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Portfolio optimization based on machine learning predictions
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School of Business |
Master's thesis
Electronic archive copy is available via Aalto Thesis Database.
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Degree programme
Language
en
Pages
33 + 23
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Abstract
I use machine learning stock return predictions to improve minimum variance and Sharpe ratio maximization portfolio optimizations. I forecasted stock returns using Ridge regression, Lasso regression, Random forest, Gradient boosted tree and artificial neural network models with 89 variables. I chose the best model, Gradient boosted tree, based on R2 for portfolio optimization. Using machine learning forecasts improve minimum variance portfolio optimization, when short-sales are not allowed. However, machine learning Sharpe ratio portfolio performs poorly due to finding local instead of global optima. This thesis contributes by providing a new methodology to optimize minimum variance portfolios without short sales for practitioners.