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Nonlinear trading models through Sharpe Ratio maximization

M Choey1, A S Weigend

  • 1Advanced Technology Group, Siemens Nixdorf Information Systems, Inc., Burlington, MA 01803, USA. choey@ix.netcom.com

International Journal of Neural Systems
|August 1, 1997
PubMed
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This study introduces a novel nonlinear trading strategy that directly maximizes the Sharpe Ratio, a key measure of risk-adjusted performance. This approach outperforms traditional methods in optimizing investment returns on real-world financial data.

Area of Science:

  • Quantitative Finance
  • Machine Learning in Finance
  • Algorithmic Trading

Background:

  • Traditional trading strategies often focus on price prediction, which may not align with traders' primary goal of optimizing risk-adjusted returns.
  • The Sharpe Ratio is a critical metric for evaluating investment performance relative to risk.
  • Existing methods may not effectively capture the complex, nonlinear dynamics inherent in financial markets.

Purpose of the Study:

  • To develop and evaluate a novel nonlinear trading strategy designed to explicitly maximize the Sharpe Ratio.
  • To implement this strategy using a neural network model for determining optimal asset allocation.
  • To compare the performance of Sharpe Ratio maximization against profit optimization and probability matching strategies.

Main Methods:

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  • A neural network model was developed to output position sizes between risky and risk-free assets.
  • Iterative parameter update rules were derived for the neural network.
  • The strategy was rigorously tested and analyzed using both simulated and real-world financial data, including the German DAX index.

Main Results:

  • The Sharpe Ratio maximization strategy demonstrated superior performance compared to profit optimization and cross-entropy optimization (probability matching).
  • The nonlinear approach successfully optimized out-of-sample risk-adjusted profits.
  • The neural network model effectively generated optimal trading positions based on the maximization objective.

Conclusions:

  • Explicitly maximizing the Sharpe Ratio is an effective strategy for improving risk-adjusted investment performance.
  • Nonlinear models, such as the proposed neural network, can successfully implement Sharpe Ratio maximization in financial trading.
  • This approach offers a promising alternative to traditional trading strategies for achieving superior risk-adjusted returns.