2018/04/05 by Daniel Kinn, Kinn, Daniel · 2 citations
Decision Sciences · Economics, Econometrics and Finance · #Financial Markets and Investment Strategies #Monetary Policy and Economic Impact #Risk and Portfolio Optimization #q-fin.PM
paper · pdf · doi:10.48550/arxiv.1804.01764
Second version: improved readability and corrected typos
arxiv created 2018/07/29 · arxiv updated 2018/07/31
In portfolio analysis, the traditional approach of replacing population moments with sample counterparts may lead to suboptimal portfolio choices. I show that optimal portfolio weights can be estimated using a machine learning (ML) framework, where the outcome to be predicted is a constant and the vector of explanatory variables is the asset returns. It follows that ML specifically targets estimation risk when estimating portfolio weights, and that "off-the-shelf" ML algorithms can be used to estimate the optimal portfolio in the presence of parameter uncertainty. The framework nests the traditional approach and recently proposed shrinkage approaches as special cases. By relying on results from the ML literature, I derive new insights for existing approaches and propose new estimation methods. Based on simulation studies and several datasets, I find that ML significantly reduces estimation risk compared to both the traditional approach and the equal weight strategy.