Investing in a Digital Asset Environment: The Effects of Staff Accounting Bulletin 121 and the Fear of Missing Out
Bibliographic record
Abstract
In recent years, regulators have become concerned that investors will be misled in the largely unregulated crypto-asset environment. Particularly, the U.S. Securities and Exchange Commission (SEC) has become concerned about the effects that the Fear of Missing Out (FoMO) could have on investors in the crypto-asset space (SEC, 2021a). This dissertation investigates, in an experimental setting, whether Staff Accounting Bulletin 121 (SAB 121) - recently issued financial reporting guidance by the U.S. Securities and Exchange Commission (SEC) - protects investors with higher levels of FoMO in the crypto-asset environment. Two experiments were carried out online through Prolific using a sample of 95 retail investors (Experiment 1) and 412 retail investors (Experiment 2). In Experiment 1, consistent with social psychology theory, I find that investors with higher levels of FoMO experience more negative emotions at the thought of missing out on future financial gains as they exhibit a higher propensity to invest in the crypto-asset market as compared to investors with lower levels of FoMO. I also find that exposure to SAB 121 decreases investors propensity to invest in the digital asset market with the effects being more pronounced for investors with higher levels of FoMO as compared to investors with lower levels of FoMO. Experiment 2 shows that SAB 121 decreases investors propensity to invest in the crypto-asset market by heightening their risk perception. I conclude the dissertation with a discussion of the implications of the findings for regulators, investors, and for accounting research.
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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 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".