Cryptocurrency investment: A safe venture or a new type of gambling?
Bibliographic record
Abstract
Investment behaviour and gambling overlap from time to time. It is stated that there is a spectrum between gambling and investment behaviour, and there are “speculative” investment tools in the middle of the spectrum. Considering that it presents a higher risk because of its high volatility compared to traditional investment instruments, trading cryptocurrencies can become pathological and gambling-like. This study aims to investigate the pathological trading behaviour and frequency among cryptocurrency investors, to investigate additional gambling disorders, and to investigate the relationship between cryptocurrency investment behaviour and impulsivity. An online questionnaire was created to investigate these issues. In the questionnaire, the Pathological Trading Scale, the South Oaks Gambling Screen Test and the Barratt Impulsivity Scale were all used. A total of three hundred persons were evaluated. We found that total pathological traders were 48.7% of all traders, impulsivity in 18–25 age group was higher, high-frequency traders were more pathological, and their impulsivity was higher; also margin traders and day traders show more pathological behaviour. It seems that an important part of cryptocurrency traders may be pathological, and certain of them may have cryptocurrency addiction, which can be evaluated as a subtype of gambling disorder.Résumé Le comportement de l’investisseur et celui du joueur se chevauchent de temps à autre. On dit qu’il existe un spectre entre ces deux comportements, au milieu duquel se trouvent des outils d’investissement « spéculatif ». Compte tenu de leur risque plus élevé dû à leur plus grande volatilité par rapport aux instruments d’investissement traditionnels, les échanges de cryptomonnaies peuvent devenir pathologiques et s’apparenter aux jeux de hasard. Cette étude vise à analyser le comportement des investisseurs de cryptomonnaies et la fréquence de leurs opérations afin d’examiner d’autres troubles liés à la pratique des jeux de hasard et la relation entre le comportement des investisseurs de cryptomonnaies et l’impulsivité. Un questionnaire en ligne a été créé à cette fin et la Pathological Trading Scale, le South Oaks Gambling Screen Test et la Barratt Impulsivity Scale y étaient utilisés. En tout, 300 personnes ont été évaluées. Nous avons constaté que les joueurs pathologiques représentaient 48,7% de tous les spéculateurs, que l’impulsivité dans le groupe des personnes de 18 à 25 ans était plus élevée, et que les spéculateurs qui effectuaient des transactions plus souvent étaient plus pathologiques et faisaient preuve d’une plus grande impulsivité; de plus, les spéculateurs sur marge et les spéculateurs sur séance affichaient un comportement plus pathologique. Il semble qu’une proportion importante des spéculateurs de cryptomonnaies peuvent être pathologiques, et que certains d’entre eux peuvent être dépendants à l’égard des cryptomonnaies, ce qui peut être évalué comme un sous-type de jeu compulsif.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".