Effect of Exchange Rate Returns on Equity Prices: Evidence from South Africa and Nigeria
Bibliographic record
Abstract
This paper examines the exchange rate returns of the Rand (relative to the US dollar) and the Naira (relative to the US dollar) for the presence of volatility. It also examines the effect of the exchange rate returns on the performance of their respective stock market. While it was found that the returns of the South African Rand was volatile, the Nigerian naira was not. Estimating the effect of exchange rate returns and crude oil price on the stock market indices of both countries showed that exchange rate return have a positive effect on the performance of the Nigerian stock exchange thus, confirming the stock flow hypothesis for Nigeria and refuting same for South Africa. Although the VAR granger causality identifies short run fluctuation of the naira as a significant factor affecting the performance of the Nigerian stock exchange in the short run, the Johannesburg stock exchange was found to be mostly affected by short run changes in the Rand and the UK FTSE 100. The paper concludes that policies aimed at stabilizing exchange rate and encouraing more non-oil stocks to be quoted in the Nigerian stock exchange will important. For the Johanesburg stock exchange, raising the listing requirement for firms quoted in the UK FTSE 100 and also seeking listing or already listed in the JSE will be a plausible idea. For both countries, however, curtailing swings in their exchange rate returns would help attract new investments and sustain existing ones hence, helping to spur growth.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 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".