Volatility Estimation and Stock Price Prediction in the Nigerian Stock Market
Bibliographic record
Abstract
This study aimed at understanding the Nigerian Stock Market with regards to volatility and prediction, to this effect the month end stock prices of four major companies from the period January 2005 to December, 2009 was used as proxy. The study made use of the Autoregressive Conditional heteroskedasticity (ARCH) to estimate and find out the presence of volatility. The study found the presence of volatility in all the four stock prices used, while stock price volatility was then regressed against stock prices to determine their predictability. The results however, revealed that out of the four companies, only two companies’ stock prices were predicted by volatility in their stock prices, while past stock prices predicted current stock prices implying that the market does not follow a random walk. As a result of these, it is recommended that activities of corporate insiders should be properly checked, to reduce the predictability of stock prices, information should be known and made public to all investors. Also policy makers are advised to review their economic policies and should be careful in their use of the Nigerian bourse as a barometer to reflect performance in the general economy as our findings suggests that this could be misleading.
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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 | 0.001 | 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) | 0.001 | 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".