Research and Analysis of Stock Prediction Based on Deep Learning

DOI

10.2991/978-94-6239-648-7_81How to use a DOI?

Keywords

Stock Prediction; Deep Learning; Convolutional Neural Network; Transformer Neural Network; Bidirectional Encoder Representations from Transformers

Abstract

In recent years, with all the relentless strides in artificial intelligence, deep learning could now be applied to almost all fields, including but not limited to health care, scientific research, and financial analytics. Among these applications, predicting and assessing stock market trends using deep learning is among the most fervent and promising as far as research is concerned. This method overcomes the shortcomings manifest in traditional statistical models, such as linear regression or ARIMA, for providing better prediction accuracy through complicated nonlinear patterns. Incorporating time-series modeling, natural language processing, and deep multimodal learning will allow an investor better to assess risk and opportunities in trading, try to understand the dynamics of the stock market. The paper at hand represents a holistic study of significant studies and updates in the field over the past late five years. Further, stock prediction using deep learning will likely achieve higher reliability, transparency, and interpretability, with mutual reinforcement of further technological innovation and the practicalities of financial management.

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  - Hanyu Zhang
PY  - 2026
DA  - 2026/04/24
TI  - Research and Analysis of Stock Prediction Based on Deep Learning
BT  - Proceedings of the International Workshop on Advances in Deep Learning for Image Analysis and Computer Vision (IWADIC 2025)
PB  - Atlantis Press
SP  - 745
EP  - 754
SN  - 2352-538X
UR  - https://doi.org/10.2991/978-94-6239-648-7_81
DO  - 10.2991/978-94-6239-648-7_81
ID  - Zhang2026
ER  -