Strategies to reduce credit risk and liquidity risk to increase bank profitability
Bibliographic record
Abstract
The purpose of this study is to examine the effect of credit risk and liquidity risk on profitability with loan restructuring and income diversification as moderating variables. The research population is all general banking companies, which were listed on the Indonesia Stock Exchange (IDX) during the period 2018-2021. The research sample was created using the purposive sampling technique and 160 observations were obtained. This study conducts panel data regression analysis using EViews 12 software. The results of this study indicate that an increase in credit risk reduces profitability, liquidity risk does not affect profitability, a loan-restructuring strategy can reduce the effect of credit risk on profitability, and an income-diversification strategy can reduce the effect of liquidity risk on bank profitability. The research findings provide an understanding of banking strategy, namely loan restructuring and income diversification can increase banking profitability under urgent conditions. This study provides support for contingency theory and stakeholder theory. The limitation of this research is that it does not discuss Islamic banking because the policies of those companies are different in terms of rules and there are limited data.
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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".