Recent studies have revealed that long-term financial news can affect on stock markets. However, previous research mainly focuses on modeling the short-term effects of financial news and suffers from the weak ability of quantifying the time-decaying influence of financial news. To fill this gap, this study introduces the Hawkes process to estimate the time-decaying influence of long-term financial news. However, the performance of the conventional Hawkes process is sensitive to the choice of kernel functions. Hence, we propose a novel multikernel-powered Hawkes process framework, which uses multiple kernels to model different time-decaying rates, thus alleviating the instability of our proposed Hawkes process based prediction model. Experimental results show that the proposed framework yields state-of-the-art stock market prediction accuracies on 515 listed companies and gains more profits in market trading simulation compared with baseline methods. News-based stock prediction can complement studies on price-volume-based stock prediction.

最近的研究表明，长期的财经新闻会影响股市。然而，以往的研究主要侧重于对财经新闻的短期影响进行建模，而在量化财经新闻的时间衰减影响方面的能力较弱。为了填补这一空白，本研究引入了霍克斯过程来估计长期财经新闻的时间衰减影响。然而，传统霍克斯过程的性能对核函数的选择很敏感。因此，我们提出了一种新的多内核驱动的 Hawkes 过程框架，它使用多个内核来模拟不同的时间衰减率，从而减轻我们提出的基于 Hawkes 过程的预测模型的不稳定性。实验结果表明，与基线方法相比，所提出的框架对 515 家上市公司产生了最先进的股票市场预测准确度，并在市场交易模拟中获得了更多利润。基于新闻的股票预测可以补充基于价格成交​​量的股票预测研究。