Trading is a losing game: An audit of deceptive choice architecture in demo-mode Contract for Difference (CFD) trading apps
Bibliographic record
Abstract
Mobile-based trading apps have made investing easier than ever before, but thisincludes enabling access to risky investments that many investors may not be able to tradesafely. The UK financial regulator thereby requires Contract for Difference (CFD) tradingapps to make disclosures such as, “89% of retail investor accounts lose money when tradingCFDs with this provider”. However, these disclosures might be counteracted by either theirsuboptimal implementation, or by other aspects of these apps’ deceptive choice architecture.Therefore, the present study audited choice architecture characteristics of demo-modes of the14 most-popular CFD trading apps in the UK. A content analysis found for example that 31.6per cent of risk warnings did not comply with the regulator’s standards, and that only 35.7 percent of apps contained risk warnings within the app’s main tabs. A thematic analysissuggested that apps’ educational resources could instil users with the hope of winning, byemphasising practice, strategies, and psychological mindset – instead of acknowledging luckas the predominant factor underlying CFD trading profitability. Overall, this study added toprevious research highlighting the similarities between certain high-risk investments andgambling, and added to the behavioural public policy literature on deceptive choicearchitecture.
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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 | 0.013 | 0.070 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.005 | 0.008 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| 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".