 |
International Journal for Research Trends and Innovation
International Peer Reviewed & Refereed Journals, Open Access Journal
ISSN Approved Journal No: 2456-3315 | Impact factor: 8.14 | ESTD Year: 2016
Scholarly open access journals, Peer-reviewed, and Refereed Journals, Impact factor 8.14 (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: April 2026
Volume 11 | Issue 4
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 : 8.14
Issue per Year : 12
Volume Published : 11
Issue Published : 119
Article Submitted : 23355
Article Published : 9033
Total Authors : 23952
Total Reviewer : 831
Total Countries : 162
Indexing Partner
Licence
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License
|
Published Paper Details
|
|
| Paper Title: | STOCK MARKET PREDICTION DURING COVID-19 USING LSTM |
| Authors Name: | Ashutosh Singh , Bhramari Verma |
| Download E-Certificate: | Download |
| Author Reg. ID: |
IJRTI_211081
|
| Published Paper Id: | IJRTI2604031 |
| Published In: | Volume 11 Issue 4, April-2026 |
| DOI: | |
| Abstract: | The conventional linear models are unable to cope with the tremendous variability that is posed by this crisis, which has engulfed the world. In this context, in this study, the power of Long Short-Term Memory has been employed to understand the effect of the disruptive footprint of this pandemic on the Healthcare, IT, and Real Estate sectors in India. Contrary to the traditional approach of treating the pandemic as an outlier, the present study integrates the daily infection curves directly into the deep learning mechanism, in effect, compelling the model to learn the pandemic’s momentum in real-time. In this regard, our assessment based on Root Mean Square Error (RMSE) metric verifies the high predictive consistency of all sectors. Unlike traditional econometric tools that fail to filter through the noise of the markets, the architecture of the LSTM model was able to decode the erratic patterns of the Indian equity markets with uniform accuracy. This shows that the layers of deep learning have the ability to tap into the non-linear patterns that exist within financial markets, even when the economy is in a state of free fall. The study thus contributes to understanding sectoral behavior under crisis conditions and provides significant implications for both investors and financial analysts. |
| Keywords: | LSTM, COVID-19, stock market prediction, Indian stock market, sectoral analysis, time series forecasting |
| Cite Article: | "STOCK MARKET PREDICTION DURING COVID-19 USING LSTM", International Journal for Research Trends and Innovation (www.ijrti.org), ISSN:2456-3315, Vol.11, Issue 4, page no.a210-a215, April-2026, Available :http://www.ijrti.org/papers/IJRTI2604031.pdf |
| Downloads: | 000109 |
| ISSN: |
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 |
| Publication Details: |
Published Paper ID: IJRTI2604031
Registration ID:211081
Published In: Volume 11 Issue 4, April-2026
DOI (Digital Object Identifier):
Page No: a210-a215 Country: Lucknow, Uttar Pradesh, India Research Area: Computer Science & Technology Publisher : IJ Publication Published Paper URL : https://www.ijrti.org/viewpaperforall?paper=IJRTI2604031 Published Paper PDF: https://www.ijrti.org/papers/IJRTI2604031 |
| Share Article: | |
|
Click Here to Download This Article |
|
| Article Preview | |
|
|
|
Major Indexing from www.ijrti.org
| Google Scholar | ResearcherID Thomson Reuters | Mendeley : reference manager | Academia.edu |
| arXiv.org : cornell university library | Research Gate | CiteSeerX | DOAJ : Directory of Open Access Journals |
| DRJI | Index Copernicus International | Scribd | DocStoc |
ISSN Details
 |
 |
|
ISSN: 2456-3315
Impact Factor: 8.14 and ISSN APPROVED,
Journal Starting Year (ESTD) : 2016
DOI (A digital object identifier)
| Providing A digital object identifier by DOI.ONE How to Get DOI? |
Conference
Open Access License Policy
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License
Important Details
Join RMS/Earn 300
WhatsApp
Click Here
Click Here
Indexing Partner |
|||
| Copyright © 2026 - All Rights Reserved - IJRTI | |||