Examinando por Materia "KNIME"

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    Ítem
    Optimización de un portafolio de inversión con acciones del Colcap aplicando técnicas de machine learning
    (Universidad EAFIT, 2024) Osorio Buitrón, Maribel; Rico Villareal, Juan David; Rojas Ormanza, Bryan Ricardo
    Based on the increases in the volumes of historical information on the shares of public companies, the question arises as to whether it is possible to create better optimized portfolios than those generated from traditional theory, making use of recent innovations in artificial intelligence and analysis of data from the last decade. For this reason, the present research aims to compare the traditional theory of portfolio optimization with the recent data analysis methodologies applied to Colcap. The methodology used is based on twelve previous investigations which had tested and demonstrated the performance of different machine learning models on stock exchanges around the world, such as S&P500, NASDAQ, DAX, SET and Colcap. From here, the best prospects were selected and applied to Colcap shares, to predict the future movement in the share price based on the historical behavior of certain significant variables and then compared with the traditional methodology. It was found that the best prediction model in the price movement applied to Colcap is the Random Forest, and the variables that best explain the future changes in the price of the shares of this exchange are the closing price of the share, the TRM index and the Colcap index. In addition, machine learning models managed to optimize portfolios with a smaller number of shares and higher returns backed by historical information.