Stock prediction using topic modeling and sentiment analysis techniques: A machine learning case study on Ecopetrol

Autores/as

  • Edwar David Valenzuela Cortés Universidad Nacional de Colombia
  • Santiago Puente Núñez Universidad Nacional de Colombia

Palabras clave:

stock market prediction, topic modeling, sentiment analysis, Machine Learning, investment strategies

Resumen

This study introduces a novel technique for predicting market movements using topic and sentiment analysis of financial news about Ecopetrol. News headlines from Hydrocarbons and La República (July 2012 to December 2023) were analyzed using BER Topic, FinBERT, and Vader. The findings show that predictive models based on news headlines are more effective over 3 and 4-week periods compared to shorter periods. The Gradient Boosting model for week 3 achieved a profitability of 49.4% and accuracy of 57%, while a Random Forest model for week 4 yielded a profitability of 33.11% with a 9.71% error, outperforming the buy and hold strategy. These results highlight the advantage of short-term trend predictions in financial decision-making.

Biografía del autor/a

Edwar David Valenzuela Cortés, Universidad Nacional de Colombia

Estudiante de Administración de Empresas de la Universidad Nacional de Colombia sede Bogotá.

Santiago Puente Núñez, Universidad Nacional de Colombia

Estudiante de Economía de la Universidad Nacional de Colombia sede Bogotá.

Citas

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Publicado

2025-07-07

Cómo citar

Valenzuela Cortés, E. D., & Puente Núñez, S. (2025). Stock prediction using topic modeling and sentiment analysis techniques: A machine learning case study on Ecopetrol. Revista Intercambio, (8), 89-124. Recuperado a partir de http://168.176.97.103/ojs/index.php/intercambio/article/view/666

Número

Núm. 8 (2024)

Sección

Artículos

Derechos de autor 2025 Revista Intercambio

Creative Commons License

Esta obra está bajo una licencia internacional Creative Commons Atribución-NoComercial-SinDerivadas 4.0.