Comparative Analysis of Machine Learning Models for Stock Market Volatility Prediction

Sophia Mitchell

Comparative Analysis of Machine Learning Models for Stock Market Volatility Prediction

Author(s)	Sophia Mitchell
Country	United States
Abstract	Stock market volatility reflects variations in asset-price movements and represents an important component of financial risk assessment, portfolio management, derivative valuation, and market monitoring. Volatility exhibits persistence, clustering, nonlinear behavior, and occasional abrupt changes, making accurate prediction methodologically challenging. Conventional econometric models provide an established foundation for volatility forecasting, while machine learning offers alternative methods capable of representing nonlinear relationships among historical volatility, returns, trading activity, and related market indicators. This hypothetical methodological study compares three machine learning approaches for stock market volatility prediction: support vector regression, random forest regression, and an artificial neural network. A fictional financial dataset representing 3,000 trading days is divided into training, validation, and testing subsets. Daily realized volatility is conceptually derived from intraday return observations, while predictor variables include lagged volatility, absolute return, trading-volume change, price range, and short- and medium-term volatility averages. The illustrative analysis evaluates forecasting performance through mean absolute error, root mean squared error, and QLIKE loss. Under the assigned hypothetical values, the artificial neural network records the lowest overall forecasting error, followed closely by support vector regression. A second analysis examines model behavior across low-, medium-, and high-volatility regimes and demonstrates that prediction errors increase during highly volatile market conditions. These numerical outcomes are entirely illustrative rather than experimentally observed. The study concludes that comparative volatility forecasting should examine several machine learning methods under identical temporal validation conditions rather than assuming that one model is universally superior. Model performance must also be assessed across different volatility regimes and against established econometric benchmarks. Actual financial forecasting claims require real market data, out-of-sample testing, statistical comparison, and evaluation over multiple market periods.
Keywords	Stock Market Volatility, Machine Learning, Volatility Prediction, Support Vector Regression, Random Forest, Artificial Neural Network, Realized Volatility, Financial Forecasting, Predictive Analytics, Quantitative Finance.
Field	Engineering
Published In	Volume 1, Issue 5, November-December 2025
Published On	2025-12-26

View / Download PDF File

E-ISSN: 3067-5073

doi

CrossRef DOI is assigned to each research paper published in our journal.

IJMLAIDSE DOI prefix is
10.00000/IJMLAIDSE

Downloads

Research Paper Format Copyright Permission and Undertaking Form

All research papers published on this website are licensed under Creative Commons Attribution-ShareAlike 4.0 International License, and all rights belong to their respective authors/researchers.

CC-BY-SA

About IJMLAIDSE Fees & Payment Current Issue Publication Archive	Submit Research Paper Track Submission Status Publication Guidelines Publication Ethics Peer Review & Plagiarism	Join as a Reviewer Editors & Reviewers Get Reviewer Membership Certi.	Website/Journal Policies Usage Policy Content Policies Privacy Policy

Contact Us	+1-872-302-3851	editor@uscse.com

International Journal of Machine Learning, AI & Data Science Evolution

A Widely Indexed Open Access Peer Reviewed Multidisciplinary Monthly Scholarly International Journal

Comparative Analysis of Machine Learning Models for Stock Market Volatility Prediction

Share this