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Media Content and Stock Returns: The Predictive Power of Press
Nicky J. Ferguson, University of Cambridge, UK
Dennis Philip, Durham University Business School, UK
Herbert Y. T. Lam, Renmin University of China, China
Jie Michael Guo, Durham University Business School, UK
This paper examines whether tone (positive and negative) and volume of firm-specific news media content provide valuable information about future stock returns, using UK news media data from 1981-2010. The results indicate that both tone and volume of news media content significantly predict next period abnormal returns, with the impact of volume more pronounced than tone. Additionally, the predictive power of tone is found to be stronger among lower visibility firms. Further, the paper finds evidence of an attention-grabbing effect for firm-specific news stories with high media coverage, mainly seen among larger firms. A simple news-based trading strategy produces statistically significant risk-adjusted returns of 14.2 to 19 basis points in the period 2003-2010. At the aggregate level, price pressure induced by semantics in news stories is corrected only in part by subsequent reversals. Overall, the findings suggest firm-specific news media content incorporates valuable information that predicts asset returns.
Keywords: news media content, stock returns, textual analysis, news-based trading strategy
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