engleski

Particle Filters in Decision Making Problems under Uncertainty

In problems of decision making under uncertainty, we are often faced with the problem of representing the uncertainties in a form suitable for quantitative models. Huge databases for the financial system now exist that facilitate the analysis of uncertainties representation. In portfolio management, one has to decide how much wealth to put in each asset. In this paper we present a decision making process that incorporates particle filters and a genetic algorithm into a state dependent dynamic portfolio optimization system. We propose particle filters and scenario trees as a means of capturing uncertainty in future asset returns. Genetic algorithm was used as an optimization method in scenario generation, and for determining the asset allocation. The proposed method shows better results in comparison with the standard mean variance strategy according to Sharpe ratio.

Uncertainty representation; Particle filters; Scenario trees

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