Influence of Financial Fraud Scandal on Listed Companies
Bibliographic record
Abstract
The revelation of financial fraud scandals will hugely impact the stock prices of listed companies. Taking the financial fraud case of Luckin Coffee in 2020 as an example, it can be shown that after the listed companies lose their investment credit, the impact on the company's development is enormous.After the fraud case of RMB 2.2 billion, Luckin Coffee's stock market continued to delist and fell into the powder sheet market. The company was in a dilemma, its development was stagnant, and it faced changes in management, equity owners, and many other aspects. Broken promises for listed companies, investment market, investors to reduce the investment confidence, management and the equity in the company all changes, the flaws of the company's financial regulation, is a great test to sound development of listed companies，Listed companies need more standardized supervision and more effective information disclosure to maintain their healthy development. This article enriches the academic literature on financial fraud and let investors know more about the impact of financial fraud on companies.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".