A Prediction Model for Stock Market:A Comparison of The Worlds Top Investors with Data Mining Method
Recently,many researches attempt to apply data mining methods to construct attractive decision support models for stock prediction.These models mainly focus on forecasting the price trend and providing advice for investors.According to the practical requirements,this paper proposes a model based on the combination of financial indicators and data mining methods to help fund managers make decision.Four industries were selected as our initial stock pool.One of the most popular data mining methods,support vector machine,was employed to construct a stock prediction model.The results indicate that our model is capable of selecting uptrend stocks.The predictive precision exceeds 60% for each industry in almost entire test period.The seven-year cumulative abnormal return exceeds 500%,much higher than the benchmark and even outperforms both Warren E.Buffetts and William J.ONeils investment methods.Although the return of our model is less than Richard Driehaus in some of test years,the Sharpe ratio of our model is much higher in the whole seven-year test period,which indicates that the return series that our model generated is more stable.Based on the above,a conclusion can be drawn that our model can provide sustained and effective guidance for fund managers on portfolio construction.
The worlds top investors Quantitative stock strategy Data mining based hybrid strategy model Warren E.Buffetts Strategy William J.ONeil s Strategy Richard Driehauss Strategy
Yong Hu Bin Feng XiangZhou Zhang XinYing Qiu Risong Li Kang Xie
Business Intelligence and Knowledge Discovery, School of Business, Guangdong University of Foreign S School of Business, Sun Yat-sen University, China School of Informatics, Guangdong University of Foreign Studies, Guangdong, China
国际会议
The Twelfth Wuhan International Conference on E-Business(第十二届武汉电子商务国际会议)
武汉
英文
336-342
2013-05-25（万方平台首次上网日期，不代表论文的发表时间）