Publication: Determinantes de la Rentabilidad en Cooperativas de ahorro y crédito de Ecuador. Un análisis mediante Machine Learning
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2020
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Abstract
El presente trabajo busca evidenciar empíricamente los principales determinantes que influyen en la rentabilidad de las Cooperativas de ahorro y crédito (COAC) de Ecuador. Para este fin, se implementarán modelos de aprendizaje automático como el árbol de clasificación CART, el Random Forest y el Gradient Boosting Machine. Mediante el empleo de estos modelos se busca llegar al objetivo principal del trabajo que es predecir los determinantes de la rentabilidad y evaluar cuál de las metodologías empleadas es más eficiente en cuanto a la clasificación de las COAC en Ecuador en rentables o no rentables. Se ha elaborado una base de datos que cuenta con un total de 510 observaciones para el año 2020. Para el análisis se han seleccionado dos variables dependientes que son el ROE y la Rentabilidad de la cartera de microcrédito y, como variables independientes, y de acuerdo con la teoría revisada, se han utilizado ratios financieros específicos para este tipo de entidades que miden la liquidez, solvencia, calidad del crédito, eficiencia y tamaño de la entidad. El presente trabajo llegó a la conclusión que, de los modelos utilizados, el modelo de Gradient Boosting Machine es el que mejor predice la rentabilidad, tanto en el caso del ROE como para la rentabilidad de la cartera de microcrédito. Además, las variables relacionadas con el tamaño de la entidad y el crédito son las que más influyen a la hora de clasificar una entidad como rentable o no rentable.
This work seeks to empirically demonstrate the main determinants that influence the profitability of savings and credit cooperatives in Ecuador. For this purpose, machine learning models will be implemented such as: Classification tree, Random Forest, and Gradient Boosting Machine through which it is sought to reach the main objective of the work, which is to predict the determinants of profitability and evaluate which of the methodologies used is more efficient regarding the classification of COACs in Ecuador as profitable or unprofitable. The analyzed database has a total of 510 observations for the year 2020. The dependent variables will be ROE and Profitability of the microcredit portfolio; As independent variables and in accordance with the revised theory, internal or specific factors of the financial institution will be used, which will be proportions that measure the liquidity, solvency, credit quality, efficiency, and size of the entity. The present work concluded that the Gradient Boosting Machine is the one that best predicts profitability. And the variables related to the size of the entity and the credit are the ones that most influence when classifying an entity as profitable or unprofitable.
This work seeks to empirically demonstrate the main determinants that influence the profitability of savings and credit cooperatives in Ecuador. For this purpose, machine learning models will be implemented such as: Classification tree, Random Forest, and Gradient Boosting Machine through which it is sought to reach the main objective of the work, which is to predict the determinants of profitability and evaluate which of the methodologies used is more efficient regarding the classification of COACs in Ecuador as profitable or unprofitable. The analyzed database has a total of 510 observations for the year 2020. The dependent variables will be ROE and Profitability of the microcredit portfolio; As independent variables and in accordance with the revised theory, internal or specific factors of the financial institution will be used, which will be proportions that measure the liquidity, solvency, credit quality, efficiency, and size of the entity. The present work concluded that the Gradient Boosting Machine is the one that best predicts profitability. And the variables related to the size of the entity and the credit are the ones that most influence when classifying an entity as profitable or unprofitable.
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Albertazzi, H., & Gambacorta, L. (2009). Bank profitability and the business cycle. Journal of Financial Stability, Volume 5, Issue 4, Pages 393-409.
Altamirano, A. (2018). Modelo de diagnóstico para medir el desempeño financiero en las Cooperativas de Ahorro y Creédito de Ecuador. Buenos Aires: Revista de investigación en modelos financieros.
Bain, J. (1951). Relation of Profit Rate to Industry Concentration: American Manufacturing, 1936-1940. Oxford: The Quarterly Journal of Economics, Vol. 65, No. 3, pp. 293 -324.
Bakar, N., & Tahir, I. (2009). Applying Multiple Linear Regression and Neural Network to Predict Bank performance. International Business Research, Vol 2(4), 176-183.
Baumol, W. (1982). Business behavior, value andgrowth. New York: Macmillan, 1959. 164 p.
BCE. (01 de 04 de 2021). Banco Central del Ecuador. Obtenido de Banco Central del Ecuador: https://www.bce.fin.ec/.
Berger, A. (1995). The Profit-Structure Relationship in Banking--Tests of Market-Power and Efficient Structure Hypotheses. Ohio: Journal of Money, Credit and Banking, Vol. 27, No. 2.
Brealey, R., Myers, S., & Marcus, A. (1996). Principios de direccion financiera. Madrid: Mcgraw Hill Editorial.
Breiman, L. (2001). Random Forests. Machine Learning, 45, 5–32, 2001.
Breiman, L., Friedman, J., Stone, C., & Olshen, R. (1984). Classification and Regression Trees (Wadsworth Statistics/Probability). Chapman and Hall/CRC; N.º 1 edición (1 enero 1984).
Catal, C. (2012). Performance Evaluation Metrics for Software. Acta Polytechnica Hungarica.
Codigo Orgánico Monetario y Financiero. (2014). Codigo Orgánico Monetario y Financiero. Quito.
(2011). Cutler, Adele; Stevens, John; Cutler, David. Machine Learning 45(1):157-176.
