Re‐stating financial statements and its reaction in financial market
Bibliographic record
Abstract
Purpose The purpose of this paper is to examine the short‐term reactions of stock prices to the announcement of earnings restatement by the public companies listed in the Toronto stock exchange in Canada. Design/methodology/approach The paper conducts an empirical study. For the purpose conducting the empirical study, a standard event study methodology has been utilized to examine the effect of restatement announcements on the stock returns. The dates of the announcement of restatement by each company have been collected and the effect of the announcement has been studied surrounding the announcement dates. Findings The results of empirical works indicate that, in general, the financial market reacts negatively to any restatement of earnings. This is evident from the fact that irrespective of the reasons for restatement, all restatements show a negative effect on the stock price. The impact of the restatement announcements is significant for all the prediction intervals. However, the long‐term reaction is more pronounced compared to short‐term reaction. In addition, the negative reaction is much higher for those reasons that are directly related to the earnings management than those that do not involve any active earnings management. Research limitations/implications Since the paper investigates only one stock exchange, it may have a limited application in other financial markets. Similar researches can be undertaken for other financial markets different in size, scope or geographical location. Practical implications The findings of the paper may have broad implications both for individual investors and corporate executives. The decisions made by executives on restatements affect stock price and hence the investors' rate of return. Since the general effect of such restatement is negative on the stock returns, it may portray a negative perception about the company. Therefore, the paper has implications on the decisions made by both the investors and the corporate executives. Originality/value The paper studied the Canadian stock market which was not studied in the past to examine the reactions of restatement.
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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 | 0.005 | 0.057 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".