Trading Gamification and Investor Behavior
Bibliographic record
Abstract
We study the effect of gamification on retail traders’ behavior using a randomized online experiment. Participants with lower financial literacy prefer platforms with hedonic gamification elements, such as confetti and achievement badges. On average, hedonic gamification increases trading volume by 5.17%. However, the difference in trading activity between gamified and nongamified platforms is driven primarily by self-selection (70%) rather than gamification (30%). Participants who prefer hedonic gamification exhibit noisy trading strategies, whereas those favoring nongamified platforms display stronger contrarian behavior. Further, price trend notifications enhance learning for investors with accurate beliefs, but they reinforce trading mistakes for those with incorrect beliefs. This paper was accepted by Jean-Edouard Colliard, Special Issue on the Human-Algorithm Connection. Funding: P. Chapkovski acknowledges funding from the Deutsche Forschungsgemeinschaft [Germany’s Excellence Strategy—EXC 2126/1-390838866]. M. Khapko and M. Zoican acknowledge the Social Sciences and Humanities Research Council of Canada [Insight Development Grant 430-2018-00125] and the Canadian Securities Institute Research Foundation [research grant]. M. Zoican acknowledges financial support from the Quantitative Management Research Initiative (QMI) under the aegis of the Fondation du Risque, a joint initiative by Université Paris-Dauphine, l’École Nationale de la Statistique et de l’Administration ParisTech, and LFIS, France. Supplemental Material: The online appendix and data files are available at https://doi.org/10.1287/mnsc.2022.02650 .
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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 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.031 | 0.002 |
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".