Returns to buying upward revision and selling downward revision: some preliminary evidence from Canada.
Bibliographic record
Abstract
Purpose - The purpose of this paper is to investigate the role of earnings forecast revisions by equity analysts in predicting Canadian stock returns. Design/methodology/approach - the sample covers 420 Canadian firms over the period 1998-2009. It analyses investors' reactions to 27,271 upward revisions and 32,005 downward revisions of analysts' forecast for Canadian quoted companies. To test whether analysts' earnings forecast revisions have impacts on stock return continuation, forecast revision portfolios similar to Jegadeesh and Titman (2001) are constructed. The paper analyses the returns gained from a trading strategy based on buying the strong upward revisions portfolio and short selling the strong downward revisions portfolio. It also separates the sample into upward and downward revisions Findings - a) new information in the form of analyst forecast revisions is not impounded efficiently into stock prices. Significant returns persist for a trading strategy that buys stocks with recent upward revisions and short sells stocks with recent downward revisions; b) good news is impounded into stock prices more slowly than bad news; c) post-earnings forecast revisions drift is negatively related to analyst coverage. The effect is particularly strong for stocks with greatest number of upward revisions; d) the introduction of the better disclosure standards has made the Canadian stock market more efficient. Originality/value - This paper adds to the limited evidence on the effect of analyst forecast revisions on the returns of Canadian stocks. It sheds light on the importance of analysts' earnings forecast information and offers support for the investor conservatism and information diffusion hypotheses. It also shows how policy can improve market efficiency.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.007 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".