From Econometrics to Machine Learning: Transforming Empirical Asset Pricing

Organised by: RCE 2 (Emerging Technologies and Innovation)

Time and Date: 15:00 - 16:00 PM, Friday, 9^th May 2025 (Beijing time)

Language: English

Onsite location: BS 542

Abstract:

We study the optimal boundary for open data in a model where participants from both financial markets and real sectors with private data can access open data. Open data, compared to privately accumulated data, offers more dimensional information that enhances productivity, but its public nature incurs privacy costs. Therefore, an optimal boundary for open data usage exists. Moreover, private data, as an alternative source of information, interacts with open data, influencing the optimal boundary and leading to a U-shaped relationship between private data endowment and the optimal open data boundary. This U-shaped relationship affects both financial market efficiency and real efficiency, resulting in non-monotonic impacts on these efficiencies as private data endowment varies. Our findings highlight the complex interplay between open and private data in determining market and real-sector efficiencies, offering important implications for data regulation policies.

Presenter:

Prof. Zhigang Qiu, Renmin University of China

Zhigang Qiu is Professor of Finance at the School of Finance, Renmin University of China. He received his PhD in Finance from the London School of Economics (LSE). Professor Qiu's research focuses on three primary areas: (1) asset pricing theory, (2) delegated portfolio management, and (3) data economics. His scholarly work has appeared in leading finance journals such as the Journal of Financial and Quantitative Analysis (JFQA), Journal of Economic Theory (JET), Journal of Banking & Finance (JBF), Journal of Financial Markets (JFM), and Journal of Economic Dynamics and Control (JEDC).