  We uncover networks from news articles to study cross-sectional stock
returns. By analyzing a huge dataset of more than 1 million news articles
collected from the internet, we construct time-varying directed networks of the
S&P500 stocks. The well-defined directed news networks are formed based on a
modest assumption about firm-specific news structure, and we propose an
algorithm to tackle type-I errors in identifying the stock tickers. We find
strong evidence for the comovement effect between the news-linked stocks
returns and reversal effect from the lead stock return on the 1-day ahead
follower stock return, after controlling for many known effects. Furthermore, a
series of portfolio tests reveal that the news network attention proxy, network
degree, provides a robust and significant cross-sectional predictability of the
monthly stock returns. Among different types of news linkages, the linkages of
within-sector stocks, large size lead firms, and lead firms with lower stock
liquidity are crucial for cross-sectional predictability.

Portfolio Management;Computation