill. See Section 2 for further discussion. 3
significant; the difference in profitability between institutions in the low-skill decile and those in the high-skill decile is approximately 40 basis points. Profitable dividend capture trading represents one way skilled institutions are able to earn abnormal trading profits. Our results also have relevance to the costly arbitrage literature [e.g., Pontiff (2006)]. Previous research documents positive abnormal ex-day returns. Consistent with prior research, abnormal volume is negatively related to risk, in particular idiosyncratic risk, as predicted if idiosyncratic risk is a relevant holding cost for potential arbitrage traders. Our results show explicitly for the first time that average ex-day returns are no longer positive once actual transaction costs are fully incorporated. This result is not surprising, because the abnormal returns are economically small in magnitude relative to transaction costs, even those for sophisticated institutional traders. Finally, our results have implications for the literature examining the determinants of ex-day returns and the horse race between taxes, transaction costs, risk and market microstructure. Related research analyzes ex-day trading using data that identify trades by different types of investors. 3 Our results also contribute to recent research examining institutional trading activity around other corporate financial events such as initial public offerings, seasoned equity offerings, and mergers. 4 We extend prior research by examining dividend capture trading by institutional investors and the profitability of these strategies. 3 Koski and Scruggs (1998) study ex-day trading activity as reported in the NYSE audit file database, which differentiates trading by securities dealers and taxable corporations. Graham and Kumar (2006) analyze dividend trading by retail investors, and Rantapuska (2008) investigates ex-day trading for all investors in Finland. 4 See for example, Chemmanur, Hu and Huang (2010) and Goldstein, Irvine and Puckett (2011) for institutional trading around initial public offerings; Chemmanur, He and Hu (2009) for seasoned equity offerings, and Bethel, Hu and Wang (2009) for mergers. 4
The remainder of this paper is organized as follows. In Section 2, we discuss the theoretical literature and derive our hypotheses related to the profitability of institutional ex-dividend trading. In Section 3, we describe the sample and our research design. Section 4 reports descriptive statistics for ex-day returns and volume, and Section 5 reports results of tests of our hypotheses related to the profitability of institutional ex-day trading. Section 6 concludes. 2. Theory and Hypotheses In the Miller and Modigliani (1961) setting, the stock price should decline by the amount of the dividend on the ex-dividend day. Extensive empirical research shows that, on average, the price decreases by less than the dividend. According to Elton and Gruber (1970), the ex-dividend price decline should reflect the marginal investor s tax rates on dividends and capital gains. As Kalay (1982), Karpoff and Walkling (1988, 1990) and others note, if the pre-tax ex-day price decline differs from the dividend by more than transaction costs, short-term traders who are taxed equally on dividends and capital gains should trade until the transaction costs of dividend capture eliminate marginal exdividend profits. Specifically, when the ex-dividend price is expected to decline by less than the dividend, dividend capture would involve buying the stock cum-dividend, receiving the dividend and selling the stock ex-dividend. The first research question we examine in this paper is whether institutions are able to trade profitably to exploit abnormal ex-day returns. The institutional traders included in our sample are either pension plan sponsors or investment managers. The pension funds in our sample are tax neutral, and all institutions in our sample are tax 5
neutral after the 2003 tax reform. 5 Institutions should also be able to transact at very low cost. Therefore, the institutions in our sample closely approximate tax-neutral dividend capture traders as modeled by Kalay (1982). Although the marginal profits of capture traders should equal zero in equilibrium, on average dividend capture should be profitable. H1. Institutions should earn positive profits after transaction costs from dividend capture trading. There are two main components of transaction costs: commissions and price impact (which includes the effect of bid-ask spreads). Prior ex-dividend studies have used bidask spreads or other proxies, under the assumption that these measures are correlated with total trading costs. Although these proxies are useful for testing cross-sectional relations, they do not allow calculation of actual dividend capture profitability. With our data, we can for the first time calculate total profits net of all transaction costs. 6 Evidence in favor of H1 supports the effectiveness of dividend capture trading. Short-term traders engage in dividend capture because, on average, they are able to earn positive profits in excess of their transaction costs. If we reject H1 and do not find evidence of profitable dividend capture, transaction costs appear to be sufficiently large in magnitude to overwhelm potential short-term trading profits. 5 Investment managers may represent a range of investors, from fully taxable individual investors to those investing through their tax exempt retirement accounts. See Appendix A for a more detailed discussion of the taxation of these two types of traders. 6 Research quantifying the magnitude of trade execution costs typically compares the execution price to some benchmark, which can be difficult to measure [Sofianos (2005), Hu (2009)]. Our goal in testing this hypothesis is to calculate profitability net of all execution costs, rather than to separately identify benchmark returns and execution costs. 6
