Due Diligence and Risk Alleviation in Innovative Ventures—An Alternative Investment Model from Islamic Finance
Bibliographic record
Abstract
Risk is a big concern for anyone contemplating investing in new, especially innovative ventures. However, if successful, the returns can be extraordinary, serving as an impetus for many venture capitalists to provide greater funding. Still, many new ventures never see the end of the tunnel, and success stories are scant. The venture capital market is growing, yet many investors feel on edge when investing in new and innovative ventures. This paper is based on field survey data to evaluate the importance of risk and return components of an alternative venture investment approach called diminishing Musharakah (DM). DM has roots in Islamic modes of investment that are more suited for ventures with a higher risk profile. This paper focuses on four key ingredients, i.e., due diligence (DD), flexibility (Flex), moral hazard reduction (MHR), and risk reduction (RR) inherent in this mode of investment. All these components contribute towards the end goal of any investment, i.e., value enhancement (VE). DM is based on investment modes approved by Islamic law, called Shariah, and Islamic jurisprudence, called Fiqh. The analysis and the paper’s results show that the proposed model is perceived as flexible enough to accommodate a wide variety of investment possibilities. The model carries the potential to encourage venture investment through various stages of growth of a venture. The findings are based on original perception data through a field survey across a broad spectrum of banking users who were interested in alternative and Islamic modes of investment. Findings and analysis of the survey data strongly support our connotations. We propose that the Shariah-based investment model presented in this paper will bring a vast new market into play, i.e., the Islamic money market, thus providing greater venture financing possibilities. As a result, we hope that the number of successful venture investment projects will significantly increase over time as we put the proposed investment model into use.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".