Strategic Delays and Clustering in Hedge Fund Reported Returns

Strategic Delays and Clustering in Hedge Fund Reported Returns George O. Aragon Arizona State University Vikram Nanda Georgia Institute of Technology November 22, 2012 Abstract We use a novel database to study the timeliness of hedge fund managers voluntary disclosures of monthly fund returns. Consistent with theoretical arguments, managers disclose returns at a much slower rate when their fund is performing poorly and when fund performance is anticipated to be stronger on account of favorable public signals, e.g., about market conditions. Managers often release the returns of two or more months together in clusters. These clusters tend to exhibit poor returns in the initial months, followed by a strong performance reversal, suggesting a type of performance smoothing. Investor capital flows are significantly lower and less performance-sensitive following greater reporting delays; estimates suggest fund managers tend to benefit from delayed disclosure when their performance is sufficiently poor. Frequent delays in disclosure presage poor performance: A portfolio strategy that buys (sells) hedge funds with historically timely (untimely) return reporting delivers style-adjusted returns of 4% per annum. We conclude that the timeliness of disclosure is an important consideration for hedge fund managers and investors. Keywords: hedge funds; smoothing; clustering; strategic delay; disclosure. JEL Codes: D82, G14, G23, G30, M41 We thank Viral Acharya, Francesco Franzoni, Mila Getmansky Sherman, Will Goetzmann, Bing Liang, Cristof Stahel, Cristian Tiu, and seminar participants at ASU, NUS, University of Melbourne, Northeastern, SEC, UNSW, UMASS-Amherst, Vanderbilt, and the Paris Hedge Fund Conference for helpful comments. Aragon is from Finance Department, W. P. Carey School of Business, Arizona State University, Tempe, AZ 85287-3906, george.aragon@asu.edu. Nanda is with Finance Department, College of Management, Georgia Institute of Technology, Atlanta, Georgia 30308, vikram.nanda@mgt.gatech.edu.

Strategic Delays and Clustering in Hedge Fund Reported Returns Abstract We use a novel database to study the timeliness of hedge fund managers voluntary disclosures of monthly fund returns. Consistent with theoretical arguments, managers disclose returns at a much slower rate when their fund is performing poorly and when fund performance is anticipated to be stronger on account of favorable public signals, e.g., about market conditions. Managers often release the returns of two or more months together in clusters. These clusters tend to exhibit poor returns in the initial months, followed by a strong performance reversal, suggesting a type of performance smoothing. Investor capital flows are significantly lower and less performance-sensitive following greater reporting delays; estimates suggest fund managers tend to benefit from delayed disclosure when their performance is sufficiently poor. Frequent delays in disclosure presage poor performance: A portfolio strategy that buys (sells) hedge funds with historically timely (untimely) return reporting delivers style-adjusted returns of 4% per annum. We conclude that the timeliness of disclosure is an important consideration for hedge fund managers and investors. Keywords: hedge funds; smoothing; clustering; strategic delay; disclosure. JEL Classification: D82, G14, G23, G30, M41

1 Introduction How does information flow from hedge fund managers to investors and the broader market? Hedge funds are largely unfettered by regulation to make public disclosures, yet many managers voluntarily report their monthly performance on a regular basis to one or more publicly available databases. Presumably, managers release performance information to garner investor attention and attract new money. An interesting question is the extent to which managers calibrate the flow of this performance information by, for instance, delaying (accelerating) the disclosure of poor (good) performance and the reaction this engenders on the part of investors. The timing patterns may also offer clues about the quality of information production within the fund and could, therefore, be predictive of its future performance. A better understanding of the disclosure policies of hedge funds is important for several reasons. Hedge funds are significant players in financial markets and the manner in which they choose to release (or not release) performance information is important for investors and for regulators concerned about the impact of hedge funds on capital markets. Further, since the quality and timing of performance information affects investor flows, a fund s disclosure policy is likely to be intertwined with its investment strategy and structure, e.g., the liquidity of assets under management and restrictions on investor withdrawals. It is also plausible that disclosure patterns could be symptomatic of managerial quality and, hence, predictive of the fund s future performance. More generally, hedge fund disclosure polices may offer insights into information provision by corporations and other entities. 1 For instance, hedge funds have discretion in terms of publicly releasing information and their choices may be instructive about the policies corporations would tend to adopt, in the absence of mandated disclosures. In the paper, we rely on a simple model to develop our main testable predictions regarding the reporting strategies of hedge fund managers. In our model, managers can strategically decide whether or not to delay the release of performance information. Outside investors may not, however, be able to distinguish between managers that choose to delay the release of poor performance and those that actually lack performance information (e.g., when assets are illiquid and hard to value). 1 There has been academic interest in the disclosure policies of corporations, even though managerial discretion on the release of material information is fairly limited. See, for instance, Verrecchia (1983) and Diamond (1985). 1