Extensive prior literature examines whether institutional investors are skilled at picking stocks [e.g., Bollen and Busse (2005) and Kacperczyk and Seru (2007)]. Perold (1988) notes that traders may be unable to profit from their stock selection ability because they are unable to implement trades at favorable prices. Anand et al. (2012) document considerable heterogeneity in realized trade execution costs across institutional investors, and show that these trading costs are persistent. Thus, traders who can effectively minimize execution costs in general may be able to use their trade implementation abilities to realize higher dividend capture profits. H2. Institutions with high trade execution skill should earn higher ex-day profits than those with low skill. Dividend capture presents a unique opportunity to examine the role of execution skill, because traders are not selecting stocks in the traditional sense. Ex-days are known in advance, and stocks are generally selected for dividend capture due to factors such as dividend yield, risk and liquidity rather than because they are undervalued. Trade execution skill may be a particularly important determinant of cross-sectional variation in institutional dividend capture profitability. Furthermore, dividend capture trading represents a potential source of the abnormal profits realized by skilled institutional investors. 3. Sample Description 7
To test these hypotheses, we use institutional trading data from Abel Noser Solutions as well as stock return and volume data from CRSP. In this section, we describe our sample selection criteria and data sources. 3.1 Institutional Trading Data Our transactions-level institutional trading data come from Abel Noser Solutions. 7 Abel Noser provides trading and transaction cost analysis for institutional investors. Institutions included in this database are either pension plan sponsors or investment managers. The database includes equity trades for a large sample of institutions. For each trade, we have the trade date, stock traded, number of shares and dollar principal traded, commissions, fees, and a buy/sell indicator. 3.2 Ex-Dividend Events We obtain dividend information, returns, and total volume data from CRSP. We include in our sample of ex-dividend events all ordinary, quarterly, taxable cash dividends paid in U.S. dollars (CRSP distcd = 1232). We include only dividends paid on ordinary common stocks (CRSP shrcd = 10 or 11) on the NYSE, and therefore exclude REITs, closed-end funds, and ADRs. Our sample of institutional trading data extends from Jan. 1999 through March 2008. We include ex-days between April 1, 1999 and Dec. 31, 2007 to ensure that we have institutional trading data for +/- 45 days relative to each ex-day. We require that the firm pay no other distributions on the ex-day. We also require that the announcement day precede the ex-day by at least 5 trading days, so announcement effects 7 Formerly known as ANcerno. See Chemmanur, He and Hu (2009), Goldstein, Irvine, Kandel and Wiener (2009), Chemmanur, Hu and Huang (2010), Puckett and Yan (2011), Goldstein, Irvine and Puckett (2011), and Anand et al. (2012) among others for recent papers using these data. 8
do not show up in our event window. To minimize noise in our measures of ex-day premiums, we exclude observations with dividends less than or equal to $0.01 per share or ex-day closing prices below $5 per share. We are left with a sample of 24,741 exdividend events for 1,351 distinct firms. Table 1, Panel A provides sample firm and ex-day characteristics for this sample. The average annualized dividend yield for the full sample is 2.24%. Trading volume on CRSP averages 1.24 million shares per day vs. 208,000 shares per day for the institutions in our sample. Our institutions therefore represent about 17% of CRSP daily trading volume. 8 3.3 Regimes Our sample period contains two major regime changes with respect to dividend related taxes and transaction costs. First, the minimum tick size changed from 1/16ths to decimals between August 28, 2000 and January 28, 2001 for NYSE stocks [Graham, Michaely and Roberts (2003)]. Second, on May 23, 2003, Congress equalized the top marginal tax rates on dividends and long-term capital gains for individual investors, and lowered both tax rates to 15%. 9 We divide our sample period into three regimes: Regime 1 (pre-decimalization and pre tax-reform), Regime 2 (post-decimalization and pre-tax reform) and Regime 3 (post-decimalization and post-tax reform). For the NYSE stocks in our sample, Regime 1 8 Throughout the remainder of this paper, when we refer to institutional trading volume we specifically mean trading by the institutions in our sample. Our total institutional trading volume is calculated by aggregating institutional buys and sells. 9 Dividends are taxed immediately, but capital gains are not taxed until realized. Therefore, the effective tax rates on realized capital gains may be smaller for a long-term investor. Chay, Choi and Pontiff (2006) show that $1 of realized capital gains is equivalent to $0.93 in unrealized gains. This difference should not be material for short-term dividend trading strategies. 9
