Refined group learning based on XCS and neural network in intelligent financial decision support system

Cooperative learning is widely defined as a process through which a group of individuals interact to achieve a learning goal. In the fluctuating stock market, investors often have various decision-making approaches. This study attempts to exploit computer technology, financial mathematics, and econometrics to make reasonable investment decisions to reduce man-made errors or mistakes and increase profits. This work integrates the extended Classifier System (XCS) and neural network modules and incorporates 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 deposit based on the Taiwan Index. The proposed system demonstrates superior performance in terms of accuracy, rate of cumulative return, and variance of return.