Advertisement

Measuring the Financial Performance of MSMEs Through Artificial Neural Networks

  • Jesus Silva
  • Lissette Hernandez
  • Ana Emilia Hernandez
  • Noel Varela
  • Hugo Hernández Palma
  • Osman Redondo Bilbao
  • Nadia Leon Castro
  • Ronald Prieto Pulido
  • Jesús García Guliany
Chapter
  • 36 Downloads
Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 637)

Abstract

Given the importance of micro, small and medium-sized enterprises (MSMEs) in Colombia, both in terms of the number of enterprises and the generation of employment, it is important to know their nature, as well as the main determinants of their financial performance. In this sense, this paper aims to provide relevant information for the formulation of strategies, programs and public policies that promote practices within companies and thus improve the performance of this segment of organizations.

Keywords

Financial performance Organizational practices Microfirms Artificial neural network (RNA) 

References

  1. 1.
    Alvarez, A.T.V., Carrasco, L.V.M., y Córdova, Z.A.F.: Estrategia organizacional y la rentabilidad en em-presas del sector automotriz de la Zona Central del Ecuador. Revista Eniac Pesquisa, 5(2), 181–192 (2016).  https://doi.org/10.22567/rep.v5i2.399CrossRefGoogle Scholar
  2. 2.
    Alsaaty, F., Zenebe, A., Sengupta, S.: The Influence of Some Macroeconomic Factors on the Growth of Micro Firms in the United States. Available at SSRN: https://ssrn.com/abstract=2775339 or http://dx.doi.org/10.2139/ssrn.2775339. Accessed on 12 June 2017
  3. 3.
    Bechara, J.E.A., Cruz, J.C.T., Ceballos, H.V.: Predicciones de modelos econométricos y redes neuronales: el caso de la acción de SURAMINV. Semestre Económico Universidad de Medellín, 12(25), 95–109 (2009). Available from http://www.scielo.org.co/scielo.php?script=sci_arttext&pid=S012063462009000300007&lng=en&nrm=i-so. Accessed on 07 Aug 2017
  4. 4.
    Bizarrón, M.E.B., Palacios, E.M.C., Bobadilla, L.I.Z., García, N.L.A.: El desarrollo de la Mipyme y la vinculación universitaria en Puerto Vallarta, Jalisco. Eur. Sci. J. ESJ 10(19) (2014). Available at http://www.eujournal.org/index.php/esj/article/view/3788/3604. Accessed on 18 June 2017
  5. 5.
    Agrawal, R., Srikant, R.: Fast algorithms for mining association rules in large databases. In: 1994. Proceedings of the 20th International Conference on Very Large Data Bases, VLDB, pp. 487–499 (1994)Google Scholar
  6. 6.
    Guzmán, M.G., Guzmán, M.M., Fuentes, M.R.: Análisis del uso de las TIC en las pymes de Guayaquil en el año 2015. Revista OIKOS 20(41), 109–119 (2016). Available at https://dialnet.unirioja.es/descarga/articulo/5841091.pdf, ISSN 0718–4670. Accessed on 13 May 2017CrossRefGoogle Scholar
  7. 7.
    Hahsler, M., Karpienko, R.: Visualizing association rules in hierarchical groups. J. Bus. Econ. 87, 317–335 (2017)Google Scholar
  8. 8.
    Silverstein, C., Brin, S., Motwani, R., Ullman, J.: Scalable techniques for mining causal structures. Data Min. Knowl. Discovery 4(2–3), 163–192 (2000)Google Scholar
  9. 9.
    Amelec, V., Carmen, V.: Relationship between variables of performance social and financial of microfinance institutions. Adv. Sci. Lett. 21(6), 1931–1934 (2015)CrossRefGoogle Scholar
  10. 10.
    Viloria, A., Lezama, O.B.P.: Improvements for determining the number of clusters in k-means for innovation databases in SMEs. Procedia Comput. Sci. 151, 1201–1206 (2019)CrossRefGoogle Scholar
  11. 11.
    Kamatkar, S.J., Kamble, A., Viloria, A., Hernández-Fernandez, L., Cali, E.G.: Database performance tuning and query optimization. In: International Conference on Data Mining and Big Data, pp. 3–11. Springer, Cham (2018)CrossRefGoogle Scholar
  12. 12.
    Viloria, A., et al.: Integration of data mining techniques to PostgreSQL database manager system. Procedia Comput. Sci. 155, 575–580 (2019)CrossRefGoogle Scholar
  13. 13.
    Lanzarini, L., Villa Monte, A., Aquino, G., De Giusti, A.: Obtaining classification rules using lvqPSO advances in swarm and computational intelligence. In: Lecture Notes in Computer Science, vol. 6433, pp. 183–193. Springer, Berlin (2015)CrossRefGoogle Scholar
  14. 14.
    Amelec, V., Carmen, V.: Validation of a model for productivity evaluation for microfinance institutions. Adv. Sci. Lett. 21(5), 1610–1614 (2015)CrossRefGoogle Scholar
  15. 15.
    Jiménez, L.R.G., Rodas, M.F.G., Quiroz, M.Q.G.: Opción de Financiamiento a Pymes ubicadas en la Provincia del Guayas enfocadas en la Búsqueda de Capitales mediante la Emisión de Títulos a través del Mercado de Valores Ecuatoriano. Empresarial 10(40), 21–30 (2017). Available at https://dialnet.unirioja.es/descarga/articulo/5924579.pdf, ISSN No. 1390-3748. Accessed on 18 May 2017

Copyright information

© Springer Nature Singapore Pte Ltd. 2020

Authors and Affiliations

  • Jesus Silva
    • 1
  • Lissette Hernandez
    • 2
  • Ana Emilia Hernandez
    • 3
  • Noel Varela
    • 3
  • Hugo Hernández Palma
    • 4
  • Osman Redondo Bilbao
    • 4
  • Nadia Leon Castro
    • 4
  • Ronald Prieto Pulido
    • 5
  • Jesús García Guliany
    • 5
  1. 1.Universidad Peruana de Ciencias AplicadasLimaPeru
  2. 2.Universidad del AtlánticoPuerto ColombiaColombia
  3. 3.Universidad de la Costa (CUC)Atlántico, BarranquillaColombia
  4. 4.Corporación Universitaria LatinoamericanaBarranquillaColombia
  5. 5.Universidad Simón BolivarBarranquillaColombia

Personalised recommendations