Optimización de portfolios y análisis de riesgo en el IBEX 35: Modelos de clasificación y predicción
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2024
Defense date
06/2024
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Abstract
El presente Trabajo de Fin de Grado se centra en la optimización de portfolios y el análisis de riesgo en el contexto del IBEX 35. Se revisan teorías clásicas como la de Markowitz y medidas de riesgo como el VaR (Value at Risk) y el ES (Expected Shortfall), además de analizar detalladamente la diversificación sectorial del índice bursátil.
En este estudio, se emplean técnicas estadísticas avanzadas como el análisis clustering con algoritmos como K-Means y algoritmo jerárquico aglomerativo junto con herramientas computacionales y técnicas de simulación como el método de Montecarlo, utilizando Python para llevar a cabo un análisis exhaustivo con datos reales del mercado. Además del análisis cluster, se utilizan modelos predictivos como los árboles de decisión y random forest, con el objetivo de comprender y predecir el comportamiento del mercado español.
Esta combinación de enfoques metodológicos permite un análisis más completo y profundo de la gestión de inversiones en el contexto específico del IBEX 35, proporcionando una perspectiva integral que abarca tanto aspectos teóricos como prácticos.
This Bachelor’s Thesis focuses on portfolio optimization and risk analysis within the context of the IBEX 35. It examines classical theories such as Markowitz's and risk measures like VaR (Value at Risk) and ES (Expected Shortfall), while also conducting a detailed analysis of sectorial diversification of the stock index. This study employs advanced statistical techniques such as clustering analysis with algorithms like K-means and hierarchical agglomerative algorithms, alongside computational tools and simulation techniques like the Montecarlo method with Python as the primary tool for conducting an exhaustive analysis using real market data. In addition to cluster analysis, predictive models such as decision trees and random forests are employed to understand and forecast the behavior of the Spanish market. This combination of methodological approaches allows for a more comprehensive and in-depth analysis of investment management within the specific context of the IBEX 35, providing an integral perspective that encompasses both theoretical and practical aspects.
This Bachelor’s Thesis focuses on portfolio optimization and risk analysis within the context of the IBEX 35. It examines classical theories such as Markowitz's and risk measures like VaR (Value at Risk) and ES (Expected Shortfall), while also conducting a detailed analysis of sectorial diversification of the stock index. This study employs advanced statistical techniques such as clustering analysis with algorithms like K-means and hierarchical agglomerative algorithms, alongside computational tools and simulation techniques like the Montecarlo method with Python as the primary tool for conducting an exhaustive analysis using real market data. In addition to cluster analysis, predictive models such as decision trees and random forests are employed to understand and forecast the behavior of the Spanish market. This combination of methodological approaches allows for a more comprehensive and in-depth analysis of investment management within the specific context of the IBEX 35, providing an integral perspective that encompasses both theoretical and practical aspects.