Bibliographic record
Abstract
This thesis is a collection of three essays on capital markets. The first essay examines how signals of reputation with non-equity stakeholders affect the market reaction to accounting restatements. Using Corporate Social Responsibility (CSR) rating as a proxy for reputation with non-equity stakeholders, I find significantly less negative market reaction to restatements for firms with better reputation. I also find that high-CSR firms experience smaller earnings-decreases and need to engage in fewer reputation restoration activities. The results suggest that a significant portion of the market value loss triggered by restatements reflects an expectation that the restating firms will face a ‘worsening of terms’ in their future transactions with the non-equity stakeholders, and CSR reputation can dampen this effect. The second essay examines the impact of accounting restatements on the information content of analyst forecast revisions (FRIC). I find that following material restatements that are perceived to be intentional, FRIC increases significantly compared to the pre-restatement period level. The results suggest that investors increase their reliance on analysts when there is uncertainty about the firm and the credibility of management disclosure is compromised. Additional tests reveal that the effect is greater for analysts who are less likely to have close ties with the management. The third essay studies how misaligned language between the investor and the firm contributes to the foreign investor bias. In particular, we document a significant US institutional investor bias against firms located in Quebec relative to firms located in the Rest of Canada (ROC). The differential bias is surprising given that Quebec and the ROC share the same country, federal law, stock exchange, accounting standards, and regulatory filings are prepared in both English and French; and given that US institutional investors are sophisticated investors at close geographic proximity to both Quebec and the ROC. We also contrast the bias against Quebec firms with different levels of French versus English online presence, and we contrast the bias of institutional investors located in the UK versus France, to bolster our conclusion that incongruent languages are a major source of bias.
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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 | 0.001 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.019 | 0.003 |
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".