Stock Market Forecasting : Comparing Machine Learning and Deep Learning with Risk-Return Model Selection and Evaluation in a Walk-forward Approach

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dc.contributor.advisorOlarte Hernández, Tomás
dc.contributor.authorCastro Marín, Carlos Andrés
dc.contributor.authorOlarte Hernández, Tomás
dc.coverage.spatialMedellín de: Lat: 06 15 00 N degrees minutes Lat: 6.2500 decimal degrees Long: 075 36 00 W degrees minutes Long: -75.6000 decimal degreeseng
dc.creator.degreeMagíster en Ciencias de Datos y Analíticaspa
dc.creator.emailccastro6@eafit.edu.co
dc.date.accessioned2024-11-01T16:59:15Z
dc.date.available2024-11-01T16:59:15Z
dc.date.issued2024
dc.description.abstractThis study compares the effectiveness of machine learning and deep learning algorithms in forecasting stock market direction using daily market data of Apple Inc. stock. We aim to determine if these algorithms can identify repeatable patterns across time using price and volume history and assess which are most capable. To ensure robustness, we employ a walk-forward validation approach to maintain the temporal dimension of the data and simulate real trading conditions. This method allows us to test models across different market conditions and measure their predictive power. We prioritize model selection and evaluation based on financial return and risk metrics, focusing on profitability rather than traditional machine learning performance metrics, which often do not correlate with financial outcomes. Our findings show that traditional machine learning algorithms, specifically Random Forest, outperform deep learning models under the selected asset and conditions tested. Machine learning models exceed the stock benchmark regarding Sharpe ratio, while deep learning models struggle to manage risk effectively, leading to poorer performance. This discrepancy is likely due to the complex solution space deep learning algorithms navigate to optimize and the amount of data required by these models. However, we hypothesize that the latter could improve its performance if tested with different architectures and hyperparameters, including newly developed transformer attention-based architectures and models such as TimeGPT and others, shown in the related work section.
dc.identifier.urihttps://hdl.handle.net/10784/34761
dc.language.isospaspa
dc.publisherUniversidad EAFITspa
dc.publisher.departmentEscuela de Ciencias Aplicadas e Ingeniería. Área Computación y Analíticaspa
dc.publisher.placeMedellín
dc.publisher.programMaestría en Ciencias de los Datos y Analíticaspa
dc.rightsTodos los derechos reservadosspa
dc.rights.accessrightsinfo:eu-repo/semantics/closedAccess
dc.rights.localAcceso cerrado
dc.subjectAprendizaje automático
dc.subjectRedes neuronales
dc.subjectAprendizaje de máquina
dc.subjectMercados financieros
dc.subjectAnalítica predictiva
dc.subjectMercado de acciones
dc.subject.keywordStock market forecasting
dc.subject.keywordMachine learning
dc.subject.keywordDeep learning
dc.subject.keywordAlgorithmic trading
dc.subject.keywordWalkforward
dc.subject.keywordFinancial metrics
dc.subject.keywordPredictive analytics
dc.subject.keywordFinancial markets
dc.subject.lembCIENCIA DE LA INFORMACIÓN
dc.subject.lembACCIONES (BOLSA)
dc.subject.lembAPRENDIZAJE AUTOMÁTICO (INTELIGENCIA ARTIFICIAL)
dc.subject.lembRIESGO (FINANZAS)
dc.titleStock Market Forecasting : Comparing Machine Learning and Deep Learning with Risk-Return Model Selection and Evaluation in a Walk-forward Approach
dc.typemasterThesiseng
dc.typeinfo:eu-repo/semantics/masterThesiseng
dc.type.hasVersionacceptedVersioneng
dc.type.localTesis de Maestríaspa
dc.type.spaArtículo

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