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 1 (January-February 2026) Submit your research before last 3 days of February to publish your research paper in the issue of January-February.
Submit Research Paper Join as a Reviewer

Plagiarism is checked by the leading plagiarism checker TurnItIn

Call for Paper

Volume 8 Issue 1
January-February 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

Machine Learning Techniques for Quantitative Stock Trading Strategies

Author(s) Mr. Kartik Garg
Country India
Abstract The paper systematically examines the conceptual underpinnings of quantitative trading and machine learning, followed by a detailed discussion of data sources and feature engineering practices commonly employed in ML-based trading systems. It reviews the use of supervised, unsupervised, deep learning, and reinforcement learning models for return prediction, portfolio optimization, and trade execution. Particular attention is given to model evaluation frameworks, backtesting methodologies, and performance metrics used to assess economic significance while mitigating risks such as overfitting and data snooping bias. The review highlights that while machine learning models often outperform traditional approaches in predictive accuracy, their real-world effectiveness is constrained by transaction costs, model decay, interpretability challenges, and market microstructure effects. Furthermore, the paper discusses emerging research directions, including explainable artificial intelligence, federated learning, ESG-aware trading, and the integration of macroeconomic and geopolitical data. Overall, the study concludes that machine learning serves as a powerful complement rather than a substitute for financial theory, and that future advancements will depend on hybrid models that balance predictive performance, economic intuition, and ethical responsibility.
Keywords Machine Learning; Quantitative Trading; Algorithmic Trading; Financial Markets; Reinforcement Learning; Feature Engineering; Stock Market Prediction
Published In Volume 8, Issue 1, January-February 2026
Published On 2026-01-08
DOI https://doi.org/10.36948/ijfmr.2026.v08i01.65636
Short DOI https://doi.org/hbjmg6
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 7 Isu 6Cover Page Vol 7 Isu 5Cover Page Vol 7 Isu 4

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