Self-organization and associative memory: 3rd edition
Self-organization and associative memory: 3rd edition
A review and comparison of six reasoning methods
Fuzzy Sets and Systems
POPFNN: a pseudo outer-product based fuzzy neural network
Neural Networks
Neural Networks in the Capital Markets
Neural Networks in the Capital Markets
Rough Sets: Theoretical Aspects of Reasoning about Data
Rough Sets: Theoretical Aspects of Reasoning about Data
Time Series Analysis: Forecasting and Control
Time Series Analysis: Forecasting and Control
Neural Networks in Finance and Investing: Using Artificial Intelligence to Improve Real World Performance
Computers and Operations Research - Special issue: Emerging economics
Financial prediction and trading strategies using neurofuzzyapproaches
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
POPFNN-AAR(S): a pseudo outer-product based fuzzy neural network
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
IEEE Transactions on Neural Networks
Learning to trade via direct reinforcement
IEEE Transactions on Neural Networks
GenSoFNN: a generic self-organizing fuzzy neural network
IEEE Transactions on Neural Networks
Fuzzy associative conjuncted maps network
IEEE Transactions on Neural Networks
An inter-market arbitrage trading system based on extended classifier systems
Expert Systems with Applications: An International Journal
Stock trading with cycles: A financial application of ANFIS and reinforcement learning
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
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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.