STOCK MARKET GRAPH PREDICTION

Registration ID: IJNRD_220490

Published ID: IJNRD2405230

DOI: Click Here to Get

M.Aruna Devi , K.S. Mukdhesh Kannapiran , S.Pravin Kumar , M.Porrus Jenix Raja

Stock market prediction, Predictive accuracy, Decision Making Algorithm Finance Investment Strategies, Algorithmic trading

Traditional methods often rely on statistical models and machine learning algorithms to forecast market trends. However, integrating class types into the prediction process can provide a deeper understanding of market behavior and improve prediction accuracy. This paper presents a novel approach to stock market graph prediction by incorporating class types into the analysis. The proposed methodology involves three main steps: data preprocessing, feature extraction, and prediction model development. Firstly, historical stock market data is collected and pre-processed to remove noise and ensure data quality. Next, various class types are identified within the data, such as bull, bear, and sideways markets, using advanced clustering techniques. Features are then extracted from each class type to capture their distinct characteristics and patterns. Based on the extracted features, prediction models are trained using machine learning algorithms such as support vector machines, random forests, and recurrent neural networks. These models are tailored to each class type, allowing for more accurate predictions under different market conditions. Additionally, ensemble techniques are employed to combine the predictions from individual models, further enhancing overall performance. Comparative analysis with traditional methods reveals significant improvements in prediction accuracy, particularly during volatile market periods. Furthermore, the interpretability of the class-based models enables investors to gain valuable insights into market dynamics and make informed decisions. In conclusion, integrating class types into stock market graph prediction offers a promising avenue for enhancing forecasting accuracy and understanding market behavior.

Published Paper   E-Certificate

M.Aruna Devi, K.S. Mukdhesh Kannapiran, S.Pravin Kumar, & M.Porrus Jenix Raja (May-2024). STOCK MARKET GRAPH PREDICTION. INTERNATIONAL JOURNAL OF NOVEL RESEARCH AND DEVELOPMENT, 9(5), c262-c271. https://ijnrd.org/papers/IJNRD2405230.pdf

Volume 9 Issue 5, May-2024

Pages : c262-c271

Paper Reg. ID: IJNRD_220490

Published Paper Id: IJNRD2405230

Downloads: 000121991

Research Area: Information Technology 

Country: Tirunelveli, Tamilnadu, India

Published Paper PDF: https://ijnrd.org/papers/IJNRD2405230.pdf

Published Paper URL: https://ijnrd.org/viewpaperforall?paper=IJNRD2405230

Journal Name: INTERNATIONAL JOURNAL OF NOVEL RESEARCH AND DEVELOPMENT(IJNRD)

ISSN: 2456-4184 | IMPACT FACTOR: 8.76 Calculated By Google Scholar | ESTD YEAR: 2016

An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 8.76 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator

Publisher: IJNRD (IJ Publication) Janvi Wave | IJNRD.ORG | IJNRD.COM | IJPUB.ORG

This work is licensed under a Creative Commons Attribution 4.0 International License and The Open Definition

Call For Paper - Volume 10 | Issue 10 | October 2025

IJNRD is a Scholarly Open Access, Peer-reviewed, and Refereed Journal with a High Impact Factor of 8.76 (calculated by Google Scholar & Semantic Scholar | AI-Powered Research Tool). It is a Multidisciplinary, Monthly, Low-Cost Journal that follows UGC CARE 2025 Peer-Reviewed Journal Policy norms, Scopus journal standards, and Transparent Peer Review practices to ensure quality and credibility. IJNRD provides indexing in all major databases & metadata repositories, a citation generator, and Digital Object Identifier (DOI) for every published article with full open-access visibility.

The INTERNATIONAL JOURNAL OF NOVEL RESEARCH AND DEVELOPMENT (IJNRD) aims to advance applied, theoretical, and experimental research across diverse fields. Its goal is to promote global scientific information exchange among researchers, developers, engineers, academicians, and practitioners. IJNRD serves as a platform where educators and professionals can share research evidence, models of best practice, and innovative ideas, contributing to academic growth and industry relevance.

Indexing Coverage includes Google Scholar, SSRN, ResearcherID-Publons, Semantic Scholar (AI-Powered Research Tool), Microsoft Academic, Academia.edu, arXiv.org, ResearchGate, CiteSeerX, ResearcherID (Thomson Reuters), Mendeley, DocStoc, ISSUU, Scribd, and many more recognized academic repositories.

How to submit the paper?

By Our website

Click Here to Submit Paper Online

By Mail

Send your paper to editor@ijnrd.org

Important Dates for Current issue

Paper Submission Open For: October 2025

Current Issue: Volume 10 | Issue 10 | October 2025

Impact Factor: 8.76

Last Date for Paper Submission: Till 31-Oct-2025

Notification of Review Result: Within 1-2 Days after Submitting paper.

Publication of Paper: Within 01-02 Days after Submititng documents.

Frequency: Monthly (12 issue Annually).

Journal Type: IJNRD is an International Peer-reviewed, Refereed, and Open Access Journal with Transparent Peer Review as per the new UGC CARE 2025 guidelines, offering low-cost multidisciplinary publication with Crossref DOI and global indexing.

Subject Category: Research Area

Call for Paper: More Details