Idiosyncratic Risk Volatility: Stock Price Informativeness or Price Error?
Bibliographic record
Abstract
Research on idiosyncratic volatility in developing countries, particularly Indonesia, is scant. This study is the first to explain idiosyncratic concepts through an information environment approach and an examination of information asymmetry. This study aims to analyze the phenomenon of idiosyncratic risk in Indonesia, whether it is related to price informativeness or price error, by considering the information environment. We identified the information environment based on the liquidity levels and stock liquidity risk. Our research revealed the relationship between information asymmetry in the information environment and idiosyncratic volatility by using a sample of 499 companies listed on the Indonesia Stock Exchange during the period 2017–2019. One thousand, two hundred and twenty-nine (firm_year) observation data were obtained. The dependent variable was idiosyncratic volatility, and the independent variable used an information environment consisting of stock liquidity, liquidity risk, and information asymmetry. The findings of this study are expected to contribute to the literature on idiosyncratic volatility by showing how it can predict the development of the information environment, and how the latter is a consequence of information asymmetry. Moreover, this study should also complement views that are related to the concept of idiosyncratic volatility equivalent to price errors; this research has been carried out in previous studies.
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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 | 0.002 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".