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News Co-Occurrences, Stock Return Correlations, and Portfolio Construction Implications

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  • Yi Tang

    (Gabelli School of Business, Fordham University, New York, NY 10023, USA)

  • Yilu Zhou

    (Gabelli School of Business, Fordham University, New York, NY 10023, USA)

  • Marshall Hong

    (Chatham High School, Chatham, NJ 07928, USA)

Abstract

In this paper, we construct a sample of news co-occurrences using big data technologies. We show that stocks that co-occur in news articles are less risky, bigger, and more covered by financial analysts, and economically-connected stocks are mentioned more often in the same news articles. We decompose a news co-occurrence into an expected component and a shock component. We find that it is the shock component that arouses abnormal retail investor attention. The expected and shock components significantly predict return correlations 12 months into the future. Finally, a global minimum variance (GMV) portfolio with the covariance matrix augmented by the predictive power of news co-occurrences for future return correlations produces relatively superior performance compared to the benchmark GMV portfolio.

Suggested Citation

  • Yi Tang & Yilu Zhou & Marshall Hong, 2019. "News Co-Occurrences, Stock Return Correlations, and Portfolio Construction Implications," JRFM, MDPI, vol. 12(1), pages 1-21, March.
    • Handle: RePEc:gam:jjrfmx:v:12:y:2019:i:1:p:45-:d:215182
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    Cited by:

    1. Shawn McCarthy & Gita Alaghband, 2023. "Enhancing Financial Market Analysis and Prediction with Emotion Corpora and News Co-Occurrence Network," JRFM, MDPI, vol. 16(4), pages 1-19, April.
    2. Marco Caiffa & Vincenzo Farina & Lucrezia Fattobene, 2021. "CEO Duality: Newspapers and Stock Market Reactions," JRFM, MDPI, vol. 14(1), pages 1-18, January.
    3. Yutong Lu & Gesine Reinert & Mihai Cucuringu, 2022. "Trade Co-occurrence, Trade Flow Decomposition, and Conditional Order Imbalance in Equity Markets," Papers 2209.10334, arXiv.org, revised Mar 2024.

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