A Research Agenda: The Investor Learning Problems in Macro-Finance Big Data
Stuart School of Business research presentation by: Assistant Professor of Finance Kingway Chun-Wei Lin
A Research Agenda: The Investor Learning Problems in Macro-Finance Big Data
- Assistant Professor of Finance Kingway Chun-Wei Lin
Abstract:
This presentation outlines a research agenda on investor learning in the context of big data in financial markets and the macroeconomy.
The first project studies how heterogeneous learning speeds about inflation among investors generate cross-sectional variation in stock returns within a Bayesian learning framework. The model shows that persistent belief heterogeneity affects firm valuation and that pricing biases become more pronounced when slow learners dominate, even though forecast errors remain limited.
The second project investigates how investors limited cognitive capacity to process complex models and data leads to different alpha decay rates across portfolios. We approach this question by introducing a novel empirical measure of portfolio complexity and examining investor learning models with heterogeneous learning speeds.
All Ï㽶´«Ã½ faculty, students, and staff are invited to attend.
The Friday Research Presentations series showcases ongoing academic research projects conducted by Stuart School of Business faculty and students, as well as guest presentations by Ï㽶´«Ã½ colleagues, business professionals, and faculty from other leading business schools.