We derive testable implications about the effect of various factors on the endogenous delay decision. For example, managers will be more likely to delay information release when their performance is poor. Delayed disclosure will, in general, be interpreted negatively by fund investors and result in outflows from the fund. However, as in Acharya, De Marzo, and Kremers (2011), investors negative assessment and fund outflows are expected to be less severe when there are, for instance, positive public signals about economic conditions and about the performance of other similar funds. Disclosure delays may also be an indication of weaker fund management and predict relatively poor future performance. We empirically test our hypotheses using an extensive and novel dataset that identifies the dates of return disclosures made by hedge fund managers to a public database. The dataset is constructed by downloading and archiving the daily updates of the Lipper/TASS database ( TASS ) over several months. By monitoring the flow of information to the database on a daily basis, we can identify the exact dates of information release by hedge fund managers and, therefore, the timeliness of performance reporting. The historical updates are necessary because data vendors do not provide the specific dates on which hedge fund managers disclose new performance information to the database. In addition, TASS overwrites the database from the prior day and provides only the most recent update of fund characteristics and its historical time-series of monthly returns. Our raw sample consists of 744 daily updates of the TASS database over the 01/2009-03/2012 period. Our evidence strongly suggests that managers delay the release of bad news. The average reporting lag the amount of time elapsed between the end of the performance period and the subsequent disclosure to TASS is about three weeks (19 days). However, the reporting lag of poorly performing funds is larger, even after controlling for various fund characteristics and month and style category dummies. Figs. 1 and 2 illustrate this main result graphically. For each monthly performance period in our sample, we track the cumulative sum of disclosures and corresponding returns for each of the twelve subsequent weeks. The results show that a significant fraction (between 25-35%) of disclosures remain outstanding three weeks after month end (Fig. 1) and, more importantly, these delayed disclosures are associated with poor performance (Fig. 2). The monthly return on the equal-weighted index tracking all reporting managers falls by 10-15 basis points after the third week as managers with the more delayed performance reports are included. We also identify several other factors that explain hedge fund manager disclosure rates. As we 2

might expect, the longest reporting lags exist in style categories in which the underlying assets are illiquid and traded infrequently. For example, funds in the relatively illiquid Event Driven style are associated with an average delay of 20 days, whereas for funds in the more liquid Managed Futures style the average delay is only 13 days. Younger funds also exhibit longer average reporting lags, possibly indicating greater caution among less-established managers in making public disclosures. In addition, our hazard analysis reveals that reporting rates are lower among managers of funds with longer lockup and redemption notice periods. The longer reporting lags are consistent with these funds having illiquid assets (see, e.g., Aragon, 2007) that are harder to value, as well as lower nondisclosure costs on account of fewer share redemptions. In the wake of the Madoff scandal, popular attention has been devoted to whether hedge funds outsource the calculation of net asset value and the preparation of investor statements to third-party administrator firms, or if instead this function is performed in-house by the hedge fund manager. 2 Interestingly, we find that funds using third-party administrators are associated with significantly longer reporting lags. This indicates that the greater transparency of third-party administrators is not accompanied by more timely disclosures of performance. Our results also indicate that disclosure rates are negatively related to public market news. As our measure of public news we use the return on the style-matched Dow Jones Credit Suisse Hedge Fund Index which is based on monthly surveys of hedge funds, with the performance information released shortly after the end of month. In particular, the negative relation between reporting rates and public signals is concentrated among managers that have relatively poor private news about fund performance. We interpret this evidence as support for Acharya, De Marzo, and Kremers (2011) argument that bad (good) public news about the industry accelerates (slows) the release of information by firms. Their argument is that when public news is bad (good), funds expect investors to place a far more (less) negative interpretation on delayed disclosures. Lastly, we find significantly lower disclosure rates in months of low market liquidity, and therefore when managers are plausibly less informed about net asset value. We investigate the possibility that delay is motivated, in part, by the hope or anticipation of better news to offset the fund s poor performance. In our sample, we find many cases in which managers delay the release of return information and then report the returns at a later date along 2 See, e.g., Crackdown on hedge funds after Madoff affair, Financial Times, December 29, 2008, http://www.ft.com/cms/s/0/3eeb2286-d5dc-11dd-a9cc-000077b07658.html\#axzz24vgbeauu 3

with the returns of intervening months, which we refer to as return clusters. Such return clusters typically contain either two (about 80% of clusters) or three (about 15%) monthly returns, but are sometimes much longer. The majority (52%) of all return clusters start with a negative excess return, with the earlier half of the cluster associated with negative excess returns (-0.20% monthly), while the latter half has positive excess returns (0.16% monthly). Return clusters are therefore associated with reversals of poor performance. Clusters are also more common among managers of younger funds. In fact, the results show that the below-average performance in the first half of a cluster are driven mainly by the subsample of young funds. This evidence suggests that the strategic motives behind the clustering decision is greatest among less established managers, for whom the incentive to protect their track record and delay releasing negative performance information is the largest. Further, managers of funds with greater redemption restrictions, like lockups and redemption notice periods, that face less risk of investor outflows, are more likely to exhibit return clusters. Next we study whether fund investor flows are related to the timeliness of disclosures. We find that flows into funds are significantly lower following delays in information production. In particular, a one standard deviation increase in reporting lags is associated with a significant 0.18% decrease in net capital flows over the subsequent month. We also find that greater reporting lags are associated with a significantly lower flow-performance sensitivity. It appears, therefore, that a fund with a sufficiently poor performance (about one to two standard deviations worse than the mean) may be better off by delaying information disclosure. The effects of delays on fund flows are concentrated among managers of funds that place few restrictions on investor redemptions, suggesting that the costs associated with reporting delays are especially large when investors can more easily redeem fund shares. In addition, we find that the penalty to managers from delaying information release is greater during periods of bad public market news about the fund s strategy, and therefore when managers are more likely to have realized lower returns. These findings are in line with our above findings on the determinants of reporting lags and the basic predictions of our model. Our analysis identifies both fund and market characteristics associated with reporting delays and return clusters, and how delays influence fund investor flows. A natural question is whether the timeliness of reporting predicts future fund performance. Prior studies find, for instance, that 4

