Appendix to 

Do Hot Hands Exist Among Hedge Fund Managers? An Empirical Evaluation

 

Abstract

The backfill bias is an important feature of hedge fund databases – the case when hedge funds bring all the history with them when they join a database. We describe a backfill bias correction methodology that could be applied if the exact length of the backfill period is unknown.

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Due to an unregulated nature of hedge funds, any rigorous research about hedge fund performance has to overcome numerous biases and irregularities in the available data. There are no legal requirements for hedge funds to report performance numbers. However, there are several different databases, to which hedge funds provide information about themselves on a voluntarily basis (we use the HFR database1 in the paper). Several papers discuss the issues related to hedge fund data, for example Ackerman, McEnally, and Ravenscraft (1999), Liang (2000), Fung and Hsieh (2000) and Fung and Hsieh (2002). Most recently, Titman and Tiu (2011) employed methodology for the backfill bias correction developed in Jagannathan, Malakhov, and Novikov (2010), spelled out below.

Here we demonstrate the importance of proper correction for the backfill bias in analyzing hedge fund relative performance. The backfill bias is an important feature of a hedge fund database – the case when hedge funds bring all the history with them when they join a database. Arguably the major reason to why a hedge fund would like to publish its returns, is for advertising purposes in order to attract additional investments.2 A hedge fund can indirectly advertise itself by publishing its relatively good past returns. The returns of such a hedge fund could be higher than the average returns of other hedge funds following the same strategy. Since only funds with relatively superior historical performance enter a database, when possible backfilling of data is ignored, this procedure introduces a bias toward¬†mistakenly assigning superior ability to managers of funds in their earlier years. Since our HFR data contains the information on when funds actually joined the database, we are able to eliminate the backfill bias by deleting all the backfill observations in our dataset. However, to highlight the importance of the complete backfill correction, we describe an alternative backfill bias correction methodology that could be applied if the exact length of the backfill period is…