Bayesian Parametric Portfolio Policies

Abstract

Parametric portfolio policies map predictors into portfolio weights without modelling returns, making them a natural tool for factor timing. With many predictors, if the policy has more coefficients than observations the data cannot pin it down. We develop Bayesian Parametric Portfolio Policies (BPPP), which discipline the policy with a prior, update it with realised utility, and average the weights over the posterior. We contribute to the factor-timing literature by estimating timing as a portfolio rule rather than from return forecasts. With 242 predictors and five factors, BPPP earns 2.08 percentage points a year from timing, mostly from aggregate anomaly returns. Under a leverage limit, averaging sizes each position by its uncertainty and comes close, without a separate scale choice, to the size that maximises timing with hindsight. A turnover penalty preserves significant gross timing at lower turnover.

arXiv comment53 pages. Revised version: reframed around factor timing with five factors and 242 predictors