Behavioral Biases of Financial Planners: The Case of Retirement Funding Recommendations
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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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 | 0.007 | 0.058 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".