|
|||||||||||||||
|
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: | Navigating Economic Uncertainty: Predicting Recessions in the Tech Industry with Data Analysis |
| Authors Name: | Divakar D , Dr. J. Jerald Inico |
| Download E-Certificate: | Download |
| Author Reg. ID: |
IJNRD_216107
|
| Published Paper Id: | IJNRD2403437 |
| Published In: | Volume 9 Issue 3, March-2024 |
| DOI: | |
| Abstract: | The world economy is by its very nature erratic, experiencing upswings and downturns frequently after periods of growth. Despite its reputation for durability and creativity, the tech sector is not impervious to the cyclical nature of economic swings. Predicting recessions is crucial for businesses and politicians to be able to proactively manage risks and allocate resources efficiently during difficult economic periods. In order to identify patterns, trends, and leading indicators of economic downturns, this survey research uses data analysis tools to examine the state-of-the-art in recession prediction within the tech sector. The first part of the abstract acknowledges that economic cycles are unpredictable and that recessions may have an effect on the technology industry. It highlights how crucial precise recession forecasting is to risk management and well-informed decision-making. The study paper's focus on using data analysis approaches to spot early warning signals of economic downturns, particularly in the IT sector, is highlighted in the abstract. The survey paper's main elements—the literature review, methodology, results and analysis, discussion, and conclusion—are then summarized in the abstract. It provides a brief overview of the methodology used to carry out the survey, highlighting the synthesis of previous studies and scholarly works on recession forecasting in the technology industry. The discussion section's critical analysis and interpretation are alluded to in the abstract, which summarizes the primary conclusions and insights from the reviewed literature. The abstract also highlights the survey paper's wider implications for corporations, politicians, and other digital industry stakeholders. It implies that in order to successfully navigate economic uncertainties and downturns, strategy planning, risk assessment, and policy formation efforts can be informed by the insights gathered from the survey. |
| Keywords: | Economic forecasting, Tech industry, Recessions, Predictive modeling, Data analysis, Economic resilience, Digital economy, Technological innovation, Macroeconomic policy, Interdisciplinary research |
| Cite Article: | "Navigating Economic Uncertainty: Predicting Recessions in the Tech Industry with Data Analysis", International Journal of Novel Research and Development (www.ijnrd.org), ISSN:2456-4184, Vol.9, Issue 3, page no.e295-e298, March-2024, Available :http://www.ijnrd.org/papers/IJNRD2403437.pdf |
| Downloads: | 00031 |
| 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:IJNRD2403437 Registration ID: 216107 Published In: Volume 9 Issue 3, March-2024 DOI (Digital Object Identifier): Page No: e295-e298 Country: Chennai, Tamil Nadu, India Research Area: Computer Science & Technology Publisher : IJ Publication Published Paper URL : https://www.ijnrd.org/viewpaperforall?paper=IJNRD2403437 Published Paper PDF: https://www.ijnrd.org/papers/IJNRD2403437 |
| 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