Posts Tagged ‘Maximum Drawdown’

In a new paper, Using Maximum Drawdowns to Capture Tail Risk, my Quantitative Value co-author Wes Gray and Jack Vogel propose a new easily measurable and intuitive tail-risk measure that they call “maximum drawdown.” Maximum drawdown is the maximum peak-to-trough loss across a time series of compounded returns. From the abstract:

We look at maximum drawdowns to assess tail risks associated with market neutral strategies identified in the academic literature. Our evidence suggests that academic anomalies are not anomalous: all strategies endure large drawdowns at some point in the time series. Many of these losses would trigger margin calls and investor withdrawals, forcing an investor to liquidate.

The authors apply their maximum drawdown metric to existing studies, for example, momentum anomaly originally outlined in Jegadeesh and Titman (1993) to demonstrate why maximum drawdown adds to the analyses:

Jegadeesh and Titman claim large alphas associated with long/short momentum strategies over the 1965 to 1989 time period. What these authors fail to mention is that the long/short strategy endures a 33.81% holding period loss from July 1970 until March 1971. When we look out of sample from 1989 to 2012, there is still significant alpha associated with the long/short momentum strategy, but the strategy endures an 86.05% loss from March 2009 to September 2009. An updated momentum study reporting alpha estimates would claim victory, an investor engaged in the long/short momentum strategy would claim bankruptcy. Tail risk matters to investors and it should matter in empirical research.

Gray and Vogel examine maximum drawdowns for eleven long/short academic anomalies:

When looking at the worst drawdown in the history of the long/short return series, we find that 6 of the 11 strategies have maximum drawdowns of more than 50%. The Oscore, Momentum, and Return on Assets, endure maximum drawdowns of 83.50%, 86.05% and 84.71%, respectively! These losses would trigger immediate margin calls and liquidations from brokers. We do find that Net Stock Issuance and Composite Issuance limit maximum drawdowns, with maximum drawdowns of 29.23% and 26.27%, respectively. If a fund employed minimal leverage, a fund implementing these strategies would likely survive a broker liquidation scenario.

In addition to broker margin calls and liquidations, investment managers face liquidation threats from their investors. Liquidations occur for two primary reasons: there are information asymmetries between investors and investment managers, and 2)investors rely on past performance to ascertain expected future performance (Shleifer and Vishny (1997)). To understand the potential threat of liquidation from outside investors, we examine the performance of the S&P 500 during the maximum drawdown period and the twelve month drawdown period for each of our respective academic anomalies. In 9/11 cases, the S&P 500 has exceptional performance during the largest loss scenarios for the value-weighted long/short strategies. In the case of the Net Stock Issuance and the Composite Issuance anomaly—the long/short strategies with the most reasonable drawdowns—the S&P 500 returns 56.40% and 49.46% during the respective drawdown periods. One can conjecture that investors would find it difficult to maintain discipline to a long/short strategy when they are underperforming a broad equity index by over 75%. Stories about the benefits of “uncorrelated alpha” can only go so far.

Gray and Vogel find that maximum drawdown events are often followed by exceptional performance for the strategy examined:

One prediction from this story is that returns to long/short anomalies are high following terrible performance. We test this prediction in Table 5. We examine the returns on the 11 academic anomalies following their maximum drawdown event. We compute three-, six-, and twelve-month compound returns to the long/short strategies immediately following the worst drawdown. The evidence suggests that performance following a maximum drawdown event is exceptional. All the anomalies experience strong positive returns over three-, six-, and twelve-month periods following the drawdown event.

Read Using Maximum Drawdowns to Capture Tail Risk.

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