General Conference

Dacheng Xiu (University of Chicago Booth School of Business) specializes in developing statistical methodologies and their applications to financial data to investigate economic implications. His earlier research involved risk measurement and portfolio management with 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 appeared in Econometrica, Journal of Political Economy, Journal of Finance, Review of Financial Studies, Journal of the American Statistical Association, and Annals of Statistics. For a more accessible introduction to his work, explore a curated list of articles in the Chicago Booth Review.

Xiu serves as a Research Associate at the National Bureau of Economic Research. He currently holds and has previously held several editorial positions, including Co-Editor of Journal of Business & Economic Statistics and Journal of Financial Econometrics, as well as Associate Editor for journals such as Journal of Finance, Review of Financial Studies, Journal of the American Statistical Association, Management Science, and Journal of Econometrics. He has received several recognitions for his research, including Fellow of the Society for Financial Econometrics, Fellow of the Journal of Econometrics, Swiss Finance Institute Outstanding Paper Award, AQR Insight Award, Dimensional Fund Advisors Prize, Bates-White Prize, and best paper prizes at various conferences. He has been recognized as one of Poets & Quants’ Best 40-under-40 Business School Professors.

Xiu earned his PhD and MA in applied mathematics from Princeton University, where he was also a student at the Bendheim Center for Finance. Prior to his graduate studies, he obtained a BS in mathematics from the University of Science and Technology of China.

David Audretsch is a Distinguished Professor and Ameritech Chair of Economic Development at Indiana University, where he directs the Institute for Development Strategies. He also holds positions at the University of Klagenfurt (Austria) and WHU–Otto Beisheim School of Management (Germany).

His research examines the links between entrepreneurship, government policy, innovation, economic development, and global competitiveness. Founder and editor-in-chief of Small Business Economics, he is coauthor of The Seven Secrets of Germany (Oxford University Press).

Recognized as a Clarivate Citation Laureate (2021) and recipient of the Global Award for Entrepreneurship Research, Audretsch is ranked the world’s leading scholar in business and management and sixth in economics and finance. He has received honorary doctorates from the Universities of Augsburg, Jönköping, and Siegen, and was awarded the Schumpeter Prize.

He has consulted for the World Bank, OECD, European Union, and United Nations, and serves on advisory boards for institutes such as the Swedish Entrepreneurship Forum and the Jackstädt Centre for Entrepreneurship.

Real Estate Day

Thies Lindenthal  is the Grosvenor Professor of Real Estate Finance at the Department of Land Economy, University of Cambridge, a professorial fellow at Pembroke College, and a JM Keynes Fellow in Financial Economics.

His research interests are twofold: First, he analyses property investments in the very long-term, tracking rents, prices, and returns for up to 500 years. The second research line focuses on applied machine learning techniques to utilize high-dimensional “Big(ish)” data. Put differently, he uses images and other data that are too complex for spreadsheets to better understand property values, household preferences, and decisions made by very human and not always rational agents.

Before joining the University of Cambridge, Thies did a postdoc at MIT’s Center for Real Estate, working on the market for virtual locations such as Internet domain names. His PhD is from Maastricht University. He has served as a board member for the American Real Estate And Urban Economics Association (AREUEA) and as an expert witness for internet domain names at US courts.