The Effects of Liquidity Risk and Interest-Rate Risk on Profitability and Firm Value among Banks in ASEAN-5 Countries
Bibliographic record
Abstract
This study explores the issues relating to liquidity risk and interest-rate risk, recognizing that existing studies are mostly vague in emerging and developing markets. Panel data estimation technique is employed in the study based on data extracted from 63 commercial banks in ASEAN-5 countries over the period 2009 to 2017 making up to 567 observations. The empirical results reveal that loan to deposit ratio have a positive significant effect on firm value while liquid asset ratio, interest rate risk (net interest margin and asset interest yield) have a negative significant effect on firm value for ASEAN. The loan to deposit ratio have a positive significant impact on return on asset, interest rate risk and banks size have a significant negative effect on return on asset for ASEAN banks while GDP and inflation have a positive significant effect on return on asset. Also, the liquidity risk have a negative significant effect on return on equity while the interest rate risk have a positive significant effect, bank size have a significant negative effect on return on equity while inflation rate have a positive significant impact on return on equity. Hence, this empirical study provides implications that emphasizes on the need for banks to adhere to prudential and regulatory guidelines and ensure corporate management with respect to liquidity exposure that is capable of critically affecting banks profitability and firm value. The dynamics of interest rate volatility in banks operating environment necessitates that financial institutions use sound risk management practices in order to obtain higher valuations, achieve better financial performance and experience diminished costs of financial distress that's useful for policy implementations in ASEAN economies and suggest that further study can explore the interaction between abnormal loan growth and non-performing loans with a robust econometrics model.
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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 | 0.002 |
| 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".