Forecasting the Next Winning Stock: A Comparative Analysis of Machine Learning Models

Stock price prediction is a common and complex problem due to the high volatility of financial markets. This master’s thesis presents a new approach to stock price forecasting by reformulating the problem as a multiclass classification task. The main objective is to predict which stock will yield the highest return the next day within a given set of features. To this end, various statistical and machine learning models are analyzed, with special emphasis on the Transformer model due to its relevance and alignment with the structure of this work. The present study proposes a novel idea to address the problem. Its contributions stand out in an initial exploratory analysis of model performance, as well as in risk minimization in investments, enabling portfolio diversification thanks to the Transformer model.

Author dc:creator

• Fernandez Mendez, Blanca Elvira

Advisor dc:contributor.advisor

• Diaz-Rodriguez, Jairo

• Applied mathematics

Statement dc:rights

• Author owns copyright, except where explicitly noted. Please contact the author directly with licensing requests.

Language dc:language

en

Handle dc:identifier.uri

https://hdl.handle.net/10315/43278

OAI identifier oai:identifier

oai:yorkspace.library.yorku.ca:10315/43278