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A dynamic network DEA model for accounting and financial indicators: A case of efficiency in MENA banking

Author

Listed:
  • Wanke, Peter
  • Abul Kalam Azad, Md
  • Emrouznejad, Ali
  • Antunes, Jorge

Abstract

Middle East and North Africa (MENA) countries present a banking industry that is well-known for regulatory and cultural heterogeneity, besides ownership, origin, and type diversity. This paper explores these issues by developing a Dynamic Network DEA model in order to handle the underlying relationships among major accounting and financial indicators. Firstly, a relational model encompassing major profit sheet, balance sheet, and financial health indicators is presented under a dynamic network structure. Subsequently, the dynamic effect of carry-over indicators is incorporated into it so that efficiency scores can be properly computed for these three substructures. The impact of contextual variables related to bank ownership, its type, and whether or not it has undergone a previous merger and acquisition process is tested by means of a stochastic non-linear model solved by differential evolution, which combines bootstrapped Simplex, Tobit, Beta, and Simar and Wilson truncated regression results. The results reveal that bank type, origin, and ownership impact efficiency levels differently in terms of profit sheet, balance sheet, and financial health indicators, although the impact of culture and regulatory barriers seem to prevail at the country level.

Suggested Citation

  • Wanke, Peter & Abul Kalam Azad, Md & Emrouznejad, Ali & Antunes, Jorge, 2019. "A dynamic network DEA model for accounting and financial indicators: A case of efficiency in MENA banking," International Review of Economics & Finance, Elsevier, vol. 61(C), pages 52-68.
    • Handle: RePEc:eee:reveco:v:61:y:2019:i:c:p:52-68
    DOI: 10.1016/j.iref.2019.01.004
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    Cited by:

