What type and size of drawdown should cause you to change an investment manager? We offer five main conclusions.

Common risk metrics reported in academia include volatility, skewness, and factor exposures. The maximum drawdown statistic is rarely calculated, perhaps because it is path dependent and estimated with greater uncertainty. In practice, however, asset managers and fiduciaries routinely use the drawdown statistic for fund allocation and redemption decisions. To help such decisions, the authors begin by quantifying the probability of hitting a certain drawdown level, given various return distribution properties. Next, they show that drawdown-based rules can be particularly useful for improving investment performance over time by detecting managers that lose their ability to outperform. This can happen as a result of structural market changes, increased competition for the type of strategy employed, staff turnover, or a fund accumulating too many assets. Finally, they show that drawdown-based rules can be used as a risk reduction technique, but this affects both expected returns and risk.


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