We examine the robustness of approaches to noise in the data and analyse out-of-sample performance using 613 live and 1,384 defunct Commodity Trading Advisors over the 1993-2015 period within the simulation framework of Molyboga and L’Ahelec (2016) 2 that incorporates the realistic constraints faced by institutional investors.įirst, we investigate the issue of sensitivity of drawdown-based approaches to noise in the data by generating 1,000 random scenarios, calculating weights that minimise each drawdown-based measure and evaluating the weights against the true optimal weights. In our research paper ‘Portfolio management with drawdown-based measures,’ forthcoming in the Journal of Alternative Investments, we present results of a comprehensive study of both established and new drawdown-based approaches and the portfolio implications of using drawdowns in allocation decisions. However, there is a lack of thorough quantitative evaluation of whether drawdown-based approaches to portfolio management positively contribute to performance. In fact, ‘Best practices in alternative investments: due diligence’ (2010) 1 require drawdown analysis as part of quantitative due diligence. Institutional investors make investment decisions based on a variety of measures of risk and risk-adjusted performance with maximum historical drawdown, defined as the largest peak-to-valley loss, among the most popular measures.
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