My work bridges the gap between unstructured data and economic theory. I specialize in applying computational methods and causal inference strategies to uncover behavioral anomalies and understand market dynamics. Currently, I am focused on constructing novel datasets from textual information to predict market returns and analyze investor behavior.
Working as RA on replicating empirical tables/figures by translating partial identification strategies into Python/R code. Designing Monte Carlo simulations to evaluate finite-sample bias and variance of new estimators.
Constructed a large-scale dataset by crawling hundreds of thousands of corporate disclosure records. Estimated the causal impact of environmental regulations on firm behavior using quasi-experimental designs.