Understanding Historical Stock Segmentation, Futures and Organizational Performance of key industrial sectors before, during and post the COVID-19 pandemic.
Coronavirus, since it's first news in December 2019 has changed almost every aspect of our lives. On March 11, 2020, WHO declared COVID-19 a global pandemic and March 12 was declared a Black Thursday in the United States, giving in to the largest single day market crash since 1987. This was no coincidence. The pandemic had officially started crippling world economy. Stock Markets do not deal well with uncertainity, as history has often shown us, this was going to be one of the most uncertain periods of time mankind had seen.
But not all has gone bad. Over the past 2 years of living with the pandemic, we as humans have grown and evolved, as we always do. Companies like Apple and Microsoft saw their all time highs during the pandemic while there were companies that went bankrupt and closed. So, what factored into these radical differences? What happened in the last 2 years that flourished some, while whitewashed others? In this project, we aim to solve this mystery. Below are some of the formal questions we would be attempting to answer:
- Analysis of repetitive patterns in companies to understand the impact of annual events (Christmas, Halloween, Black Friday, etc.) and quarterly events (dates of SOFR announcement, quarterly earnings report, etc.) on different industries.
- Observe the people’s confidence in the stock market throughout COVID-19 by analyzing the behavior of stock fluctuations in various sectors of the economy (agriculture, healthcare, consumer good, electronics, technology, etc.)
- Superimpose that analysis to specific companies that were in the news for production of vaccines. Analyse the difference in performance of vaccine-producing pharmaceutical companies to other industries throughout COVID-19.
a. Introduction to the Problem: Explanation and introduction to the problem statement.
b. Data Sources and Structure: Links to the sources of financial data and the structure of raw data being used.
c. Transformation, Cleaning and Feature Selection: Steps taken to convert the raw data to its usable, transformed form.
d. Treating Missing Values: Removing missing values from the data.
e. Analysis Results: Answering the questions asked in the problem statement and perform additional exploratory analysis to complete the project's analysis outlook.
f. Analysis through interactive charts: Building interactive plots to represent the data and corresponding analysis.
g. Conclusion and Future Work: Final comments and score for the next phase of work.
This repo was initially generated from a this bookdown template, and a special thanks to Professor Joyce for the class, concepts and the opportunity for this open-source contribution.