runs from the beginning of our sample period through Aug. 26, 2000, and Regime 2 runs from Jan. 30, 2001 through May 23, 2003. 10 Regime 3 includes the period from May 25, 2003 through the end of our sample period. A comparison of Regime 1 to Regime 2 illustrates the impact of decimalization, and a comparison of Regime 2 to Regime 3 reflects the 2003 tax law change. An analysis of Regime 3 also lets us examine a period during which all of the institutions in our sample are tax neutral. Table 1, Panel B provides sample firm and ex-dividend information for each of these three regimes. As expected with decimalization, percentage bid-ask spreads decline from 0.62% in Regime 1 to 0.31% in Regime 2. 11 Interestingly, they also decline again, to 0.15% in Regime 3. Both of these changes are highly statistically significant. Chordia, Roll and Subrahmanyam (2011) document a similar decline in transaction costs over this period. Dividend yields and risk measures also vary across regimes; we control for these factors in subsequent tests, below. 4. Abnormal Ex-Day Returns and Volume In this section, we provide descriptive statistics, including ex-dividend premiums, returns, and volume for our sample. 4.1 Ex-Day Premiums and Returns Graham, Michaely and Roberts (2003) and Jakob and Ma (2004) test the impact of decimalization on ex-day pricing, and Cloyd, Li and Weaver (2006), Chetty, Rosenberg 10 We exclude the transitional period while decimalization was being phased in. We also define the range of ex-dividend dates included in each regime so that both the cum-day and the ex-day fall in the same regime. 11 Percentage bid-ask spreads are calculated using closing quotes from TAQ during days [-5,+5] relative to ex-day 0. 10
and Saez (2007), and Zhang, Farrell and Brown (2008) test for changes in returns associated with the May 2003 tax reform. To compare our sample to these papers, and also to document ex-day premiums and returns in anticipation of our profitability tests, we compute summary premiums and ex-day abnormal returns for the full sample and by regime. To control for price movements within the ex-day, we also adjust the ex-dividend price for daily expected returns, calculated using a market model. The ex-day premium for ex-dividend event i adjusted for market movements is given by Prem i Pcum, i Pex, i / (1 + ER ( i )) = Divi where cum, i P, P ex, i and Div i are the closing cum-day price, ex-day price and dividend amount for a given ex-dividend event i. ER ( i ) is the stock s expected return, estimated using the market model with CRSP value weighted returns using daily data over the benchmark period. For each ex-dividend event, the benchmark period is days -45 through -6 and days +6 through +45 and the event window is days -5 through +5 relative to ex-day 0. Following Graham, Michaely and Roberts (2003), so that outliers do not drive our results we winsorize premiums at the upper and lower 2.5% level. Similarly, ex-day abnormal returns are calculated as AR i P P + Div ex, i cum, i i = P cum, i E( R ) i In the Miller-Modigliani setting, ex-day premiums should equal one, and ex-day abnormal returns should equal zero. Table 2 reports results for premiums (Panel A) and returns (Panel B), with and without the market adjustment. Unadjusted premiums are statistically significantly less than one for the full sample and for each regime. Mean 11
premiums increase from Regime 1 to Regime 2, as expected given the reduction in transaction costs associated with discreteness. However, they subsequently decrease significantly by Regime 3, which is unexpected given the reduction in the tax penalty of dividends associated with the 2003 tax reform. Analogous results hold for unadjusted returns. In contrast, mean market adjusted premiums increase consistently to values closer to one across regimes, as predicted given discreteness and the tax law change. However, these changes are not statistically significant. Notably, market adjusted premiums are still statistically significantly below one even in Regime 3. Similarly, market adjusted abnormal returns decrease across regimes, but are still significantly positive in Regime 3. The reduction in market adjusted abnormal returns associated with discreteness is significant for means, and the reduction associated with the 2003 tax reform is significant for medians. Overall, these results are generally consistent with those found previously in the literature. 4.2 Institutional Ex-Dividend Trading Volume To establish whether institutions trade during ex-dividend periods, we compute trading volume statistics [Lakonishok and Vermaelen (1986), Koski and Scruggs (1998)]. Following Michaely and Vila (1996), abnormal volume for trading day t relative to exdividend event i is defined as AV TO = it, it, 1 ATOi 12
