Answer-first summary for fast verification
Answer: The sample is too small
## Explanation Survivorship bias occurs when only successful funds that have survived are included in the analysis, while failed funds that went out of business are excluded. This leads to an upward bias in performance measures like Sharpe ratios. **Why option A is correct:** - Survivorship bias effectively reduces the sample size by excluding failed funds - The analysis only includes "surviving" funds, creating a non-representative sample - This makes the sample artificially small in terms of representing the true population of all funds (both successful and failed) **Why other options are incorrect:** - **B:** Historical window length is not directly related to survivorship bias - **C:** While risk metrics are important, this doesn't address the core issue of survivorship bias - **D:** This is a general warning about performance persistence, not a specific methodological criticism of survivorship bias Survivorship bias is a significant concern in hedge fund performance analysis because failed funds (which typically have poor performance) are systematically excluded from databases, making the remaining funds appear more successful than they actually are on average.
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A factor analysis of returns for hedge funds employing a equity market-neutral strategy produces strongly positive performance information for the strategy (for example, impressive Sharpe ratios). However, the analysis is guilty of neglecting the effects of survivorship bias. If the problem is survivorship bias, which of the following criticisms of the methodology is best?
A
The sample is too small
B
The historical window is too short
C
Risk metrics needs to be included along with return metrics
D
Past performance is no guarantee of future performance
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