I was reading an article recently that pointed out the dangers of using statistical inference in evaluating active managers as the power of statistical tests diminishes with the variance of the underlying data. I can understand the case for requiring more stringent tests like White's reality check, but could you ever make the case that tests for statistical significance should have a lower bar or that a t-test is not useful in evaluating active managers?


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The main problem with hypothesis testing is going to be survivorship bias. Any manager with a track record you're looking at is only there because they haven't performed badly -- if they perform badly then investors withdraw their money, they collapse, and you don't have their data to do the hypothesis test on! So even if all the managers were investing in geometric Brownian motions then survivorship bias will make a lot of managers for which there is sufficient data pass the skill hypothesis test with flying colours.

The other issue is that most of the data you'll be able to get is monthly. You need to do a power estimation of your test if you're using monthly data.


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