From Traditional to Intelligent: The Theoretical Foundations, Method Comparisons, and Challenges of Stock Price Prediction Models

Qiancheng Rong

2025

Abstract

Stock prices are a fundamental component of financial markets, encapsulating collective investor expectations and serving as a crucial basis for economic decision-making. The accurate prediction of price movements remains a longstanding challenge in financial research, driven by the complex, nonlinear, and dynamic nature of market behavior. With the proliferation of high-frequency financial data and advancements in computational methodologies, a diverse array of predictive models has been developed, ranging from traditional statistical techniques to sophisticated machine learning algorithms. This paper aims to provide a comprehensive review of the principal methodologies and recent advancements in stock price forecasting. It covers traditional statistical approaches such as Support Vector Machines (SVM), Random Forests (RF), Long Short-Term Memory networks (LSTM), Convolutional Neural Networks (CNN), and Reinforcement Learning (RL). By examining the underlying mechanisms, performance metrics, and implementation challenges, this paper offers a structured perspective on the diverse methodologies employed in stock price prediction, which contributes to a deeper understanding of their theoretical foundations and key characteristics.

Paper Citation

in Harvard Style

Rong Q. (2025). From Traditional to Intelligent: The Theoretical Foundations, Method Comparisons, and Challenges of Stock Price Prediction Models. In Proceedings of the 2nd International Conference on Data Science and Engineering - Volume 1: ICDSE; ISBN 978-989-758-765-8, SciTePress, pages 660-666. DOI: 10.5220/0013703700004670

in Bibtex Style

@conference{icdse25,
author={Qiancheng Rong},
title={From Traditional to Intelligent: The Theoretical Foundations, Method Comparisons, and Challenges of Stock Price Prediction Models},
booktitle={Proceedings of the 2nd International Conference on Data Science and Engineering - Volume 1: ICDSE},
year={2025},
pages={660-666},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0013703700004670},
isbn={978-989-758-765-8},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 2nd International Conference on Data Science and Engineering - Volume 1: ICDSE
TI - From Traditional to Intelligent: The Theoretical Foundations, Method Comparisons, and Challenges of Stock Price Prediction Models
SN - 978-989-758-765-8
AU - Rong Q.
PY - 2025
SP - 660
EP - 666
DO - 10.5220/0013703700004670
PB - SciTePress