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.
Highlights
Big data is rapidly changing the way financial markets work
Built on the evidence that news co-occurrences are significantly related to economic linkages and the shock components of news co-occurrences significantly impact retail investor attention, we argue that stock prices of firms that appear in the same news article are expected to move strongly together
We show that a global minimum variance (GMV) portfolio with the covariance matrix augmented by the predictive power of news co-occurrences for future return correlations produces smaller ex-post variance than the benchmark GMV portfolio
Summary
Big data is rapidly changing the way financial markets work. Banks use big data analytics as a tool in credit risk management. Built on the evidence that news co-occurrences are significantly related to economic linkages and the shock components of news co-occurrences significantly impact retail investor attention, we argue that stock prices of firms that appear in the same news article are expected to move strongly together. Different from previous asset pricing studies that primarily analyze the lead-lag return relations between connected stocks, we focus on the predictability of news co-occurrence for future return correlations. Unlike past studies on portfolio construction that attempted to improve estimation of the covariance matrix using historical time-series data, we explore the rich information in the large cross-section of news co-occurrences and build the predictive power of news co-occurrences for future return correlations into the covariance matrix.
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