A Study of Using Extended Learning Classifier System on Price-Difference Behavior of Overnight － an Example of Taiwan Weighted Index
|關鍵字:||隔夜價差;隔夜效應;分類元系統;overnight price-difference;overnight effect;classifier system|
This study attempted to applied the artificial intelligence approach, extend learning classifier system (XCS) with dynamical learning ability, on overnight price-difference behavior for stock market analysis. The restriction on the trading time in stock market brings the investment risks and the overnight risk which is effected investors who make any bargain during the close time of this today to the opening of the following trading day. By the way, it was mightily interrelated with the trend prediction of the stock market and the investment profit. Investors often could not achieve the better profits due to they did not know this phenomenon and then must take hedging or to stop the profits while uncertainly situations rise. However, investors will obtain more portfolios by this overnight price-difference, and then they will take the better investment to offset or to hedge on that right time. Thus, this study built up a model to predict the trend of the stock market. The input factors of this model have been comprised not only the relative information between two global stock markets but also the autocorrelations of long term and short term of stock trading data. By the generalization and association abilities of XCS, it is applied in this study to produce some rules of the behavior patterns by trained from historical trading data. After that, to evaluate the performance of the model is using these rules as investment strategies, to the empirical experiments. Meanwhile, it’s accumulated profits with four kinds of strategies. The result has demonstrated that the overnight effect indeed effected the Taiwan economic market, and the proposed model successfully has been simulated the phenomenon with remarkable outcome.
|Appears in Collections:||Thesis|