Nonlinear trading models through Sharpe Ratio maximization.
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.
Choey
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Advanced Technology Group, Siemens Nixdorf Information Systems, Inc., Burlington, MA 01803, USA. choey@ix.netcom.com
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D000465:Algorithms; D001185:Artificial Intelligence; D003627:Data Interpretation, Statistical; D018803:Models, Economic; D016571:Neural Networks, Computer; D017711:Nonlinear Dynamics