Stock investment strategy analysis using support vector

Sheng Tun Li, Ming Lung Hsu, Meng Huah Huang

Research output: Contribution to journalArticlepeer-review


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.

Original languageEnglish
Pages (from-to)325-336
Number of pages12
JournalInternational Journal of Operations and Quantitative Management
Issue number4
Publication statusPublished - 2005

All Science Journal Classification (ASJC) codes

  • Business and International Management
  • Strategy and Management
  • Management Science and Operations Research
  • Information Systems and Management
  • Management of Technology and Innovation


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