Vanguard’s digital advisor and the European robo-advisory market
Bibliographic record
Abstract
Robo-advisors base their investment strategy on low-cost indexed funds. Vanguard Group announced the launch of its US Robo-advisor in the third quarter of 2020. Being one of the largest providers of indexed funds, Vanguard concentrates both the provision of automated advisory services and the management of the underlying investment vehicles. This study aims to determine if Vanguard’s model results in a relevant reduction of costs for investors and therefore in a competitive advantage against competitors. Our focus is on the European market, where Vanguard is currently expanding its investment services and the leading Robo-advisory providers struggle to expand. To test the hypothesis that a European Robo-advisor by Vanguard would have a determinant cost advantage, we simulate its costs by extrapolating the incremental costs of Vanguard services in the UK against the ones in the US. The results show that independent Robo-advisors cannot compete with Vanguard Digital Advisor on a cost basis.
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.037 | 0.002 |
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".