Asaf Manela, Bryan Kelly, Dacheng Xiu, and Leland Bybee speaking on "Business News and Business Cycles"

The Hoover Institution announces a new seminar series on Using Text as Data in Policy Analysis, co-organized by Steven J. Davis and Justin Grimmer. These seminars will feature applications of natural language processing, structured human readings, and machine learning methods to text as data to examine policy issues in economics, history, national security, political science, and other fields.

This eleventh session features a conversation with Asaf Manela, Bryan Kelly, Dacheng Xiu, and Leland Bybee speaking on Business News and Business Cycles on Tuesday, April 12, 2022 from 9:00AM – 10:30AM PT and the paper under discussion can be found here.


ABOUT THE SPEAKERS:

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Bryan Kelly is Professor of Finance at the Yale School of Management, a Research Fellow at the National Bureau of Economic Research, Associate Director of SOM’s International Center for Finance, and is the head of machine learning at AQR Capital Management. Professor Kelly’s primary research fields are asset pricing, machine learning, and financial econometrics. He is interested in issues related to expected return, volatility, tail risk, and correlation modeling in financial markets; financial sector systemic risk; financial intermediation; and financial networks.  He has served as co-editor of the Journal of Financial Econometrics and associate editor of Journal of Finance and Journal of Financial Economics. Before joining Yale, Kelly was a tenured professor of finance at the University of Chicago Booth School of Business.  He earned an AB in economics from University of Chicago, MA in economics from University of California San Diego, and a PhD and MPhil in finance from New York University’s Stern School of Business. Kelly worked in investment banking at Morgan Stanley prior to his PhD.

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Dacheng Xiu is Professor of Econometrics and Statistics at the University of Chicago Booth School of Business. His current research focuses on developing machine learning solutions to big-data problems in empirical finance. Xiu’s work has appeared in the Journal of Finance, Review of Financial Studies, Econometrica, Journal of Political Economy, the Journal of the American Statistical Association, and the Annals of Statistics. He is a Co-Editor for the Journal of Financial Econometrics, an Associate Editor for the Review of Financial Studies, Journal of Econometrics, Management Science, Journal of Business & Economic Statistics, etc. He has received several recognitions for his research, including the Fellow of the Society for Financial Econometrics, Fellow of the Journal of Econometrics, AQR Insight Award, EFA Best Paper Prize, and Swiss Finance Institute Outstanding Paper Award. Xiu earned his PhD and MA in applied mathematics from Princeton University.

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Leland Bybee is a PhD student in financial economics at the Yale School of Management.  Bybee's research focuses on asset pricing, financial econometrics, text data, and high-dimensional Bayesian statistics.  Before Yale, Bybee was a researcher at the Booth School of Business, received a master's degree in statistics from the University of Michigan, and completed an undergraduate degree in economics at the University of Chicago.

 

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Asaf Manela is an Associate Professor of Finance at Reichman University and at Washington University in St. Louis. He serves as an Associate Editor of the Journal of Finance. He was one of the world’s best 40 business school professors under the age of 40 (Poets&Quants, 2014). He received his PhD in Finance and an MBA from the University of Chicago. Before pursuing an academic career, he was a software engineer. He holds a BA in Economics and Computer Science from Boston University. Professor Manela conducts empirical research in finance, which focuses on improving our understanding of two broad questions: (1) How valuable is information to financial market participants? (2) How important is leverage to financial intermediaries? His attempts to answer these and related questions in papers coauthored with leading researchers have been published in the Journal of Financial Economics, the Review of Financial Studies, and the Journal of Finance. His papers won best paper awards at the FMA Conference on Derivatives and Volatility at CBOE, the Financial Research Association Meetings, and the LBS Doctoral Conference. He has been invited to present in numerous seminars at leading academic institutions and conferences. His current research combines machine learning methods with structural economic models to predict key economic variables and asset returns with high-dimensional data like text. 


WATCH THE DISCUSSION

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