Familiarity Breeds Institutional Investment: Evidence from US Defined Benefit Pension Plans
Bibliographic record
Abstract
This paper provides new evidence that familiarity bias affects the portfolios of \ninstitutional investors. Using a sample of large US defined-benefit pension plans for the period \n1992 to 2002, we show that the corporate focus of the sponsoring firm has an impact on the \ninvestment policy of the pension plan. Pension plans sponsored by firms with a high proportion \nof foreign sales are more likely to invest in international assets, plans sponsored by firms that \nare active in research and development are more likely to invest in private equity, and plans \nwith sponsors that have more fixed assets are more likely to invest in real estate and \nmortgages. Comparing to existing explanations of why plans tilt their portfolios towards the \nsponsor’s focus, familiarity bias is the most compelling one. The worse performance of pension \nplans with such portfolio allocation bias is consistent with pension managers being over-confident about familiar assets.
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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 | 0.002 | 0.015 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".