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Anastasiia Korzhylova
Description of the Problem
Description of the Problem
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The unprecedented challenges posed by COVID-19 in recent years showed the world how important it is to strategically approach crisis management. For investors and businesses in particular, understanding how financial markets respond to the progression of a pandemic is crucial to assess and manage risk, make predictions for investment decisions and develop long-term plans for the future.
Our project aims to investigate the impact of the COVID-19 crisis on the world's economy through the analysis of two comprehensive datasets containing stock market information and Covid-related death toll. Focusing on stocks in NASDAQ, S&P500, and NYSE listed companies, the study explores market trends, sector-specific performance and the behaviour of various technical indicators during different phases of the crisis.
Key Questions to Address
Key Questions to Address
How did stock markets perform during different phases of the Coronavirus crisis?
When did significant market fluctuations occur, and how were they related to major Coronavirus-related events? What trends can be observed?
Did the stock market exhibit heightened volatility during significant spikes in the COVID-19 death toll?
Is there a strong correlation between NASDAQ, S&P500, and NYSE indices, or do they exhibit unique patterns? Were there any notable differences in resilience between the indices?
What were the top-performing and bottom-performing stocks during the crisis, and how did individual stock performance correlate with overall market trends?
Which sectors were most and least affected by the Coronavirus crisis? Did any sectors experience growth during the pandemic?
What conclusions and forecasts can be made about crisis management in different companies?
The results of the analysis will provide insights into stock performance, volatility, and potential investment strategies, contributing to the understanding of the economic implications of the global health crisis.
Description of the Data
Description of the Data
Stock Market Dataset
Stock Market Dataset
Size: 10.23 GB
Source: Kaggle
Content: Daily stock market prices for NASDAQ, S&P500, and NYSE listed companies from March 1980 to October 2022 in CSV format. Additionally includes companies from the Forbes Global 2000 ranking. Columns: Date, Volume, High, Low, Closing Price.
COVID-19 Global Excess Deaths Dataset
COVID-19 Global Excess Deaths Dataset
Size: 10.54 GB
Source: Kaggle
Content: Daily Covid-related death numbers in CSV format classified by area, income group and calculation method. Also provides daily infection numbers by country. Data spans from January 2020 to November 2023.
To find out more about how to access the data in Cloud, its obtaining and its cleansing process, consult the Data documentation page.
Need for Big Data Processing
Need for Big Data Processing
Why Big Data?
Why Big Data?
Data Volume: Processing gigabytes of data distributed over extended periods requires Big Data processing due to its scale.
Data Variety: Cleansing, unifying and analyzing two differently structured datasets with numerous correlated tables poses a challenge to conventional data processing methods.
Limited Local Storage: Utilizing cost-effective Cloud-based solutions is essential for storing the large dataset.
Tools and Infrastructure
Tools and Infrastructure
GitHub Repository: code base, version control, collaboration, backup management.
Python (PySpark, Pandas, matplotlib): programming logic, statistical analysis, visualization.
Google Cloud Platform (GCS, Dataproc, Monitoring): large data storage, parallel processing, resource management, computation outsourcing, process monitoring.
Google Sites: project webpage.
For more details about the (cloud-based) tools, infrastructure and models used in the project, refer to the Tools documentation page. For the app's logic description, code baseline and link to the GitHub repository, proceed to the Code documentation. To see the results of our study, including performance evaluation, reproducible test cases and conclusions, visit the Results webpage.
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