What Do Robo-Advisors Recommend? - An Analysis of Portfolio Structure, Performance and Risk

The growing demand for digital investment advisory services and the advancing technological process led to increased attention to this topic in recent literature. In light of these developments, the question arises whether conventional and digital advisors behave differently in their investment advisory decisions. I therefore conducted a systematic literature review and evaluated 97 publications on the determinants of conventional and digital investment advisory decisions. Based on the literature, five main determinants were identified that are important for investment advisory decisions. These determinants are identical for both variants of the advice, but there are differences in the way they are addressed. This systematic literature review provides an overview of the current state of research and can therefore help identify areas where investment advice can be improved in the future.

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Robo-advisors as part of industry 4.0 in financial markets: Evolutionary development, methods, and first performance insights

Holtfort

Horsch²,

Schwarz

2022

RGC

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Today, an essential disruptive trend of the fourth industrial revolution is robo-advisors that offer innovative asset management services (Tao, Su, Xiao, Dai, & Khalid, 2021). They are automated investment platforms that use quantitative algorithms to produce advice to investors to help them manage their portfolios and are accessible to clients online (Beketov, Lehmann, & Wittke, 2018). Until now, there has been no comprehensive analysis of the development of these innovative advisors, the asset allocation methods used, and the performance (also concerning the Corona crisis). Thus, the paper takes robo-advisory-related research a step further by analyzing the development of robo-advisory on a global scale from an evolutionary point of view, at the same time focusing on the variety of methods applied by the advisors and the factors influencing their performance between 2018 and 2021 by regression analysis. Our results show that modern portfolio theory remains the primary framework used by robo-advisors, even though some use new approaches. The average performance of robo-advisors appears to beat the market benchmark, however not significantly during the Corona-crash period. Important factors influencing their performance are the number of allocation methods applied and, specifically, the technique of rebalancing. The findings demonstrate that in the context of Industry 4.0, robo-advisors can offer advantages not only in terms of costs and technical processes but also in terms of performance.

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What Do Robo-Advisors Recommend? - An Analysis of Portfolio Structure, Performance and Risk

Cited by 4 publications

References 17 publications

Trust me, I am a Robo-advisor

Trust me, I am a Robo-advisor

Determinants of conventional and digital investment advisory decisions: a systematic literature review

Robo-advisors as part of industry 4.0 in financial markets: Evolutionary development, methods, and first performance insights

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