Applying Learning Classifier System to Stock Market Based on Institutional Analysis

• Main Author: 林如茵
• Other Authors: 陳安斌
• Format: Others
• Language: en_US
• Published: 2004
• Online Access: http://ndltd.ncl.edu.tw/handle/21838888984102726284

碩士 === 國立交通大學 === 資訊管理研究所 === 92 === Artificial intelligence technique in stock market fluctuation evaluation has been documented in other papers and applied in many application domains. Based on previous surveys, to accurately handle the uncertainties and variations of the environment is a significant task. The thesis tries to probe into the judgment of future price trend on the basis of the institutional factors. Firstly, the traditional statistical tests are performed to discover whether there exists a relationship between the experimental indicators or not. The correlation analysis is performed on the five indicators as the condition part and future price trend. However, the experiment indicates low or non correlation between the stock price and the institutional factors. On the other hand, using institutional indicators as the simulated factors on the stock market has been proved to be quite persuasive in many documents. Thus, the simple regression analysis is used to perform for the prediction of the future price trend. However, according to the results, the future price trend cannot be accurately predicted based on the historical data. Though the consequences are unsatisfactory, the selected institutional indicators are certainly valuable for the dynamic stock market. The poor outcome has to be understood and discussed for discovering the statistical pitfalls. Therefore, the thesis attempts to apply learning classifier system (LCS), which is intended as a rule-based framework that integrates the concept of genetic algorithms, to learn and interact with the stock market environment. There are a large number of elements affecting the stock environment, selecting the most significant ones is capable of making the best investment strategies and improving the system performance. As mentioned above, the thesis focuses on the institutional indicators for modeling the behaviors in such complex environment to help investors obtaining optimal and satisfactory profits. Surveys on the selected institutional indicators, such as buy/sell of qualified foreign institutional investors (QFII), buy/sell of securities investment trusts, balance of margin purchasing, balance of short selling, and trading volume, show that market prices can be directly influenced by the above-mentioned indexes. The promising results demonstrate that, by implementing the LCS model, the rules that are discovered can be utilized to make investment strategies with progressive benefits. The statistical pitfalls might be occurred due to the incapability of understanding and modeling the uncertainties in such situation. However, the learning classifier system is capable of taking the complicated factors into account for discovering the unknown behaviors and learning the inward knowledge of the environment.