Apply Integrated Classification Model to Taiwan Stock Trend Prediction

• Main Authors: Yu-Chun Liao, 廖祐君
• Other Authors: Chein-Shung Hwang
• Format: Others
• Language: zh-TW
• Online Access: http://ndltd.ncl.edu.tw/handle/64121132701333969249

碩士 === 中國文化大學 === 資訊管理研究所 === 98 === In the era of low interest rate, how does one learn to invest and manage the wealth? How to retain the capital on hands and not let it depreciate through inflation? Effective investment suddenly becomes the subject that everyone cares about. Adage like “The accumulation of wealth starts from investment and wealth management” is well said under backdrop of high conscientiousness for wealth management nowadays. Since investment merchandises have considerably been diversified and stock market naturally becomes one of the most popular targets for investment monies. Nonetheless, how to hedge the risk so as to locate the best timing for targeting your investment, to acquire high investment returns and low risk exposures as the outcomes for wealth management, would be the absolute necessities for all investor studies. Therefore, investors not only select the stocks of their own interest as the stock investment targets, but also will jointly face the dilemma of when for the actual transactions to take place. Hence, all kinds of technical analysis methods for stock market are widely researched and applied as well. This research employs a variety of technical indicators in the sense of complementing each other. In addition, this research also intends to locate the relevancies between technical indicators and the market trends, and predict the timing for adequate investment between the relationships from a variety of these indicators. During research, Bayesian, SVM, KNN classifications were established accordingly. And these three classification algorithm modules were used to predict the validity of timing for the buy-transaction in the stock market. Lastly, the composite multiple classification module will be used to predict the time points for buy and sell transactions so as to facilitate the investors in increasing the investment returns in addition to lower the unnecessary risks. This research has been proven through lab experiments that, in the performance for integrated classification model used in the research, the average return ratio for integrated prediction for the market index is better than that from using the single classifier approach. Within the investment strategies selected by voting method, for those aggressive investment strategies that invest during identical large categories, the respective trading lasts 279 days with 12.88 points gain of the market index during each transaction. And those conservative types of investment strategy lasting 87 days which would trade during identical small categories, they managed with 27.6 points gain of the market index. We can see that all of these can effectively facilitate the investors in making the right decisions so as to prevent from exposures to unnecessary investment risks.