Research on Using Technical Analysis and Machine Learning to Investment Decision

• Main Authors: Hung-Wen Chiang, 蔣鴻文
• Other Authors: Sun-Jen Huang
• Format: Others
• Language: zh-TW
• Published: 2017
• Online Access: http://ndltd.ncl.edu.tw/handle/an4744

碩士 === 國立臺灣科技大學 === 資訊管理系 === 105 === This research project aims to assist the investment decision of buying or selling for 14 China equity funds by using the machine learning method. We divided the investment decision into three categories: buying、selling and holding. By using the best investigate point of Moving Average Convergence-Divergence (MACD) method in the fund open data as the training data, we built the non-linear Support Vector Machine (SVM) model and Back Propagation Network (BPN) model to execute the daily trading forecast from 2014 to 2016. The results of average rate of return of investment (ROI) of three models were compared and analyzed. The findings of this research are presented as follows: 1.The average rate of ROI of MACD model, the best market trading strategy, of all China stock funds is 47.85%. Comparing to Shanghai Composite Index 47.13%, Shanghai B-share index 34.84% and the Shenzhen B-share index 30.17%, they are almost the same. 2.Under the China stock market’s bumpy ride in 2015, the average rate of ROI of BPN model is 86.59% which is higher than the SVM model’s 63.45%. BPN model is better than SVM model. The significant difference is showed under the verification of statistical test. 3.The average rates of ROI of both SVM and BPN models are all better than MACD model. The significant differences are also showed under the verification of statistical test.