Methodology of data envelopment analysis (DEA) - GLMNEt for assessment and forecasting of financial efficiency in a free trade zone - Colombia

被引:0
作者
Fontalvo T.J. [1 ]
De La Hoz E.J. [2 ]
Olivos S. [1 ]
机构
[1] Facultad de Ingeniería, Programa de Ingeniería Industrial, Universidad Libre, Barranquilla
[2] Facultad de Ingeniería, Programa de Ingeniería Industrial, Univ. Tecnológica de Bolívar, Campus Tecnológico, Cartagena
来源
Informacion Tecnologica | 2019年 / 30卷 / 05期
关键词
DEA; Efficiency; Free trade zone; GLMNET; Machine learning;
D O I
10.4067/S0718-07642019000500263
中图分类号
学科分类号
摘要
This research work proposes a methodology for evaluation and forecasting for companies located in the Industrial Port Zone of the city of Barranquilla, Colombia. Based on an empirical and rational analysis, supported by the concepts of technical efficiency, purely technical efficiency, additive efficiency, efficiency of scale and of mixing, as well as in the algorithm for machine learning GLMNET. Work was done with 29 companies that presented their complete financial statements for the year 2017 in the Chamber of Commerce of Barranquilla - Colombia. As a result, it was found an average technical efficiency of 72.79%, a purely technical efficiency of 82.54% and an additive efficiency of 59.45%. In addition, the projections required to make inefficient organizations achieve efficiency are contributed. From the study, it can also be observed that 11 companies were constituted as benchmarks to measure the companies of the Free Zone of the Port of Barranquilla. It is noteworthy that the algorithm GLMNET delivered a good result in the prediction of group membership of efficient and inefficient enterprises, with an accuracy of 93.1%. © 2019 Centro de Informacion Tecnologica. All rights reserved.
引用
收藏
页码:263 / 270
页数:7
相关论文
共 28 条
  • [11] Fontalvo T.J., Mendoza A., Visbal D., Efficiency in logistics processes in Medellin BASC certified companies through data envelopment analysis, Revista U.D.C.A Actualidad & Divulgación Científica, 17, 1, pp. 265-274, (2014)
  • [12] Fontalvo T.J., Mendoza A., Visbal D., Análisis comparativo de eficiencia financiera: Estudio de un caso sectorial en Barranquilla, Prospectiva, 13, 2, pp. 16-24, (2015)
  • [13] Friedman J., Hastie T., Tibshirani R., Regularization paths for generalized linear models via coordinate descent, Journal of Statistical Software, 33, 1, pp. 1-22, (2010)
  • [14] Galagedera D.U., Roshdi I., New network DEA model for mutual fund performance appraisal: An application to US equity mutual funds, Omega, 77, 1, pp. 168-179, (2018)
  • [15] Gonzalez P., Bermudez T., Una Aproximación al Modelo de Toma de Decisiones Usado por los Gerentes de las Micro, Pequeñas y Medianas Empresas Ubicadas en Cali, Colombia desde un Enfoque de Modelos de Decisión e Indicadores Financieros y no Financieros, Contaduria Unviersidad De Antioquia, 52, 1, pp. 131-154, (2008)
  • [16] Hans C., Bayesian lasso regression, Biometrika, 96, 4, pp. 835-845, (2009)
  • [17] Hoerl A.E., Kennard R., Ridge regression: Biased estimation for nonorthogonal problems, Technometrics, 12, 1, pp. 55-67, (1970)
  • [18] Hong H.K., Ha S., Evaluating the efficiency of system integration projects using data envelopment analysis (DEA) and machine learning, Expert Systems with Applications, 16, 3, pp. 283-296, (1999)
  • [19] Kocak H., Efficiency examination of Turkish airport with DEA approach, International Business Research, 4, 2, pp. 204-212, (2011)
  • [20] Lin W.Y., Hu Y., Tsai C., Machine learning in financial crisis prediction: A survey, IEEE Transactions on Systems, Man, and Cybernetics, Part C, 42, 4, pp. 421-436, (2012)