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We study how the social transmission of public news influences investors' beliefs and securities markets. Using data on investor social networks, we find that earnings announcements from firms in higher-centrality locations generate stronger immediate price, volatility, and trading volume reactions. Post announcement, such firms experience weaker price drift and faster volatility decay but higher and more persistent volume. This evidence suggests that greater social connectedness promotes timely incorporation of news into prices, but also opinion divergence and excessive trading. We propose the social churning hypothesis and present supporting evidence with granular data from StockTwits messages and household trading records.

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A war-related factor model derived from textual analysis of media news reports explains the cross section of expected asset returns. Using a semi-supervised topic model to extract discourse topics from 7,000,000 New York Times stories spanning 160 years, the war factor predicts the cross section of returns across test assets derived from both traditional and machine learning construction techniques, and spanning 138 anomalies. Our findings are consistent with assets that are good hedges for war risk receiving lower risk premia, or with assets that are more positively sensitive to war prospects being more overvalued. The return premium on the war factor is incremental to standard effects.