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

Gianluca Anese, Marco Corazza (), Michele Costola and Loriana Pelizzon ()

No 322, SAFE Working Paper Series from Leibniz Institute for Financial Research SAFE

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.

Keywords: Public financial news; Stock market; NLP; Dictionary; LSTM neural networks; Investor sentiment; S&P 500 (search for similar items in EconPapers)
JEL-codes: C45 C63 G14 G17 (search for similar items in EconPapers)
Date: 2021
New Economics Papers: this item is included in nep-big, nep-cmp, nep-fmk and nep-rmg
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Journal Article: Impact of public news sentiment on stock market index return and volatility (2023)
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Persistent link: https://EconPapers.repec.org/RePEc:zbw:safewp:322

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