An investigation on impacts of structural changes in stocks’ past returns on financial analysts’ earnings forecasting rationality
Bibliographic record
Abstract
Purpose The purpose of this paper is to test whether financial analysts’ rationality in making stocks’ earnings forecasts is homogenous or not across different information regimes in stocks’ past returns. Design/methodology/approach By treating stocks’ past returns as the information variable in this study, the authors employ a threshold regression model to capture and test threshold effects of stocks’ past returns on financial analysts’ rationality in making earnings forecasts in different information regimes. Findings The results show that three significant structural breaks and four respective information regimes are identified in stocks’ past returns in the threshold regression model. Across the four different information regimes, financial analysts react to stocks’ past returns quite differently when making one-quarter ahead earnings forecasts. Furthermore, the authors find that financial analysts are only rational in a certain information regime of stocks’ past returns depending on a certain return-window such as one-quarter, two-quarter or four-quarter time period. Originality/value This study is different from those in the existing literature by arguing that there could exist heterogeneity in financial analysts’ rationality in making earnings forecasts when using stocks’ past returns information. The finding that financial analysts react to stocks’ past returns differently in the different information regimes of past returns adds value to the research on financial analysts’ rationality.
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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 | 0.006 | 0.057 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".