The Three Musketeers of Portfolio Allocation: Risk, Return, and Machine Learning: A data-driven approach to portfolio allocation using machine learning and Markowitz in the Norwegian equity market

Abstract

The portfolio selection problem is one of the most discussed topics in financial literature. Harry Markowitz (1952) is recognized as the first to formalize the risk-reward trade-off methodology used in portfolio selection. Through his mean-variance framework, he detailed the importance of diversification and laid the foundation for the modern portfolio theory we know today. This thesis explores a novel approach to portfolio allocation enabling the mean-variance framework and machine learning. We employ machine learning to predict the quarterly expected return and the associated covariance matrix for stocks trading on Oslo Stock Exchange. To construct the predictions, we deploy the renowned Extreme Gradient Boosting algorithm, also called XGBoost. We investigate the opportunity to use quarterly reports, macroeconomic and economic variables as predictors of quarterly stock returns and covariances. Furthermore, we apply these predictions in the mean-variance framework from Markowitz to construct quarterly portfolios. The results from the Thesis Model are disappointing. The objective of the quarterly portfolio optimization is to maximize the Sharpe ratio. Unfortunately, the Thesis Model is not able to construct portfolios that reliably aligned with this goal. Nevertheless, the model initially yields an impressive one-year return. However, under new conditions the performance change drastically. The statistical evaluation of the XGBoost prediction models entails that they both deliver highly inaccurate predictions, which propagates further through to the portfolio allocation process. Moreover, there is little evidence that the models can detect any patterns in the data beneficial for portfolio construction. In sum, the model struggles to foresee market developments, which accumulates into a model incapable of consistently performing with satisfying financial results.