Stock Market Performance Prediction: A Comparative Study Between Econometric Models and Artificial Intelligence-Based Models[Prédiction de la performance boursière, une étude comparative entre modélisations économétriques et modélisations basées sur l’intelligence artificielle]

In this article, we present a comparative study of the performance of econometric models (Mundlak model and GEE-Logit model) and artificial intelligence based models, such as stacking model and ensemble model integrating XG-Boost and LightGBM, as well as deep learning models (LSTM, GRU, Transformer-based encoder-decoder, TCN) in a classification task of listed securities into underperfor- ming and outperforming stocks, with a one-year investment horizon. We use annual historical data from 2019 to 2021. The results show that a stacking classification model out-performs the other models and offers a better balance between the true positive rate (70%) and the true negative rate (67%).