Stock prediction using topic modeling and sentiment analysis techniques: A machine learning case study on Ecopetrol - Valenzuela Cortés, Edwar David; Puente Núñez, Santiago (Revista Intercambio, 2025)
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.