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Traditional stock market prediction methods commonly only utilize thehistorical trading data, ignoring the fact that stock market fluctuations canbe impacted by various other information sources such as stock related events.Although some recent works propose event-driven prediction approaches byconsidering the event data, how to leverage the joint impacts of multiple datasources still remains an open research problem. In this work, we study how toexplore multiple data sources to improve the performance of the stockprediction. We introduce an Extended Coupled Hidden Markov Model incorporatingthe news events with the historical trading data. To address the data sparsityissue of news events for each single stock, we further study the fluctuationcorrelations between the stocks and incorporate the correlations into the modelto facilitate the prediction task. Evaluations on China A-share market data in2016 show the superior performance of our model against previous methods.

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