Early Results on Depth of the Nascent Kenyan Derivative Market
Bibliographic record
Abstract
Despite their importance in hedging against risk and reducing price uncertainty, derivative markets remain undeveloped or absent in many African countries. This paper describes market depth using key trends observed in the Kenyan derivatives market for the first 30 weeks of trading using mixed methods. Market depth was measured by the number of open interests of 142 trading days (30 weeks. The market was described using trend analysis, tests of means, and thematic analysis. The results revealed a market highly dominated by one company's single stock futures (Safaricom Plc), whose overall trade was 68% of the 6179 open contracts. Further, the market has strong weekly swings fluctuating from no trade to a high of 326 and a weekly average of 206 contracts. The market segment of single stock futures is significantly deeper than that of equity index futures. The qualitative study attributed the results to limited knowledge on derivatives amongst investors, unclear market policies, few derivatives products, and skepticism associated with developing financial markets. The Nairobi Securities Exchange (NSE) is advised to intensify investor education, introduce market makers, add new derivatives products, and transform the Nairobi Securities Exchange Clearing House into a full Central Counterparty (CCP) structure to accelerate market depth. This will create a pathway to market depth through efficiency and reduction of operational risks.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".