operational risk, such as the failure of operational, control, and accounting systems, is associated with poor performance. 3 Therefore, we might expect a negative relation between reporting lags and future performance, to the extent that reporting lags are reflective of operational risk. We uncover a strong negative relation between historical reporting lags and future excess returns. For example, a real-time portfolio tracking the most timely hedge funds (i.e., bottom quintile of historical reporting lags) delivers average excess returns of 0.09% per month, as compared to -0.24% for the portfolio of the least timely hedge funds. The difference, 4% per year on average, is significant and consistently positive across our sample period. We conclude that the timeliness of hedge fund manager reporting has significant investment value for fund investors. While there is limited literature on the voluntary release of performance information by hedge fund managers, there exists a fairly well developed literature on the disclosure policies of firms. 4 The regulatory regimes under which firms and hedge funds operate is, of course, radically different. While corporate managers have some limited discretion in the timing and quality of information released, they are governed by myriad rules and requirements that come from the SEC, GAAP rules, stock exchanges, threat of shareholder lawsuits and other sources. Hedge fund managers, in contrast, are largely free to set their own disclosure policies. Hence, the study of hedge fund manager disclosure may have much to teach us about the equilibrium demand and supply of information, since there are relatively few restrictions in place. As it turns out, despite managerial discretion, there is a steady flow of information from many hedge fund managers. While some managers do engage in timing the release of performance information, there are market consequences from delaying the release of information once the fund has (implicitly) committed to providing the information through a data provider such as TASS. A notable difference between funds and corporations, however, is that fund investors can redeem their shares (subject to restrictions), which gives them relatively more control and, presumably, enhances managerial incentives to provide information. Our paper is related to an extensive literature on the timing of information releases, usually in the context of information releases by publicly traded firms. Early and influential papers on the incentives of firms to disclosure information include Grossman (1981), Milgrom (1981), Verrecchia (1983), Diamond (1985), and Ross (1979). More recent studies of the strategic issues in disclosures 3 Brown et al. (2008, 2009), for example, develop an operational risk measure using hedge fund manager filings of Form ADV. 4 See, for example, Verrecchia (1983), Diamond (1985), Skinner (1994) and Song-Shin (2003, 2006). 5

by firm include Song-Shin (2003, 2006), Teoh and Hwang (1991) and Genotte and Trueman (1996). Papers that examine issues of herding by managers in disclosure decisions include Tse and Tucker (2007) while incentives for disclosure stemming from concerns over litigation are analyzed in papers such as Skinner (1994), Trueman (1997), and Hanley and Hoberg (2012). A recent paper that examines the timing of disclosure and, in particular, the effect of public signals on disclosure by firms is Acharya, De Marzo, and Kremer (2011). While some of the issues in disclosure by hedge fund managers are similar to those faced by corporations, there are also striking differences since, as noted above, hedge funds are largely unregulated and their disclosures are of a voluntary nature. Among other issues is that managers of hedge funds often hold illiquid assets and may, therefore, have difficulty in ascertaining net asset value in a timely fashion. There are relatively few studies of disclosure strategies of hedge funds and the ones that exist are largely focused on the issue of distortion of return information. For example, Getmansky, Lo, and Makarov (2004) show that a drop in volatility of reported returns can be achieved by performance smoothing i.e., the practice of reporting only part of the gains in months of positive returns in order to offset the reporting of losses in months of negative returns. Likewise, the clustering strategy we identify is a form of performance smoothing, in the sense that the volatility of the average returns reported within a cluster is lower than the volatility of returns reported separately outside of clusters. Unlike performance smoothing, however, the clustering strategy involves a delay, rather than a distortion of true, economic returns. 5 Finally, our paper also adds to the literature on hedge fund flows and performance. For example, prior studies focus on the shape of the flow-response to past performance and its interactions with investor share restrictions. 6 Our findings indicate that both the level and shape of the flowresponse is significantly related to the speed with which managers disclose information about fund performance. Prior studies also find that fund performance is related to fund-level variables, including lockup provisions (Aragon, 2007), features of the manager s compensation contract (Agarwal, Daniel, and Naik, 2009), and past performance. 7 Our evidence shows that, in addition these factors, 5 See, also, Bollen and Poole (2006, 2009, 2012), Agarwal, Daniel, and Naik (2011), Cassar and Gerakos (2011), and Patton, Ramadorai, and Streatfield (2012). Agarwal et al. (2012) and Aragon, Hertzel, and Shi (2012) study the confidentially-held positions of Section 13(f) hedge fund managers in a setting where portfolio disclosure is mandatory. 6 See, e.g., Goetzmann, Ingersoll, and Ross (2003), Agarwal, Daniel, and Naik (2004), Baquero and Verbeek (2009), and Ding et al. (2009). 7 See, e.g., Kosowski, Naik, and Teo (2007), Boyson (2008), Fung et al. (2008), and Jagannathan, Malenko, and Novikov (2010). 6

