Portfolio Optimization with Industry Return Prediction Models
52 Pages Posted: 1 Aug 2017
Date Written: June 30, 2017
We postulate that utilizing return prediction models with fundamental, macroeconomic, and technical indicators instead of using historical averages should result in superior asset allocation decisions. We investigate the predictive power of individual variables for forecasting industry returns in-sample and out-of-sample and then analyze multivariate predictive regression models including OLS, a regularization technique, principal components, a target-relevant latent factor approach, and forecast combinations. The gains from using industry return predictions are evaluated in an out-of-sample Black-Litterman portfolio optimization framework. We provide empirical evidence that portfolio optimization utilizing industry return prediction models significantly outperform portfolios using historical averages and those being passively managed.
Keywords: Portfolio Optimization, Return Forecasts, Predictive Regression, Three-Pass Regression Filter, Black-Litterman Model
JEL Classification: G17, G11, C53
Suggested Citation: Suggested Citation