Un análisis de los factores más significativos en la contratación de distintos productos financieros por parte de las familias en España

  1. Llorent Jurado, Julián 1
  2. Ordaz Sanz, José Antonio 1
  3. Melgar Hiraldo, Mª Carmen 1
  4. Guerrero Casas, Flor María 1
  1. 1 Universidad Pablo de Olavide
    info

    Universidad Pablo de Olavide

    Sevilla, España

    ROR https://ror.org/02z749649

Revista:
Anales de ASEPUMA

ISSN: 2171-892X

Any de publicació: 2018

Número: 26

Tipus: Article

DIALNET GOOGLE SCHOLAR lock_openDialnet editor

Altres publicacions en: Anales de ASEPUMA

Resum

On December 2017, Spanish families’ savings in financial assets got to 262 billion in investment funds, 111 billion euros in pension funds and 4 billion euros in life insurance primes. The relevance of these data suggests an approach to the study of main factors that may have influence in Spanish family units of hiring these financial products. Some techniques of multivariate analysis, as well as Financial Survey of Families of the Bank of Spain, may be very useful to achieve this aim.

Referències bibliogràfiques

  • Akkoç, S. (2012). “An empirical comparison of conventional techniques, neural networks and the three stage hybrid Adaptive Neuro Fuzzy Inference System (ANFIS) model for credit scoring analysis: The case of Turkish credit card data.” European Journal of Operational Research, 222(1), 168–178.
  • Altman, E. (1968). “Financial ratios, discriminant analysis and the prediction of corporate bankruptcy.” The Journal of Finance.
  • Beaver, W. (1966). “Financial ratios as predictors of failure.” Journal of Accounting Research. Disponible en: http://www.jstor.org/stable/2490171
  • Berger, A. N., Imbierowicz, B., Y Rauch, C. (2016). “The Roles of Corporate Governance in Bank Failures during the Recent Financial Crisis.” Journal of Money, Credit and Banking, 48(4), 729–770.
  • Blanco, A., Pino Mejías, R., Lara, J., Y Rayo, S. (2013). “Credit scoring models for the microfinance industry using neural networks: Evidence from Peru.” Expert Systems with Applications, 40(1), 356–364.
  • Dutta, A., Bandopadhyay, G., Y Sengupta, S. (2015). “Prediction of stock performance in indian stock market using logistic regression.” International Journal of Business and Information, 7(1).
  • Hair, J. F., Black, W. C., Babin, B. J., Anderson, R. E., Y Tatham, R. L. (1998). “Multivariate data analysis”. 5(3), 207–219. Upper Saddle River, NJ: Prentice hall.
  • Hosmer, D. W., Taber, S., Y Lemeshow, S. (1991). “The importance of assessing the fit of logistic regression models: a case study.” American Journal of Public Health, 81(12), 1630–1635.
  • Martínez, R. (2008). “El análisis multivariante en la investigación científica.” Cuadernos de Estadística, 1. Editorial La Muralla. Madrid.
  • Mures, M. J., García, A., Y Vallejo, M. E. (2005). “Aplicación del análisis discriminante y regresión logística en el estudio de la morosidad en las entidades financieras: comparación de resultados.” Pecunia: Revista de La Facultad de Ciencias Económicas y Empresariales, Universidad de León, 0(1), 175.
  • Nikolic, N., Zarkic Joksimovic, N., Stojanovski, D., Y Joksimovic, I. (2013). “The application of brute force logistic regression to corporate credit scoring models: Evidence from Serbian financial statements”. Expert Systems with Applications, 40(15), 5932–5944.
  • Ohlson, J. (1980). “Financial ratios and the probabilistic prediction of bankruptcy.” Journal of Accounting Research.
  • Ren, Y. Y., Zhou, L. C., Yang, L., Liu, P. Y., Zhao, B. W., Y Liu, H. X.(2016). “Predicting the aquatic toxicity mode of action using logistic regression and linear discriminant analysis.” SAR and QSAR in Environmental Research, 27(9),721–746.
  • Shinmura, S. (2015). “A Trivial Linear Discriminant Function.” Statistics, Optimization & Information Computing, 3(4), 322–335.
  • Silva, L. C., Y Barroso, I. M. (2004). “Regresión logística.” Cuadernos de Estadística, 27. Ed. La Muralla, Hespérides.
Fuente de los datos: Dialnet