Determining investor's risk group using fuzzy logic under the UCITS

Abstract

There are many approaches that examine the classification of investor risk. The most used approach offered by asset management companies and brokers is an investment questionnaire, which determines the risk group to which the investor belongs. A common problem with this approach is that a potential investor is fully classified into one category, even though he may be close to the border with the second category. This deficiency can be improved by using the European UCITS directive, which sets out 7 risk groups according to the synthetic risk and loss indicator SRRI. Using fuzzy logic, we determine with what affiliation the investor meets each question. This makes it possible to more accurately define the investor profile through SRRI with a higher granularity of risk groups. The Python programming language in the Jupyter Notebook environment is used for the analysis. This article aims to bring a more accurate classification of investors into risk groups, since the creation of an investment questionnaire with fuzzy logic using the European directive has not yet been implemented.