To students who are interested in my research,
I hope the following provides insights into my research interests and agenda.
Primarily, I aim to delve both theoretically and empirically into how the connections among firms, financial, physical, and technological, influence corporate actions, portfolio management, business cycles, and systemic risk. I employ microdata to substantiate the macro narratives.
Besides, I am also super interested in exploring the application of Machine Learning, Deep Learning, and Reinforcement Learning in economics, finance, and business decisions which presently constitutes my research focus. I have initiated multiple projects in this regime and welcome students with robust backgrounds in Math, Computer Science, or Statistics. My collaboration spans several disciplines from Finance and Economics to Mathematics, Statistics, Physics, and Computer Science across various institutions.
My expertise also lies in harnessing big data and enormous datasets to unveil micro channels that bolster a vibrant macro picture.
My research to date falls into three domains:
1. The first delves into the tangible and intangible linkages between firms, examining their implications on corporate finance, governance, monetary policy, and the broader economy, an area in which I have a special interest
2. The second explores the employment of statistical learning, deep learning, and reinforcement learning techniques in portfolio or asset management, enriched by regular interdisciplinary discussions with my co-authors from fields like finance, statistics, and computer science across various institutions.
3. The third investigates the interplay between non-structural data (like text, video, and graph) with deep learning and asset management.
My students have been placed in the very top institutions like UPenn Wharton, Princeton, MIT, DE SHAW, Morgan Stanley, Jane Street, Ubiquant Investment, Optival etc.,
