Neue Veröffentlichungen auf der FinanceCom 2020

Die folgenden Beiträge wurden auf der FinanceCom 2020 veröffentlicht:

Torno, A. and Schildmann, S. 2020. “What do Robo-Advisors Recommend? - An Analysis of Portfolio Structure, Performance and Risk ,“ in: FinanceCom 2020. Lecture Notes in Business Information Processing, forthcomming.


Abstract: Robo-Advisors guide investors through an automated investment advisory process, recommend personalized portfolio assignments based on their individual risk-affinity as well as investment goals and rebalance the portfolio automatically over time. Giving basic investment advice to customers, it can provide a useful way to reduce risk by diversifying and mitigating biases, while keeping a certain degree of performance at low costs. To verify these claims we conduct a sophisticated analysis of recommended portfolios of 36 Robo Advisors, based on six distinct model customers with different risk-affinities and investment horizons, resulting in 216 recommended portfolios. We find that the analyzed Robo-Advisors provide distinct recommended portfolios for the different risk/investment horizon combinations, while sharing similarities in used products for portfolio allocation. We also find issues within the recommended portfolios, e.g. a low degree of distinctiveness between different investment horizons and a high amount of equities even in the short-term investment horizon.



Bankamp, S. and Muntermann J. 2020. “Portfolio Rankings on Social Trading Platforms in Uncertain Times ,“ in: FinanceCom 2020. Lecture Notes in Business Information Processing, forthcomming.


Abstract: On social trading platforms, anyone can create investment portfolios and share them with others. This leads to an abundance of portfolio strategies available to investors. However, this variety comes with the drawback of high search costs for investors looking for the most promising portfolios to invest in. Platform operators provide a ranking on these portfolios, thus making it easier for investors and reducing search costs. In our study, we analyze the value of this ranking regarding various aspects and especially against the background of its protective function in turbulent market phases. For this purpose, we look at the stock market crash in connection with the Covid-19 pandemic. Our results show (i) that investors on social trading platforms are making use of the ranking, (ii) that the ranking offers value for these investors, and (iii) protects them from ex-treme losses in downward periods. The ranking we analyzed rather identifies portfolios with a more successful market timing than those with an effective stock selection. We further show that changes in the ranking tend to contribute to pro-cyclical behavior (iv). Finally, we provide preliminary evidence that indicates rankings’ influence on the behavior of traders.