Excessive trading, a gambling disorder in its own right? A case study on a French disordered gamblers cohort
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
INTRODUCTION: Trading and gambling appear to share some similarities. Indeed, traders can get professionally involved in high-risk types of trading as if it were gambling. This research explores whether excessive trading can be conceptualized as a subset of gambling disorders. OBJECTIVE: To better acknowledge the existence of an addictive-like trading behavior and to discuss its phenomenological similarities with gambling disorders. METHODS: The data of 8 excessive traders out of a cohort of 221 outpatients seeking treatment in our Problem Gambling unit were analyzed. RESULTS: Our case series revealed important similarities with gambling disorders in terms of diagnosis, trajectory and comorbidities. Like many disordered gamblers, excessive traders of this study experienced a number of small early wins, chased their losses, and ended up losing control over the money they invested. All of them invested in very risky stocks associated with short-term trading leading to potential large gains, but also with very significant losses. The structure itself of the two activities (gambling and trading) is very close. CONCLUSION: Our results tended to support the idea of an addictive-like trading behavior as a subset of gambling disorders. Investing is not a form of gambling, but some people gamble with investments. Several observations and recommendations can be made: (i) conduct researches; (ii) build and validate specific assessment tools; (iii) develop strategies for prevention and treatment; and (iv) conduct more rigorous studies to clarify what we named an addictive-like trading behavior.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 it