Determinants of Equity Share Price Movement: Evidence From the Nigerian Banking Industry (2000 – 2014)
Bibliographic record
Abstract
Profit maximization is the primary focus of investors. The banking industry is a veritable sector for investment, however, understanding the determinants of profitability is paramount as it assists investors to know where their money should go. This study, therefore, investigates the influence that Earnings per share (EPS) and Non-Financial factors namely: inflation, exchange rate, and interest rate have on share price movement. The Ex-post factor was adopted as the research design. The data on EPS was collected from the Central Bank of Nigeria (CBN), Factbook, and the financial reports of the selected banks. The data on the Interest rate, Inflation, and Exchange Rate were collected from the Bulletin of CBN. The time-series data were diagnosed using the Unit root test; they were detrended where necessary to avoid a spurious result. The data were then analyzed using multiple regression. Also, Variance inflation factors (VIF) were engaged to test for the multicollinearity of the selected variables; while a heteroskedasticity test was carried out for a result free of heteroskedasticity. The outcome from the analysis displayed a positive but insignificant relationship between EPS and the market price of shares (MPS;); The study also revealed a negative and significant relationship between Inflation share price; while Interest Rate is insignificantly and negatively influencing the share price. Finally, Exchange Rate showed a significant influence on the share price. The researcher, therefore, recommends among others the need for Nigerian listed Banks to endeavor to improve on their EPS as this will increase their share price even though it won't be significant. Inflation displayed a negative and significant effect on the share prices of the quoted Banks in Nigeria; policies that will reverse the geometric rise in the inflation presently experienced in Nigeria should be enacted by the Government.
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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 | 0.000 |
| 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".