Stock Change Prediction Based on Artificial Intelligence Algorithm

DOI

10.2991/978-94-6463-823-3_69How to use a DOI?

Keywords

Artificial Intelligence; Neural network; Stock Market Forecast

Abstract

Nowadays, the increasing instability of the global economy (such as economic crises happen frequently, data explosion, etc.) has generated the demand for stock market dynamics prediction. Among them, the stock market has the characteristics of nonlinear, violent fluctuations, and easy to be affected by environmental variables, and artificial intelligence is good at analyzing a large number of complex nonlinear data at a short time, and has an outstanding ability to remember historical data, not only stronger than manual analysis, but also stronger than traditional computers, AI can well monitor this situation, so as to improve the prediction results. This paper compares the traditional stock prediction model Particle Swarm Optimization (PSO) and its branch Inertia Weight PSO, discrete PSO, Cooperative PSO, Inertia weight PSO. Moreover, the algorithm combining particle swarm optimization and Principal Component Analysis (PCA) is listed and analyzed. These algorithms are then compared with two Transformer-based algorithms, the Stockformer model and the HIST model. Then, this paper analyzes the basic information of the above algorithms, such as calculation accuracy, data demand, calculation time, calculation cost, interpretability, dynamic adaptability and multi-source data support, and lists which algorithm is the best algorithm under which circumstances. At the end of the article, the contents of the full text are summarized and future prospects.

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  - Zi Wang
PY  - 2025
DA  - 2025/08/31
TI  - Stock Change Prediction Based on Artificial Intelligence Algorithm
BT  - Proceedings of the 2025 3rd International Conference on Image, Algorithms, and Artificial Intelligence (ICIAAI 2025)
PB  - Atlantis Press
SP  - 701
EP  - 712
SN  - 2352-538X
UR  - https://doi.org/10.2991/978-94-6463-823-3_69
DO  - 10.2991/978-94-6463-823-3_69
ID  - Wang2025
ER  -