Database selection and self-selection bias

Since reporting to database vendors occurs on a voluntary basis, the sample of hedge funds observed does not constitute a true random sample of the entire population. Characteristics from reporting funds may differ widely from characteristics from non- reporting funds. Additionally, hedge fund managers may opt to report to one or two database vendors, but rarely report to all. Thus, selecting a database for statistical analysis resulted in a sample selection bias towards particular segments of hedge funds (some providers exclude certain investment strategies from their database). By comparing the data of multiple providers, the impact of selection bias could be mitigated.

Some examples from research into the TASS database and at least one complementary database: In Liang (2000) the joint sample of HFR (without managed futures) and TASS resulted in a common sample of 465 funds versus 2,324 unique funds; Fung and Hsieh (2004b) established that from 1,061 hedge funds in TASS, 1,151 in HFR and 909 in Zurich Capital, only 305 funds reported to all three databases (for all funds up to December 2000).

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Some hedge funds choose not to publish their performance - either because the performance does not appear satisfactory or they have already reached their critical size (self-selection bias). It is therefore difficult to know whether this bias has a positive or negative impact on the average performances. Empirical research suggests, however, that funds that stop reporting do so because of underperformance rather than reaching the cap of their capital requirements (Grecu, Malkiel & Saha, 2007).

3.5.2 Survivorship

Survivorship bias results from the tendency of funds to be excluded from databases for the simple reason that they no longer exist. Thus, performance assessment based on surviving funds is likely to positively skew the expected performance of the average hedge fund. However, the reasons for exclusion from a database can be many: the fund has been liquidated due to financial losses, the fund has been closed (no more investors will be allowed into the fund), the fund has been merged with another fund, or the fund has simply stopped reporting for different reasons without being liquidated.

The potential survivorship bias could be addressed by looking at both reporting funds, as well as funds that stopped reporting to the database vendor. It was found that the monthly returns were overstated for most hedge fund strategies, when the graveyard funds were excluded from the analysis. Since TASS has included defunct funds in a graveyard database since 1994, their database is a popular starting point to quantify survivorship bias. However, considering funds that were dropped from the sample prior to 1994, a certain degree of remaining survivorship bias is to be expected for the following years.

Fung and Hsieh (2000: 294–297) quantified survivorship bias as the expected returns between an observable portfolio investing in all funds in a database and the surviving portfolio excluding defunct funds. For 1994 through 1998, the survivorship bias was 3.0 percent per annum (p.a.) and 1.3 percent p.a. for single manager funds and FoHFs respectively. An identical approach was employed for managed futures in the TASS database, setting survivorship bias at 3.5 percent p.a. for 1989 through 1995 (Fung & Hsieh, 1997b) and 3.6 percent for 1991 through 1997 (Fung & Hsieh, 2000). Liang

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(2001) confirmed the results for single manager and FoHFs up to 1999, quantifying TASS survivorship bias at 2.4 percent annually.

Barry (2002) established that survivorship has increased in the years following 1998 due to higher attrition among managed futures and fixed income arbitrage strategies. The average performance difference between surviving and defunct hedge funds was 3.8 percent p.a. for the seven year period prior to June 2001. For the same time period, Amin and Kat (2003) found that annual survivorship bias amounted to 1.9 percent for single manager funds and 0.6 percent for FoHFs. However, Amin and Kat (2003) emphasised that survivorship bias may be much higher for small, young and leveraged funds. In addition, bias appeared persistent in the estimation of higher moments of the return distribution.

Getmansky et al. (2004: 75–76) expanded previous research by including a much larger observation period from November 1977 to January 2001. For hedge funds with a continuous five-year track record, the annual performance difference over the 24-year period between alive and dead funds was 4.1 percent. Considering the inherent bias of limiting the analysis to funds with a minimum return history and the 1994 TASS database cut-off for defunct funds, the results may not be directly comparable to previous research. Malkiel and Saha (2005) increased the survivorship bias to 4.4 percent p.a. for TASS from 1996 to 2003. For 1995 to 2006, Ibbotson and Chen (2006) set the return difference at 2.8 percent p.a. before accounting for backfill bias.

Whilst not quantifying the performance difference between survivors and defunct funds in the TASS database, Grecu et al. (2007) found that funds perform significantly worse shortly before they stop performing, suggesting that funds cease to report to database vendors due to inferior performance. More recently, Aggarwal and Jorion (2010) identified a previously unreported bias in TASS hedge fund returns due to the merger of Tremont with TASS. By subdividing the analysis in two sample periods (1994-2001 and 2002-2008), they found that the returns of the survived Tremont funds are on average 5.4 percent p.a. higher than those of the TASS pre-Tremont funds.

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Survivorship estimates for other databases vary considerably, depending on compositional differences in the databases, the methodologies used in constructing survivor and defunct portfolios, the inclusion of FoHFs, and the specified timeframe. For the HFR database, Ackermann, McEnally and Ravenscraft (1999) estimated average survivorship bias at approximately 0.2 percent per month. Edwards and Caglayan (2001) approximated that the performance difference between dead and surviving funds of the MAR database was as high as 1.9 percent p.a.

A further component to survivorship bias is look-ahead bias, which stems directly from the methodology employed: An ex-post analysis of hedge fund time series may suffer from implicit survivorship bias if funds are selected on the basis of their past track record. While funds entering the sample may be derived from survivor and graveyard databases, in an ex post framework, the study may still be biased towards hedge funds with a minimum number of consecutive return observations. Baquero, Horst and Verbeek (2005) placed look-ahead bias at 3.8 percent at the one-year horizon for the TASS database by estimating persistence in hedge fund returns. Since attrition is higher in hedge funds than in mutual funds, look-ahead bias is of more severe consequence.