Forecasting S&P 500 stock index futures with a hybrid AI system

Abstract

This study presents a hybrid AI (artificial intelligence) approach to the implementation of trading strategies in the S&P 500 stock index futures market. The hybrid AI approach integrates the rule-based systems technique and the neural networks technique to accurately predict the direction of daily price changes in S&P 500 stock index futures. By highlighting the advantages and overcoming the limitations of both the neural networks technique and rule-based systems technique, the hybrid approach can facilitate the development of more reliable intelligent systems to model expert thinking and to support the decision-making processes. Our methodology differs from other studies in two respects. First, the rule-based systems approach is applied to provide neural networks with training examples. Second, we employ Reasoning Neural Networks (RN) instead of Back Propagation Networks. Empirical results demonstrate that RN outperforms the other two ANN models (Back Propagation Networks and Perceptron). Based upon this hybrid AI approach, the integrated futures trading system (IFTS) is established and employed to trade the S&P 500 stock index futures contracts. Empirical results also confirm that IFTS outperformed the passive buy-and-hold investment strategy during the 6-year testing period from 1988 to 1993.