Asset quality is an important indicator of the stability of the banking sector, which in turn can potentially affect the stability of an economy. Hence, understanding factors that affect the bank’s asset quality can have significant policy implications. This article aims at identifying the factors that affect the asset quality of a bank after controlling for macroeconomic variables, that is, annual GDP growth rate, inflation rate and macroeconomic shocks such as the global financial crisis and the COVID-19 pandemic. To this end, we construct multidimensional indices of bank-specific variables and use them to find the determinants of asset quality. The article also uses election year dummies to capture the effect of potential political influence on asset quality. We use unbalanced panel data for 51 banks over the period of 18 years (2006–2023). The empirical results reveal that earnings, liquidity and operational efficiency have a significant impact on asset quality. The significant coefficients of election dummies reflect the consequence of evolving policies undertaken and the ongoing political affairs at that time. The article also extracts some important policy implications for better governance of the asset quality of a bank.
AborJ., & BiekpeN. (2007). Small business reliance on bank financing in Ghana. Emerging Markets Finance and Trade, 43(4), 93–102. https://doi.org/10.2753/REE1540-496X430405
2.
AliyevN., HuseynovF., & RzayevK. (2021). Does algorithmic trading impact investment? Algorithmic trading and investment-to-price sensitivity. Edinburgh Research Explorer, 1–69. https://www.pure.ed.ac.uk/ws/portalfiles/portal/245328777/AT_18122021.pdf
3.
Al-NajjarD., & AssousH. F. (2021). Key determinants of deposits volume using CAMEL rating system: The case of Saudi banks. PLOS One, 16(12), 1–15. https://doi.org/10.1371/journal.pone.0261184
4.
AltanM., YusufazariH., & BedukA. (2014, October28). Performance analysis of banks in Turkey using CAMEL approach [Conference session]. 14th International Academic Conference, Malta. http://proceedings.iises.net/index.php?action=proceedingsIndexConference&id=9
5.
ArellanoM., & BondS. (1991). Some tests of specification for panel data: Monte Carlo evidence and an application to employment equations. The Review of Economic Studies, 58(2), 277–297. https://doi.org/10.2307/2297968
6.
ArellanoM., & BoverO. (1995). Another look at the instrumental variable estimation of error-components models. Journal of Econometrics, 68(1), 29–51. https://doi.org/10.1016/0304-4076(94)01642-D
7.
ArrawatiaR., DawarV., MaitraD., & DashS. R. (2019). Asset quality determinants of Indian banks: Empirical evidence and policy issues. Journal of Public Affairs, 19(4), 1–11. https://doi.org/10.1002/pa.1937
8.
BandyopadhyayT. (2020). Pandemonium: The great Indian banking tragedy. Roli Books.
9.
BardhanS., & MukherjeeV. (2016). Bank-specific determinants of nonperforming assets of Indian banks. International Economics and Economic Policy, 13(3), 483–498. https://doi.org/10.1007/s10368-016-0344-4
10.
BardhanS., SharmaR., & MukherjeeV. (2019). Threshold effect of bank-specific determinants of nonperforming assets: An application in Indian banking. Journal of Emerging Market Finance, 18(1S), 1S–34S. https://doi.org/10.1177/0972652719831546
11.
BaulchB., & QuisumbingA. (2010). Testing and adjusting for attrition in household panel data. Chronic Poverty Research Centre: United Kingdom Government. Toolkit Note. Chronic Poverty Research Centre. https://assets.publishing.service.gov.uk/media/57a08b2740f0b652dd000b22/NOTE-ON-ATTRITION-revfinal.pdf
12.
BawaJ. K., GoyalV., MitraS. K., & BasuS. (2019). An analysis of NPAs of Indian banks: Using a comprehensive framework of 31 financial ratios. IIMB Management Review, 31, 51–62. https://doi.org/10.1016/j.iimb.2018.08.004
13.
BeckettiS., GouldW., LillardL., & WelchF. (1988). The panel study of income dynamics after fourteen years: An evaluation. Journal of Labour Economics, 6(4), 472–492. https://doi.org/10.1086/298192
14.
BergerA. N., & DeYoungR. (1997). Problem loans and cost efficiency in commercial banks. Journal of Banking and Finance, 21(6), 849–870. https://doi.org/10.1016/S0378-4266(97)00003-4
15.
BlundellR., & BondS. (1998). Initial conditions and moment restrictions in dynamic panel data models. Journal of Econometrics, 87(1), 115–143. https://doi.org/10.1016/S0304-4076(98)00009-8
16.
CameronA. C., & TrivediP. K. (2005). Microeconometrics: Methods and applications. Cambridge University Press.
17.
DaherD., & NobaneeH. (2020). CAMELS ratio system approach for the Bank of America. https://www.researchgate.net/publication/342703270_CAMELS_Ratio_System_Approach_for_the_Bank_of_America
18.
