The Effects of Corporate Financial Disclosure on Stock Prices: A Case Study of Korea’s Compulsory Preliminary Earnings Announcements
Bibliographic record
Abstract
This paper examines the effects of Korea’s compulsory preliminary earnings announcements on stock prices using individual corporate financial disclosure data. Korea’s compulsory preliminary earnings announcements are similar to the US’s fair disclosures in that they are preliminary settlement disclosures. Disclosure regulation aims to prevent insider trading and resolve information asymmetry among investors by promptly disclosing unconfirmed internal settlement information prior to an external audit. The disclosure of such changes in profit or loss is generally expected to affect stock prices. Many studies have analyzed the relationship between accounting profit disclosure and stock prices, but most have focused on the relationship between net profit disclosure and stock price without considering other disclosure information such as sales and operating profit. In addition, previous studies analyzed the information effect of accounting profits based on annual reports, which are based on analysts’ predicted values and limited datasets. This study investigates the impact of Korea’s compulsory disclosure on stock prices through a multiple regression analysis, considering three types of accounting information, including sales, operating profit, and net profit, based on actual announcement data and daily trading volumes. The effect of corporate financial disclosure might vary with stock market type and industry sector. For this reason, we analyze the relationship between financial disclosure and stock prices for different stock market types and industry sectors. Results show that sales information affected KOSPI-listed companies’ stock prices, and operating profit information affected KOSDAQ-listed companies’ stock prices. In terms of financial market efficiency, the results show weak-form efficiency for both the KOSPI and KOSDAQ markets in general. However, this implies that there is still information asymmetry in sales information for the KOSPI, which consists of large and valued stocks and is not completely efficient, whereas information asymmetry might occur in operating profit information for the KOSDAQ, which consists of relatively small-to-medium innovative growing companies. In addition, results show that operating profits affect manufacturing industries’ stock prices, and that trading volumes significantly impact stock prices for all markets and industries.
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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".