Comparative Analysis of Financial Performance before And during Covid-19 Pandemic
Bibliographic record
Abstract
This study assesses the financial performance of technology sector firms listed on the IDX by utilizing various financial ratios, including Return on Assets, Total Assets Turnover, Current Ratio, Debt to Equity Ratio, and Sales Growth. The study employs a quantitative approach with multiple regression analysis, and the research relies on secondary data gathered from financial reports spanning from the third quarter of 2018 to the second quarter of 2021. The sample selection method employed purposive sampling, resulting in a sample size of nine companies. The normality of the data was assessed using the Kolmogorov-Smirnov method, revealing a non-normal distribution. As a result, the non-parametric Wilcoxon Signed Rank test was applied. The findings indicate significant disparities in the financial performance of technology sector companies listed on the IDX before and during the Covid-19 pandemic, particularly in metrics such as Total Assets Turnover, Current Ratio, Debt to Equity Ratio, and Sales Growth. However, the Return on Assets variable did not significantly differ before and during the Covid-19 pandemic. These insights can be valuable for stakeholders such as investors, creditors, and regulators in comprehending the associated risks and potential impacts when considering investment or extending credit to these entities
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".