Examining high-frequency patterns in Robinhood users’ trading behavior
Bibliographic record
Abstract
Using intraday (hourly) and overnight changes in the number of Robinhood (RH) investors holding a stock, we examine their high-frequency trading behaviors in response to contemporaneous and lagged returns. RH investors do not react to contemporaneous returns. However, they respond to lagged intraday or overnight returns, exhibiting three high-frequency behaviors: (i) the number of RH investors increases more for stocks with extreme lagged returns than for those with moderate returns, suggesting attention-driven buying; (ii) this reaction is asymmetric, with larger increases in the number of RH users following extreme negative returns compared to extreme positive returns, suggesting that their contrarian buying is stronger than their momentum buying; (iii) this asymmetry is strongest immediately after extreme returns and dissipates over time. Compared to findings from daily data, our analysis shows that daily data underestimates this asymmetry. Further analyses reveal greater attention to overnight movements, exacerbated behaviors during COVID-19, and variation across firm sizes, with more contrarian buying for larger-cap firms. • Robinhood investors do not react to contemporaneous returns but respond to lagged intraday and overnight returns. • They show attention-driven buying, with more buying after extreme lagged returns than moderate ones. • Their reaction is stronger after extreme lagged negative returns. • This asymmetry is underestimated in daily data and is more pronounced for overnight movements, large-cap firms, and during COVID-19.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 teacher head, 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".