The intertwining of financial analyst behavior and webbased performance transparency
Bibliographic record
Abstract
The purpose of this study is to assess how a firm’s disclosure regarding its web-based performance disclosure maps into financial analysts’ earnings forecasts. We assert and empirically test that the determination of a firm’s web-based performance disclosure and financial analysts’ earnings forecasting work are closely intertwined processes. However, such endogeneity in capital markets’ performance information dissemination and use is strongly influenced by a country’s governance regime. On the one hand, North American countries (United States and Canada) exhibit a corporate governance regime that encompasses strong investor protection, largely diffuse corporate ownership and a focus on shareholder value creation. On the other hand, continental European countries (France, Germany, Belgium, the Netherlands) exhibit a corporate governance regime with weaker investor protection, a prevalence of large-block shareholding and significant input into the corporate decision-making process by non-shareholder groups such as labour and other social interest organisations. The continental European context will lead corporate managers to refrain from some disclosures and will attenuate the value of financial analysts’ activities. Our sample comprises 678 firms, with web-based performance information being collected from corporate web sites and analysts’ earnings forecasts being obtained from IBES. Results from simultaneous equation regressions document significant interrelationships between financial analysts’ activities and corporate disclosure transparency for North American firms. We observe that analyst following drives web-based performance disclosure and results in the web-based performance disclosure that reduces the dispersion of analysts' earnings forecasts. These results are consistent with the shareholder model of corporate governance. For continental European firms, no significant relationships emerges between web-based performance disclosure, analyst following and analyst forecasts' dispersion, although web-based performance disclosure related to intangible capital, and quantitative/monetary disclosure content are associated with market-to-book premium. Overall our results suggest that in continental Europe web-based performance disclosure is much less affected by financial market concerns than in North America, a result consistent with a less unilaterally focused stakeholder model of corporate governance.
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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 | 0.051 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| 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".