| Summary: | 博士 === 國立交通大學 === 資訊管理研究所 === 94 === Artificial intelligence has been applied in numerous studies to predict stock trends after years of development. The fluctuating and chaotic nature of the stock market has long been a topic of interest for investors and financial researchers. As most learning models form the past studies were based on trial and error, they produced unsatisfactory performances in terms of efficiency and accuracy, or could only be applied in a closed form environment. Since the power of computers has been improved tremendously in recent years, the learning model of artificial intelligence should also be re-examined to improve decision quality.
This study built an intelligent group learning model based on the group learning concept in human behavior in traditional learning theories. Cooperative learning is widely defined as the process through which a group of individuals interact to achieve their goal. In the fluctuating stock market, investors often have various decision making approaches. This work integrates eXtended Classifier System (XCS) and neural network modules incorporating features such as dynamic learning and group decision making. An empirical study is conducted by comparing the profitability of the proposed system with that of investment strategies based on simple rules with single technical indices, individual learning XCS, buy and hold, and six-year term deposits based on the Taiwan Index. The proposed system demonstrates superior performance in terms of accuracy and the rate of cumulative return.
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