Impact of public news sentiment on stock market index return and volatility

  • Recent advances in natural language processing have contributed to the development of market sentiment measures through text content analysis in news providers and social media. The effectiveness of these sentiment variables depends on the imple- mented techniques and the type of source on which they are based. In this paper, we investigate the impact of the release of public financial news on the S&P 500. Using automatic labeling techniques based on either stock index returns or dictionaries, we apply a classification problem based on long short-term memory neural networks to extract alternative proxies of investor sentiment. Our findings provide evidence that there exists an impact of those sentiments in the market on a 20-minute time frame. We find that dictionary-based sentiment provides meaningful results with respect to those based on stock index returns, which partly fails in the mapping process between news and financial returns.

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Author:Gianluca Anese, Marco Corazza, Michele CostolaORCiD, Loriana PelizzonORCiDGND
Parent Title (German):SAFE working paper ; No. 322
Series (Serial Number):SAFE working paper (322)
Place of publication:Frankfurt am Main
Document Type:Working Paper
Year of Completion:2021
Year of first Publication:2021
Publishing Institution:Universit├Ątsbibliothek Johann Christian Senckenberg
Release Date:2021/10/11
Tag:Dictionary; Investor sentiment; LSTM neural networks; NLP; Public financial news; S&P 500; Stock market
Issue:October 2021
Page Number:43
Wissenschaftliche Zentren und koordinierte Programme / House of Finance (HoF)
Wissenschaftliche Zentren und koordinierte Programme / Center for Financial Studies (CFS)
Wissenschaftliche Zentren und koordinierte Programme / Sustainable Architecture for Finance in Europe (SAFE)
Dewey Decimal Classification:3 Sozialwissenschaften / 33 Wirtschaft / 330 Wirtschaft
Licence (German):License LogoDeutsches Urheberrecht