fund performance is related to managerial delays in reporting fund returns. The remainder of the paper is organized as follows. Section 2 presents a simple model of strategic delays by hedge fund managers. Section 3 describes the data used in the empirical analysis. Sections 4 and 5 discuss our main empirical results; robustness and extensions are in Section 6. Section 7 concludes. 2 Empirical Hypotheses and a Simple Model of Delay We develop our testable hypotheses with the aid of a simple model of strategic information release by a hedge fund. We obtain time-series and cross-sectional predictions about the effect of a fund s performance and the arrival of public signals, on the fund s decision to release information. The model is outlined below, with details in the Appendix. 2.1 Outline of Model We consider a two-period set-up in which the investments made by a hedge fund manager generate stochastic returns r 1 & r 2 on dates t = 1 & 2, respectively. If the manager receives information about r 1 by date 1, he can release it on the same date. On the other hand, he can choose to wait till date 2 to disclose r 1. There is, however, some probability Γ that the fund manager does not learn about r 1 till date 2. The probability Γ can be regarded as a type of illiquidity measure, reflecting the difficulty the manager faces in ascertaining the value and returns on fund investments in a timely fashion. Outsiders have no way to determine whether a fund manager who does not release r 1 chooses to do so for strategic reasons or because he does not have the information. Date 2 is the terminal date, by when information about r 1, r 2 is always available and disclosed. All market participants are assumed to be rational and risk-neutral. Manager s Objective: The fund manager s objective function depends on fund performance as well as the timing of the information release. While not explicitly modeled, there are several reasons to expect the timing of information release to matter and for funds to find it more attractive to exhibit stronger performance earlier, rather than later. One reason may have to do with asymmetric costs of inflows and outflows once flows have come in, investors may be less inclined to withdraw and reallocate their investments. Second, flows that come in earlier will have more periods over which the manager s fixed fees are charged. A third reason is that if the disclosure is likely to 7

trigger a withdrawal of investor funds, it may be less costly for the manager to delay the release and arrange for a careful liquidation of fund assets. Finally, if the manager expects performance to be stronger in the near term, say next period, it may be in his interest to withhold current information and to release it together with better subsequent performance (we refer to this as return clustering ). In choosing whether or not to disclose his performance on date 1 (if he has the information), the manager compares the actual performance r 1 with what outside investors would infer as his performance, E(r 1 ), if he did not disclose r 1 till date 2. Market Conditions: In developing our empirical predictions, we consider some factors that can affect investor assessment of fund performance and a fund s decision to delay releasing its performance information. Specifically, we first examine the possibility of a public signal that can arrive on date 1, in time to affect a fund s disclosure decision. The public signal is assumed to provide information to the fund manager and investors about market conditions and/or the performance of other funds. As a result, the public signal affects investor expectations E(r 1 ) about fund performance and may, therefore, affect the disclosure decision. Empirical Predictions: We state the main empirical predictions that are tested subsequently, beginning with predictions about a fund s decision to delay disclosure (additional details are in the Appendix): Prediction-1: The likelihood that a fund delays the release of its return information will be: i Decreasing in the level of its performance, ii Decreasing when there is unfavorable public information about the economy or performance of similar funds. The intuition for the above proposition is simply that fund managers will be more willing to delay disclosure when their expected costs (in terms of investor withdrawals) are lower. Hence, if the fund does poorly, it may choose to delay disclosure if it expects the negative consequences of delay to be less than those of reporting its realized returns. In addition, if there is a positive public signal about the performance of funds, then funds that have done relatively poorly have a greater incentive to delay. This is because investors expect the typical fund to be doing relatively well, given the favorable public signal, thereby lowering the cost of delay. This is similar to the argument made in Acharya, De Marzo, and Kremer (2011). We now turn to predictions about the impact of 8

