Stock investment strategy analysis using support vector

Sheng Tun Li, Ming Lung Hsu, Meng Huah Huang

研究成果: Article同行評審


Financial investment is a knowledge-intensive industry. For many investors, investment strategy is a key point in financial investment. With useful investment strategies, investors can profit in the financial market. However, investors always fall into the logic puzzle and make a decision subjectively. On the other hand, the evaluation of investment strategy is one of the most essential tasks of investment analysis, and it is usually time-consuming and laborious for investment experts.In this study, we integrate the technical analysis of financial markets with an emerging neural network model, Support Vector Machine (SVM), to solve the investment strategy problem in Taiwan Futures Market (TAIFEX). Unlike most of the previous studies, this effective and efficient decision support tool could significantly alleviate investor's burden and improve decision quality. In addition, financial experts can benefit from the ability of verifying or refining their tacit investment knowledge. Experimental results from a real-case study demonstrate its salient features of generalization and usability compared with original technical analysis.

頁(從 - 到)325-336
期刊International Journal of Operations and Quantitative Management
出版狀態Published - 2005

All Science Journal Classification (ASJC) codes

  • 商業與國際管理
  • 策略與管理
  • 管理科學與經營研究
  • 資訊系統與管理
  • 技術與創新管理


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