Time: 2024/11/08, 10:00-11:30 (Beijing Time)

Title:  A Dynamic Semiparametric Characteristics-based Model for Portfolio Selection

Venue: 106 Zhonghui Building

About the speaker:

Shaoran Li, assistant professor at School of Economics, Peking University, obtained  PhD in economics from University of Cambridge. His research mainly focuses on financial econometrics, asset pricing and machine learning. His work has been published at Journal of Econometrics , Journal of Business & Economic Statistics  and  Journal of the Royal Statistical Society: Series A (Statistics in Society).

Abstract:

This paper introduces a two-step methodology for portfolio weight selection linked to a characteristics-based factor model with time-varying factor risk premia. This portfolio management strategy extends the expected utility maximization framework to accommodate a large number of assets and to integrate the insight from an asset pricing model. The first step, termed factor tilting, finds flexible factor-mimicking sub-portfolios through linear combinations of characteristics- based factor loadings. The second step, factor timing, dynamically combines these factor-mimicking sub-portfolios based on the predictability of factor premia by a time-varying signal – a single index function of state variables. We develop a two-stage semiparametric estimator and perform hypothesis tests on the significance of state variables. Applying our methodology and theories to data from the Center for Research in Security Prices (CRSP) and the Federal Reserve Economic Data (FRED), we identify significant roles for some dynamic predictors, which yield excellent in-sample and out-of-sample performance consistent with varying levels of investor risk aversion