delayed disclosure on investor expectations and, consequently, on fund flows: Prediction-2: A delay in the release of performance information will lead to investors having a poorer assessment about the fund manager s ability and is likely to lead to more net outflows from the fund. If the delay occurs when there is also an unfavorable public signal about the economy or performance of similar funds, then investor assessment will be even more negative and lead to greater outflows. In addition to testing the predictions above, we investigate the possible impact of using thirdparty (compared to internal) administrators and spillovers between funds from the same family on performance disclosure and investor response. We also explore the possibility that a fund s proclivity to delay the release of its performance information offers clues into underlying problems, such as operational risk, and is a predictor of poor future performance. We can state the testable prediction as follows (discussed in the Appendix): Prediction-3: Assume that there is heterogeneity in managerial ability to produce returns and that lower ability managers are also less likely to produce performance information in a timely fashion. In this case, we expect that even after controlling for past performance, funds that tend to delay more frequently will deliver worse future performance than other funds. 3 Data and Summary Statistics In this section we describe the data used in the empirical analysis of hedge fund manager disclosure decisions. We also define our measures of disclosure date and reporting lag, discuss sample selection criteria, and provide summary statistics of the sample funds. 3.1 TASS database Our main data source is the Lipper/TASS database ( TASS ). Although hedge funds are generally not required to make public disclosures, many funds voluntarily report historical performance and other information to commercial data vendors. 8 TASS is among the largest hedge fund data vendors, with over 18,000 funds, and has been used in several prior studies of hedge fund performance. Effective October 2003, hedge fund managers can register for a TASS Database listing online and use 8 One exception is that the U.S. Securities and Exchange Commission requires large investment managers, including hedge fund mangers, to disclose certain long positions in U.S. equity securities on a quarterly basis. See Section 13(f) of the Securities Exchange Act. 9

the web to update their performance or any other information carried by the database. The updated database is generally available for download by its subscribers every Tuesday-Saturday, except U.S. holidays. Each update contains the most recent snapshot of fund characteristics, including the manager s compensation contract, investor liquidity restrictions, and the identity of fund service providers (e.g., administrators). Each update also contains the most recent historical time-series of monthly returns and assets under management (AUM) for each individual fund, including live funds as well as those that have stopped reporting ( defunct ). The key variable in our empirical analysis are the dates that a hedge fund manager reports monthly returns to a public database. Although this information is not directly provided by data vendors, we can infer it by monitoring the daily changes in the history of reported returns. 9 One complication is that the historical daily updates are not available from TASS, since each daily update overwrites the prior day s database. We overcome this issue by downloading and archiving 744 daily updates in real time over the 01/2009-03/2012 period. Our raw sample contains 703,141,162 observations of monthly returns for 18,279 individual hedge funds. Compared to prior studies that use only a single TASS update, our raw sample is much larger since we are combining 744 updates of TASS. Therefore, for each fund, we potentially have several observations of the same monthly performance period. From the raw sample, we monitor changes in the daily updates to decipher the disclosure date of each return in the database. Specifically, we define the report date of each fund s monthly return observation as the earliest date that a non-missing return for that fund/month appears in our sample. Next we define the reporting lag of each monthly performance period as the number of days between month end and the corresponding report date. Report date returns often differ from returns reported in subsequent updates for the same fund and month. It is plausible that later disclosures are more precise given that managers have more information about asset values. Therefore, in our analysis of future fund performance and capital flows, we use the latest available monthly returns reported to TASS at the end of our collection period (03/2012). 10 We also use the end-of-collection period AUM for each fund/month observation 9 Although TASS provides the date each individual fund was initially added to the database (see, e.g., Aggarwal and Jorion, 2010), it does not provide the date that each individual return was initially reported to the database. This is a key distinction that highlights the novelty of our data. 10 We find similar results if we instead compute flows and performance using report date returns. Patton, Ramadorai, and Streatfield (2012) use monthly updates to track revisions in reported returns, and find that return revisions are associated with lower fund performance. In contrast, we use daily updates to measure reporting lags and find that reporting delays are associated with lower fund performance. 10

reported to TASS, denoted by AUM. We convert all AUM figures into US dollars using fund currency codes (from TASS) and the corresponding monthly exchange rates from DataStream and the Federal Reserve Bank of St. Louis, and then winsorize the AUM observations at the 1st and 99th percentiles. 11 Our analysis features fund-level variables that are defined relative to other funds managed by the same management firm or investment advisor as part of a larger fund family. In particular, age is defined as the number of years between the monthly performance period and the earliest inception date across all funds linked to the same family. We also use the reported administrator to define third-party that is, an indicator variable for whether the fund s administrator and family firm are different. 12 Lastly, we match fund characteristics (e.g., incentive fee, lockup period) with monthly returns using the characteristics reported to TASS at the corresponding month-end. 13 After defining these variables from the raw sample, we then focus on the sample of 1,197,974 unique fund/month return observations. 3.2 Sample selection We impose other selection criteria to focus our analysis on the strategic disclosure decisions of fund managers. In particular, we exclude all returns from each fund s earliest available snapshot in our sample. This criterion has two effects. First, it excludes all returns that were reported to the database prior to the start of our collection period, and therefore returns for which we cannot accurately measure the report date. Second, for funds that are added to the database during our sample period, the criterion excludes all returns that were generated during the pre-inclusion period ( backfilled data ). 14 Next, for each fund, we exclude all returns after the fund first appears in the 11 Many funds have either a partial or complete set of missing AUM values in the database. Returns are never missing, suggesting that return disclosures are required for ongoing inclusion in the live fund database, whereas AUM reporting is optional. If reported, the initial AUM report date often occurs after (but never before) the report date for the same fund/month and, like returns, are often restated. Since our focus is on return disclosures, we leave an analysis of strategic AUM disclosures for future research. 12 Our classification of funds into families and administrator clienteles involves a name-matching procedure based on the reported names of the fund s investment advisor or management firm, and the fund s administrator, respectively. Details are available from the authors upon request. However, our qualitative results are unchanged if instead we use the TASS-provided company identification numbers to identify families and administrators. 13 For some fund/months, fund characteristics data are not reported to TASS, and we therefore use the last available fund characteristics reported in prior months. In fewer cases, fund characteristics data are not reported to TASS in either the current or previous months, and we therefore use the fund characteristics reported in the earliest subsequent disclosure. 14 The backfilled period might represent a period of fund incubation, during which multiple funds are managed with the purpose of generating a favorable track record and are not generally open to the public. Alternatively, the observations could correspond to a period during which the fund was reporting to an alternative (non-tass) 11