where TO i,t is the daily turnover (shares traded relative to shares outstanding), and ATO i is the average daily turnover during the benchmark period. To minimize the impact of extreme outliers, we winsorize AV statistics at the 99.9% level. 12 From Table 3, Panel A, we see that institutional AV is 8.6% (t-stat = 11.25) during the event window. Abnormal CRSP volume during the event window is 4.4%, which is also highly statistically significant. Abnormal institutional volume is almost double that of CRSP (and this difference is statistically significant), consistent with our expectations that the institutions in our sample should be active traders during the exdividend period. Both institutional and CRSP AV are also significantly positive during each regime. If decimalization reduced transaction costs, abnormal trading volume by institutions should increase after decimalization. The 2003 tax reform reduced tax heterogeneity. According to Michaely and Vila (1995, 1996), reduced tax heterogeneity should be associated with lower trading volume. However, we find no statistically significant changes in institutional AV across regimes. Therefore, for subsequent analyses we focus our discussion on full sample results. According to Lakonishok and Vermaelen (1986), potential dividend capture trading profits will be higher for high yield and low transaction cost stocks. Heath and Jarrow (1988) note that short-term dividend capture trading is not arbitrage, because it is risky. Michaely and Vila (1996) and Michaely, Vila and Wang (1996) develop models in which short-term ex-day trading is negatively related to the risk of dividend capture. 12 Our expectation is that dividend capture trades may be very large. We chose to report results for this cutoff for winsorizing to balance the need to retain potential dividend capture trades with our desire to prevent a small number of extreme values from driving the results. Our main inferences hold if we do not winsorize, or if we winsorize at different levels. 13
Therefore, we expect that abnormal dividend trading volume should be positively crosssectionally related to dividend yield, and negatively related to transaction costs and risk. In Table 3, Panel B, we report event window institutional AV for ex-dividend events sorted into quintiles by dividend yield, transaction cost, and risk. We use percentage bid-ask spreads to measure transaction costs [e.g., Karpoff and Walkling (1990)]. Our measure of total risk σ i σ m is the standard deviation of returns for the exdividend firm divided by the standard deviation of returns on the CRSP value-weighted index, calculated during the benchmark period. Following Michaely and Vila (1996), we also decompose risk into idiosyncratic risk and systematic risk. Idiosyncratic risk and beta are estimated from a market model regression of daily returns on the CRSP valueweighted index during the benchmark period. Idiosyncratic risk is defined as σ σ ε m, the ratio of the standard deviation of the residuals to the standard deviation of market returns, and systematic risk is the beta from the market model. Results in Panel B show that, consistent with prior literature, abnormal institutional trading volume is significantly positively related to dividend yield and negatively related to all of our risk measures. However, in contrast to prior literature institutional AV is significantly positively related to our proxy for transaction costs, bidask spreads. 13 Similar results hold in unreported results for CRSP AV. Our sample includes a more recent period during which transaction costs were much lower than in 13 This result is robust to alternative definitions of transaction costs including several previously used in the ex-dividend literature: effective spreads [Graham, Michaely and Roberts (2003)], the log of firm size [Naranjo, Nimalendran and Ryngaert (2000)], the inverse of the cum-dividend price [Dhaliwal and Li (2006)], and the Amihud (2002) illiquidity measure. 14
previous studies. 14 Also, transaction costs are highly correlated with other relevant measures such as risk and dividend yield. To control for these correlations, in Panel C we report results of regressions of institutional AV on yield, bid-ask spread and risk. In Model 1 we include a measure of total risk. Subsequent models decompose risk into idiosyncratic risk and beta. Following Michaely and Vila (1996), we include firm size (defined as the log of equity market capitalization calculated during the benchmark period) in the regressions. Grinstein and Michaely (2005) show that institutional ownership is associated with payout policy. Given our focus on dividend trading by institutions, in our final specification we therefore also include institutional ownership as a control variable. 15 Results in Panel C show that abnormal volume is significantly positively related to yield and negatively related to total risk. Once we control for other factors, transaction costs as measured by bid-ask spreads are no longer significantly related to abnormal volume. Firm size is significantly negatively related to abnormal volume. Michaely and Vila (1996) find that the coefficient on firm size is significantly positive in similar regressions when it is included as the only measure of transaction costs, and insignificantly negative when bid-ask spreads are also included. Blume and Keim (2012) document that institutions (especially hedge funds) have significantly increased their holdings of smaller firms over time. Our results suggest that abnormal institutional exday volume is also greater for smaller firms. 14 For example, percentage spreads were 1.53% for the full sample in Michaely and Vila (1996) vs. 0.29% for our full sample. 15 Data on institutional holdings are obtained from the Thomson Reuters Institutional 13-F stock holdings, accessed through WRDS. The variable equals total institutional shares held at the end of the quarter prior to the ex-day, scaled by total shares outstanding from CRSP. 15
