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

Author

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  • Xi Zhang
  • Yixuan Li
  • Senzhang Wang
  • Binxing Fang
  • Philip S. Yu

Abstract

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.

Suggested Citation

  • Xi Zhang & Yixuan Li & Senzhang Wang & Binxing Fang & Philip S. Yu, 2018. "Enhancing Stock Market Prediction with Extended Coupled Hidden Markov Model over Multi-Sourced Data," Papers 1809.00306, arXiv.org.
  • Handle: RePEc:arx:papers:1809.00306
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    References listed on IDEAS

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    1. Paul C. Tetlock & Maytal Saar‐Tsechansky & Sofus Macskassy, 2008. "More Than Words: Quantifying Language to Measure Firms' Fundamentals," Journal of Finance, American Finance Association, vol. 63(3), pages 1437-1467, June.
    2. Sanjiv R. Das & Mike Y. Chen, 2007. "Yahoo! for Amazon: Sentiment Extraction from Small Talk on the Web," Management Science, INFORMS, vol. 53(9), pages 1375-1388, September.
    3. Paul C. Tetlock, 2007. "Giving Content to Investor Sentiment: The Role of Media in the Stock Market," Journal of Finance, American Finance Association, vol. 62(3), pages 1139-1168, June.
    4. Xi Zhang & Yunjia Zhang & Senzhang Wang & Yuntao Yao & Binxing Fang & Philip S. Yu, 2018. "Improving Stock Market Prediction via Heterogeneous Information Fusion," Papers 1801.00588, arXiv.org.
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    Cited by:

    1. Thomas Dierckx & Jesse Davis & Wim Schoutens, 2020. "Using Machine Learning and Alternative Data to Predict Movements in Market Risk," Papers 2009.07947, arXiv.org.
    2. Ashish Kumar & Abeer Alsadoon & P. W. C. Prasad & Salma Abdullah & Tarik A. Rashid & Duong Thu Hang Pham & Tran Quoc Vinh Nguyen, 2021. "Generative Adversarial Network (GAN) and Enhanced Root Mean Square Error (ERMSE): Deep Learning for Stock Price Movement Prediction," Papers 2112.03946, arXiv.org.

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