Nonlinear trading models through Sharpe Ratio maximization.

M Choey; A S Weigend

doi:10.1142/s0129065797000410

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Nonlinear trading models through Sharpe Ratio maximization.

M Choey·A S Weigend

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Abstract

While many trading strategies are based on price prediction, traders in financial markets are typically interested in optimizing risk-adjusted performance such as the Sharpe Ratio, rather than the price predictions themselves. This paper introduces an approach which generates a nonlinear strategy that explicitly maximizes the Sharpe Ratio. It is expressed as a neural network model whose output is the position size between a risky and a risk-free asset. The iterative parameter update rules are derived and compared to alternative approaches. The resulting trading strategy is evaluated and analyzed on both computer-generated data and real world data (DAX, the daily German equity index). Trading based on Sharpe Ratio maximization compares favorably to both profit optimization and probability matching (through cross-entropy optimization). The results show that the goal of optimizing out-of-sample risk-adjusted profit can indeed be achieved with this nonlinear approach.

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Algorithms Artificial Intelligence Data Interpretation, Statistical Models, Economic Neural Networks, Computer Nonlinear Dynamics Journal Article Research Support, U.S. Gov't, Non-P.H.S.

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M Choey, A S Weigend. 1997. Nonlinear trading models through Sharpe Ratio maximization.. https://doi.org/10.1142/s0129065797000410

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