Cryptocurrency Investments: The Role of Advisory Sources, Investor Confidence, and Risk Perception in Shaping Behaviors and Intentions
Bibliographic record
Abstract
The rapid growth and increasing adoption of cryptocurrencies have reshaped the investment landscape, presenting unique opportunities and challenges for investors. This study examines how advisory information sources influence cryptocurrency investment behaviors and intentions among U.S. investors. Using data from the 2021 National Financial Capability Study, it explores how reliance on financial professionals, media, and social networks shapes investment decisions. The motivation for this research lies in the need to understand the divergent roles of these sources in an era where traditional and emerging financial advice coexist. Findings reveal that reliance on financial advisors correlates with reduced cryptocurrency investment and future investment intentions, reflecting advisors’ cautious stance toward volatile assets. Conversely, reliance on media and social networks significantly increases both current investments and future intentions. The findings also highlight that investor confidence is positively associated with the likelihood and intentions to invest in cryptocurrency. Conversely, heightened risk perceptions associated with cryptocurrency reduce both the likelihood and intentions to invest. The study calls for financial professionals to enhance client education on cryptocurrency risks and for policymakers to strengthen regulations, ensuring accurate information dissemination through media and social networks. By providing a nuanced understanding of advisory influence and investors’ characteristics, this research offers valuable insights for financial professionals, policymakers, and investors navigating the complexities of cryptocurrency investments.
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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".