Abstract
Most models for prediction of the stock market focus on individual securities. In this paper we introduce a rank measure that takes into account a large number of securities and grades them according to the relative returns. It turns out that this rank measure, besides being more related to a real trading situation, is more predictable than the individual returns. The ranks are predicted with perceptrons with a step function for generation of trading signals. A learning decision support system for stock picking based on the rank predictions is constructed. An algorithm that maximizes the Sharpe ratio for a simulated trader computes the optimal decision parameters for the trader. The trading simulation is executed in a general purpose trading simulator ASTA. The trading results from the Swedish stock market show significantly higher returns and also Sharpe ratios, relative the benchmark.
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© 2001 Springer-Verlag Berlin Heidelberg
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Hellström, T. (2001). Optimizing the Sharpe Ratio for a Rank Based Trading System. In: Brazdil, P., Jorge, A. (eds) Progress in Artificial Intelligence. EPIA 2001. Lecture Notes in Computer Science(), vol 2258. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-45329-6_16
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DOI: https://doi.org/10.1007/3-540-45329-6_16
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