NDLI: Can Artificial Neural Networks Predict the Survival Capacity of Mutual Funds? Evidence from Spain

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Can Artificial Neural Networks Predict the Survival Capacity of Mutual Funds? Evidence from Spain

Content Provider	MDPI
Author	Gl, òria Barberà-Mariné Antonio, Terceño Fabregat-Aibar, Laura Sorrosal-Forradellas, Maria-Teresa
Copyright Year	2021
Description	Recently, the total net assets of mutual funds have increased considerably and turned them into one of the main investment instruments. Despite this increment, every year a considerable number of funds disappear. The main purpose of this paper is to determine if the neural networks can be a valid instrument to detect the survival capacity of a fund, using the traditional variables linked to the literature of disappearance funds: age, size, performance and volatility. This paper also incorporates annualized variation in return and the Sharpe ratio as variables. The data used is a sample of Spanish mutual funds during 2018 and 2019. The results show that the network correctly classifies funds into surviving and non-surviving with a total error of 13%. Moreover, it shows that not all variables are significant to determine the survival capacity of a fund. The results indicate that surviving and non-surviving funds differ in variables related to performance and its variation, volatility and the Sharpe ratio. However, age and size are not significant variables. As a conclusion, the neural network correctly predicts the 87% of survival capacity of mutual funds. Therefore, this methodology can be used to classify this financial instrument according to its survival or disappearance.
Starting Page	695
e-ISSN	22277390
DOI	10.3390/math9060695
Journal	Mathematics
Issue Number	6
Volume Number	9
Language	English
Publisher	MDPI
Publisher Date	2021-03-23
Access Restriction	Open
Subject Keyword	Mathematics Social Sciences, Interdisciplinary Mutual Funds Neural Network Survival Capacity Spanish Market
Content Type	Text
Resource Type	Article

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