Journal Article Summary Report

Authors: Beketov, Mikhail, Kevin Lehmann, and Manuel Wittke

Title: Robo Advisors: quantitative methods inside the robots. Journal of Asset Management

Name: Journal of Asset Management

Issue and page numbers: pages363–370(2018)

Year of Journal: 27 September 2018

The study aims to collect and analyze information on asset allocation and the optimization of portfolio methods presently implemented in the current Robo Advisors (RA) globally. The study investigates the methods of applications and their combinations for assets under management (AuM) methods while considering future methodological solutions. This research is crucial because it facilitates establishing the financial theories employed in the models’ execution. The study discovered that the modern portfolio theory was the main framework employed in the RAs. Secondly, the research was vital because it established instances where AuMs are used in high volume.

The study analyzes 219 RAs that were selected based on the listing of previous scholars. The research rigorously analyzes the RA systems’ web pages collected from 28 countries, comprising 30% of firms in the US, 20% in Germany, 14% in the UK, 9% in Switzerland, and 27% in other remaining countries. The RA data sets were established between the years 1997 and 2017. Notably, the average establishment year was 2014, while the most frequent years were 2016, 2015, 2017, and 2014. Notably, the AuM volumes analyzed in the study were within the range of 1-93,000 million USD.

The most interesting part of the article is the RA building blocks, which constitute five steps: asset universe selection, investor profile identification, the optimization of portfolios, monitoring and rebalancing, and reviewing the performance and reporting. Artificial intelligence has been developed to the extent that it facilitates creating an investment portfolio, which is a challenging aspect.