STOCK MARKET ANALYSIS AND VOLATILITY BEHAVIOR DURING THE COVID-19 PANDEMIC

Authors

• Manoj Venkatachalaiah, Harrisburg University of Science and TechnologyFollow
• Soon Leong Yeap, Harrisburg University of Science and TechnologyFollow
• Salman Ahmed Lnu, Harrisburg University of Science and TechnologyFollow
• Sangwhan Cha, Harrisburg University of Science and TechnologyFollow

Document Type

Article

Publication Date

Summer 6-1-2026

Abstract

This report outlines the structural design, cloud implementation, and analytical findings of a scalable Big Data architecture deployed on Google Cloud Platform (GCP). The primary objective is to investigate the macroeconomic and microeconomic disruption caused by the COVID-19 pandemic on global equities, focusing on two dominant digital business models: online retail/cloud computing (Amazon, Inc. - AMZN) and subscription-based digital streaming entertainment (Netflix, Inc. - NFLX). Through a serverless orchestration pipeline leveraging GCP Cloud Run, automated workflows fetched and blended high-velocity epidemiological metrics alongside daily financial asset layers. Data transformations and parallel analytical calculations were executed utilizing Apache Beam pipelines inside Google Cloud Dataflow, with analytical persistency handled via Google BigQuery. The empirical visualizations confirm a strong positive structural growth pattern for home-bound digital service sectors, as well as distinct volatility structures when exposed to varying market stress levels ('Panic Index').

Program or Discipline Name

Computer and Information Sciences

Recommended Citation

Venkatachalaiah, M., Yeap, S., Lnu, S., & Cha, S. (2026). STOCK MARKET ANALYSIS AND VOLATILITY BEHAVIOR DURING THE COVID-19 PANDEMIC. Retrieved from https://digitalcommons.harrisburgu.edu/other-works/31