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Can Artificial Neural Networks Predict the Survival Capacity of Mutual Funds? Evidence from Spain.
- Source :
- Mathematics (2227-7390); Mar2021, Vol. 9 Issue 6, p695-695, 1p
- Publication Year :
- 2021
-
Abstract
- 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. [ABSTRACT FROM AUTHOR]
- Subjects :
- ARTIFICIAL neural networks
MUTUAL funds
SHARPE ratio
FINANCIAL instruments
NET worth
Subjects
Details
- Language :
- English
- ISSN :
- 22277390
- Volume :
- 9
- Issue :
- 6
- Database :
- Complementary Index
- Journal :
- Mathematics (2227-7390)
- Publication Type :
- Academic Journal
- Accession number :
- 149500947
- Full Text :
- https://doi.org/10.3390/math9060695