The Influence of Liquidity, Exchange Rate Profitability and Firm Size on Hedging Decision Making in Bank Companies on the Indonesian Stock Exchange
Bibliographic record
Abstract
Risk within banking companies' operational activities is a crucial thing. Investment in the banking financial market has a performance decline as there is uncertainty in gaining higher profits. GDP in the financial service sector declined from 4.49% in the second quarter of 2019 to 1.03% in the second quarter of 2020 with the amount of decline about -77.06% (BPS, 2020). Therefore, banking companies need to do hedging in order to mitigate the risk. This study was quantitative and had a Systematic Literature Review. Moreover, the population was banking companies that had complete financial statements during 2018-2022 and were listed on IDX. Furthermore, the data were secondary and library research. The data analysis technique used discriminant analysis and descriptive analysis. Additionally, the statistical test results showed that liquidity, exchange rate, and firm size of banking companies did not affect hedging decisions. However, profitability which was referred to as ROA affected hedging decisions. It meant the function of discriminant showed that ROA had a strong divide in the companies' tendency of hedging. As a suggestion, the next researcher needed to use other variables outside the study with different years of observation and analysis models; in order to have optimal output
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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 | 0.003 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".