We combine modern methods from Speech Emotion Recognition and Natural Language Processing with high-frequency financial data to precisely analyze how the vocal emo- tions and language of ECB President Mario Draghi affect the yields and yield spreads of major euro area economies. This novel...

This paper compares the statistical and economic performance of state-of-the-art highfrequency based multivariate volatility models with a simpler, widely used alternative-the Exponentially Weighted Moving Average (EWMA) filter. Using over two decades of 100 U.S. stock returns (2002-2023), we...

This paper investigates the speed of price discovery when information becomes publicly available but requires costly processing to become common knowledge. We exploit the unique institutional setting of hacks on decentralized finance (DeFi) protocols. Public blockchain data provides the precise...

We study the relationship between investors' social media activity and earnings announcement returns. To distinguish between information contained in peer-to-peer interaction and user-posted content, we analyze conversation networks on Reddit using centrality metrics from network science and...