22 Pages Posted: 22 Dec 2010 Last revised: 4 Jan 2011
Date Written: December 22, 2010
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.
Keywords: Clustering methods, performance measure, hedge funds, stochastic dominance
JEL Classification: C14, C32, C51, C61, C81, G11
Suggested Citation: Suggested Citation
Glaffig, Clemens, Dominating Randomness - Applications of State Contingent Stochastic Ordering Methods to the Clustering and Performance Measurement of Trading Strategies (December 22, 2010). Available at SSRN: https://ssrn.com/abstract=1729726 or http://dx.doi.org/10.2139/ssrn.1729726