Measuring the gap between elicited and revealed risk for investors: An empirical study
Bibliographic record
Abstract
Abstract Financial advisors use questionnaires and discussions with clients to determine investment goals, elicit risk preference and tolerance and establish a suitable portfolio allocation for different risk categories. Financial institutions assign risk ratings to their financial products. Advisors use these ratings to categorize products into the same risk categories used for portfolio allocation. Subsequently, clients select a portfolio of assets whose risk profile we call revealed risk. This paper proposes a novel methodology for comparing an individual's elicited and revealed risk. We propose using Value‐at‐Risk to measure elicited and revealed risk and the discrepancy between them, showing whether clients are over‐risked or under‐risked. We demonstrate the methodology using a dataset from a Canadian private financial dealer. We find that elicited risk is consistently higher than revealed risk–advisors build a safety buffer into their recommendations–and elicited risk varies with respect to demographic features and trading behaviors in expected ways–investors are receiving sound advice. This risk discrepancy could be used, for example, to gauge the quality of financial advice an individual is receiving, or it could be used to help advisors communicate inconsistencies between client trading actions and client goals. Our methodology falls into the interest realms of advisors, regulators, and dealerships.
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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 | 0.014 | 0.090 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".