Examinando por Materia "Red neuronal"
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Publicación El incumplimiento crediticio mediante un análisis comparativo de diferentes técnicas de predicción en la geografía colombiana(Universidad EAFIT, 2021) Peñaloza Martínez, Eliana María; Tamara Ayús, Armando LeninPublicación La probabilidad de incumplimiento y su estimación en una cartera de consumo en una empresa del sector de combustible(Universidad EAFIT, 2023) Narváez Quintero, Juan Diego; Jaramillo Jaramillo, Luis Javier; Tamara Ayus, Armando LeninPublicación Modelo de negocio para la creación de una IPS de imagenología apoyada en nuevo software de diagnóstico en la ciudad de Medellín(Universidad EAFIT, 2023) Sierra Castillo, Santiago; Reyes Sarmiento, Martha EugeniaPublicación Predicción de direcciones de activos financieros basados en la volatilidad en series temporales utilizando machine learning(Universidad EAFIT, 2026-02-24) Holguín Carvalho, Mateo; Velasco Vera, Henry GiovannyIdentifying effective trading signals in financial assets is a challenge that draws attention across multiple disciplines due to the volatile and dynamic nature of financial markets. The complexity investors face stems from the wide range of factors that influence asset prices, including macroeconomic variables, corporate decisions, and unexpected events, making it difficult to obtain precise estimates of future movements. This is particularly relevant for investors seeking to build portfolios that maximize returns. In this context, some variables exhibit stronger relationships with market-driven factors, making them useful indicators for anticipating price direction. Nevertheless, recent advances in computing and in Machine Learning and Deep Learning techniques have enabled the development of more sophisticated models that facilitate this task. This study compares time-series-based machine learning methodologies, specifically LSTM neural networks and LightGBM decision-tree models, while incorporating Conditional Heteroskedasticity models (GARCH) to improve the classification of buy and sell signals in financial instruments, accounting for both historical patterns and external variables affecting asset behavior. The results show that LightGBM achieved the best predictive performance, with notable metrics such as an F1 Score of 0.823 and an AUC-ROC of 0.923 in validation, whereas LSTM delivered the best financial performance, reaching a cumulative return of 28.05% and a Sharpe Ratio of 0.70, clearly outperforming a Buy-and-Hold strategy. These findings suggest that although daily directional prediction is inherently complex, advanced Machine Learning models can transform weak signals into profitable trading strategies.