A Review of Stock Market Volatility Prediction Techniques Based on Machine Learning

Yangzhiping Chen

doi:10.54254/2755-2721/2025.LD28448

Applied and Computational EngineeringOpen access

A Review of Stock Market Volatility Prediction Techniques Based on Machine Learning

Research Article

Open Access

A Review of Stock Market Volatility Prediction Techniques Based on Machine Learning

Yangzhiping Chen ^1*

¹ School of Mathematics and Economics, Sichuan University, Chengdu, China

^*Corresponding author: chenyangzhiping@163.com

Published on 22 October 2025

ACE Vol.196

ISSN (Print): 2755-273X

ISSN (Online): 2755-2721

ISBN (Print): 978-1-80590-451-9

ISBN (Online): 978-1-80590-452-6

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Abstract

Volatility is a crucial indicator for quantifying risk levels, guiding as- set allocation, and assisting in formulating macro policies, so realizing the prediction has important signiﬁcance. Researchers are increasingly applying machine learning techniques, such as support vector machines, LSTM, and deep convolutional networks, to enhance the accuracy of volatility predictions in complex environments. Although numerous studies have proposed diverse methods that combine with machine learning for volatility forecasting, systematic reviews that sort out these methods remain scarce. To ﬁll this gap, this paper systematically summarizes novel models developed by scholars for forecasting stock market volatility, aiming to provide valuable references for researchers to develop new forecasting technologies. By checking 19 empirical studies and 6 review articles, along with authoritative writings, this paper systematically organizes foundational methods related to stock market volatility prediction and demonstrates machine learning- based forecasting techniques. The review finds that machine learning methods such as support vector machine (SVM) and random forest (RF) enhance prediction accuracy by efficiently capturing the nonlinear characteristics of financial data through kernel functions and ensemble learning. Deep learning models, such as LSTM and GRU, excel in forecasting stock market volatility by capturing long-term dependencies and processing complex sequential data, signiﬁcantly improving prediction accuracy compared to traditional models like GARCH. In addition, hybrid models (such as GARCH-LSTM and GARCH-MIDAS) further combine the advantages of econometrics and machine learning, and have demonstrated superiority in multiple empirical studies.

Keywords:

Machine learning, Volatility prediction, Stock market

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