Spillover Effects of Fraud Allegations and Investor Sentiment
Bibliographic record
Abstract
ABSTRACT We examine whether a stock price spillover effect spreads through the method of listing or country of origin and whether this spillover effect changes when investor sentiment shifts. Using a sample of fraud allegations against Chinese companies that became public through Chinese reverse mergers (CRMs), we investigate whether firms that experienced negative spillover effects on their stock prices are those from the same country and/or with the same method of listing as the firms accused of fraud. We first show that the negative spillover effect channeled through the firm's country of origin becomes stronger when investor sentiment about Chinese companies becomes pessimistic, as evinced by significant declines in the stock prices of non‐fraudulent Chinese companies, including both CRMs and Chinese IPOs. Second, we show that the negative spillover effects on CRMs are stronger than those on Chinese IPOs and non‐Chinese reverse mergers, suggesting that both country and listing method are applicable to CRMs. Our findings indicate that (i) investor sentiment plays an important role in the spillover process involving fraud allegations and (ii) while the two channels could coexist, negative spillover effects that spread through the country of origin play a more prominent role than those that spread through the method of listing.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.010 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".