My current research primarily focuses on the importance of retail investors' attention and sentiment generated on social media platforms such as Twitter, StockTwits, and Reddit, for the functioning of financial markets from an empirical perspective. In several research projects, we aim to address two key objectives. On one hand, we assess the causal impact of social media attention on retail investors' trading behavior in classical stock markets. On the other hand, we strive to enhance models for financial forecasting by directly incorporating information from social media, such as investors' sentiment, attention, and the network structure formed by retail investors on these platforms. This approach helps us detect volatility regimes, analyze spillovers between assets, and derive asset pricing implications across various markets, including, but not limited to, stock and cryptocurrency markets. Current Projects include:
De Giorgi, Enrico Giovanni, C. Hirt, J. Schüttler (2024). From Memes to Markets: How WallStreetBets Attention Drives Robinhood Trading - A Causal Machine Learning Approach. Presented at the 18th International Joint Conference CFE-CMStatistics, London, UK.