Single Family Offices as Firm Owners: Comparative Performance Effects
Bibliographic record
Abstract
Single Family Offices (SFOs) have become an important vehicle for transgenerational wealth management. They address succession issues in family-owned firms, act as an investment vehicle for the business-owning family, and provide administrative services. Yet, we know little about the role of SFOs as firm owners, particularly how they influence the performance of the firms they own. Our study addresses this question and investigates the performance of SFO-owned firms. Arguing from an agency and monitoring perspective, we propose that SFO-owned firms will underperform firms directly owned by families. We further postulate that this underperformance will be mitigated when members of the owning family are involved in the management or supervisory board of the SFO-owned firm, and when the SFO-owned firm is publicly listed. Our results partially support these hypotheses and show that SFO-owned firms do indeed exhibit weaker financial performance than family-owned firms. This effect is diminished when a family member is directly involved in the management or supervisory board of the SFO-owned firm. Significant performance differences were not found between private versus listed SFO-owned firms. Our study contributes to the corporate governance and family business literatures on the performance effects of firm owners and blockholders. It also extends the nascent but growing literature on family offices. Practical implications are drawn for business owning families seeking to set up a family office as vehicles for succession and transgenerational wealth management.
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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 | 0.004 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.021 | 0.001 |
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".