Examinando por Materia "S&P500"

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    Comparación de la eficiencia de portafolios : un análisis entre la teoría de Markowitz y un modelo basado en Random Forest
    (Universidad EAFIT, 2025) Vargas Galvis, Estefanía; Botero Ramírez, Juan Carlos
    This study compares the efficiency of investment portfolio construction using two approaches: the classic Markowitz Theory and a model based on Random Forest. Thus, it can be said that while Markowitz optimizes the portfolio under parametric and linear assumptions, Random Forest leverages the ability to capture nonlinear and dynamic patterns in complex financial data, especially in volatile markets. The research is based on real data from the 30 most liquid stocks in the S&P 500, between 2020 and 2024, evaluating the performance of both models through key metrics such as the Sharpe Ratio, variance, and return predictive capacity in January 2025. The results obtained will demonstrate whether the Random Forest model can match or exceed Markowitz's efficiency in the risk-return relationship, offering an adaptive and robust alternative for portfolio management in current financial environments.
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    Modelo fundamental de crecimiento en utilidades y price-to-earnings ratio, P/E, de los índices accionarios internacionales
    (Universidad EAFIT, 2025) Jiménez Benítez, Daniel; Sandino Perdomo, Daniel; Navarrete Quintero, Nicolás; Diaz, Walter; Durango Gutiérrez, María Patricia
    The S&P500, a barometer of the U.S. economy, is one of the world's leading stock market indices. The price-to-earnings ratio (P/E) is a valuation measure that compares a stock's market price to its earnings per share, and is commonly used to assess whether stocks are overvalued or undervalued. Forecasting the P/E ratio is complex due to factors that can influence the ratio: interest rates, economic growth, market sentiment, and financial projections for companies, among others. In this research, two recurrent neural network models were implemented: LSTM (long short-term memory) and GRU (gated recurrent unit), as well as two machine learning models: XGBoost (extreme gradient boosting) and LigthGBM (light gradient boosting machine), to forecast the P/E ratio of the S&P500 using historical data between January 1990 and October 2024. The results show that all four models perform well, although the GRU model stands out in terms of accuracy and computational efficiency, without leaving aside the LightGBM model, a boosting algorithm, which also shows competitive results. The research offers valuable information on the use of the four models to forecast valuation ratios, and can be useful as support in investment decision making.