Forecasting Foreign Stock Index Futures: An Application of Neural Networks

• Main Authors: Lai, Charles C., 賴俊霖
• Other Authors: Ray Tsaih, Yenshan Hsu
• Format: Others
• Language: zh-TW
• Published: 1996
• Online Access: http://ndltd.ncl.edu.tw/handle/06505677755300294661

碩士 === 國立政治大學 === 資訊管理學系 === 84 === This research adopts a hybrid approach to implementing the trading strategies in the S&P 500 index futures market. The hybrid approach integrates both the rule-based systems technique and the neural networks technique. Our methodology is different from previous studies in two aspects. First, we employ Reasoning Neural Networks (RN) instead of back propagation networks to resolve the undesired predicaments of local minimum and the unknown of the number of hidden nodes. Second, the rule-based systems approach is applied to provide neural networks with good training examples. We, first, categorize the daily conditions of the futures market into a variety of cases through processing futures historical data. Then, the dual-forecast models, FFM (futures forecast model) and EFFM (extended futures forecast model), are proposed to predict the direction of daily price changes. The rule-based model, FFM, is designed to deal with the obvious cases and to provide the neural network-based model, EFFM, with good training examples. Meanwhile, EFFM, which consists of four RNs and a voting mechanism, is designed to handle the non-obvious cases. The simulation results show that the cooperation of FFM and EFFM does a good job in predicting the direction of daily price change of S&P 500 index futures. Based on FFM and EFFM, the integrated futures trading system (IFTS) is developed and employed to trade the S&P 500 index futures contracts. The results show that IFTS outperforms the passive buy-and-hold investment strategy over the six-year testing period from 1988 to 1993.