The Predictability of Stock Price: Empirical Study onTick Data in Chinese Stock Market

TL;DR

This study investigates the predictability of Chinese stock prices using high-frequency tick data and entropy-based measures, revealing significant predictability for most stocks and factors affecting accuracy.

Contribution

It introduces an entropy-based approach to assess stock predictability and compares models against theoretical bounds, highlighting the potential for improved forecasting in Chinese markets.

Findings

Over 73% of stocks have prediction accuracy above 70%

Significant gap between model performance and theoretical upper bounds

Negative correlation between stock price volatility and prediction accuracy

Abstract

Whether or not stocks are predictable has been a topic of concern for decades.The efficient market hypothesis (EMH) says that it is difficult for investors to make extra profits by predicting stock prices, but this may not be true, especially for the Chinese stock market. Therefore, we explore the predictability of the Chinese stock market based on tick data, a widely studied high-frequency data. We obtain the predictability of 3, 834 Chinese stocks by adopting the concept of true entropy, which is calculated by Limpel-Ziv data compression method. The Markov chain model and the diffusion kernel model are used to compare the upper bounds on predictability, and it is concluded that there is still a significant performance gap between the forecasting models used and the theoretical upper bounds.Our work shows that more than 73% of stocks have prediction accuracy greater than 70% and RMSE less than 2 CNY under different quantification intervals with different models. We further take Spearman's correlation to reveal that the average stock price and price volatility may have a negative impact on prediction accuracy, which may be helpful for stock investors.