MACHINE-LEARNING FOR DYNAMIC REVERSE ENGINEERING OF HEDGE FUNDS

摘要：

The leave-one-out cross-validation in nested sets of data models is traditionally considered in Machine Learning as the basic instrument of finding the most appropriate subset of features or regressors in pattern recognition and regression estimation.We extend the notion of a nested set of models onto the problem of time-varying regression estimation, which implies, in addition to the generic challenge of choosing the subset of regressors, also the inevitable necessity to choose the appropriate level of model volatility, ranging from the full stationarity of instant models in time to their absolute independence of each other.So, there are, at least, two axes of model nesting in the problem of nonsta-tionary regression estimation, first, the relevant size of the set of regressors and, second, the level of model volatility in time.We use the leave-one-out measure of the model fit as quality indicator along both nesting axes.We apply the proposed technique to analysis of a hedge funds returns and reverse-engineering its strategies.

关键词： Time-varying regression Subset of regressors Time volatility level Leave-one-out procedure Investment portfolio Hedge fund Dynamic style analysis

作者: MICHAEL MARKOV ILYA MUCHNIK VADIM MOTTL OLGA KRASOTKINA

作者单位: Markov Processes International, 428 Springfield Ave., Summit, NJ 07901, USA DIMACS, Rutgers University, P.O.Box 8018, Piscataway, NJ 08854, USA Computing Center of the Russian Academy of Sciences, Vavilov St.40, Moscow, 119991, Russian Federati Tula State University, Lenin Ave.92, Tula, 300600, Russian Federation

会议类型: 国际会议

会议名称: 2007 International Conference on Machine Learning and Cybernetics(IEEE第六届机器学习与控制论国际会议)

会议地点: 香港

会议语种:英文

页码: 2805-2811

在线出版日期: 2007-08-19（万方平台首次上网日期，不代表论文的发表时间）