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.Publicación Construcción de un portafolio en el mercado brasileño : un enfoque basado en el modelo de Black-Litterman(Universidad EAFIT, 2024) Palacios Racines, Lizeth Carolina; Miller Rengifo, Genner Enrique; Botero Ramírez, Juan CarlosThe equity market represents a significant trading volume, modeling price formation and offering good investment opportunities in both national and international markets. In this context, and with the aim of developing an investment portfolio in Brazil, this research focuses on constructing a portfolio composed of equity assets using the Black-Litterman (BL) model to evaluate its performance compared to other models such as Markowitz and CAPM. Portfolios with weekly periodicity and a time horizon exceeding 10 years will be modeled to compare the returns of BL and the aforementioned models. The findings indicate that the Markowitz model provides more diversified investment opportunities, while BL is more concentrated but aligns with higher return expectations.Ítem Constructing Black Litterman optimal portfolios based on Wilcoxon test(Universidad EAFIT, 2020) Graciano Londoño, Mateo; Laniado Rojas, Henry; Laniado Rodas, HenryPublicación 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 performancePublicación 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 CarlosPublicación 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, VivianPublicación Optimización de portafolios : un análisis comparativo de estrategias convencionales vs. algoritmos genéticos para los mercados de Estados Unidos y Colombia(Universidad EAFIT, 2024) Vélez Bunzl, Juan José; Botero Ramírez, Juan CarlosInvestment portfolio optimization is an aspect of vital interest for investors; considering there are different methods for achieving this goal, the question arises about which of them allows to obtain the optimal portfolio. The purpose of this thesis is to perform a comparison between different approaches such as Markowitz and inverse variance, among others, with an optimization technique using genetic algorithms. Furthermore, it is intended to analyze the results obtained by these methodologies in a developed market like the United States and in a less developed one like Colombia. To do this, historical data on 10 of the leading stocks from the S&P500 and Colcap indexes were collected, while portfolios were constructed using Matlab and Phyton; with this, it is expected to demonstrate whether the use of artificial intelligence (AI) techniques such as genetic algorithms, can generate portfolios with a better performance than those produced through other methodologies. Also, it is important to assess the influence of the type of market we are dealing with in this outcome.Publicación Optimización de portafolios de inversión en el contexto de Big Data : integrando aprendizaje automático y técnicas de descomposición espectral(Universidad EAFIT, 2025) Hernández Slait, Jhon Jairo; Almonacid Hurtado, Paula MaríaPublicación 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 GuillermoPublicación Portfolio Optimization Using Predictive Auxiliary Classifier Generative Adversarial Networks : Application to the Colombian stock market(Universidad EAFIT, 2024) Arango López, Federico; Castellanos Ríos, SantiagoÍtem Riqueza generacional : estrategias de portafolios financieros(Universidad EAFIT, 2024) Jaramillo Correa, Felipe; Giraldo Arango, Mateo