Humboldt-Universität zu Berlin - High Dimensional Nonstationary Time Series

IRTG1792DP2021 023

Networks of News and Cross-Sectional Returns

Junjie Hu
Wolfgang Karl Härdle

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.

Networks, Textual News, Cross-Sectional Returns, Comovement, Network Degree

JEL Classification:
G11, G41, C21