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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

Morstad, Hans Oscar; Bock, Lars Tobias
Master thesis
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https://hdl.handle.net/11250/2982776
Utgivelsesdato
2021
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  • Master Thesis [3762]
Sammendrag
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.

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