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Can artificial neural networks predict the survival capacity of mutual funds? Evidence from Spain

  • Datos identificativos

    Identificador: imarina:9187029
    Autores:
    Fabregat-Aibar, LauraSorrosal-Forradellas, Maria-TeresaBarbera-Marine, GloriaTerceno, Antonio
    Resumen:
    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 disappear-ance.
  • Otros:

    Autor según el artículo: Fabregat-Aibar, Laura; Sorrosal-Forradellas, Maria-Teresa; Barbera-Marine, Gloria; Terceno, Antonio
    Departamento: Gestió d'Empreses
    Autor/es de la URV: Barberà Mariné, Maria Glòria / Fabregat Aibar, Laura / Sorrosal Forradellas, Maria Teresa / Terceño Gómez, Antonio
    Palabras clave: Survival capacity Spanish market Neural network Mutual funds Improve Failure
    Resumen: 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 disappear-ance.
    Áreas temáticas: Química Mathematics (miscellaneous) Mathematics (all) Mathematics General mathematics Engineering (miscellaneous) Computer science (miscellaneous) Astronomia / física
    Acceso a la licencia de uso: https://creativecommons.org/licenses/by/3.0/es/
    Direcció de correo del autor: laura.fabregat@urv.cat gloria.barbera@urv.cat gloria.barbera@urv.cat antonio.terceno@urv.cat antonio.terceno@urv.cat mariateresa.sorrosal@urv.cat
    Identificador del autor: 0000-0002-0077-161X 0000-0003-2578-1301 0000-0003-2578-1301 0000-0001-5348-8837 0000-0001-5348-8837 0000-0003-4719-452X
    Fecha de alta del registro: 2024-09-28
    Versión del articulo depositado: info:eu-repo/semantics/publishedVersion
    Enlace a la fuente original: https://www.mdpi.com/2227-7390/9/6/695
    URL Documento de licencia: https://repositori.urv.cat/ca/proteccio-de-dades/
    Referencia al articulo segun fuente origial: Mathematics. 9 (6): 695-
    Referencia de l'ítem segons les normes APA: Fabregat-Aibar, Laura; Sorrosal-Forradellas, Maria-Teresa; Barbera-Marine, Gloria; Terceno, Antonio (2021). Can artificial neural networks predict the survival capacity of mutual funds? Evidence from Spain. Mathematics, 9(6), 695-. DOI: 10.3390/math9060695
    DOI del artículo: 10.3390/math9060695
    Entidad: Universitat Rovira i Virgili
    Año de publicación de la revista: 2021
    Tipo de publicación: Journal Publications
  • Palabras clave:

    Computer Science (Miscellaneous),Engineering (Miscellaneous),Mathematics,Mathematics (Miscellaneous)
    Survival capacity
    Spanish market
    Neural network
    Mutual funds
    Improve
    Failure
    Química
    Mathematics (miscellaneous)
    Mathematics (all)
    Mathematics
    General mathematics
    Engineering (miscellaneous)
    Computer science (miscellaneous)
    Astronomia / física
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