| Summary: | 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.
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