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Non-performing loans decision making in the Romanian banking system

Author

Listed:
  • Pop Ionuț-Daniel

    (The Bucharest University of Economic Studies – ASE, Bucharest, Romania)

  • Chicu Nicoleta

    (The Bucharest University of Economic Studies – ASE, Bucharest, Romania)

  • Răduțu Andrei

    (The Bucharest University of Economic Studies – ASE, Bucharest, Romania)

Abstract

Non-Performing Loans (NPLs) are representing nowadays one of the main challenges for the banking systems all over the world. Therefore, a sustainable decision-making process should be implemented, for minimizing the effects of credit risk. The current paper uses a dynamic panel regression model to present the determinants of NPLs for the largest five banks of the Romanian Banking System during 2007-2016. A Generalized Method of Moments (GMM) regression is used and defined under three different types of variables: bank specific indicators, macroeconomic indicators and qualitative variables. Other studies illustrated also the determinants of NPLs in various banking systems from all around the world, such as Japan, China or several CEE countries (especially the emergent ones). After an in-depth analysis of the literature and Romanian market, the following variables were found to be relevant and were introduced into a dynamic data panel model: unemployment rate, annual average growth rate of gross domestic product, return on equity (ROE), loan to deposit ratio (LTD). The existing literature presents ROE as having a negative impact on NPLs, unemployment rate being positive correlated with NPLs and a negative relationship between economic growth and such loans. Our contribution to the current literature is represented by the introduction of two additional qualitative variables (Board Risk Management Ratio (BRMR), as the proportion of risk managers within the Board of Directors of each bank in question and the Expert Aggregate Priority Vector (EAPV), as the aggregated perceived risk regarding the NPLs). The decision of introducing these variables relies on previous research made in this area, results being validated by experts from the Romanian Banking System, according to the BASEL III and NBR criteria. The results of the current paper are consistent with the existent literature, the correlations and impact of the variables being relevant for the subject matter.

Suggested Citation

  • Pop Ionuț-Daniel & Chicu Nicoleta & Răduțu Andrei, 2018. "Non-performing loans decision making in the Romanian banking system," Management & Marketing, Sciendo, vol. 13(1), pages 761-776, March.
    • Handle: RePEc:vrs:manmar:v:13:y:2018:i:1:p:761-776:n:4
    DOI: 10.2478/mmcks-2018-0004
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    References listed on IDEAS

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    1. Didar ERDINC & Eda ABAZI, 2014. "The Determinants of NPLs in Emerging Europe, 2000-2011," Journal of Economics and Political Economy, KSP Journals, vol. 1(2), pages 112-125, December.
    2. Chortareas, Georgios & Logothetis, Vassileios & Magkonis, Georgios & Zekente, Kalliopi-Maria, 2016. "The effect of banking supervision on central bank preferences: Evidence from panel data," Economics Letters, Elsevier, vol. 140(C), pages 11-13.
    3. Berger, Allen N. & DeYoung, Robert, 1997. "Problem loans and cost efficiency in commercial banks," Journal of Banking & Finance, Elsevier, vol. 21(6), pages 849-870, June.
    4. Virgil DAMIAN & Cosmin – Octavian CEPOI, 2016. "Volatility Estimators With High-Frequency Data From Bucharest Stock Exchange," ECONOMIC COMPUTATION AND ECONOMIC CYBERNETICS STUDIES AND RESEARCH, Faculty of Economic Cybernetics, Statistics and Informatics, vol. 50(3), pages 247-264.
    5. Louzis, Dimitrios P. & Vouldis, Angelos T. & Metaxas, Vasilios L., 2012. "Macroeconomic and bank-specific determinants of non-performing loans in Greece: A comparative study of mortgage, business and consumer loan portfolios," Journal of Banking & Finance, Elsevier, vol. 36(4), pages 1012-1027.
    6. Cosmin Octavian Cepoi & Filip Mihai Toma, 2016. "Estimating Probability of Informed Trading on the Bucharest Stock Exchange," Czech Journal of Economics and Finance (Finance a uver), Charles University Prague, Faculty of Social Sciences, vol. 66(2), pages 140-160, April.
    7. Dimitrios, Anastasiou & Helen, Louri & Mike, Tsionas, 2016. "Determinants of non-performing loans: Evidence from Euro-area countries," Finance Research Letters, Elsevier, vol. 18(C), pages 116-119.
    8. Vasiliki Makri & Athanasios Tsagkanos & Athanasios Bellas, 2014. "Determinants of Non-Performing Loans: The Case of Eurozone," Panoeconomicus, Savez ekonomista Vojvodine, Novi Sad, Serbia, vol. 61(2), pages 193-206, March.
    9. Barseghyan, Levon, 2010. "Non-performing loans, prospective bailouts, and Japan's slowdown," Journal of Monetary Economics, Elsevier, vol. 57(7), pages 873-890, October.
    10. Rinaldi, Laura & Sanchis-Arellano, Alicia, 2006. "Household debt sustainability: what explains household non-performing loans? An empirical analysis," Working Paper Series 570, European Central Bank.
    11. Manuel Arellano & Stephen Bond, 1991. "Some Tests of Specification for Panel Data: Monte Carlo Evidence and an Application to Employment Equations," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 58(2), pages 277-297.
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