Behavioral Biases of Financial Planners: The Case of Retirement Funding 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
We examine whether financial planners display common behavioral biases and whether these biases affect their recommendations for various home equity release options to fund retirement income. First, we show that different factors explain different behavioral biases. Second, we show that different behavioral biases affect financial planners’ comfort level and recommendations for various options to fund extra income during retirement. For instance, female planners, planners with advanced degrees and those from non-bank institutions display less mental accounting bias, while older and high-income planners display lower loss aversion. Specifically, our findings reveal that biases, notably mental accounting and herding, influence planners’ willingness to recommend home equity utilization. Furthermore, these biases significantly affect the ranking of retirement income strategies. Planners exhibiting mental accounting or gambler’s fallacy prioritize selling investments, whereas those with loss aversion lean toward selling and downsizing. Our findings have important implications for financial planning and advising practices as they illuminate the nuanced interplay between planner biases and advisory practices in retirement planning.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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 it