Survivorship bias


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Survivorship bias

Usually pertaining to fund manager or individual investor performance. Suppose we examined the performance over the last ten years of a group of managers that exist today. This performance is biased upwards because we are only considering those that survived for 10 years. That is, some dropped out because of poor performance. Hence, in evaluating performance, one has to be careful to include both the current and the managers that dropped out of the sample due to poor performance.

Survivorship Bias

In finance, the tendency to exclude failed companies or managers from performance evaluations or studies simply because they do not exist. Survivorship bias can result in skewed findings in a study and lead a casual reader to believe that a study shows a rosier picture than it really does. Mutual funds, especially smaller ones, are especially susceptible to survivorship bias. At any given time, 90% of mutual funds will claim to be in the top 25% of performers. Technically, they are correct, but only because the other 75% have closed or merged. Manager universe comparisons have also been criticized for exhibiting signs of survivorship bias. It is also known as survivor bias.
References in periodicals archive ?
The SS&C GlobeOp Hedge Fund Performance Index is transparent, consistent in data processing, and free from selection or survivorship bias.
The tests were carefully constructed to be free of both look-ahead and survivorship bias.
Survivorship bias in marketing material is often high.
Investigators included living and deceased patients to avoid survivorship bias.
Malkiel (1995) presents two important findings for a sample of equity mutual funds examined from 1971-1991: in the aggregate, funds underperformed the market, with the S&P 500 as the benchmark and significant survivorship bias exists, which may be leading to erroneous finds of performance persistence.
Besides using more robust performance evaluation techniques (conditional multi-factor models) to analyse overall performance as well as timing and selectivity abilities of fund managers, the analysis is complemented with robustness tests to check for possible effects that may arise from survivorship bias, management fees and spurious regressions.
Ackermann, McEnally, and Ravenscraft (1999) pointed out that hedge fund databases have survivorship bias, liquidation bias, backfill bias, and selection bias.
Survivor Bias Free Database -- data is available on inactive companies allowing researchers to remove survivorship bias from their research.
Aharony and Falk (1992) test for the presence of survivorship bias arising out of the non-inclusion of failed firms.
The results can be quite sensitive to survivorship bias, as well as to how one treats firms that disappear from the datasets.
Finally, there may be a serious survivorship bias with respect to US data.
The difference in returns is actually understated, due both to survivorship bias (poorly performing funds disappeared from the data) and the impact of taxes on fund distributions.