| Summary: | 碩士 === 國立交通大學 === 資訊管理研究所 === 94 === The characteristics of financial market are nonlinear and semi structure. Thus, the behavior of the dynamic market is difficult to catch by using static approaches. Furthermore, artificial intelligence was widely applied to solve financial problem due to its flexible. Extended Classifier Systems (XCS) is a novel methodology in artificial intelligence, which consists of machine learning and reinforcement learning technique that can be used to interact with a given environment. By the generalization and online learning ability of XCS, it can generate initial rules from training data and keep evolving rules in testing environment. In this study, XCS with continuous-valued inputs (XCSR) is applied to develop a portfolio construction model which can adapt to the dynamic financial market.
The experiment is designed to demonstrate the predictive ability of XCSR. The investing targets of this research are Taiwan 50 index constituents. Five technical indicators are taken as input factors. The simulation and statistical results show that XCSR portfolio construction model is able to achieve a positive excess return in out-of-sample simulated trading. The performance of XCSR model is obviously superior to other investing strategies and that market return, and XCSR portfolio construction model can be concluded which is suitable for fund managers to manage the portfolio of equity fund.
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