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Title: Applying Machine Learning to investment management. Building a strategy to consistently beat the benchmark
Author: Gine Rabadan, Joan
Tutor: García Agudiez, David
Others: Solé-Ribalta, Albert  
Abstract: The aim of this work is to build a portfolio that is able to outperform the S&P 500 stock index. This portfolio will be composed by units of the Exchange Trading Funds of the most important sectors of the U.S. economy. The time series of the unit price of the ETFs and the stock index will be obtained with a depth of 10 years. Likewise, it will be attempted to incorporate additional variables that can provide predictive power to the model, such as inflation or gross domestic product. Next, regression machine learning models will be trained in order to determine, for each month, the portfolio that can provide the maximum return. The obtained models will undergo a backtest to determine their predictive capacity. First, a cross-validation will be carried out; if the result is satisfactory, a backtest will be done by means of an out-ofsample sample. Finally, the selected models will be compared with other risk-oriented portfolio management models such as an equal-weighted portfolio or an efficient frontier limit portfolio.
Keywords: investments
S&P 500
Exchange Trading Fund
Document type: info:eu-repo/semantics/masterThesis
Issue Date: 9-Jan-2024
Publication license:  
Appears in Collections:Bachelor thesis, research projects, etc.

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