Event Description

False findings in financial research can lead to costly misallocations, flawed regulation, and misguided strategies. This event explores why these errors occur, focusing on the widespread assumption that financial time series are stationary and a-Holder continuous- an assumption at odds with markets shaped by regime shifts, stochastic volatility, and structural breaks. Through case studies spanning AI-driven models and advanced quantitative techniques, Bloch argues for a fundamental shift toward methodologies that embrace non-stationary dynamics and recognize the limits of prediction in complex systems.