Dominating Randomness - Applications of State Contingent Stochastic Ordering Methods to the Clustering and Performance Measurement of Trading Strategies
The rise in popularity of benchmark free and complex trading strategies throughout the last decade has made available a large variety of risk and performance profiles. As a consequence, to account for their complex performance characteristics, a lot of effort has been devoted to classify and value the performance of these strategies by the alterations of previous- or innovative measures. However, as most measures are often still simple path- and context independent statistics, most often the information provided proves inadequate to separate performance characteristics - as evidenced by the latest crisis. This paper provides a methodology that integrates the clustering and performance measurement of trading strategies in a context and preference based environment. It decomposes preferred performance characteristics into fragments of context dependent behaviour for clustering purposes. It subsequently aggregates these fragments of performance characteristics into a performance measure. The methodology allows for consideration of path dependencies. Two applications, in the clustering of hedge fund styles and the ordering of alternative equity strategies are given. A further application in the statistical replication of trading strategies is highlighted.