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International Journal for Research Trends and Innovation
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ISSN Approved Journal No: 2456-3315 | Impact factor: 8.14 | ESTD Year: 2016
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Impact Factor : 8.14
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| Paper Title: | Comparative Study of Traditional (Pre-ML) and Modern (Post-ML) Methods to Predict the Indian Stock Market Index |
| Authors Name: | Mr. Rameshbhai M. Patelia , Dr. Nayan S. Patel |
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IJRTI_205047
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| Published Paper Id: | IJRTI2506192 |
| Published In: | Volume 10 Issue 6, June-2025 |
| DOI: | |
| Abstract: | This paper presents a comparative analysis of traditional (pre-machine learning) and modern (post-machine learning) approaches used to predict the movement of the Indian stock market index, specially for Nifty. Traditional methods such as technical analysis, fundamental analysis, Sentiment analysis and option chain analysis are evaluated along with modern machine learning and deep learning algorithms. The aim is to explore the advantages, disadvantages, and predictive power of both paradigms in the context of the Indian stock market. |
| Keywords: | Nifty prediction, traditional methods, machine learning, deep learning, stock market forecasting, Indian stock index, time series analysis |
| Cite Article: | "Comparative Study of Traditional (Pre-ML) and Modern (Post-ML) Methods to Predict the Indian Stock Market Index", International Journal for Research Trends and Innovation (www.ijrti.org), ISSN:2455-2631, Vol.10, Issue 6, page no.b756-b762, June-2025, Available :http://www.ijrti.org/papers/IJRTI2506192.pdf |
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2456-3315 | IMPACT FACTOR: 8.14 Calculated By Google Scholar| ESTD YEAR: 2016 An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 8.14 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator |
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Published Paper ID: IJRTI2506192
Registration ID:205047
Published In: Volume 10 Issue 6, June-2025
DOI (Digital Object Identifier):
Page No: b756-b762 Country: Anand, Gujarat, India Research Area: Computer Science & Technology Publisher : IJ Publication Published Paper URL : https://www.ijrti.org/viewpaperforall?paper=IJRTI2506192 Published Paper PDF: https://www.ijrti.org/papers/IJRTI2506192 |
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ISSN: 2456-3315
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