    1. Sungmin Park & Pansoo Kim, 2021. "Operational Performance Evaluation of Korean Ship Parts Manufacturing Industry Using Dynamic Network SBM Model," Sustainability, MDPI, vol. 13(23), pages 1-20, November.
    2. Cândida Ferreira, 2020. "Evaluating European Bank Efficiency Using Data Envelopment Analysis: Evidence in the Aftermath of the Recent Financial Crisis," International Advances in Economic Research, Springer;International Atlantic Economic Society, vol. 26(4), pages 391-405, November.
    3. Kuo‐Cheng Kuo & Wen‐Min Lu & Thanh Nhan Dinh, 2020. "Firm performance and ownership structure: Dynamic network data envelopment analysis approach," Managerial and Decision Economics, John Wiley & Sons, Ltd., vol. 41(4), pages 608-623, June.
    4. Mahmoudabadi, Mohammad Zarei & Emrouznejad, Ali, 2019. "Comprehensive performance evaluation of banking branches: A three-stage slacks-based measure (SBM) data envelopment analysis," International Review of Economics & Finance, Elsevier, vol. 64(C), pages 359-376.
    5. González, Luis Otero & Razia, Alaa & Búa, Milagros Vivel & Sestayo, Rubén Lado, 2019. "Market structure, performance, and efficiency: Evidence from the MENA banking sector," International Review of Economics & Finance, Elsevier, vol. 64(C), pages 84-101.
    6. Jéfferson Colombo & Peter Wanke & Jorge Antunes & Abul Kalam Azad, 2022. "Unveiling endogeneity between competition and efficiency in European banks: a robust econometric-neural network approach," SN Business & Economics, Springer, vol. 2(3), pages 1-46, March.
    7. Azad, A.S.M. Sohel & Azmat, Saad & Hayat, Aziz, 2023. "What determines the profitability of Islamic banks: Lending or fee?," International Review of Economics & Finance, Elsevier, vol. 86(C), pages 882-896.
    8. Wanke, Peter & Tsionas, Mike G. & Chen, Zhongfei & Moreira Antunes, Jorge Junio, 2020. "Dynamic network DEA and SFA models for accounting and financial indicators with an analysis of super-efficiency in stochastic frontiers: An efficiency comparison in OECD banking," International Review of Economics & Finance, Elsevier, vol. 69(C), pages 456-468.
    9. Yong Tan & Peter Wanke & Jorge Antunes & Ali Emrouznejad, 2021. "Unveiling endogeneity between competition and efficiency in Chinese banks: a two-stage network DEA and regression analysis," Annals of Operations Research, Springer, vol. 306(1), pages 131-171, November.
    10. M.V. Leonov, 2021. "Review of Modern Approaches for Assessing the Effectiveness of Banking," Journal of Applied Economic Research, Graduate School of Economics and Management, Ural Federal University, vol. 20(2), pages 294-326.
    11. Pooja Bansal & Aparna Mehra & Sunil Kumar, 2022. "Dynamic Metafrontier Malmquist–Luenberger Productivity Index in Network DEA: An Application to Banking Data," Computational Economics, Springer;Society for Computational Economics, vol. 59(1), pages 297-324, January.
    12. Zhishuo Zhang & Yao Xiao & Huayong Niu, 2022. "DEA and Machine Learning for Performance Prediction," Mathematics, MDPI, vol. 10(10), pages 1-23, May.
    13. Ying Li & Yung‐ho Chiu & Ying Yu Chen & Lihua Wang & Yi‐Nuo Lin & Su‐Wan Wang, 2022. "The impact of market share on efficiency of commercial banks: Resampling slacks‐based measure data envelopment analyses model," Managerial and Decision Economics, John Wiley & Sons, Ltd., vol. 43(5), pages 1621-1634, July.
    14. Moraes, Ricardo Kalil & Wanke, Peter Fernandes & Faria, João Ricardo, 2021. "Unveiling the endogeneity between social-welfare and labor efficiency: Two-stage NDEA neural network approach," Socio-Economic Planning Sciences, Elsevier, vol. 77(C).
    15. Hong‐Jing Lin & Che‐Chien Chen & Yung‐ho Chiu & Tai‐Yu Lin, 2022. "How financial technology (fintech) can improve the business performance of securities firms by using the dynamic data envelopment analysis modified model," Managerial and Decision Economics, John Wiley & Sons, Ltd., vol. 43(4), pages 1113-1132, June.
    16. Huang-Ping Yen & Po-Chi Chen & Kung-Cheng Ho, 2021. "Analyzing Destination Accessibility From the Perspective of Efficiency Among Tourism Origin Countries," SAGE Open, , vol. 11(2), pages 21582440211, April.
    17. Koronakos, Gregory & Sotiros, Dimitris & Despotis, Dimitris K. & Kritikos, Manolis N., 2022. "Fair efficiency decomposition in network DEA: A compromise programming approach," Socio-Economic Planning Sciences, Elsevier, vol. 79(C).
    18. Pérez-Cárceles, María Concepción & Gómez-García, Juan & Gómez Gallego, Juan Cándido, 2019. "Goodness of governance effect on European banking efficiency," International Review of Economics & Finance, Elsevier, vol. 64(C), pages 29-40.
    19. Del Barrio-Tellado, María José & Gómez-Vega, Mafalda & Herrero-Prieto, Luis César, 2023. "Performance of cultural heritage institutions: A regional perspective," Socio-Economic Planning Sciences, Elsevier, vol. 87(PB).
    20. Chao Wang & Xi Chu & Jinyan Zhan & Pei Wang & Fan Zhang & Zhongling Xin, 2019. "Factors Contributing to Efficient Forest Production in the Region of the Three-North Shelter Forest Program, China," Sustainability, MDPI, vol. 12(1), pages 1-19, December.

    More about this item

    Keywords

    Banks; MENA; Dynamic; Network; DEA; Stochastic optimization;
    All these keywords.

    JEL classification:

    • H81 - Public Economics - - Miscellaneous Issues - - - Governmental Loans; Loan Guarantees; Credits; Grants; Bailouts
    • E44 - Macroeconomics and Monetary Economics - - Money and Interest Rates - - - Financial Markets and the Macroeconomy
    • G21 - Financial Economics - - Financial Institutions and Services - - - Banks; Other Depository Institutions; Micro Finance Institutions; Mortgages

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