PhD Candidate: Ali Moin
Start: Fall 2024
My research lies at the intersection of empirical asset pricing, econometrics, and machine learning. I study how financial markets respond to information, how return predictability varies across countries and sectors, and how modern statistical methods can help uncover economically meaningful patterns in increasingly high-dimensional financial data.
A recurring question in my work is how to balance global commonality with local heterogeneity. Financial markets are internationally connected, but they are not identical: news, institutions, industries, and accounting structures differ across settings. This means that models of expected returns should neither pool everything together nor treat every market as fully separate. In my research, I develop and apply methods that allow for both shared structure and meaningful local variation.
By combining advances in econometrics and machine learning with substantive questions in finance, I hope to contribute to a better understanding of expected returns in global equity markets.