The decomposition of risk into idiosyncratic risk and beta shows that abnormal volume is specifically associated with idiosyncratic risk. Given the low transaction costs during our period, these results are consistent with Michaely and Vila (1995, 1996), who argue that in the absence of transaction costs, trading volume is negatively related to idiosyncratic risk and unrelated to systematic risk. Our results are also consistent with Pontiff (2006), who notes that idiosyncratic risk exposure is an important holding cost which inhibits arbitrage trading. Overall, we document significant abnormal institutional ex-day volume, consistent with dividend capture trading by institutions. Volume increases with yield and decreases with idiosyncratic risk, as predicted by the dividend capture theory. Controlling for other factors, there is no significant relation between abnormal volume and transaction costs as measured by bid-ask spreads. 4.3 Ex-Day Returns after Transaction Costs Are ex-day returns still positive after we incorporate transaction costs? Based on statistics provided earlier, although transaction costs are small, they are large relative to the magnitude of abnormal ex-day returns (e.g., bid-ask spreads of 0.29% and percentage commissions of 0.12% in Table 1.A, versus ex-day returns of 0.17% in Table 2.B). If traders trade within the spread, bid-ask spreads may overstate transaction costs. However, full transaction costs include commissions, spreads and price impact, and therefore may be larger than quoted bid-ask spreads. 16 An advantage of the Abel Noser 16 Koski (1996) adopts the ex-day equilibrium pricing equations to incorporate bid-ask spreads by modeling purchases at the ask quote and sales at the bid quote. Other research uses estimates for the magnitude of transaction costs; for example, Rantapuska (2008) computes returns after transaction costs using representative costs for different types of investors. 16
database is that we have actual trading data for the institutions in our sample that incorporate commissions, spreads and price impact for each transaction. Table 4 reports average ex-day returns calculated at the ex-day event level. This analysis supplements the results in Table 2, where ex-day returns are calculated with CRSP closing prices. Here, we also report average ex-day returns using actual execution prices realized by institutional investors. To compute ex-day returns, we calculate the volume-weighted average execution prices (VWAP) on the cum- and ex-days. 17 Returns computed with these VWAP prices can be interpreted as the ex-day return realized by the aggregate institutional traders in our sample. We report results based on CRSP closing prices, actual prices realized by our institutions (pre-commissions), and with prices realized by our institutions after adjusting for commissions paid (post-commissions). Ex- Day Premium is the median ex-day premium across all ex-dividend events (Panel A) or the value weighted median (Panel B). To be included in this analysis, there must be at least one institutional purchase on the cum-day and at least one institutional sale on the ex-day. The resulting sample (15,932 ex-day events) is somewhat smaller than the full set of ex-days we analyze (24,741 events from Table 1). Results in Table 4 show that ex-day returns calculated using CRSP data are a significantly positive 0.17% (same as in Table 2). Returns calculated using the precommission institutional prices are also significantly positive. Once we incorporate commissions, however, institutional returns fall dramatically and become negative (significantly so with equal weighting). These results suggest that although ex-day returns 17 More specifically, for the institutional trades the cum-dividend volume-weighted average price (VWAP cum ) is a volume weighted average of purchases on the cum-dividend day, and the ex-dividend VWAP (VWAP ex ) is the volume weighted average of ex-dividend sales. For CRSP, VWAP prices are just the cum- and ex-dividend closing prices. 17
measured using CRSP are statistically significant, they are consistent with a costly arbitrage equilibrium in the sense that they are eliminated once all of the relevant transaction costs are incorporated. They also confirm our intuition based on summary statistics from Tables 1 and 2; average ex-day returns are very small relative to percentage bid-ask spreads and trading commissions. Positive ex-day returns disappear on average across all ex-days once we account for actual execution prices and costs. This finding is consistent with the notion of an implementation shortfall as discussed by Perold (1988); there is an economically significant performance difference between the returns to a paper portfolio (using CRSP prices) and the returns to a real portfolio. The performance difference illustrates the difficulty of implementing profitable dividend capture strategies. Next, we examine whether certain institutions can avoid this drag on performance, and whether institutional profitability is related to trader skill. 5. Results: Profitability of Institutional Trading Average ex-day returns are no longer significantly positive after we incorporate transaction costs. However, average returns make no allowance for whether a specific institution has a net long or short position over the ex-day. Also, they don t account for the fact that institutions may not trade uniformly across ex-day events; certain institutions may be realizing profitable capture strategies for a subset of ex-dividend events. Finally, institutional ex-day trading strategies may involve trades that are spread out over several days; profits to this type of strategy would not be captured by ex-day returns. Anand et al. (2012) describe institutional execution as a joint production process that includes the decisions of both portfolio managers and their trading desks. One of the investment 18
