Machine learning for stock selection
Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining
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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.