Paper Info
Title
Nonlinear Trading Models Through Sharpe Ratio Maximization
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.
Year
DOI
Venue
1997
10.1142/S0129065797000410
INTERNATIONAL JOURNAL OF NEURAL SYSTEMS
Keywords
DocType
Volume
sharpe ratio
Journal
8
Issue
ISSN
Citations 
4
0129-0657
11
PageRank 
References 
Authors
1.30
2
2
Name
Order
Citations
PageRank
M Choey1111.30
Andreas S. Weigend2576112.30