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 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 in a 20-min time frame. We find that dictionary-based sentiment provides meaningful results that outperform those based on stock index returns, which partly fails in the mapping process between news and financial returns.