decisions made by the portfolio manager is to specify a trading horizon which could span multiple days [Hu (2009)]. Perold (1988) points out that execution costs will be lower for institutions that trade slowly and patiently. Therefore, although average ex-day returns are not significantly positive after transactions costs, it is possible some institutions earn positive profits from dividend capture trading. In this section, we estimate the profitability of institutional ex-day trading strategies to test our hypotheses. 5.1 Ex-Day Profitability at the Client/Manager Level Abel Noser provides data on trades executed by a particular money management firm on behalf of a particular client. We calculate profitability at the client/manager pair; each observation is the collection of trades executed by a particular money management firm on behalf of a particular client during an individual ex-dividend event window. 18 Our definition of profitability includes profits from positions held at the end of the ex-day window in addition to profits on round trip trades. To estimate profitability, we compare total cash outflows and inflows during the event window at the client/manager pair level. Cash outflows are the total amount spent to acquire shares, calculated using actual transaction prices after all commissions and related trading costs. Cash inflows consist of the sum of proceeds from shares sold net of commissions, total cash dividends paid on the cum-dividend position, and the dollar value of any remaining shares held at the end of the event window. We illustrate this 18 See Jame (2012) for more details about the Abel Noser client/manager identification. Because an individual manager s trades across different clients are likely to be correlated, we use two-way clustered standard errors in our tests of statistical significance. 19
calculation for the [-5,+5] window with an example in Appendix B. 19 We focus on the [- 5,+5] window for several reasons. First, this window is used by the ex-dividend literature to measure dividend capture trading. 20 Second, the timing of trades is one way that trading desks can add value to performance. Perold (1988) asserts that execution costs can be lowered by trading patiently over a longer window, and low trading costs are an important component of profitable dividend capture. To compare profitability across positions of different sizes, we divide total net trading profits by the total investment on the cum-dividend day. We separately report profitability depending on whether the net cum-dividend position is long or short. To minimize the impact of outliers, we winsorize profitability at the upper and lower 2.5% level. Our first hypothesis predicts that institutions are able to earn positive profits after transaction costs from dividend capture trading. In Table 5, we report results for the profitability of institutional trading. Results are reported equally weighting these observations, value weighting each observation by the cum-dividend value of the share position, and winsorizing the value-weighting factor at 2.5% and 97.5% to reduce the influence of outliers. Results in Table 5 show that profitability calculated using CRSP prices when the cum-day position is long (Panel A) are statistically significantly positive. Institutional profits from long positions pre-commission (which incorporate bid-ask spreads and price 19 In this calculation, we subtract estimated commissions (calculated as the total dollar commission paid on all trades for that client/manager pair during that event window, divided by the total dollar volume of trades) on the marked-to-market portion of the position, to reflect realizable proceeds if this position were sold on the last day of our event window at the market price. Our profitability calculation is similar to Irvine, Lipson and Puckett (2007). We explore the robustness of our results to alternative definitions of profitability (see footnote 21). 20 Eades, Hess and Kim (1984) show evidence of abnormal returns for several days during the ex-dividend window. Lakonishok and Vermaelen (1986) and Michaley and Vila (1996) examine abnormal volume during an 11-day window surrounding ex-days to test for dividend-related trading strategies. 20
