Comparative Analysis of Banking Financial Performance Pre and Post Covid-19 Pandemic
Bibliographic record
Abstract
Covid-19 struck the Indonesian banking industry in particular ASEAN, through the weaker economic growth, which resulted in a slowdown in credit growth and eventually reduce profitability. This study aimed to analyze the financial performance of banks before and after the occurrence of a covid-19 pandemic and formulate alternative strategies to improve the financial performance of Indonesian banks. The study sample consisted of four banks with saturated sampling method (census) are owned banks (State Bank) listed on the Stock Exchange Indonesia. The data in this research is secondary data obtained from the bank's annual report period 2019 until the second quarter of 2020 which is accessed via the IDX website. Performance is measured using the six financial ratios namely ROA, BOPO, NPL, NIM, CAR and LDR with different test analysis method (Paired T-Test). The study found that in the form of financial ratios ROA, BOPO, CAR and LDR pre and post Covid-19 pandemics have significantly different values, while the NPL and NIM did not differ significantly.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".