Risk is in the eye of the investor: Cryptocurrency investors’ engagement with risk, regulatory advice, and regulatory institutions
Bibliographic record
Abstract
Despite regulators’ warnings that investing in cryptoassets is highly risky, cryptocurrency investments are prevalent. To explore investors’ engagement with regulatory risk advice, we conducted two surveys. Cryptocurrency investors residing in the UK and the US were asked about their interpretation of the notion of risk, awareness of regulatory risk advice, and attitudes towards the advice and the regulators. Investors were also asked whether they followed the advice. Qualitative content analysis of their answers suggests that people often invest in cryptocurrencies although they understand the risks involved and are aware of the regulators’ advice. They do so due to their risk propensity, self-reliance, criticism of the informativeness of the advice, or attitudes towards regulators. Furthermore, negative attitudes towards regulators often stem from lack of trust and the perception that regulators are dated. This study suggests that regulators could benefit investors by providing them with more informative advice and addressing their attitudes.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".