Pre and Post-COVID-19 Financial Performance of Oil and Gas Companies: An Absolute and Relational Study of Financial Variables
Bibliographic record
Abstract
The outbreak of COVID-19 affected all aspects of individuals at a worldwide level.It harmed the physically and economically of individuals, and lower the growth of economies, globally.India the third largest consumer of oil and gas was affected by the pandemic, negatively.The study is based on the data collected from the financial statements of Indian oil and gas companies available on the websites.The purpose of the study is to know the financial performance of the Indian oil and gas companies pre and post-COVID-19 pandemic period.The absolute and relational financial variables are applied to get the absolute trend and relational growth of the financial performance of Indian oil and gas companies in graphical form.To explore the profit-earning capacity, short-term paying ability, and long-term paying ability of the Indian oil and gas companies, profitability (profit before tax ratio) ratio, liquidity (current ratio) ratio, and solvency (Debt-Equity ratio) ratio were applied.From 2015 to 2021, the absolute values of revenues, total expenses, and profit before tax were applied to get the trend in graphical form while stacked column charts were prepared to compare the profitability, liquidity, and long-term paying ability of the Indian oil and gas companies.During the pandemic period, total revenues, total expenses, profits, and profitability declined while liquidity and solvency status was unaffected by COVID-19 in Indian oil and gas companies.The profitability of the smaller Indian oil and gas companies improved more than the larger Indian oil and gas companies after the COVID-19 pandemic.It is found that the large-scale production of Indian oil and gas companies was affected more than the smallerscale production of Indian and gas companies by the negativity of the pandemic COVID-19 due to fixed expenses.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".