Investing in the crypto-asset environment: the effects of risk disclosures and the fear of missing out
Bibliographic record
Abstract
Purpose The psychology literature describes several methods of mitigating the effects of Fear of Missing Out (FoMO) in a social setting. However, the management literature lacks an empirically validated method of attenuating the effects of FoMO in an investing setting. Given that FoMO exposes investors to significant risks in volatile markets, this study aims to understand whether risk disclosures are an effective method of attenuating the effects of investment-related FoMO (I-FoMO) in the crypto-asset environment. Design/methodology/approach In an experimental setting, retail investors (n = 412) evaluate the share price of a publicly traded crypto-asset exchange. Investors are randomly assigned to one of two conditions: no risk disclosure or risk disclosure. This paper also measures investors’ level of I-FoMO. Findings Investors with higher levels of I-FoMO exhibit a higher propensity to invest in the crypto-asset market. Furthermore, exposure to a risk disclosure decreases investors’ propensity to invest in the crypto-asset environment with the effect being more pronounced for investors with higher levels of I-FoMO. The results show that risk disclosures decrease investors’ propensity to invest in the crypto-asset market by heightening their risk perception. Research limitations/implications This paper employs a one-shot experiment where we expose retail investors to a single risk disclosure. Although the manipulation of risk disclosure attenuates the effects of I-FoMO in the short term, future research should explore how long the effect of this manipulation will last. Practical implications Given that exposure to risk disclosures increases investors’ risk perception, investors should collect all relevant information prior to making investment decisions. Originality/value This study is among the first to show an empirically validated method of attenuating the effects of FoMO in an investing setting.
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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 | 0.003 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".