Scientific Development of Robo-Advisor: A Bibliometric Analysis
Bibliographic record
Abstract
This study addresses Robo-advisor, a relevant and current topic.Robo-advisor is an emerging business model that aims to popularize the investment advisory service by fully automating it.This work investigates the main research topics and the most important authors, as well as the journals and countries where this scientific research is carried out.The study uses two authoritative, multidisciplinary databases, Web of Science and Scopus, to select 219 research papers spanning from 2015 to May 21, 2022.It presents an overview of research on Roboadvisor, using a bibliometric analysis.To study the main interest of Robo-advisor research, we have reviewed the abstracts of the analyzed articles.Furthermore, to provide a comprehensive overview of current research, we extracted the main objectives from the articles of our corpus published in 2022.This review identifies 2018 as the moment from which this topic begins to grow, both in terms of scientific research interest and assets under management.The analysis of the abstracts, allowed us to highlight three major topics that focus academic research on Robo-advisor at present, namely (1) Low-human factor related, which includes those concepts such as asset selection and Roboadvisor implementation; (2) High-human factor related, dedicated to those actions in which the human factor plays a major role; and (3) Compliance, which includes topics related to the regulatory aspects of Robo-advisor.Our findings may be useful for professionals, future researchers, and academics.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.017 | 0.095 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.245 | 0.268 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.009 | 0.007 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".