2023/07/05 by Yueshan Chen, Xingyu Xu, Chen, Yueshan +5 · 1 citation
Computer Science · Decision Sciences · Economics, Econometrics and Finance · #Complex Systems and Time Series Analysis #FOS: Mathematics #Numerical Analysis (math.NA) #Stock Market Forecasting Methods #Time Series Analysis and Forecasting
paper · pdf · doi:10.48550/arxiv.2307.02099
openalex publication_date 2023/07/05 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
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.