Measurement Biases in Hedge Fund Performance Data: An Update

Tending to be static and single-database oriented, existing models for correcting performance measurement biases are unable to detect potential data errors arising from (1) hedge funds that migrate from one database vendor to another and (2) merged databases. In general, return measurement biases can be traced to two key events: when a hedge fund elects to enter one or more databases (backfill bias) and when a hedge fund exits a database (survivorship bias). Artificial rules (e.g., ignoring the first x number of months of performance history to minimize backfill bias) and survivorship statistics based on a single database vendor are susceptible to another form of bias as databases evolve and consolidate. The authors posit that one must be mindful of how much of the hedge fund industry one is observing before passing judgment on the performance statistics of the hedge fund industry as a whole.