Computational Approaches to Stock Price Prediction

DOI

10.2991/978-94-6239-648-7_57How to use a DOI?

Keywords

Stock Price Prediction; Machine Learning; Deep Learning; Financial Forecasting; Computational Finance

Abstract

Stock price prediction is one of the most significant and difficult problems of computational finance. As the volume of data and computing capabilities have rapidly increased, forecasting models have shifted toward a data-driven forecasting method based on machine learning and deep learning. In this paper, a comparative review of three key groups of stock prediction methods is presented. The initial part is about the conventional methods that are defined by technical and fundamental indicators wherein the interpretability of the technique and its limitation is discussed. The second section will discuss machine learning techniques of regression models, support vector machines, and a tree-based algorithm using an empirical example of Apple Inc. And Google to explain performance and errors. The third segment presents the deep learning algorithms, such as the Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU), and Convolutional Neural Network (CNN) that are the current leading edge in the field of financial forecasting. This paper finds that the deep learning models tend to be superior in nonlinearity and long-term modeling nonlinear and long-term dependencies compared to classical methods, with the tradeoff that such models do need large datasets and are more expensive to compute. The results indicate the way in which the transformation between traditional analysis and the deep learning reflects the broader transformation in global financial data analysis and decision-making.

Copyright

© 2026 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  - Jiachen Liu
PY  - 2026
DA  - 2026/04/24
TI  - Computational Approaches to Stock Price Prediction
BT  - Proceedings of the International Workshop on Advances in Deep Learning for Image Analysis and Computer Vision (IWADIC 2025)
PB  - Atlantis Press
SP  - 516
EP  - 523
SN  - 2352-538X
UR  - https://doi.org/10.2991/978-94-6239-648-7_57
DO  - 10.2991/978-94-6239-648-7_57
ID  - Liu2026
ER  -