A Contract Theory Approach to Islamic Financial Securities with an Application to Diminishing Mushārakah
Bibliographic record
Abstract
This paper demonstrates how the contract theory framework can and should complement standard financial mathematics for analysing Islamic financial securities (IFSs). It is motivated by the perception that most valuations of IFSs are rather simplistic and are as simple as risk and reward, leading to very simplistic investment strategies, especially by buyers. In fact, there are more dimensions to IFSs and IF in general which can only be properly analysed with more advanced approaches, such as contractual issues which are well-recognised and discussed in the fields of Islamic commercial law and contract theory but not always considered in valuation models. Contract theory can bring together financial mathematics and contractual issues, providing a more sophisticated framework for analysing IFSs. This paper aims to demonstrate this by providing a brief outline of the contract theory approach, followed by a simple demonstration of its use in the analysis of diminishing mushārakah (DM) contracts. The resulting model led to three main conclusions regarding DM contracts: That (i) finance seekers have no ready incentive to spend on asset maintenance, (ii) finance seekers will only spend on asset maintenance if their marginal benefit from the asset’s appreciation is greater than the financier’s share of the asset, and (iii) if the magnitude of asset appreciation and depreciation is equal, an increase in either will also increase the optimal level of spending on asset maintenance.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.007 |
| Scholarly communication | 0.002 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".