Dang, U. (2011). THE CAMEL RATING SYSTEM IN BANKING SUPERVISION: A CASE STUDY. Arcada University of Applied Sciences, International Business.
Demirgüç-Kunt, A., & Huizinga, H. (2000). Financial structure and bank profitability. Washington: The World Bank.
Demsetz, H. (1973). Indusry structure, Market rivalry and public policy . The Journal of Law and Economics, Vol. 16 No. 1, pp. 1-9.
Drzewiecki, W. (2017). Thorough statistical comparison of machine learning regression models and their ensembles for sub-pixel imperviousness and imperviousness change mapping. Geodesy an Cartography, 66(2), 171-209.
Efron, B. (1979). Bootstrap Methods: Another Look at the Jackknife. Ann. Statist. 7(1): 1-26 (January, 1979).
Erdal, H., & Karahanoglu, I. (2016). Bagging ensemble models for bank profitability: An emprical research on Turkish development and investment banks. Applied soft computing 49, 861-867.
Fawcett, T. (2006). An introduction to ROC analysis. Pattern Recognition Letters 27 (2006) 861–874.
Friedman, J. (2001). Greedy function approximation: A gradient boosting Machine. The Annals of Statistics, 29(5), 1189-1232.
Friedman, J. (2002 ). Stochastic gradient boosting. Computational Statistics & Data Analysis, 38, 367-378.
Goddard, J., Molyneux, P., & Wilson, J. (2004). THE PROFITABILITY OF EUROPEAN BANKS:A CROSS-SECTIONAL AND DYNAMIC PANEL ANALYSIS. The Manchester School Vol 72 No. 3.
Gonzáles Perez, A., Rodríguez Correa, A., & Acosta Molina, M. (2002). Factores determinantes de la rentabilidad financiera de las PYMES. Revista Española De Financiación Y Contabilidad, 31(112), 395-429.
Gonzáles, L. (2019). Análisis de rentabilidad del sistema bancario panameño . Madrid: Universidad Complutense de Madrid.
González, N., & Taborda, A. (2015). ANÁLISIS DE COMPONENTES PRINCIPALES SPARSE: Formulación, algoritmos e implicaciones en el análisis de datos.
Gu, Q., Zhu, L., & Cai, Z. (2009). Evaluation Measures of the Classification Performance of Imbalanced Data Sets. Computational Intelligence and Intelligent Systems, 461-471.
Haslem, J. (1968). A Statistical Analysis of the Relative Profitability of Commercial Banks. The journal of finance.
Hastie, T., Tibshirani, R., & Friedman, J. (2009). The Elements of Statistical Learning: Data Mining, Inference, and Prediction. Springer Series in Statistics.
Heggestad, A., & Mingo, J. (1976). Prices, Nonprices, and Concentration In Commercial Banking. Ohio: Journal of Money, Credit and Banking.
Hollis, A., & Sweetman, A. (1998). Microcredit: What Can we Learn from the Past? World Development.
Jolliffe, T. (2002). Principal Component Analysis, Second Edition. Encyclopedia of Statistics in Behavioral Science, 30(3), 487.
Lapo, M., & Tello, M. (2021). Rentabilidad, capital y riesgo crediticio en bancos ecuatorianos. Investigación Administrativa 50(127):18-39.
Lawrence, S., & Joe, Z. (1999). Profitability and Marketability of the Top 55 U.S. Commercial Banks. Maryland: Managment Science.
Ledgerwood, J. (1999). Sustainable Banking with the poor, Microfinance handbook. Washington D.C: The World Bank.
Ley Orgánica de Economía Popular y Solidaria . (2011). Ley Orgánica de Economía Popular y Solidaria. Quito.
Peltzman, S. (1977). The Gains and Losses from Industrial Concentration. Journal of Law & Economics, 20(2), 229-264.
SEPS. (Abril de 2017). Super Intendencia de Economia Popuar y Solidaria. Obtenido de Fichas Metodológicas de Indicadores Financieros: https://www.seps.gob.ec/documents/20181/594508/NOTA+TE%CC%81CNICA+
PARA+PUBLICAR+-FICHA+METODOLOGICAS+DE+INDICADORES.pdf/a71e5ed1-7fae-4013-a78d-425243db4cfa.
SEPS. (01 de Abril de 2021). Superintendencia de Economía Popular y Solidaria. Obtenido de Superintendencia de Economía Popular y Solidaria : https://www.seps.gob.ec/#.
Short, B. (1979). The relation between commercial bank profit rates and banking concentration in Canada, Western Europe, and Japan. Journal of Banking & Finance Volume 3, Issue 3, Pages 209-219.
Smirlock, M. (1985). Evidence on the (Non) Relationship between Concentration and Profitability in Banking. Ohio: Journal of Money, Credit and Banking ,Vol. 17, No. 1.
Staikouras, C., & Wood, G. (2004). The Determinants Of European Bank Profitability. International Business & Economics Research Journal (IBER).
Uddin, M., Habib, T., Chi, G., & Al Janabi, M. (2020). Leveraging random forest in microenterprises credit riskmodelling for accuracy and interpretability. International Journal of Finance and Economics. 2020; 1– 17.
Vapnik, V. (1995). The Nature of Statistical Learning Theory. New York: Springer.