标题： 基于多源数据的扩展耦合隐马尔可夫模型增强股票市场预测 显示英文标题

标题： Enhancing Stock Market Prediction with Extended Coupled Hidden Markov Model over Multi-Sourced Data

摘要： 传统股票市场预测方法通常只利用历史交易数据，忽略了股票市场波动可能受到各种其他信息源的影响，例如与股票相关的事件。 尽管一些近期的研究通过考虑事件数据提出了基于事件的预测方法，但如何利用多种数据源的联合影响仍然是一个开放的研究问题。 在本工作中，我们研究如何探索多种数据源以提高股票预测的性能。 我们引入了一个扩展的耦合隐马尔可夫模型，将新闻事件与历史交易数据结合。 为了解决单个股票新闻事件的数据稀疏性问题，我们进一步研究了股票之间的波动相关性，并将这些相关性纳入模型中以促进预测任务。 在2016年中国A股市场数据上的评估显示，我们的模型相比之前的方法表现出更优的性能。 显示英文摘要

摘要： Traditional stock market prediction methods commonly only utilize the historical trading data, ignoring the fact that stock market fluctuations can be impacted by various other information sources such as stock related events. Although some recent works propose event-driven prediction approaches by considering the event data, how to leverage the joint impacts of multiple data sources still remains an open research problem. In this work, we study how to explore multiple data sources to improve the performance of the stock prediction. We introduce an Extended Coupled Hidden Markov Model incorporating the news events with the historical trading data. To address the data sparsity issue of news events for each single stock, we further study the fluctuation correlations between the stocks and incorporate the correlations into the model to facilitate the prediction task. Evaluations on China A-share market data in 2016 show the superior performance of our model against previous methods.