TY  - UNPD
A1  - Anese, Gianluca
A1  - Corazza, Marco
A1  - Costola, Michele
A1  - Pelizzon, Loriana
T1  - Impact of public news sentiment on stock market index return and volatility
T2  - SAFE working paper ; No. 322
N2  - 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.
T3  - SAFE working paper - 322 
KW  - Public financial news
KW  - Stock market
KW  - NLP
KW  - Dictionary
KW  - LSTM neural networks
KW  - Investor sentiment
KW  - S&P 500
Y1  - 2021
UR  - http://publikationen.ub.uni-frankfurt.de/frontdoor/index/index/docId/63339
UR  - https://nbn-resolving.org/urn:nbn:de:hebis:30:3-633392
UR  - https://ssrn.com/abstract=3937901
IS  - October 2021
PB  - SAFE
CY  - Frankfurt am Main
ER  -