Gambling on the stock market: the behavior of at-risk online traders
Bibliographic record
Abstract
Purpose Online investment platforms offer an environment that may lead some traders into excessive behaviors akin to gambling. Over the last decade, gambling behaviors associated with the stock market have attracted the attention of many researchers but the literature on the subject remains scarce. This study aims to present the results of live interviews with a sample ( N = 100) of retail investors trading online, and contrasts trading habits with gambling behaviors. Design/methodology/approach Participants are divided in three groups according to their score on an adapted version of the Problem Gambling Severity Index (referred to as the PGSI-Trading), and their trading habits and behaviors are compared. Findings The authors find that traders with higher PGSI-Trading scores are more likely to display gambling-related behaviors such as trading within a short timeframe, being motivated by making money quickly and experiencing high sensations when trading. Research limitations/implications The sample is small but the authors proceeded this way in order to gather some qualitative data that would be helpful to clinicians in the Province of Quebec. The questionnaire used to classify traders at risk of being gamblers (PGSI-Trading) has not been validated. Practical implications The findings of this study will be helpful to clinicians who hwork with patients suffering from excessive online stock trading habits. Social implications Clinicians observe an increasing number of patients who consult with excessive stock trading habits. This study has brought new information allowing clinicians to better understand how gambling manifests itself on the stock market. Originality/value To the authors’ knowledge, this study is the first to investigate the trading habits of individuals classified in terms of their score on an adapted PGSI questionnaire.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".