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.