Please use this identifier to cite or link to this item: https://hdl.handle.net/10419/243176 
Year of Publication: 
2021
Series/Report no.: 
SAFE Working Paper No. 322
Publisher: 
Leibniz Institute for Financial Research SAFE, Frankfurt a. M.
Abstract: 
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 implemented 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.
Subjects: 
Public financial news
Stock market
NLP
Dictionary
LSTM neural networks
Investor sentiment
S&P 500
JEL: 
G14
G17
C45
C63
Document Type: 
Working Paper

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