Earnings Surprises and Stock Price Reactions of Quoted Companies in Nigeria
Bibliographic record
Abstract
This study focuses on examining the relationship between stock prices and earnings surprises in quoted companies of Nigeria. This study applied a longitudinal research design which studies the effect of earnings surprises on stock prices using panel data. A sample of 64 companies was chosen to study in all sectors of the Nigerian Stock Exchange. The research data were obtained from secondary sources of the annual reports for the selected companies covering the period from 2013 to 2017. The measurement for earnings surprises used in the study is the residual or unexplained component of earnings persistence model commonly referred to as first-order autoregressive AR (1) regression of reported earnings. Were, the data analysis was carried out by regression using the generalised least squares technique. The regression results for positive earnings surprise shows that share prices react negatively to positive surprises with a coefficient of (-2.4109) in tandem with the return news hypothesis which suggests that positive earnings news results in a negative stock-price reaction. The negative earnings surprise results show that stock prices react positively to negative earnings surprises with a positive coefficient of (0.1136). This is in line with the premise of return news, which indicates that negative earnings news leads to a positive reaction to the share price. The study recommends that there is a need to regulate the stock market to improve the level of market efficiency in stock markets. This will improve the rate at which earnings news will be reserved at stock prices. Secondly, there is a need to improve investor confidence in the disclosed profits made by companies.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".