|
|||||||||||||||
|
INTERNATIONAL JOURNAL OF NOVEL RESEARCH AND DEVELOPMENT International Peer Reviewed & Refereed Journals, Open Access Journal ISSN Approved Journal No: 2456-4184 | Impact factor: 8.76 | ESTD Year: 2016 Scholarly open access journals, Peer-reviewed, and Refereed Journals, Impact factor 8.76 (Calculate by google scholar and Semantic Scholar | AI-Powered Research Tool) , Multidisciplinary, Monthly, Indexing in all major database & Metadata, Citation Generator, Digital Object Identifier(DOI) |
||||||||||||||
Issue: May 2024
Volume 9 | Issue 5
Review Result and Publication of Paper within : 2-3 days
Click Here For more DetailsFor Authors
Forms / Download
Published Issue Details
Editorial Board
Other IMP Links
Facts & Figure
Impact Factor : 8.76
Issue per Year : 12
Volume Published : 9
Issue Published : 96
Article Submitted :
Article Published :
Total Authors :
Total Reviewer :
Total Countries :
Indexing Partner
Join RMS/Earn 300
Licence
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License
|
Published Paper Details
|
|
| Paper Title: | Comparative Analysis Of Various Hybrid Models Over Stock Market Dataset |
| Authors Name: | Rishabh Saxena , Sandeep Kumar |
| Download E-Certificate: | Download |
| Author Reg. ID: |
IJNRD_216482
|
| Published Paper Id: | IJNRD2403521 |
| Published In: | Volume 9 Issue 3, March-2024 |
| DOI: | |
| Abstract: | The world of financial markets faces a formidable challenge when it comes to accurately anticipating stock market movements. Conventional prediction techniques have struggled to contend with the intricate and uncertain nature of market dynamics, often yielding suboptimal outcomes. Inaccurate forecasts can have far-reaching implications, impacting investment strategies, financial choices, and overall economic stability. Consequently, there is an urgent demand for the exploration of fresh and inventive methods that can bolster our capacity to forecast stock prices with increased accuracy and dependability. This study investigates the potential of hybrid Machine-Learning (ML) models as a promising remedy to this persistent issue. This research presents a comparative analysis between multiple hybrid models applied to stock market datasets. These models were assessed using three distinct datasets spanning the years 2022-2023, 2021-2023, and 2018-2023 for five major stocks: RELIANCE, TCS, HDFC, ITC, and INFOSYS. Result dictate that ARIMA-HMM and RDAWA are the two models from the chosen ones that provide good results. Out of the two, ARIMA gives the best perform with an accuracy of 80% and metrics sitting under 0.3 for all datasets. Following that, RDAWA gives a good and robust perform with an accuracy of 70% to 75% with metrics sitting under 0.3 for RELIANCE and TCS. |
| Keywords: | Stock market, forecasting models, predicting model, market closing price, hidden Markov model, ARIMA, MLP, Random Forest, RDA |
| Cite Article: | "Comparative Analysis Of Various Hybrid Models Over Stock Market Dataset", International Journal of Novel Research and Development (www.ijnrd.org), ISSN:2456-4184, Vol.9, Issue 3, page no.f194-f205, March-2024, Available :http://www.ijnrd.org/papers/IJNRD2403521.pdf |
| Downloads: | 00035 |
| 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 |
| Publication Details: |
Published Paper ID:IJNRD2403521 Registration ID: 216482 Published In: Volume 9 Issue 3, March-2024 DOI (Digital Object Identifier): Page No: f194-f205 Country: Greater Noida, Uttar Pradesh, India Research Area: Computer Science & Technology Publisher : IJ Publication Published Paper URL : https://www.ijnrd.org/viewpaperforall?paper=IJNRD2403521 Published Paper PDF: https://www.ijnrd.org/papers/IJNRD2403521 |
| Share Article: |
|
|
Click Here to Download This Article |
|
| Article Preview | |
|
|
|
Major Indexing from www.ijnrd.org
| Semantic Scholar | Microsaoft Academic | ORCID | Zenodo |
| Google Scholar | ResearcherID Thomson Reuters | Mendeley : reference manager | Academia.edu |
| arXiv.org : cornell university library | Research Gate | CiteSeerX | PUBLON |
| DRJI | SSRN | Scribd | DocStoc |
ISSN Details
 |
 |
|
ISSN: 2456-4184
Impact Factor: 8.76 and ISSN APPROVED
Journal Starting Year (ESTD) : 2016
DOI (A digital object identifier)
Conference
Open Access License Policy
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License
Important Details
Social Media
Licence
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License
Join RMS/Earn 300
| Copyright © 2024 - All Rights Reserved - IJNRD |
Facebook Twitter Instagram LinkedIn