19 Pages Posted: 24 Apr 2020 Last revised: 5 May 2020
Date Written: May 4, 2020
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, we begin by quantifying the probability of hitting a certain drawdown level, given various return distribution properties. Next, we 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, we show that drawdown-based rules can be used as a risk reduction technique, but this impacts both expected returns and risk.
Keywords: Trading strategies, alpha, outperformance, crowding, downside risk, skewness, hitting time, allocation, redemption, Type I error, Type II error, drawdown, Sharpe ratio, structural breaks, Corona crash, COVID-19 crash
JEL Classification: G11, G12, G17, G41, G23, C58
Suggested Citation: Suggested Citation