Overconfidence, Financial Advice Seeking and Household Portfolio Under-Diversification
Bibliographic record
Abstract
This paper examines the relationship between overconfidence and losses from under-diversification among Dutch investors. We find that a lack of proper portfolio diversification is positively associated with overconfidence. Part of this relationship is mediated through the lower propensity of overconfident individuals to hire a professional financial adviser. We use data from the 2005 wave of the DNB Dutch Household Survey that provides us with detailed portfolio data of 257 investors. We proxy for overconfidence by exploiting the difference between measured and self-assessed financial literacy, and use this proxy in a regression model (with and without mediation) to explain the difference between the actual households return and the return that could have been obtained by selecting a portfolio on the efficient frontier with equivalent risk. Our results contribute to the current discussion among policy makers on the role of financial advice and self-perceptions in household financial decision-making.
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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 | 0.001 | 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".