Exploring Nvidia's evolution, innovations, and future stock trends

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Authors
Wang, John
Hsu, Jeffrey
Qin, Zhaoqiong
Advisors
Issue Date
2024-06
Type
Article
Keywords
Innovations , AI , Machine learning , Random Forest , Support vector machine , Deep learning applications
Research Projects
Organizational Units
Journal Issue
Citation
Wang, J., Hsu, J., & Qin, Z. (2024). Exploring NVIDIA's evolution, innovations, and future stock trends. Journal of Management & Engineering Integration, 17(1), 21-33. https://doi.org/10.62704/10057/28082
Abstract

This paper undertakes a thorough examination of Nvidia's stock market performance, intertwining historical analysis with forward-looking projections to illuminate the dynamic trajectory of this semiconductor industry giant. Commencing with a retrospective review, the authors delve into pivotal milestones, technological innovations, and strategic maneuvers that have shaped Nvidia's stock evolution. Utilizing advanced machine learning algorithms, including Random Forest and Support Vector Regression (SVR), alongside traditional statistical forecasting methods, we forecast future patterns. Through the incorporation of emerging industry dynamics, technological advancements, and market forecasts, our goal is to furnish stakeholders with insights pivotal for strategic decision-making. This dual perspective, encompassing both historical retrospection and future outlook, weaves a holistic narrative capturing the essence of Nvidia's stock market journey. It serves as a valuable resource for avid readers navigating the ever-changing landscape of the semiconductor market, especially considering the expanding role of Nvidia's GPUs in Artificial Intelligence (AI) and deep learning applications.

Table of Contents
Description
Published in SOAR: Shocker Open Access Repository by the Wichita State University Libraries Technical Services, July 2024.
Publisher
Association for Industry, Engineering and Management Systems (AIEMS)
Journal
Book Title
Series
Journal of Management & Engineering Integration
v.17 no.1
PubMed ID
ISSN
1939-7984
EISSN