Robo Advising and Investor Profiling

Raquel M. Gaspar; Madalena Oliveira

doi:10.20944/preprints202311.0228.v1

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preprints.org > business, economics and management > finance > doi: 10.20944/preprints202311.0228.v1

Preprint Article Version 1 Preserved in Portico This version is not peer-reviewed

Robo Advising and Investor Profiling

Raquel M. Gaspar

^* and

Madalena Oliveira

^†

The opinions here expressed are those of the authors and not necessarily those of AXCO Consulting. This study was developed for academic purposes only.

Version 1 : Received: 30 October 2023 / Approved: 31 October 2023 / Online: 3 November 2023 (09:45:42 CET)

A peer-reviewed article of this Preprint also exists.

Gaspar, R.M.; Oliveira, M. Robo Advising and Investor Profiling. FinTech 2024, 3, 102-115. Gaspar, R.M.; Oliveira, M. Robo Advising and Investor Profiling. FinTech 2024, 3, 102-115.

Abstract

The rise of digital technology and artificial intelligence has led to a significant change in the way financial services are delivered. One such development is the emergence of robo-advising, which is an automated investment advisory service that utilizes algorithms to provide investment advice and portfolio management to investors. Robo-Advisors collect information about their clients’ preferences, financial situation and future goals through questionnaires, then recommend ETF based portfolios supposed to fit investor’s risk profile. However, questionnaires seem to be vague, and robos do not reveal the methods used for investor profiling or asset allocation. This study aims at contributing by proposing an investor profiling method, based only on the level of relative risk aversion (RRA) of investors. We show how to determine RRA optimal portfolios and compare the performance of our portfolios with portfolios proposed by the Riskalyze platform.

Keywords

robo-advisor; mean variance theory; expected utility theory; sharpe ratio

Subject

Business, Economics and Management, Finance

Copyright: This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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