Please use this identifier to cite or link to this item: https://hdl.handle.net/10419/324548 
Year of Publication: 
2021
Citation: 
[Journal:] Central European Economic Journal (CEEJ) [ISSN:] 2543-6821 [Volume:] 8 [Issue:] 55 [Year:] 2021 [Pages:] 269-284
Publisher: 
Sciendo, Warsaw
Abstract: 
In this study we utilise artificial neural networks to classify equity investment funds according to two fundamental risk measures - standard deviation and beta ratio - and to investigate the fund characteristics essential to this classification. Based on a sample of 4,645 monthly observations on 37 equity funds from the largest fund families registered in Poland from December 1995 to March 2018, we allocated funds to one of the classes generated using Multilayer Perceptron (MLP) and Radial Basis Function (RBF). The results of the study confirm the legitimacy of using machine learning as a tool for classifying equity investment funds, though standard deviation turned out to be a better classifier than the beta ratio. In addition to the level of investment risk, the fund classification can be supported by the fund distribution channel, the fund name, age, and size, as well as the current economic situation. We find historical returns (apart from the last-month return) and the net cash flows of the fund to be insignificant for the fund classification.
Subjects: 
open-end investment fund classification
equity funds
artificial neural networks
emerging market
JEL: 
G23
C38
C45
Persistent Identifier of the first edition: 
Creative Commons License: 
cc-by-nc-nd Logo
Document Type: 
Article

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