Title: Stock price direction prediction by directly using prices data: an empirical study on the KOSPI and HSI
Authors: Yanshan Wang
Addresses: School of Industrial Management Engineering, Korea University, Seoul, 136-713, Korea
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.
Keywords: stock direction prediction; co-movement; principal component analysis; PCA; support vector machines; SVM; Korean composite stock price index; KOSPI; Hang Seng index; HSI; stock prices; stock market direction; early recommendation; short-term investors; early warning; financial distress warning; long-term shareholders.
International Journal of Business Intelligence and Data Mining, 2014 Vol.9 No.2, pp.145 - 160
Received: 12 May 2014
Accepted: 01 Jun 2014
Published online: 24 Oct 2014 *