Impact of Foreign Direct Investment on the Financial Performance of Listed Deposit Banks in Nigeria
Bibliographic record
Abstract
In this study, we examine the impact of foreign direct investment (FDI) on the financial performance of Nigerian listed deposit banks. We collected secondary data from the annual reports and accounts of 14 banks between 2010 and 2017. We employed the Tobin Q quantitative method for the analysis. We adopted the theoretical framework of pecking order theory since the analysis of the impact of FDI on the financial performance of these banks are both inward and outward FDI. The Tobin Q method was used as the dependent variable and FDI as an independent variable. Board size, firm size, equity capital and reinvested earnings were all financial performance indicators employed to test the impact of FDI on the financial performance of the banks on understudy in Nigeria. The result of the data analysis and findings showed that FDI had contributed positively to the development and performance of the deposit banks over the period under consideration. Our theoretical findings suggest a positive relationship between FDI and profit maximization. This support the FDI theory that banks or organisations are financed partly with debt-equity, both used by the banks to balance the cost and benefit financing decisions by the management. In the case of the empirical findings, the results of hypothesis testing show a significant effect on the banks’ financial performances. Given these results, we conclude that FDI has made a positive impact on the development and financial performances of the listed deposit banks under study which resulted in some of the banks’ growth from local banks in Nigeria into some of the leading international banks in Africa.
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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 | 0.003 |
| 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 | 0.002 | 0.001 |
| 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".