Effect of Financial Reporting Quality on the Cost of Capital and Investment in China
Bibliographic record
Abstract
Financial reporting helps reduce information asymmetry between external personnel and the internal members of the company.A high-quality financial reporting can help people better understand the company's business operations and financial performance and make the best investment decisions.Since the financial reports in the U.S. can reduce the cost of capital, the purpose of this paper is to look for the possibility that any financial reporting could have effects on Chinese companies.The data samples in this paper include thirty listed Chinese companies from 2000 to 2020.By comparing these Chinese companies' data, the statistics show that high-quality financial reporting could not reduce the cost of capital or bring more investment income to Chinese companies.This result may be due to language barriers and different investment habits.Moreover, the auditing system and reporting standards may vary for different listed companies in Asia and Europe.This paper will further analyze why the conclusion drawn from the U.S. companies' data may not apply to Chinese companies.In conclusion, using the initial public offering of equity securities (IPO), research, and analysis, financial statements have little impact on the cost of capital and investments.Also, due to the information asymmetry and different writers of prospectuses, the result from U.S. companies may not be applicable to Chinese businesses.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".