International Journal For Multidisciplinary Research

E-ISSN: 2582-2160   •   Impact Factor: 9.24

A Widely Indexed Open Access Peer Reviewed Multidisciplinary Bi-monthly Scholarly International Journal

Call for Paper Volume 8, Issue 2 (March-April 2026) Submit your research before last 3 days of April to publish your research paper in the issue of March-April.
Submit Research Paper Join as a Reviewer

Plagiarism is checked by the leading plagiarism checker TurnItIn

Call for Paper

Volume 8 Issue 2
March-April 2026

Submit your research paper

Indexing Partners
Academia Advanced Sciences Index Bielefeld Academic Search Engine CiteSeer DRJI Google Scholar Independent Search Engine & Directory Network ISI (International Scientific Indexing) Issuu Mendeley Research Networks
RefSeek ResearcherId - Thomson Reuters ResearchGate Scirus Scribd Semantic Scholar UTeM - Universiti Teknikal Malaysia Melaka Wiki for Call for Papers WorldCat Zenodo

Predictive Analytics in Algorithmic Trading: Evaluating Model Robustness Under Market Volatility

Author(s) Mr. Kushal Dutia
Country India
Abstract Predictive analytics has emerged at the heart of the development of algorithmic trading allowing traders to identify trends in intricate financial data sets, predict market volatility and maximize execution algorithms. Though these models tend to perform well in steady states, their resilience is commonly challenged when markets are hit with sudden shocks or regime changes. This article analyses the performance of various prediction methods ranging from traditional statistical models to complex machine learning and deep learning architectures under unstable environments. Previous research indicates that AI-based trading systems are capable of providing higher returns and better risk management than traditional methods. Yet, empirical evidence indicates that algorithmic strategies could feed back instability due to feedback loops, data drift and investor sentiment dynamics. Novel adaptive and ensemble models show promise to enhance stability and predictive performance under uncertainty. Through integration of recent literature insights, this paper presents a framework to assess model robustness with a focus on trade-offs between accuracy, interpretability and adaptability. The results identify predictive analytics not as a promise of certainty but as a means to develop resilient strategies capable of navigating turbulent financial markets.
Keywords Predictive Analytics, Algorithmic Trading, Model Robustness, Market Volatility, Machine Learning in Finance, Deep Learning, Risk Management, Investor Sentiment, Ensemble Models, Financial Forecasting
Field Sociology > Banking / Finance
Published In Volume 7, Issue 5, September-October 2025
Published On 2025-10-02
DOI https://doi.org/10.36948/ijfmr.2025.v07i05.56960
View / Download PDF File

Share this



E-ISSN 2582-2160
CrossRef

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

IJFMR DOI prefix is
10.36948/ijfmr

Downloads
Research Paper Format Copyright Permission Form and Undertaking Form Cover Page Vol 8 Isu 2Cover Page Vol 8 Isu 1Cover Page Vol 7 Isu 6

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 Open Access

About IJFMR
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
Reviewer Referral Program
Get Reviewer Membership Certi. 		Website/Journal Policies
Usage Policy
Content Policies
Privacy Policy
Contact Us +91-9687-828-838 editor@ijfmr.com
Powered by Sky Research Publication and Journals