Corporate Sectoral Investments and Economic Growth in Nigeria: Evidence from the Capital Market
Bibliographic record
Abstract
This study aims at articulating an empirical basis for prioritizing corporate sectoral investments in the Nigerian capital market and also, evaluating the extent to which market capitalization of the Nigeria stock exchange reflects the net sectoral investments of corporate organizations quoted therein. Covering the period 1984 to 2009 (26yrs), the study population consists of all the thirty (30) classified sectors of the market, while the study sample is made up of the eighteen (18) sectors with operational activities over the period of study. Multiple correlation and stepwise regression techniques are utilised and the relevant hypotheses tested at 0.05 level of significance. The F-test and F-change test statistics are employed. The results establish a significant multiple correlation between the Nigerian Stock Market Capitalization and Corporate net sectoral investments, while net corporate investments in four sectors of capital market activity – petroleum marketing, building materials, packaging and banking are found to significantly contribute to variations in Nigeria’s GDP. It is recommended that these four sectors should continually enhance their capitalizations to facilitate further investments and also, engage in product diversification. Further, the banking sector is recommended to adopt sectoral contributions to the GDP as one of the plausible criteria for lending decisions, while the resolution of an optimal portfolio for sectoral investments in the Nigerian Capital Market is recommended as an issue arising from this study for further research.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| 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".