An Empirical Analysis of the Effects of Online Trading on Stock Price and Trading Volume Reactions to Earnings Announcements*
Bibliographic record
Abstract
Abstract This study provides evidence regarding the effects of online trading on stock price and trading volume reactions to quarterly earnings announcements. We test for differences in stock price and volume reactions to quarterly earnings announcements between a period with a significant amount of online trading (1996‐99) and a period without online trading (1992‐95). We conjecture that online trading has increased the proportion of naive investors in the market. We predict that this will result in (1) a decrease in the average precision of investor information prior to earnings announcements leading to higher earnings response coefficients (ERCs), (2) an increase in differential interpretation of earnings leading to higher trading volume reactions that are unrelated to price change, and (3) a decrease in differential prior precision leading to a decrease in the association between trading volume and absolute price change. We find evidence consistent with all three predictions. Our findings are relevant for assessing the validity of concerns about online trading expressed by regulators and the validity of theoretical models of trade with asymmetrically informed investors.
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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 | 0.036 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".