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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.
