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

Yi Tang, Yilu Zhou and Marshall Hong
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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

JRFM, 2019, vol. 12, issue 1, 1-21

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.

Keywords: big data; news co-occurrence; stock return correlation; portfolio construction; global minimum variance portfolio (search for similar items in EconPapers)
JEL-codes: C E F2 F3 G (search for similar items in EconPapers)
Date: 2019
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (3)

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