Maximizing winning trades using a novel RSPOP fuzzy neural network intelligent stock trading system |
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Authors: | A Tan C Quek K C Yow |
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Affiliation: | (1) Centre for Computational Intelligence, School of Computer Engineering, Nanyang Technological University, Block N4 #2A-32, Nanyang Avenue, Singapore, 639798, Singapore |
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Abstract: | The increasing reliance on Computational Intelligence techniques like Artificial Neural Networks and Genetic Algorithms to
formulate trading decisions have sparked off a chain of research into financial forecasting and trading trend identifications.
Many research efforts focused on enhancing predictive capability and identifying turning points. Few actually presented empirical
results using live data and actual technical trading rules. This paper proposed a novel RSPOP Intelligent Stock Trading System,
that combines the superior predictive capability of RSPOP FNN and the use of widely accepted Moving Average and Relative Strength
Indicator Trading Rules. The system is demonstrated empirically using real live stock data to achieve significantly higher
Multiplicative Returns than a conventional technical rule trading system. It is able to outperform the buy-and-hold strategy
and generate several folds of dollar returns over an investment horizon of four years. The Percentage of Winning Trades was
increased significantly from an average of 70% to more than 92% using the system as compared to the conventional trading system;
demonstrating the system’s ability to filter out erroneous trading signals generated by technical rules and to preempt any
losing trades. The system is designed based on the premise that it is possible to capitalize on the swings in a stock counter’s
price, without a need for predicting target prices. |
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Keywords: | Intelligent stock trading system Rough-set based Maximizing winning trades Fuzzy neural networks Moving average Relative strength indicator Multiplicative returns |
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