Examinando por Materia "Optimización de portafolios"
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Ítem Aplicación de modelos de inteligencia artificial y aprendizaje automático para la previsión de precios y la optimización de portafolios : un enfoque integrado con datos estructurados y no estructurados con el fin de compararse con el S&P 500 como benchmark(Universidad EAFIT, 2023) Vélez García, Santiago; Botero Ramírez, Juan CarlosThis study presents an integrated approach of artificial intelligence and machine learning models, combining neural networks for price forecasting and portfolio optimization in the financial industry. The results show that the integrated approach outperforms other financial analysis methods and provides more effective tools for market professionals compared to a buy and hold strategy represented in the analysis by the S&P500. The artificial intelligence and machine learning models used in this study enable the identification of patterns and trends in financial data, helping investors make more informed and accurate decisions. Furthermore, the study demonstrates that the inclusion of unstructured data, such as news and social networks, in financial analysis can significantly improve the accuracy of price predictions achieving an R2greater than 65% and portfolio optimization.Ítem Constructing Black Litterman optimal portfolios based on Wilcoxon test(Universidad EAFIT, 2020) Graciano Londoño, Mateo; Laniado Rojas, Henry; Laniado Rodas, HenryÍtem Evaluación del efecto de incluir la predicción de rendimientos mediante la técnica de Support Vector Machines en la eficiencia del modelo de media-varianza de Markowitz(Universidad EAFIT, 2024) Aristizábal Nieto, Eliana Jiset; García Agudelo, Estefanía; Botero Ramírez, Juan CarlosPortfolio investment optimization aims to maximize expected returns given certain levels of risk. This process requires dealing with different variables in a nonlinear, noisy system due to market complexity. This is understood as a system that is affected by different external conditions that may be uncontrollable, where volatility influenced by unpredictable factors is present. In this study, an analysis of the results obtained by integrating machine learning techniques, specifically the set of algorithms called Support Vector Machines (SVM), into classical portfolio construction models is conducted. These algorithms allow for the analysis of large amounts of data and the estimation of asset return time series, resulting in a hybrid optimization model. Historical data from the stock markets of the United States and Colombia are used for numerical experiments; one set of data is used for model training (Training Set) and another for testing (Testing Set). Finally, the efficiency of the model is evaluated comparatively with the mean-variance portfolio selection theory proposed by Markowitz.Ítem Modelo de Black-Litterman para la optimización de portafolios con views obtenidos por modelación de volatilidad(Universidad EAFIT, 2018) Valencia García, Jorge Andrei; Trespalacios Carrasquilla, AlfredoThe Black-Litterman model incorporates the market equilibrium returns and investors views to generate a new prediction of the return of the portfolio -- This model is applied for the optimization of stock portfolios in Colombia -- The main difference compared to the existing literature in Colombia is the use of GARCH processes for forecasting the returns that are used as views in the optimizer -- Portfolios are modeled weekly with a horizon of 20 trading days for the second semester of 2017 and the real returns of those portfolios adjusted by Black-Litterman versus the reference portfolios are compared -- It is found that 58.82% of portfolios outperform COLCAP with the suggested methodology. In addition, comparisons are made with respect to the measure of value aggregation (α), with Black-Litterman presenting a better performanceÍtem Modelo de optimización de portafolio renta fija por factores(Universidad EAFIT, 2024) Correa Restrepo, Daniel; Uribe Osorio, Camilo Mateo; González Jaramillo, Nicolás; Grajales Correa, Carlos Alexander; Botero Ramírez, Juan CarlosÍtem Optimización de portafolios : integración de modelos de aprendizaje automático y estrategias tradicionales para una gestión eficiente de inversiones(Universidad EAFIT, 2024) Agudelo Niño, Yolanda María; Cruz Castañeda, VivianÍtem Optimización de portafolios financieros mediante enfoques de machine learning y computación cuántica : un caso de estudio(Universidad EAFIT, 2024) Agudelo Zuluaga, Mariana; Almonacid Hurtado, Paula María; Lalinde Pulido, Juan GuillermoÍtem Portfolio Optimization Using Predictive Auxiliary Classifier Generative Adversarial Networks : Application to the Colombian stock market(Universidad EAFIT, 2024) Arango López, Federico; Castellanos Ríos, Santiago