impact for the actual institutional trades) are also significantly positive. Pre-commission institutional returns are significantly higher than CRSP returns, suggesting that institutions have trading skill [Puckett and Yan (2011), Anand et al. (2012)]. Once we incorporate commissions, institutional returns fall dramatically, but they are still positive (although statistically significantly so in only two of three specifications). 21 Returns to short positions (Panel B) are consistently negative, indicating that institutions do not execute profitable short dividend capture strategies. Our profitability measure defines dividend capture trades as any client/manager combination that accumulates a non-zero position from the start of the cum-dividend event window through day -1. During our sample period, institutions were gradually purchasing more stock over time [see, e.g., Blume and Keim (2012)]. Clearly, not all institutional purchases are dividend related, and this trend makes it more challenging to identify dividend capture trades. In unreported results we explore alternative definitions of dividend capture (or footprints ). These footprints look at variations in cum-dividend volume or order imbalances relative to ex-dividend values. In general, institutional profits pre-commission are similar to or higher than CRSP profits across footprints, and institutional profits post-commission are positive. Our main inferences are therefore robust to alternative definitions of dividend capture. 5.2 Targeting Ex-Days 21 We conduct several robustness checks for our profitability measures (untabulated): not winsorizing the returns, including Nasdaq and Amex stocks in addition to NYSE, calculating profits at the manager level only (rather than for the client/manager pair), and eliminating the (round-trip) commission on the markedto-market portion of the profit calculation. Inferences are very similar to those reported herein. 21
Results in Table 5 suggest that some institutions are able to earn significant profits after all transaction costs. One potential reason is that there are specific ex-day events when many client/manager pairs trade, and they earn high profits around these events. The institutional trading process begins with the portfolio manager making investment decisions [Hu (2009)]. For example, the portfolio manager could identify the ex-day events on which to focus dividend capture strategies, and the trading desk then implements the optimal execution strategy. To investigate this possibility further, we sort ex-dividend events into quintiles based on the number of buy trades by our institutions on the cum-day (the buy intensity ). The total number of client/manager positions in the quintile of ex-day events with the highest buy intensity is approximately equal to the sum of the number of client/manager positions in the lower four quintiles, evidence that institutions concentrate their ex-dividend trading around certain events. Table 6 reports institutional profitability for events in the quintile with high buy intensity and for events in the lower four quintiles. Results show that equal weighted institutional profitability is significantly higher both pre- and post-commission for the exday events with high buy intensity. Profits around events with high buy intensity are also higher when we value weight profitability, although the differences are smaller and no longer statistically significant. In Panel C of Table 6, we report summary ex-day characteristics based on buy intensity. Institutions concentrate their trading around exday events for stocks with low spreads and low idiosyncratic risk. These differences are highly statistically significant. Counter to our expectations, events with strong buy 22
intensity have significantly lower dividend yields than those with lower levels of institutional trading. Overall, these results confirm that some institutions earn significant ex-day profits by targeting certain ex-day events. 5.3 Trader Skill Using Abel Noser data, Anand et al. (2012) show that some institutions have trade execution skill, and that this skill is persistent. Specifically, they show that trading-desk skill is related to an institution s trade execution abilities, and trading desks can contribute to relative outperformance as a result of these abilities. This execution skill is separate from stock picking skill, and allows institutions to avoid an implementation shortfall due to their ability to achieve superior execution quality. Given the size of exday returns relative to bid-ask spreads and trading commissions, execution skill may be very relevant for dividend capture profitability. Thus, we focus on whether institutions can use execution skill to implement trading strategies that successfully capture the wellknown abnormal returns around ex-days. Our second hypothesis predicts that institutional trading skill is persistent; institutions with high skill should earn higher ex-day profits than those with low skill. To test this hypothesis, we define two different measures of execution skill. We note that both of these measures are exogenous in the sense that they are based on trade execution skill outside the ex-dividend event window rather than execution prices for the dividend capture trades themselves. Therefore, we test whether institutions that are skilled in general are also able to implement profitable dividend capture strategies. 23
