Integration of Macroeconomic Data into Multi-Asset Allocation with Machine Learning Techniques

Abstract

In this paper, we propose a new way to predict market returns for multi-assets (equity, fixed-income and commodity) by extracting features from macroeconomic data and performing machine learning algorithms for both regression and classification. Our approach aims to select robust models to build alternative risk premia portfolio. We apply machine learning algorithms to our investment universe and then apply different portfolio allocation methods. We discover the importance of integrating macroeconomic data to build portfolio, especially with classification techniques which enhance the Sharpe ratios of strategies.