Abstract:

This study examines the efficiency of Robo-Advisors within the broader context of Fintech in wealth and asset management, aiming to determine their performance relative to traditional asset management strategies and benchmarks. Positioned as a case study, the research explores the performance of a particular leading Robo-Advisor in Japan, decomposing returns into tactical and strategic components, alongside various risk metrics. The work is structured into three key parts: theoretical foundations, methodological development, and empirical analysis.

First a classification framework is proposed to capture the essential features of Robo-Advisory services globally. Then, the experiment is developed and conducted: Empirical results reveal that, across varied market conditions, Robo-Advisors do not consistently outperform a simple fixed-weight strategy, which holds assets at predetermined allocations without frequent adjustments. Furthermore, the study’s comparison with the Japanese Fund Market suggests no statistically significant difference in asset management outcomes between Robo-Advisors and conventional fund strategies.

Desing/Approach:

A case study methodology is employed, focusing on a leading Japanese Robo-Advisor. Theoretical foundation of the Robo-Advisor methodology and data from the Japanese Fund Association are utilized to construct a composite benchmark and evaluate returns, which are decomposed into tactical and strategic components. Statistical tests and risk metrics are applied to data to determine significant performance differences between the Robo-Advisor and traditional asset management strategies.