We proxy for trade execution skill using measures of execution quality, which we define with the following general form: PE P B Execution Quality= * Side PB where P E is the execution price, P B is a benchmark price, and Side is an indicator variable that equals one for a sell and minus one for a buy. Thus, sell trades that execute above the benchmark price and buy trades that execute below the benchmark price exhibit positive execution quality. Higher values of execution quality are indicative of higher trader skill. There is a debate in the literature over the choice of a benchmark price. Sofianos (2005) discusses two popular measures, one of which uses a pre-trade benchmark, and an alternative which uses a VWAP (volume-weighted average price during the trading day) benchmark. 22 Therefore our first skill measure uses a pre-trade benchmark price, and the second measure uses a VWAP benchmark. Our first trader skill measure is from Anand et al. (2012). 23 They compare a trader s execution price with a pre-trade benchmark price that is observed when the trading desk submits the order. This trader skill measure is calculated monthly for each institution ( clientcode ) by estimating institution fixed-effect regressions of execution shortfall on the economic determinants of trading cost (see p. 569 of Anand et al. (2012) for further details). Institutions are assigned a percentile value, and then sorted into deciles by trading skill (the AIPV trader skill measure). We use the AIPV measure during the month prior to the ex-day as our first measure of trade execution skill. 22 See also Berkowitz, Logue, and Noser (1988) and Hu (2009). 23 We are extremely grateful to Andy Puckett for providing us with the trader skill measures. 24
Our second proxy for trader skill is in the spirit of the VWAP-based measure of Hu (2009). 24 Specifically, we compute the average execution quality relative to the VWAP for all institutional trades during the trading day, calculated for all trades made during the 80-day benchmark period by a given client/manager around a given ex-day event (the ex-day benchmark trader skill measure). This measure of trading skill is based on trades during the benchmark period (rather than the ex-dividend window), so it also does not depend on the execution skill for dividend capture trades. Table 7 reports institutional profitability for the high and low skill deciles when sorted by AIPV skill and also by ex-day benchmark skill. Analogous to Table 5, we report results when profitability is equally weighted (Panel A) and value weighted (Panel B). Results in Table 7 show that the ex-day profitability of institutions in the high trader skill quintile is statistically significantly greater than profitability of low-skill institutions. These differences are economically large, approximately 40 basis points. Overall, our results strongly support H2; institutions with trade execution skill that is unrelated to ex-dividend capture are able to trade profitably around ex-days. Our results provide further evidence that institutional trading skills are persistent [Puckett and Yan (2011), Anand et al. (2012)]. Furthermore, dividend capture profits could represent one source of the abnormal profits realized by skilled institutions. 5.4 Cross-Sectional Determinants of Institutional Profitability Results so far show that some institutions are able to earn significant abnormal profits by targeting certain ex-days and through skilled trade execution. Is trade execution skill 24 Hu (2009) formulates the measure as an implicit trading cost, such that negative values indicate better execution quality. Our measure is constructed such that larger (positive) values indicate better execution quality and higher trader skill. See p. 422 of Hu (2009) for details. 25
correlated with the other determinants of ex-day trading? To examine this question, we explore the cross-sectional determinants of institutional profitability. We estimate OLS regressions in which the dependent variable is institutional profitability. Explanatory variables include the variables we use to explain abnormal ex-day volume in Section 4.2: dividend yield, percentage bid-ask spread and idiosyncratic risk, beta, the natural logarithm of market capitalization, and institutional ownership. We also include a second measure of trading costs, percentage trading commissions. Trading commissions are dollar commissions as a percent of the total dollar value of the trade. Finally, we include our two measures of trade execution skill, the AIPV measure and our ex-day benchmark measure. Results in Table 8 show that institutional profitability is significantly or marginally significantly negatively related to spreads, beta and firm size. Otherwise profitability is unrelated to the other determinants of abnormal volume that we consider (yield, idiosyncratic risk or institutional ownership). Both the Anand et al. (2012) and the ex-day benchmark measures of trader skill are significantly positively related to institutional profitability both pre- and post-commissions. 25 High trader skill is economically meaningful for post-commission institutional profits. A one standard deviation increase in ex-day benchmark trader skill leads to a 0.10% improvement in post-commission profitability, which represents 60% of the mean ex-day return (0.17%) and 100% of the mean ex-day abnormal return (0.10%). 25 In the regressions, we use percentile rank for the Anand et al. (2012) measure of skill. In the Anand et al. (2012) paper, traders are ranked based on decreasing skill. We reverse the order of the sort so that trader skill is increasing in percentile rank, to aid in the interpretation and comparison of the trader skill regression coefficients. 26