Impact from a Financial Perspective on Retailing, Luxury, and Technological Companies that COVID-19 Caused and How It Continues
Bibliographic record
Abstract
In 2020, the whole society was hit by an unexpected virus -- COVID-19. The virus expanded fast and influenced all around the world. It changed the routine lives of people, and it affected the economy in some ways. The consumption habits of consumers were also affected. This paper will discuss three main industries --Retailing, Luxury, and Technology, and evaluate how COVID-19 impacted them, by choosing six companies from these three industries and importing Income Statements, Balance Sheets, and Cash flows from 2019, which before COVID-19, to 2022. In order to digging and support the effects, this paper chooses to use some financial metrics to evaluate different aspects. The metrics that I choose are Profitability Metrics, including Earnings Per share, and Sales Growth, Liquidity Metrics, which reflect the company’s ability to pay back its short-term liabilities. This article finds out that although COVID-19 influenced these industries from 2020 to 2021, they all demonstrated flexibility in facing the global crisis and led to growth after the pandemic.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".