The Quality of Financial Advice: What Influences Client Recommendations?
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
In this paper, we conduct an experiment with a large sample of financial planner professionals in Canada to elicit factors which may influence client recommendations. Using repeated client vignettes, we find that recommendations are often in-line with what one would expect from economic theory. In particular, advice is sensitive in expected ways to relative costs and benefits of particular options. In some domains, we find evidence that planners are more likely to recommend products they own themselves, their spouse owns, or they are licensed to sell. In the investment domain, we also find that planners are more likely to recommend products that clients inquire about even when this type of solicitation is randomized across clients and options. Finally, we find that planners are systematically sensitive to the gender of the client even when gender is uninformative regarding which recommendation to make.
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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 | 0.013 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 it