Earticle

초록

영어

The support vector machine (SVM) is a fast, and reliable machine learning mechanism. In this paper, we evaluated the stock price predictability of SVM, which is a kind of fundamental analysis that predicts the stock price from corporate intrinsic value. Corporate financial statements are used as input into SVM. Based on the results, we predicted the rise or drop of the stock. In addition, we evaluated how long a given financial statement can be used to predict a stock’s price. Compared to the experts forecast, the results of SVM show good predictability. However, as times goes on, the predictability begins to drop. These predictions based on financial statements are excellent, but after a short period, the dissonance between financial statements and stock price can be offset by reasonable investors. These results support the efficient market hypothesis.

목차

Abstract
 1. Introduction
 2. Description of the Issues
 3. Description on Data
 4. SVM
 5. Experiments
 6. Conclusion
 References

키워드

저자

• Junyoung Heo [ Department of Computer Engineering, Hansung University ]
• Jin Yong Yang [ Department of Computer Engineering, Hansung University ]

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  International Journal of Hybrid Information Technology

• 간기

  격월간

• pISSN

  1738-9968

• 수록기간

  2008~2016

• 십진분류

  KDC 505 DDC 605