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

Manel Labidi, Ying Zhang (), Matthieu Petit Guillaume () and Aurélien Krauth ()
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Manel Labidi: LEVIATAN
Ying Zhang: LEVIATAN
Matthieu Petit Guillaume: BH - Beyond Horizon - BH - Beyond Horizon
Aurélien Krauth: LEVIATAN

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Abstract: 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%).

Keywords: Long Short-Term Memory; Gestion de portefeuilles décision d'investissement eXtreme Gradient Boosting Long Short-Term Memory Light Gradient Boosting Gated Recurrent Unit Temporal Convolutional Network modèle à pile modèle GEE-Logit modèle de Mundlak Portfolio management investment decision eXtreme Gradient Boosting Long Short-Term Memory Light Gradient Boosting Gated Recurrent Unit Temporal Convolutional Network stacking model GEE-Logit model Mundlak model 1. Autoregressive moving-average model 2. Autoregressive Integrated Moving Average model 3. Autoregressive Conditional Heteroskedasticity model 4. Generalized AutoRegressive Conditional Heteroskedasticity model; Gestion de portefeuilles; décision d'investissement; eXtreme Gradient Boosting; Light Gradient Boosting; Mundlak model 1. Autoregressive moving-average model 2. Autoregressive Integrated Moving Average model 3. Autoregressive Conditional Heteroskedasticity model 4. Generalized AutoRegressive Conditional Heteroskedasticity model; GEE-Logit model; stacking model; investment decision; modèle de Mundlak Portfolio management; modèle GEE-Logit; modèle à pile; Temporal Convolutional Network; Gated Recurrent Unit (search for similar items in EconPapers)
Date: 2025-07-02
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Published in Conférence Nationale en Intelligence Artificielle (CNIA), Conférence Nationale en Intelligence Artificielle (CNIA), Jul 2025, Dijon, France

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