معرفی
Dacheng Xiu is a Professor at the University of Chicago Booth School of Business and affiliated with the National Bureau of Economic Research (NBER). His research spans finance, machine learning, and econometrics, focusing on asset pricing, volatility modeling, and high-frequency data analysis.
His work includes developing machine learning frameworks for financial applications, such as return prediction, factor models, and text mining of market data. Recent publications emphasize leveraging large language models (e.g., BERT, GPT) and deep learning architectures (e.g., autoencoders) to address challenges in empirical asset pricing and portfolio optimization.
He has collaborated extensively with scholars like Bryan T. Kelly and Stefano Giglio, contributing to high-impact journals and working papers. His research also explores the statistical limits of arbitrage and weak signal detection in financial markets, with applications to risk premium estimation and factor zoo regularization.
