Networks of News and Cross-Sectional Returns

TL;DR

This paper constructs dynamic news-based networks from over a million articles to analyze their influence on stock return comovement and predictability within the S&P 500, revealing significant cross-sectional effects.

Contribution

It introduces a novel algorithm for constructing directed news networks from large datasets and demonstrates their predictive power for stock returns, accounting for firm-specific news structures.

Findings

News-linked stock returns show strong comovement and reversal effects.

Network degree predicts monthly stock returns reliably.

Within-sector and large, less liquid firms are key for predictability.

Abstract

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.