What is a 'Maximum Drawdown (MDD)'
A maximum drawdown (MDD) is the maximum loss from a peak to a trough of a portfolio, before a new peak is attained. Maximum Drawdown (MDD) is an indicator of downside risk over a specified time period. It can be used both as a stand-alone measure or as an input into other metrics such as "Return over Maximum Drawdown" and Calmar Ratio. Maximum Drawdown is expressed in percentage terms and computed as:
(Trough Value – Peak Value) ÷ Peak Value
BREAKING DOWN 'Maximum Drawdown (MDD)'
Consider an example to understand the concept of maximum drawdown.
Assume an investment portfolio has an initial value of $500,000. The portfolio increases to $750,000 over a period of time, before plunging to $400,000 in a ferocious bear market. It then rebounds to $600,000, before dropping again to $350,000. Subsequently, it more than doubles to $800,000. What is the maximum drawdown?
The maximum drawdown in this case is = ($350,000 – 750,000) / $750,000 = –53.33%
Note the following points:
- The initial peak of $750,000 is used in the MDD calculation. The interim peak of $600,000 is not used, since it does not represent a new high. The new peak of $800,000 is also not used since the original drawdown began from the $750,000 peak.
- The MDD calculation takes into consideration the lowest portfolio value ($350,000 in this case) before a new peak is made, and not just the first drop to $400,000.
MDD should be used in the right perspective to derive the maximum benefit from it. In this regard, particular attention should be paid to the time period being considered. For instance, a hypothetical long-only U.S. fund Gamma has been in existence since 2000, and had a maximum drawdown of -30% in the period ending 2010. While this may seem like a huge loss, note that the S&P 500 had plunged more than 55% from its peak in October 2007 to its trough in March 2009. While other metrics would need to be considered to assess Gamma fund's overall performance, from the viewpoint of MDD, it has outperformed its benchmark by a huge margin.