Comparison of Psychological Status and Investment Style Between Bitcoin Investors and Share Investors
Bibliographic record
Abstract
Bitcoin has unique characteristics that have inspired people to invest in it as well as distinct drawbacks. With a rapid increase in Bitcoin prices in the short term, more investors enthusiastically began investing in it, raising concerns about a speculative bubble. This study investigated the multiple factors involved in the Bitcoin craze despite concerns about its shortcomings. In what concerns to personality traits and psychological states, online use patterns, and investment patterns, we first hypothesized that Bitcoin investors would show differences in multiple factors when compared to share investors. Based on our assumptions about these differences, we secondly hypothesized that investors’ personality, psychological states, and investment patterns could predict whether they would invest in Bitcoin or shares. In total, 307 respondents completed the research protocol and were sorted into Bitcoin investors (n = 101), share investors (n = 102), and non-investors (n = 104). A self-report questionnaire on demographic data, online use patterns, investment patterns as well as the Fear of Missing Out (FoMO) scale, Temperament and Character Inventory-Revised-Short (TCI-RS), Mood Disorder Questionnaire (MDQ), trait anxiety part of the State-Trait Anxiety Inventory (STAI-T), and the Korean version of the Canadian Problem Gambling Index (K-CPGI) were administered. The results of this study indicated that Bitcoin investments can be attributed to the interaction of multiple factors, among which personality, psychological states, and investment patterns are particularly important. Specifically, the investment pattern is the strongest predictive factor for Bitcoin investment. Bitcoin investors were distinct with regard to higher novelty seeking, higher gambling tendencies, and unique investment patterns. Thus, personality, psychological states, and investment patterns could explain the substantial investments in Bitcoin.
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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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".