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Boosting-Based Framework For Portfolio Strategy Discovery And Optimization

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  • VALERIY V. GAVRISHCHAKA

    (Head of Quantitative Research, Alexandra Investment Management, 767 3-d Avenue New York, New York 10017, USA)

Abstract

Increasing availability of the multi-scale market data exposes limitations of the existing quantitative models such as low accuracy of the simplified analytical and statistical frameworks as well as insufficient interpretability and stability of the best machine learning algorithms. Boosting was recently proposed as a simple and robust framework for intelligent combination of the clarity and stability of the analytical and parsimonious statistical models with the accuracy of the adaptive data-driven models. Encouraging results of the boosting application to symbolic volatility forecasting have also been reported. However, accurate forecasting does not always warrant optimal decision making that leads to acceptable performance of the portfolio strategy. In this work, a boosting-based framework for a direct trading strategy and portfolio optimization is introduced. Due to inherent adaptive control of the parameter space dimensionality, this technique can work with very large pools of base strategies and financial instruments that are usually prohibitive for other portfolio optimization frameworks. Unlike existing approaches, this framework can be effectively used for the coupled optimization of the portfolio capital/asset allocation and dynamic trading strategies. Generated portfolios of trading strategies not only exhibit stable and robust performance but also remain interpretable. Encouraging preliminary results based on real market data are presented and discussed.

Suggested Citation

  • Valeriy V. Gavrishchaka, 2006. "Boosting-Based Framework For Portfolio Strategy Discovery And Optimization," New Mathematics and Natural Computation (NMNC), World Scientific Publishing Co. Pte. Ltd., vol. 2(03), pages 315-330.
    • Handle: RePEc:wsi:nmncxx:v:02:y:2006:i:03:n:s1793005706000506
    DOI: 10.1142/S1793005706000506
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    References listed on IDEAS

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    1. Francis Eng-Hock Tay & Lixiang Shen & Lijuan Cao, 2003. "Ordinary Shares, Exotic Methods:Financial Forecasting Using Data Mining Techniques," World Scientific Books, World Scientific Publishing Co. Pte. Ltd., number 5027, December.
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    Cited by:

    1. Kim, Min Jae & Kim, Sehyun & Jo, Yong Hwan & Kim, Soo Yong, 2011. "Dependence structure of the commodity and stock markets, and relevant multi-spread strategy," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 390(21), pages 3842-3854.

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