A Quantitative Performance Analysis of Robo-Advisors in Germany

• 발행기관: People & Global Business Association
• 저자명: Mahan Tahvildari
• 간행물 정보: 『Global Business and Finance Review』Vol.30 No.8, 1~18쪽, 전체 18쪽
• 주제분류: 경제경영 > 경영학
• 파일형태: PDF
• 발행일자: 2025.08.31

국문 초록

Purpose: This study provides a performance analysis of robo-advisors (RAs) operating in Germany from January 2017 to February 2022, evaluating their strategies against straightforward benchmark portfolios. Addressing a critical gap in quantitative research, it explores whether RAs can outperform traditional allocations under varying market conditions. Design/methodology/approach: The analysis used monthly price data from 16 RAs offering 86 portfolios, categorised into ten benchmark groups by equity allocation. Standard performance measures were employed, including annualised return, volatility, Sharpe ratio, maximum drawdown, and CAPM-based measures. Two time frames were examined: the total period and a COVID-19 sub-period. Findings: Overall, RAs underperformed their benchmarks, with considerably higher drawdowns and longer recovery times. While some RAs occasionally outperformed on specific measures, there was no consistent outperformance. The COVID-19 crisis highlighted the inability of RAs to effectively mitigate losses or capitalise on the rapid market recovery, resulting in systematic underperformance. Research limitations/implications: The study is limited to a five-year window, ending before the full impact of post-COVID market developments and geopolitical tensions. Future research could extend the analysis to include more RAs and longer time periods and also explore potential factors for the performance differences. The findings highlight the need for greater transparency and algorithmic improvements in RAs. As an alternative to RAs, investors can simply set up their portfolios like the constructed benchmarks, depending on their risk preferences (equity exposures). Originality/value: This research is one of the few large-scale empirical assessments of RA performance against simple, low-cost benchmark portfolios. By rigorously comparing RAs in different benchmark groups, it provides new insights into RA performance. The findings suggest that RAs need to refine their investment strategies, rebalancing, and risk management to deliver superior results and justify their fees in a competitive asset management landscape.

목차

I. Introduction

II. Literature Review

III. Methods

IV. Results

V. Discussions

VI. Conclusions

References

키워드

Robo-Advisor Financial Advice Performance Analysis Artificial Intelligence COVID-19