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

TL;DR

This paper presents an empirical study on predicting stock market directions using only price data, demonstrating high accuracy on KOSPI and HSI indices and their constituents.

Contribution

It introduces a practical method for stock direction prediction using only price data, avoiding reliance on macroeconomic indicators.

Findings

High hit ratios in predicting index movements

Effective prediction of individual stock directions

Applicable to KOSPI and HSI indices

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.