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.

翻译：股票是否具备可预测性一直是数十年来备受关注的话题。有效市场假说（EMH）认为，投资者难以通过预测股价获得超额收益，但这一观点可能并非绝对成立，尤其是在中国股市中。为此，我们基于广泛研究的高频数据——逐笔数据，探索了中国股市的可预测性。通过引入真实熵的概念（采用Limpel-Ziv数据压缩方法计算），我们获得了3,834只中国股票的可预测性。采用马尔可夫链模型与扩散核模型对比预测上限，结果表明当前预测模型与理论上限之间仍存在显著性能差距。我们的研究显示：在不同量化区间及不同模型下，超过73%的股票预测准确率高于70%，且均方根误差（RMSE）低于2元人民币。我们进一步采用斯皮尔曼相关性分析发现，平均股价与价格波动性可能对预测准确率产生负面影响——这一发现或对股票投资者具有参考价值。