Effect of Globalization on Nigerian Financial Sector
Bibliographic record
Abstract
The study examined the effect of globalization on the Nigerian financial sector and to ascertain the contribution of globalization on the Nigerian stock exchange and commercial banks. Assets of the Nigerian stock exchange and commercial banks were used as performance indicators. The data used are Nigerian yearly data from 1983 to 2014; the data were analyzed using descriptive statistics, ordinary least square statistical technique, Johannes’s co-integration and error correction mechanism. We used Augmented Dickey-fuller statistics test for stationary. We proxy globalization with degree of openness measured by total trade divided by gross domestic product, foreign direct investment flows, Real Gross Domestic Product, external debt flows, nominal exchange rate and gross capital formation. Two null hypotheses were formulated and were tested. They were rejected based on overall significant of models using F statistics at 5 percent level of significance. The result of our estimate based on overall significant of models using F statistics at 5 percent level of significance shows that Nigerian financial sector as a whole has benefited from globalization. Some of the globalization proxy variables take out a priori signs while some did not. However, the foreign direct investment flows and Real Gross Domestic Product affected the performance of the Nigeria Stock Exchange and commercial banks positively while degree of openness, external debt flows, nominal exchange rate and gross capital formation affected the Nigeria stock exchange and Commercial Banks negatively. This shows that Nigerian foreign trade is low. External debt flow has a negative effect on the Nigerian stock Exchange and positive on commercial banks. Nigeria should discourage external loans. Gross capital formation and external debt flows affected the Nigeria stock exchange negatively. We therefore recommend that the recent re-capitalization and debt recovery exercise and monitoring macroeconomic stability be encouraged to gain confidence by investors in the financial sector.
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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 | 0.002 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".