An Enhanced Mean-Variance Framework for Robo-Advising Applications

24 Pages Posted: 6 Nov 2019

See all articles by Moris Simon Strub

Moris Simon Strub

Southern University of Science and Technology - Business School

Duan Li

Chinese University of Hong Kong; City University of Hong Kong

Xiangyu Cui

Shanghai University of Finance and Economics - School of Statistics and Management

Date Written: May 11, 2019

Abstract

Any robo-advisor needs to decide on a framework to model the preferences of its investors over uncertain outcomes. As of today, most robo-advisors model their investors as mean-variance optimizers. While the mean-variance framework is intuitive and optimal investment strategies have been derived in various settings, it suffers from serious drawbacks due to its time-inconsistency and non-monotonicity. We propose an enhanced mean-variance framework for robo-advising applications which is based on the equivalence between the mean-variance objective and quadratic utility functions. By introducing a flexible weight on the decreasing part of the quadratic utility function, we can alleviate the issues of time-inconsistency and non-monotonicity while keeping the features leading to the popularity of the mean-variance framework. We show how the new framework can be calibrated by means of questionnaires and discuss the advantages of the novel framework in terms of the resulting terminal wealth distributions.

Keywords: robo-advising, portfolio choice, decision support, mean-variance optimization, expected utility maximization

JEL Classification: C61, G11

Suggested Citation

Strub, Moris Simon and Li, Duan and Cui, Xiangyu, An Enhanced Mean-Variance Framework for Robo-Advising Applications (May 11, 2019). Available at SSRN: https://ssrn.com/abstract=3302111 or http://dx.doi.org/10.2139/ssrn.3302111

Moris Simon Strub (Contact Author)

Southern University of Science and Technology - Business School ( email )

1088 Xueyuan Ave
Shenzhen, Guangdong
China

HOME PAGE: http://sites.google.com/view/morisstrub/home

Duan Li

Chinese University of Hong Kong ( email )

Shatin, New Territories
Hong Kong

City University of Hong Kong

Tat Chee Avenue
Kowloon Tong
Kowloon
Hong Kong
852 3442 8591 (Phone)

Xiangyu Cui

Shanghai University of Finance and Economics - School of Statistics and Management ( email )

777 Guoding Road
Shanghai, Shanghai 200433
China

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