Talmud and Markowitz Diversification Strategies: Evidence from the Nigerian Stock Market
Bibliographic record
Abstract
The object of this study is to investigate Talmud and Markowitz diversification strategies using stocks quoted on the Nigerian Stock Exchange. The essence is to determine how each of these strategies compare with one another in terms of generating superior performance based on maximizing returns and minimizing risks. In addition, it examines the applicability of diversification to the Nigerian stock exchange regarding risk reduction and return maximization. This involved data on quarterly closing prices of 17 assets (companies) drawn from the Nigerian stock exchange for 17 years, equivalent to 68 periods. The three hypotheses formulated in the course of this study were tested using the difference between independent sample means (t – test). The null hypotheses of the three hypotheses were accepted. By implication this means that diversification can diversify away a reasonable amount of risk. Hence we recommend that Nigerian investors should apply Talmud diversification strategy since diversification is applicable to the Nigerian stock market. We further recommend that more sophisticated investors could still adopt Markowitz strategy since they possess the skills to do so. Investors should exercise caution by seeking the opinion of experts before committing their funds in the market.
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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 | 0.007 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".