Stock price direction prediction by directly using prices data: An empirical study on the KOSPI and HSI

Research output: Contribution to journalArticlepeer-review

10 Scopus citations

Abstract

The prediction of a stock market direction may serve as an early recommendation system for short-term investors and as an early financial distress warning system for long-term shareholders. Many stock prediction studies focus on using macroeconomic indicators, such as CPI and GDP, to train the prediction model. However, daily data of the macroeconomic indicators are almost impossible to obtain. Thus, those methods are difficult to be employed in practice. In this paper, we propose a method that directly uses prices data to predict market index direction and stock price direction. An extensive empirical study of the proposed method is presented on the Korean Composite Stock Price Index (KOSPI) and Hang Seng Index (HSI), as well as the individual constituents included in the indices. The experimental results show notably high hit ratios in predicting the movements of the individual constituents in the KOSPI and HIS.

Original languageEnglish (US)
Pages (from-to)145-160
Number of pages16
JournalInternational Journal of Business Intelligence and Data Mining
Volume9
Issue number2
DOIs
StatePublished - 2014

Keywords

  • Co-movement
  • HSI
  • Hang seng index
  • KOSPI
  • Korean composite stock price index
  • PCA
  • Principal component analysis
  • SVM
  • Stock direction prediction
  • Support vector machine

ASJC Scopus subject areas

  • Management Information Systems
  • Statistics, Probability and Uncertainty
  • Information Systems and Management

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