Unveiling Stock Market Trends by Deep Learning Insights With Correction Factor and Recurrent Neural Networks

ABSTRACT Understanding financial behaviour, particularly in the stock market, has attracted significant interest in recent years due to advancements in artificial intelligence and its impact on the global economy. The field of stock market prediction, which explores the interaction between finance and computer science to create predictive models, aims to forecast the behaviour of various securities in the financial market. One of the most well‐known and widely used techniques is Deep Learning, which employs different deep neural network structures for learning nonlinear models. In this study, we used open data from some of the largest companies in Brazil—Petrobras (PETR4), Itaúsa (ITSA4), and Vale (VALE3)—provided by BovDB, a historical dataset containing the stock prices of all companies listed on the Brazilian stock exchange (B3) from 2000 to 2020. As part of the preprocessing, a price correction factor was applied to neutralise the effects of market events on stock behaviour, enabling the recurrent neural network (RNN) model to process this information better. The results showed that using this correction factor significantly improves predictions, reducing abrupt behaviours in stock prices and decreasing the model's prediction error. For instance, the prediction error in VALE3 stock was reduced by < 10% compared with uncorrected data. These findings highlight the potential of using an event correction factor in stock data processed by an RNN, facilitating its training and providing more reliable forecasts.