Insights on Crypto Investors from a German Personal Finance Management App
Bibliographic record
Abstract
This study investigates the socio-economic characteristics, behavioral preferences, and consumption of individuals who own crypto-assets. Our empirical analysis utilizes data from a German personal finance management app where users connect their bank accounts and depots. We conducted a survey and elicited behavioral factors for financial decision-making. By combining survey with account and security account data, we identify crypto investors’ preferences for financial decision-making and financial advice. Our results suggest that, in particular, students or self-employed, young, and male individuals who are risk-seeking and impatient are more likely to have invested in crypto-assets. Most crypto owners have less experience with financial advisory. They see it as too time-consuming and qualitatively poor, and instead, they prefer to decide on their own as they have self-reported high financial literacy. Investigating their consumption in more detail we conclude that crypto investors more often spend on travelling, electronics, and food delivery and less on health. Our findings suggest policymakers in identifying high-risk consumers and investors, and help financial institutions develop appropriate products.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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".