How should investors think about maximum drawdown (MaxDD) as a portfolio/strategy performance metric? In their April 2020 paper entitled “Drawdowns”, Otto Van Hemert, Mark Ganz, Campbell Harvey, Sandy Rattray, Eva Martin and Darrel Yawitch examine usefulness of MaxDD for portfolio/strategy performance evaluation. They first quantify how MaxDD relates to key return statistics based on 100,000 simulations of monthly returns for each variation. They then investigate use of MaxDD for detecting portfolio/strategy failure due to strategy crowding or other market changes. Finally, they assess MaxDD-based rules for portfolio risk reduction. Using pure simulations and simulations based on actual U.S. stock market monthly returns since 1926, they find that:
- Key determinants of MaxDD risk are evaluation horizon (time to dig a hole, with longer worse), Sharpe ratio (ability to climb out of a hole, with lower worse) and autocorrelation of returns (streakiness, with more worse). Simulations based on an ideal (normal, with zero autocorrelation) return distribution indicate:
- For a 10-year horizon, 10% annualized volatility and 0.5 annualized Sharpe ratio, probabilities of experiencing -10%, -20%, -30% and -40% MaxDDs are 97.1%, 43.0%, 9.9% and 1.5%, respectively.
- Increasing (decreasing) the horizon naturally raises (lowers) probabilities.
- Changing volatility, with Sharpe ratio held constant, has little effect on probabilities.
- Increasing (decreasing) Sharpe ratio, with volatility held constant, lowers (raises) probabilities.
- Increasing (decreasing) autocorrelation of monthly returns raises (lowers) probabilities. Increasing autocorrelation from zero to 0.10 has about the same effect as reducing Sharpe ratio from 0.5 to 0.4.
- Simulations based on actual U.S. stock market monthly returns since 1926 increases MaxDD probabilities compared to the ideal case due to both non-normality of monthly returns and clustering of volatility (for example, from 43% to 55% for MaxDD -20% over 10 years).
- The probability of a deep MaxDD also increases with the likelihood of sharp (gap) moves in monthly returns, but the impact is less important than those for investment horizon and Sharpe ratio.
- Drawdown-based risk management rules are reasonable under the belief that portfolio/strategy performance tends to degrade over time due to crowding/market adaptation, because they emphasize recent history. An example of such a rule is 50% reduction in allocation to a portfolio if its MaxDD falls below some threshold, with full allocation restored after recovery of half the drawdown. However, failure to compensate for MaxDD-based risk reduction by increasing risk in some other investment generally leads to lower overall expected return.
- A time-varying MaxDD drawdown rule is advisable, with the threshold decreasing over time to recognize an increasing probability of drawdown.
In summary, simulations indicate that MaxDD-based rules with thresholds decreasing over time are useful for portfolio/strategy risk management under the belief that performance tends to degrade over time.
Cautions regarding findings include:
- Findings derive from simulations rather than actual portfolio/strategy performance data.
- It may be psychologically difficult for investors to accept deeper and deeper MaxDD risk management thresholds over time.
For some related research, see results of this search.
