• Medientyp: E-Book
  • Titel: Networks of News and Cross-Sectional Returns
  • Beteiligte: Hu, Junjie [Verfasser:in]; Härdle, Wolfgang K. [Verfasser:in]
  • Erschienen: [S.l.]: SSRN, 2021
  • Umfang: 1 Online-Ressource (47 p)
  • Sprache: Englisch
  • DOI: 10.2139/ssrn.3904012
  • Identifikator:
  • Entstehung:
  • Anmerkungen: Nach Informationen von SSRN wurde die ursprüngliche Fassung des Dokuments August 11, 2021 erstellt
  • Beschreibung: 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
  • Zugangsstatus: Freier Zugang