Therefore, when evaluating the returns of mutual funds (regardless of the time horizon), it is important to consider all mutual funds that meet the criteria for the study.

The net effect would be positively-skewed results that would fail to accurately depict the actual returns realized by all mutual funds. However, during the recession that the funds have survived, other mutual funds were likely forced to shut down due to poor performance. The mutual funds have survived difficult economic conditions such as a recession through either their composite breakdown or the ability of the fund’s management to react to the market and adjust their investment strategy. Going back to the mutual fund returns example above, a study that exhibits survivorship bias can skew the returns positively as it only considers mutual funds that are currently in existence. The bias occurs because the “surviving” observations often tend to have survived due to their stronger-than-average resilience to difficult conditions, and leaves out other observations that have ceased to exist as a result of such conditions. Generally speaking, survivorship bias tends to create conclusions that are overly optimistic, and that may not be representative of real-life environments. In such a case, financial analysts base their evaluation solely on companies that currently exist in the market. Another example of survivorship bias could occur when studies on the profitability of certain industries fail to include financial information about acquired or bankrupt companies. The mutual funds could’ve been closed for a variety of reasons such as mergers and acquisitions, restructuring, or poor financial performance.

In finance, an example of survivorship bias is when studies on mutual fund returns only use databases that contain data about mutual funds that currently exist, and fail to include data about funds that are no longer existing. Survivorship bias is a type of sample selection bias that occurs when a data set only considers “surviving” or existing observations and fails to consider observations that already ceased to exist.

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