Data Mining and Forecasting in Peaks and Troughs of Taiwan Stock Market

• Main Authors: Yung-Ying Fang, 方永盈
• Other Authors: Hung-Chang Lee
• Format: Others
• Language: zh-TW
• Published: 2016
• Online Access: http://ndltd.ncl.edu.tw/handle/03072329209005583594

碩士 === 淡江大學 === 資訊管理學系碩士班 === 104 === The total market value of listed shares in Taiwan was 26.25 trillion and the total turnover volume was 21.90 trillion in year 2014. These was 1.6 times and 1.3 times of Taiwan GDP in that year. That indicated the profound effect of Taiwan stock market. Even with adaptive value, features of investing in stock are still high profit as well as high risk. How to reduce risk and, at the same time, produce high profit is an interesting challenge. In this paper, we try to build a model which can predict the peaks and troughs of Taiwan stock market. First, we define peaks and troughs in short-term and medium-term period, respectively. And then, by scanning the historical data, we find the indicators like Volume, Relative Strength Index, Bias Ratio of Stock Price, Stochastic Oscillator…etc. to get information from the peaks and troughs of Taiwan stock market. After that, we use SVM along with some notable features to forecast peaks and troughs of Taiwan Stock Market. Within that evaluation, two kinds of accuracy, the general accuracy (correct or incorrect) and operation accuracy (in the range of indicating or not), is defined. Finally, we discuss parmeters and mark criterions about training sets and test sets in varities of condition. The result shows that prediction accuracy for peaks is highly sensitive and it’s easily effected by varieties of environment. Among these, we present a model for medium-term peaks which the average Cross-Validation Accuracy for the training set is up to 80%, accuracy for the testing set is up to 70%, and average operation accuracy for test set is 93.75%. On the other side, the result shows that models for troughs more easily to predict than that for peaks. For both troughs of short-term and medium-term, they can be found a model with combined features that produce a average operation accuracy for testing set up to 80%. These promising results show that the proposed model can provide a significant value for the investors.