A two-stage architecture for stock price forecasting by integrating self-organizing map and support vector regression |
| |
| Authors: | Sheng-Hsun Hsu JJ Po-An Hsieh Ting-Chih Chih Kuei-Chu Hsu |
| |
| Affiliation: | 1. Department of Business Administration, Chung Hua University, No. 707, Sec. 2, WuFu Road, Hsinchu, Taiwan;2. Department of Management and Marketing, The Hong Kong Polytechnic University, Hong Kong;3. Department of Information Management, Chung Hua University, Taiwan;1. Department of Bioresource Engineering, Faculty of Agricultural and Environmental Sciences, McGill University, 21 111 Lakeshore, Ste Anne de Bellevue, Quebec H9X 3V9, Canada;2. Department of Environment Protection and Development, Environmental Engineering Faculty, Warsaw University of Technology, ul. Nowowiejska 20, 12 Warsaw 00-653, Poland;1. Department of Mathematical Sciences and Technology, Norwegian University of Life Sciences, P.O. Box 5003, NO-1432 Ås, Norway;2. Kenya Agricultural Research Institute, Kenya Soil Survey, P.O. Box 14733-00800, Nairobi, Kenya;3. Department of Economics and Computer Sciences, Faculty of Arts and Sciences, Telemark University College, NO-3800 Bø, Norway;4. Department of Environmental Sciences, Norwegian University of Life Sciences, P.O. Box 5003, NO-1432 Ås, Norway;1. Department of Economics and Management, Zaozhuang University, Zaozhuang, Shandong, 277100, China;2. College of Finance & Economics, Wuxi Institute of Technology, Wuxi, Jiangsu, 214121, China;3. School of Business Administration, Shanghai Lixin University of Accounting and Finance, Shanghai, 201209, China;4. Glorious Sun School of Business and Management, Donghua University, Shanghai, 200051, China;5. Business Economics College, Shanghai Business School, Shanghai 200235, China;6. School of Art and Design, Shanghai University of Engineering Science, Shanghai, 201620, China;7. College of Business, Southern Illinois University, Carbondale, 62901, United States;8. International College, Renmin University of China, Suzhou, Jiangsu, 215123, China |
| |
| Abstract: | Stock price prediction has attracted much attention from both practitioners and researchers. However, most studies in this area ignored the non-stationary nature of stock price series. That is, stock price series do not exhibit identical statistical properties at each point of time. As a result, the relationships between stock price series and their predictors are quite dynamic. It is challenging for any single artificial technique to effectively address this problematic characteristics in stock price series. One potential solution is to hybridize different artificial techniques. Towards this end, this study employs a two-stage architecture for better stock price prediction. Specifically, the self-organizing map (SOM) is first used to decompose the whole input space into regions where data points with similar statistical distributions are grouped together, so as to contain and capture the non-stationary property of financial series. After decomposing heterogeneous data points into several homogenous regions, support vector regression (SVR) is applied to forecast financial indices. The proposed technique is empirically tested using stock price series from seven major financial markets. The results show that the performance of stock price prediction can be significantly enhanced by using the two-stage architecture in comparison with a single SVR model. |
| |
| Keywords: | Stock price prediction Support vector machine Self-organizing map |
| 本文献已被 ScienceDirect 等数据库收录！ |
|
