Machine Learning in Automated Asset Management Processes 4.1

Marcus Becker, Mikhail Beketov and Manuel Wittke

Die Unternehmung - Swiss Journal of Business Research and Practice, 2021, vol. 75, issue 3, 411-431

Abstract: The traditional (human driven) process of Asset Management has become automatized by algorithmic decision trading with so called Robo Advisors (RAs). With an increasing amount of publicly available financial data, the foundation for applying machine learning (ML) algorithms has been paved. We examine the question in which process steps of automated investment advice ML algorithms could be applied and investigate which implementations have already been placed on the market. As the following study shows, (surprisingly) ML is globally still under its development phase in Robo Advisory. German and Swiss FinTech companies thereby contribute about a third to the ML solutions in our sample. The most promising technique is the usage of Text Mining for sentiment analyses, which can be used for monitoring and rebalancing purposes or future performance forecasting. Furthermore, Text Mining algorithms can be helpful for reducing information asymmetries. Embedded into early warning systems, the derived sentiment scores can be used for hedging against future price losses. This approach would be inevitably linked to an increased access of highly sensible data. Furthermore, we try to provide an explanation for the lack of acceptance of the application of ML in RA distributions. Possible reasons for this can be found in the current MiFID II regulations, which are not specified for ML. Based on these insights, we formulate first recommendations for both the provider of RA solutions as well as for the regulator.

Date: 2021
References: Add references at CitEc
Citations:

Downloads: (external link)
https://www.nomos-elibrary.de/10.5771/0042-059X-2021-3-411 (application/pdf)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:nms:untern:10.5771/0042-059x-2021-3-411

Ordering information: This journal article can be ordered from
Nomos Verlagsgesellschaft mbH & Co. KG, Waldseestraße 3-5, 76530 Baden-Baden, Germany
https://www.nomos-sh ... w.aspx?product=13597

DOI: 10.5771/0042-059X-2021-3-411

Access Statistics for this article

Die Unternehmung - Swiss Journal of Business Research and Practice is currently edited by Prof. Dr. Artur Baldauf, Universität Bern, Prof. Dr. Manfred Bruhn, Universität Basel, Prof. Dr. Pascal Gantenbein, Universität Basel (geschäftsführend), Prof. Dr. Markus Gmür, Universität Fribourg, Prof. Dr. Klaus Möller, Universität St. Gallen, Prof. Dr. Günter Müller-Stewens, Universität St. Gallen, Prof. Dr. Dr. h.c. Margit Osterloh, Universität Zürich, Prof. Dr. Dieter Pfaff, Universität Zürich and Prof. Dr. Martin Wallmeier, Universität Fribourg

More articles in Die Unternehmung - Swiss Journal of Business Research and Practice from Nomos Verlagsgesellschaft mbH & Co. KG
Bibliographic data for series maintained by Nomos Verlagsgesellschaft mbH & Co. KG ().