Analyst Recommendations: Does Home Bias Exist with Canadian Firms?
Bibliographic record
Abstract
We examine the investment value of analysts ’ recommendations for Canadian firms made by Canadian and U.S. analysts. Prior research suggests that “home bias ” or “local analyst advantage ” is attributable to the informational advantage of domestic analysts. The possibility exists, however, that apparent superior investment value is derived from decreased market friction encountered by investors in the home market. We use a sample of analyst recommendations issued on Canadian firms by both Canadian and U.S. analysts and exploit the unique situation that Canadian shares trade as ordinary shares in U.S. markets as opposed to depository receipts. This “flat trade ” reduces the potential role of market frictions as an explanation of home bias. Our results are consistent with notion that there is no significant home bias is attributable to informational advantage of local (Canadian) analysts. On the contrary, we find that U.S. analysts issuing recommendations on Canadian firms create an announcement effect reaction that is on the order of twice as large as the average announcement effect reaction created by their Canadian analyst counterparts.
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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 | 0.004 | 0.037 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| 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".