There is an obscure corner of the theory of probability and statistics which describes the behavior of statistics such as the maximum monthly drop. Along the way I will show how to estimate the most likely size of the maximum monthly drop, and its sampling variability. I will give formulas that show exactly how unreliable an observed maximum monthly drop statistic really is. I would like to provide some theoretical support for this position, via an examination of a statistic that is closely related but much easier to analyze: the maximum monthly drop. He found considerable variation in the size of the maximum drawdown, and concluded from this that Eric Mintz is right, the maximum drawdown is too unreliable. Each time, he simulated 150 monthly returns of a screen with CAGR = 42 and GSD = 35, and calculated the maximum drawdown. GSD is a vastly superior predictor of drawdowns."Įlan Caspi illustrated his doubts about drawdowns by repeating an experiment 10 times. In the ensuing discussion, Eric Mintz replied, "The data we've seen so far supports the idea that the size of past drawdowns is a very poor predictor of future drawdowns. Drawdown, not volatility, is what we fear the most, second only to inadequate returns." For example, Len Kogan commented in 2002 that "Drawdown, along with -2 and -3sigma are better indicators than the usually observed standard deviation and Sharpe Ratio volatility indicators. Reliability of the Maximum Drawdown by Loren Cobbįrom time to time, mechanical investors have debated the question of the size and accuracy of the observed maximum drawdown of a screen.
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