The Impact of Earnings Announcements Before and After Regular Market Hours on Asset Price Dynamics in the Fintech Era
Bibliographic record
Abstract
With the recent increase in retail investor participation led by commission-less fintech trading applications and new features like fractional trading, we now have higher volatility and significantly quicker price changes. This makes it hard to make informed trading decisions. Moreover, these effects are exacerbated even further around earnings announcements days. In this paper, we use Nasdaq data feed at a minute frequency and show that there is a significant increase in the slope of the price–volume structure during extended hours (after-hours, or pre-market hours) as compared with the ones observed during regular market times. Our analysis shows that the liquidity is much less during the extended market hours. As such, earnings announcements of stocks during these times have a significantly larger price impact than those stocks that have their earnings announced during regular trading hours. This significant difference can be explained by observing the limit order book structures during these different trading periods. We suggest that the earnings announcements should not be made during extended hours given the significantly lower liquidity and thus, the significantly larger price impact that not only determines the prices for the next trading session but also sets the new “fundamental” price signals for the stocks.
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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 | 0.000 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".