The Application of Variance-based Structural Equation Modeling for Predicting the Intermediation Margin of Islamic Banking Industry
Bibliographic record
Abstract
Abstract The purpose of this paper is to predict the determinants of bank margins (bankspecific as well as macroeconomic condition) in Islamic banks by applying SEM-PLS. Data were collected through financial statements of 11 Islamic banks in Indonesia obtained in each website of bank, covering bank quarter observations for the period of 2013 to the second quarter of 2018. The results of this study indicate the specific factors of banks that have the greatest influence on NIM are liquidity variables. In contrast, macroeconomic factors (GDP and inflation) do not have a significant effect on Islamic bank NIMs, but specific bank and macroeconomic factors together affect Islamic bank NIMs. while the macroeconomic condition is not significant. In this study using the method of investigating the determinants of the margin of financial intermediation for Islamic banks operating in Indonesia by applying SEM-PLS. This finding improves our understanding on the usage of SEM-PLS to predict the margin intermediation of Islamic banks. This study provides a guidance and strategy for Islamic bank managers to manage their intermediation margin which can affect their customers interest to use Islamic banking services.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".