Individual Cryptocurrency Investors: Evidence From A Population Survey
Bibliographic record
Abstract
Cryptocurrencies, such as Bitcoin, are a highly volatile asset class where very high returns are offset by large losses. This study examines the financial success of individual investments in cryptocurrencies and analyzes whether it relates to similar explanatory factors as for investments in other asset classes. For this purpose, a nationally representative survey data set of 3,864 German citizens is used, of which 354 (9.2%) reported owning cryptocurrencies in March 2019. We analyze the subpopulation of 225 cryptocurrency owners who classify as investors. 56% of them experienced positive returns, while 29% had negative results. The remaining respondents broke even. The average investment was €1,773 in a portfolio of two cryptocurrencies. At the time of the survey, the average portfolio value had risen to €7094 — an average gain of 300%. While nearly half of the investors (44%) outperformed Bitcoin market returns, not a single one of the early investors (2009–2012) did. We find that net income, the degree of cryptocurrency knowledge and the degree of ideological motivation for owning cryptocurrency have positive effects on returns. This first scientific analysis of individual investment in cryptocurrencies provides a basis for future research and for regulatory decision-making.
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".