returns graveyard database (i.e., defunct ). This is not to say that we exclude defunct funds, however, since many of our sample funds are ultimately classified as defunct by the end of our sample period. The resulting sample contains 236,209 observations for 11,463 individual hedge funds. We also detect a few situations in which a manager is already reporting to the database, but at some later update adds monthly performance data from an earlier period, thereby creating backfilled data. We therefore drop all observations from any performance period that precedes the most recent performance period on which the fund has reported. We also exclude all returns corresponding to months before 01/2009 or after 12/2011. We do this to avoid any effects of our collection period on the distribution of reporting lags. In other words, since our collection procedure begins at the start of 2009, we do not observe pre-2009 returns that were reported prior to 2009, thereby leading to longer average reporting lags among the pre-2009 returns that we do observe. On the other hand, since our collection procedure ends in 03/2012, we do not observe the delayed returns for the final months in our collection period, thereby leading to shorter average reporting delays among the returns we do observe. The resulting sample contains 213,640 observations for 10,914 individual hedge funds. Lastly, we drop monthly returns that are either less than -100% or greater than 200%, since these extreme observations are likely misstatements. The final sample contains 213,505 monthly returns contained in 201,356 separate disclosures for 10,914 individual hedge funds, and corresponding to monthly performance periods in years 2009 (66,673), 2010 (68,410), and 2011 (78,422). 3.3 Summary Statistics Table 1 presents summary statistics for the main variables in the analysis. The key variable reporting lag reveals that monthly performance results are delayed by roughly three weeks (19.36 days), on average, following each month end. However, as shown in Fig. 1, the reporting lags can be substantially greater, with roughly 30% of the returns remaining unreported even three weeks after the performance period. We also find that the pooled average and standard deviation of monthly fund returns are 0.28% and 4.59%, respectively. In comparison, over the same period the S&P 500 has a monthly return mean and standard deviation of 1.07% and 5.48%, respectively. The median commercial database, and therefore using TASS updates would not allow us to measure the initial disclosure date. 12

fund size is $28.98 million and there are several months for which AUM was never reported to the database (even though returns were reported). Despite positive average fund returns, monthly net fund flows are negative, -.82%, on average. 15 Towards the bottom of Table 1 we summarize fund characteristics at the end of our sample period. For example, the average lockup and redemption notice periods are 1.79 months and 29.67 days, respectively. This suggests that the typical liquidity provided to hedge fund investors is somewhere between that provided by a no-load U.S. mutual fund and a private equity firm. Lastly, we find that the majority of funds (85%) use third-party administrators and (in untabulated results) that the absence of a third-party administrator is more likely among older and smaller AU M funds. Table 2 provides summary statistics for the reporting lags across various subsamples. As the table shows, reporting lags vary significantly across style categories. The most timely reporting funds belong to the managed futures (12.69) and global macro (13.79) style categories, whereas much longer average reporting lags are found among fund of funds (23.40) and event driven funds (20.22). This evidence is broadly consistent with a negative relation between timely reporting and investment strategies that involve illiquid assets (see Getmansky, Lo, and Makarov, 2004, their Table 8). Asset liquidity is unlikely to be the only factor in explaining reporting lags, however, since there is significant within-style variation in reporting lags. We also report results for subsamples of observations that correspond either to the top or bottom quintiles for various fund characteristics, including AU M, age, and returns. For each monthly evaluation period in our sample, we sort funds into quintiles based on characteristics measured at the end of the corresponding month. The table shows longer average reporting lags among smaller (18.79 vs. 18.13 days) and younger funds (20.85 vs. 18.65 days). 16 In addition, the table shows evidence that longer delays are associated with poor performance: the reporting lag of funds in the top and bottom style-adjusted return quintiles averages 17.51 and 20.95 days, respectively. Reporting lags are also lower among unrestricted funds that is, funds that impose few restrictions on investor redemptions. This subsample likely corresponds to observations where there are relatively high costs from delay, since managers of unrestricted funds may be more concerned about 15 Net flows are -1.1%, on average, during 2009, as compared to -.56%, and -.81% in 2010 and 2011, respectively. This is consistent with a delayed response, possibly from restrictions on investor liquidity, to negative returns during the Financial Crisis. The measure for Net Flows is defined in Section 4.3. 16 Both top and bottom AUM quintiles show lower average reporting lags than the full sample because the average reporting lag is lower (18.39) for the subsample of non-missing AUM observations. 13

