Scoring models for roboadvisory platforms: a network approach

Paolo Giudici and Gloria Polinesi

Journal of Network Theory in Finance

Abstract: Automated digital consultancy platforms (“robot advisors†) reduce costs and im- prove the perceived service quality, speeding it up and making user involvement more transparent. These improvements are often offset by risk classification models that are simpler than those employed in traditional consultancy. In this paper, the;authors;show how to exploit the available data to build portfolios that better fit the risk profiles of investors. This is made possible, on the one hand, by constructing groups of homogeneous risk profiles based on user responses to the markets in financial instruments directive (MIFID) questionnaire, and, on the other hand, by constructing homogeneous clusters of financial assets based on their risk and return performance. They also;also show that machine learning methods and, specifically, neural network models can be used to “automatize†the previous classifications and, eventually, to assess whether an investor’s portfolio matches their risk profile.

References: Add references at CitEc
Citations:

Downloads: (external link)
https://www.risk.net/journal-of-network-theory-in- ... s-a-network-approach (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:rsk:journ8:7411161

Access Statistics for this article

More articles in Journal of Network Theory in Finance from Journal of Network Theory in Finance
Bibliographic data for series maintained by Thomas Paine ().