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Dacheng Xiu specializes in developing statistical methodologies for the application of financial data to investigate economic implications. His research involves risk measurement, portfolio management, high-frequency data, and econometric modeling of derivatives. Currently, he focuses on developing machine learning solutions for big-data problems in empirical asset pricing. His research has been published in prestigious journals such as Econometrica, the Journal of Political Economy, the Journal of Finance, the Review of Financial Studies, the Journal of the American Statistical Association, and the Annals of Statistics. For an accessible introduction to his work, he has a curated list of articles available through the Chicago Booth Review. Xiu also serves as a Research Associate at the National Bureau of Economic Research. He has held editorial positions, including Co-Editor of the Journal of Business & Economic Statistics and the Journal of Financial Econometrics, and he has been an Associate Editor for several other journals. His research has received multiple recognitions, including Fellow distinctions in the Society for Financial Econometrics and the Journal of Econometrics, as well as various research paper prizes.
The doctoral program at Booth is organized into 'dissertation areas' which include Accounting, Behavioral Science, Econometrics and Statistics, Finance, Marketing, and Operations Management.