investor redemptions. The reporting lags are greater when the market is less liquid, following Gatev and Strahan (2006) in measuring market illiquidity as the difference between the yield on 3-month high-grade non-financial commercial paper minus the yield on 3-month T-bills. It is possible that higher market illiquidity makes it harder to assess net asset value. The reporting lags are smaller when there is negative public news about the performance of funds in the same style category (Public signal). Our proxy for the public signal is the early estimate of the monthly return on the fund s style-matched Dow Jones Credit Suisse Hedge Fund Index. Early estimates are based on surveys of hedge funds and are published on the Credit Suisse Hedge Index website, usually within 10 days, following each month end. 17 The evidence is consistent with our predictions and Acharya, De Marzo, and Kremer s (2011) argument that bad public market news accelerates the release of information. We also find significantly higher reporting lags (23.75 days, on average) among funds that are considered defunct at the end of the sample period. This evidence is consistent with longer delays being predictive of either fund liquidation and/or other non-liquidation reasons for ceasing to report to the database. Lastly, we find lower average reporting lags (16.92) among funds that do not use a third-party for fund administration services (nonthird party) that is, the fund s administrator is the same firm as the fund s management firm or investment advisor. This suggests that the use of a third-party administrator tends to slow the rate of disclosure by hedge fund managers. 4 Hedge Fund Disclosure: Analysis and Results In this section we discuss the methodology and results on hedge fund disclosure policies. First, we use a hazard model of reporting lags to study the timeliness of hedge fund manager disclosures in a multivariate setting. Second, we examine return clusters to test whether managers delay the reporting of poor performance in anticipation of reversals from future performance. Third, we measure the costs of delay using the response of net investor flows to a lack of timeliness in the release of information by fund managers. 17 Source: http://www.hedgeindex.com/hedgeindex/en/prlist.aspx?cy=usd. Early estimates are later confirmed at the end of the second week. For a few months in our sample, we use confirmed index returns where no early estimates are provided. 14

4.1 Hazard model of disclosure The raw data clearly suggest that several factors are associated with reporting delays. To validate these differences, we report a proportional hazard model of reporting lags. The time variable in our model equals the reporting lag and yields the baseline hazard rate for our hedge fund sample. We also include several explanatory variables that plausibly shift the baseline hazard rate, such as the fund s within-style return rank, the natural logarithms of age, AU M, lockup period, redemption notice period, incentive fee, and management fee, and indicator variables for high-water mark, third party, domicile country, style category, and calendar month. The return rank is the fractional rank of the fund s reported return across all other funds in the same month and style. We expect higher returns to lead to an increase in the hazard rate, and this would be reflected in hazard ratios (coefficients) greater than one. In contrast, we expect lower reporting rates among funds with longer lockup and notice periods, and therefore hazard ratios less than one. The hazard analysis strongly suggests that managers are slower in disclosing poor fund performance (Table 3). Model 4 shows a coefficient on Return rank of 1.0454, meaning that reporting rates are 4.54% higher per one standard deviation increase in fund performance. Note that this increase goes beyond what one would predict based on the specific calendar month or style category, due to the presence of month and style dummies. Further analysis (not tabulated) shows that this finding is stable across styles and months. In style-by-style estimation, the coefficient on Return rank is greater than one for all styles categories, and significantly so for eight (of twelve) style categories. In month-by-month estimation, the coefficient is greater than one for 28 (of 36) months, and the average coefficient is 1.049 (t=5.71). The point estimates also suggest that reporting rates are lower among managers of funds with longer lockup and redemption notice periods. This finding is consistent with less disclosure when the funds are less concerned about costs from delay, since greater share restrictions make it more difficult for investors to redeem their shares. The finding is also consistent with longer reporting lags among managers that are uninformed about net asset value, since funds with lockups tend to hold more illiquid assets (see, e.g., Aragon, 2007). Lastly, the coefficient on third party validates our univariate comparisons. In particular, Model 4 indicates a 3.5% slower reporting rate among funds for which the administrator is different from the management firm. This is interesting in light of the recent investor push for greater trans- 15

parency through the use of third-party (versus in-house) administrators. 18 Apparently, any increase in transparency associated with third-party administrators must come from sources other than a more timely reporting of fund performance. For example, the slower disclosure rates among funds that use third-party administrators is consistent with a tradeoff between the precision and timeliness of reported returns, to the extent that third-party administrators place a greater importance on the former. Next we test whether a manager s rate of disclosure is also influenced by market conditions. Therefore, we include in the hazard model the Paper-bill spread (a measure of market illiquidity) and Public signal (a measure of public news about the fund s strategy). We expect lower reporting rates when market illiquidity is higher, since managers are plausibly less informed about net asset value. We also expect worse public news about the fund s strategy to increase nondisclosure costs, thereby accelerating the rate of disclosure. We include indicator variables for whether the fund s raw return is greater than (Good private news) or less than (Bad private news) the public signal. All other explanatory variables in Table 3 (except month dummies) are included in the analysis but not reported in the tables. The results in Table 4 strongly suggest that both market liquidity and public signals of fund performance are significantly related to the manager s disclosure decision. For example, in Model 4 the coefficient on Paper-Bill spread is 0.9608, meaning that reporting rates are 3.92% lower per one standard deviation increase in market illiquidity. 19 In addition, a one standard deviation rise in the public signal of fund performance is associated with a 3.40% drop in reporting rates. The results further show that the negative relation between reporting rates and public signals is concentrated among managers that have relatively poor private news about fund performance. For example, in Model 4 we estimate that, among managers with bad private news, the drop in reporting rates is 6.04% per one standard deviation increase in the public signal. Overall, the results are consistent with our hypotheses and Acharya, De Marzo, and Kremer s (2011) prediction that good public news decreases disclosure rates. 18 See http://www.forbes.com/sites/halahtouryalai/2012/06/11/ goldman-sachs-deal-would-put-state-street-on-top-in-hedge-fund-world/ 19 We again find a negative relation between reporting rates and market illiquidity when we measure liquidity as in Pastor and Stambaugh (2003). 16

