Advancing Stock Return Prediction: A Comprehensive Study of Traditional Machine Learning and Deep Learning Models

DOI

10.2991/978-94-6463-652-9_93How to use a DOI?

Keywords

Machine Learning; Deep Learning; Stock Return

Abstract

Stock markets exhibit high volatility, driven by numerous factors, making accurate stock return prediction a challenging task. This paper provides a comprehensive review of machine learning models used to stock return forecasting, comparing traditional models like Linear Regression (LR), Support Vector Machine (SVM), and Random Forest (RF) with deep learning techniques such as Artificial Neural Networks (ANN), Convolutional Neural Networks (CNN), and Long Short-Term Memory (LSTM) networks. Traditional methods are praised for their interpretability and computational efficiency but frequently fail to convey the non-linear and time-dependent complexities of financial data. In contrast, deep learning models, particularly LSTM, are highly effective in modeling long-term dependencies in stock data but face challenges related to overfitting, interpretability, and generalizability across different market conditions. The paper also highlights the limitations of current models, including their inability to integrate external factors such as geopolitical events and policy changes. To address these issues, potential future research avenues are explored, focusing on enhancing interpretability using interpretability techniques as well as leveraging transfer learning and domain adaptation to improve scalability and model robustness. These advancements could significantly enhance the practical application of machine learning in stock return prediction, offering more reliable and interpretable solutions for real-world financial decision-making.

Copyright

© 2025 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - Yupeng Liu
PY  - 2025
DA  - 2025/02/24
TI  - Advancing Stock Return Prediction: A Comprehensive Study of Traditional Machine Learning and Deep Learning Models
BT  - Proceedings of the International Workshop on Navigating the Digital Business Frontier for Sustainable Financial Innovation (ICDEBA 2024)
PB  - Atlantis Press
SP  - 869
EP  - 876
SN  - 2352-5428
UR  - https://doi.org/10.2991/978-94-6463-652-9_93
DO  - 10.2991/978-94-6463-652-9_93
ID  - Liu2025
ER  -