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Stock Market Prediction from WSJ: Text Mining via Sparse Matrix Factorization

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  • Felix Ming Fai Wong
  • Zhenming Liu
  • Mung Chiang

Abstract

We revisit the problem of predicting directional movements of stock prices based on news articles: here our algorithm uses daily articles from The Wall Street Journal to predict the closing stock prices on the same day. We propose a unified latent space model to characterize the "co-movements" between stock prices and news articles. Unlike many existing approaches, our new model is able to simultaneously leverage the correlations: (a) among stock prices, (b) among news articles, and (c) between stock prices and news articles. Thus, our model is able to make daily predictions on more than 500 stocks (most of which are not even mentioned in any news article) while having low complexity. We carry out extensive backtesting on trading strategies based on our algorithm. The result shows that our model has substantially better accuracy rate (55.7%) compared to many widely used algorithms. The return (56%) and Sharpe ratio due to a trading strategy based on our model are also much higher than baseline indices.

Suggested Citation

  • Felix Ming Fai Wong & Zhenming Liu & Mung Chiang, 2014. "Stock Market Prediction from WSJ: Text Mining via Sparse Matrix Factorization," Papers 1406.7330, arXiv.org.
    • Handle: RePEc:arx:papers:1406.7330
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    References listed on IDEAS

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    1. Huina Mao & Scott Counts & Johan Bollen, 2011. "Predicting Financial Markets: Comparing Survey, News, Twitter and Search Engine Data," Papers 1112.1051, arXiv.org.
    2. Casey Dougal & Joseph Engelberg & Diego García & Christopher A. Parsons, 2012. "Journalists and the Stock Market," The Review of Financial Studies, Society for Financial Studies, vol. 25(3), pages 639-679.
    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.
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    Cited by:

    1. Qiong Wu & Christopher G. Brinton & Zheng Zhang & Andrea Pizzoferrato & Zhenming Liu & Mihai Cucuringu, 2019. "Equity2Vec: End-to-end Deep Learning Framework for Cross-sectional Asset Pricing," Papers 1909.04497, arXiv.org, revised Oct 2021.
    2. František Dařena & Jan Přichystal, 2018. "Analysis of the Association between Topics in Online Documents and Stock Price Movements," Acta Universitatis Agriculturae et Silviculturae Mendelianae Brunensis, Mendel University Press, vol. 66(6), pages 1431-1439.
    3. Stefan Feuerriegel & Helmut Prendinger, 2018. "News-based trading strategies," Papers 1807.06824, arXiv.org.
    4. Pooja Gupta & Angshul Majumdar & Emilie Chouzenoux & Giovanni Chierchia, 2020. "SuperDeConFuse: A Supervised Deep Convolutional Transform based Fusion Framework for Financial Trading Systems," Papers 2011.04364, arXiv.org.
    5. Souta Nakatani & Kiyohiko G. Nishimura & Taiga Saito & Akihiko Takahashi, 2020. "Interest Rate Model with Investor Attitude and Text Mining (Published in IEEE Access)," CARF F-Series CARF-F-479, Center for Advanced Research in Finance, Faculty of Economics, The University of Tokyo.
    6. Souta Nakatani & Kiyohiko G. Nishimura & Taiga Saito & Akihiko Takahashi, 2020. "Interest Rate Model with Investor Attitude and Text Mining," CIRJE F-Series CIRJE-F-1152, CIRJE, Faculty of Economics, University of Tokyo.
    7. Kübler, Raoul V. & Colicev, Anatoli & Pauwels, Koen H., 2020. "Social Media's Impact on the Consumer Mindset: When to Use Which Sentiment Extraction Tool?," Journal of Interactive Marketing, Elsevier, vol. 50(C), pages 136-155.
    8. Sun, Andrew & Lachanski, Michael & Fabozzi, Frank J., 2016. "Trade the tweet: Social media text mining and sparse matrix factorization for stock market prediction," International Review of Financial Analysis, Elsevier, vol. 48(C), pages 272-281.
    9. Frantisek Darena & Jonas Petrovsky & Jan Zizka & Jan Prichystal, 2016. "Analyzing the correlation between online texts and stock price movements at micro-level using machine learning," MENDELU Working Papers in Business and Economics 2016-67, Mendel University in Brno, Faculty of Business and Economics.

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