4.2 Return Clusters In the above analysis we present strong evidence that managers delay the release of poor performance. With a sufficiently long delay, say a month or more, the manager can use the performance in subsequent months, if favorable, to offset the negative information in prior months. In particular, since disclosure is both voluntary and irreversible, we might expect managers to withhold poor performance news if it is likely to be subsequently reversed insofar as investors would react more negatively to disclosure than non-disclosure. Redemptions by investors in response to poor fund performance increase trading costs and lower AU M. In this context, reporting poor performance along with better subsequent returns could be helpful by moderating investor redemptions and limiting the impact on fund profitability and manager fees. In these cases, we would expect poor performance to be often reported along with the performance in subsequent months that, at least partially, reversed the poor performance. In this section, we study such return clusters, defined as a disclosure that contains at least two monthly return observations. 20 4.2.1 Characteristics of return clusters From the final sample of 213,505 returns we drop an additional 985 returns that were reported in clusters together with other returns from outside our final sample. The resulting sample contains either non-clusters or clusters that only contain returns from our sample period, thereby allowing us to precisely characterize the earlier and latter halves of clusters. The final sample contains 9,468 clusters completed over our sample period, and corresponding to 21,232 monthly returns, or, 10% of all returns in our sample. Table 5 summarizes the characteristics of excess returns for various subsamples of return clusters. Excess returns are computed by subtracting from raw returns the average return of all funds in the same style category. The first row shows that the majority (52%) of all return clusters start with a negative excess return, thereby suggesting that poor performance triggers a manager s decision to initiate a return cluster. In addition, the earlier half of a cluster (First) has a negative average return (-0.20% monthly) while the latter half (Second) has a positive average return (0.16% monthly). A similar pattern is found when returns are cumulated within each cluster. The differences, about 0.36%, are both economically and statistically significant. Return clusters are 20 As noted above, return clusters are not identifiable from the standard database, because data vendors do not provide information on when managers report new information about fund returns. 17

therefore associated with reversals of poor performance. This suggests that clusters may serve as a way to report smoother performance outcomes: disclosing poor returns with delay in the hope of clustering them with better performance in subsequent months. Although a majority (over 80%) of clusters contain only two monthly returns, a similar pattern is found among return clusters that contain three returns. The table also presents results depending on the year the return cluster is reported. For example, as noted above, our collection period ends in March 2012. Among all daily updates collected in 2012, we identify 457 disclosures that contain at least two monthly returns generated over 2009-2011. Overall, the pattern of worse performance during the earlier half of a cluster is consistent across years in our sample. Clusters that begin with poor performance are also more common among younger (53% vs. 49%) and smaller (58% vs. 46%) funds. This pattern suggests that the incentive to strategically delay reporting poor performance is greatest among managers that are less established and may be especially concerned about protecting their track record. One way to view the economic effects from clustering is as a reduction in the volatility of news about fund performance, where news is the average of all incrementally disclosed returns. In Panel B we quantify these effects by comparing the return distributions of news reported in clusters and non-clusters. The evidence shows that clustering is associated with a 25-30% drop in the volatility of reported news. The economic importance of this result is highlighted by recent models of performance smoothing. For example, Getmansky, Lo, and Makarov (2004) allow for distortions between reported and true, economic returns, in which only a fraction of economic returns are reflected in contemporaneous reported returns. This can result from illiquid asset exposure or deliberate smoothing. They show that, depending on the level of distortions, the volatility of reported returns can be much lower than that of true, economic returns. The drop in volatility we observe from clustering corresponds to a smoothing strategy where 32% of the fund s true, economic return is withheld from reported returns. 21 However, the clustering strategy achieves this result through delay, rather than distortions between reported and true, economic returns. 21 To see this, note that Getmansky, Lo, and Makarov (2004) express report returns as R t = K k=0 θ krt k, where Rt is the fund s true, economic return in month t and K k=0 θ k ( = 1. This implies a standard deviation of reported θ ) 2 returns is then σ 0 + θ2 1 + θ2 k, where σ is the standard deviation of true, economic returns. Assuming K = 1, a reduction in volatility of 25% corresponds to a smoothing coefficient θ 0 satisfying: 0.75 = θ 2 0 + (1 θ0)2, or, θ 0 = 0.68. This corresponds to about one (=(.92-.68)/.26) standard deviation below their sample mean (their Table 8). 18