DangD. (2011). The CAMEL rating system in banking supervision: A case study. Arcada University of Applied Sciences—International Business. https://urn.fi/URN:NBN:fi:amk-2011123019231
19.
DharS., & BakshiA. (2015). Determinants of loan losses of Indian banks: A panel study. Journal of Asia Business Studies, 9(1), 1–24. https://doi.org/10.1108/JABS-04-2012-0017
20.
FazzariS. M., HubbardR. G., & PetersenB. C. (1988). Financing constraints and corporate investment. Brookings Papers on Economic Activity, 1988(1), 141–195. https://doi.org/10.2307/2534426
21.
GhoshS. (2022). Political connections and bank behaviour. Economic Notes, 52(1), 1–21. https://doi.org/10.1111/ecno.12209
22.
GreeneW. H. (2011). Econometric analysis. Pearson.
23.
GujaratiD. N., & PorterD. C. (2010). Basic Econometrics. McGraw-Hill.
24.
GulatiR., & SinghN. (2019). Analysing bank stability in India: Evidence from 2007/08–2016/17. Journal of Applied Finance & Banking, 9(6), 101–125.
25.
GunjalS. P. (2017). Political intrusion on firms: Effects of elections on bank lending in India [Master’s theses] (pp. 239). https://repository.usfca.edu/thes/239
26.
KhwajaA. I., & MianA. (2005). Do lenders favor politically connected firms? Rent provision in an emerging financial market. The Quarterly Journal of Economics, 120(4), 1371–1411. https://doi.org/10.1162/003355305775097524
27.
KjosevskiJ., & PetkovskiM. (2021). Macroeconomic and bank-specific determinants of non-performing loans: The case of Baltic states. Empirica, 48, 1009–1028. https://doi.org/10.1007/s10663-020-09491-5
28.
KonstantakopoulouI. (2022). Does health quality affect tourism? Evidence from system GMM estimates. Economic Analysis and Policy, 73(C), 425–440. https://doi.org/10.1016/j.eap.2021.12.007
29.
LouzisD. P., VouldisA. T., & MetaxaxV. L. (2010). Macroeconomic and bank-specific determinants of non performing loans in Greece: A comparative study of mortgage, business and consumer loan portfolios [Bank of Greece Working Paper No. 118]. http://dx.doi.org/10.2139/ssrn.1703026
30.
MacroTrends (n.d.). India inflation rate 1960-2024. Retrieved, 20April2024, from https://www.macrotrends.net/global-metrics/countries/IND/india/inflation-rate-cpi
31.
MakriV., TsagkanosA., & BellasA. (2014). Determinants of non-performing loans: The case of Eurozone. Panoeconomicus, 2, 193–206. https://doi.org/10.2298/PAN1402193M
32.
MasoodO., GhauriS. M. K., & AktanB. (2016). Predicting Islamic banks performance through CAMEL Strating model. Banks and Bank Systems, 11(3). 37–43. https://doi.org/10.21511/bbs.11(3).2016.04
33.
MaudeF. A., & DogarawaA. B. (2016). A critical review of empirical studies on assessing bank performance using CAMEL framework. Nigerian Journal of Management Technology and Development, 7(2), 21–32. http://dx.doi.org/10.2139/ssrn.3492008
34.
MishraA. K., JainS., AbidM., & ManognaR. L. (2020). Macro-economic determinants of non-performing assets in the Indian banking system: A panel data analysis. International Journal of Finance & Economics, 26(3), 3819–3834. https://doi.org/10.1002/ijfe.1989
35.
OECD. (2008). Handbook on constructive composite indicators: Methodology and user guide.
36.
Outes-LeonI., & DerconS. (2008). Survey attrition and attrition bias in young lives. Young Lives Technical Note No. 5. University of Oxford. https://citeseerx.ist.psu.edu/document?repid=rep1&type=pdf&doi=8cbac7c06ba3f159d4fd19c14fc88d9fd5c697c6
37.
ÖzgüzelC. (2019). Agglomeration effects in a developing economy: Evidence from Turkey [Working Paper No. 1341]. Economic Research Forum. https://erf.org.eg/publications/agglomeration-effects-in-a-developing-economy-evidence-from-turkey/
38.
ParkJ. (2012). Corruption, soundness of the banking sector, and economic growth: A cross-country study. Journal of International Money and Finance, 31(5), 907–929. https://doi.org/10.1016/j.jimonfin.2011.07.007
39.
PaulS., & RoyM. (2021). Is priority sector lending responsible for higher NPA in the banking industry?Neoliberalism in the emerging economy of India: The political economy of international trade, investment and finance (pp. 89–102). Routledge. https://doi.org/10.4324/9781003131762-7
40.
PodpieraJ., & WeillL. (2008). Bad luck or bad management? Emerging banking market experience. Journal of Financial Stability, 4, 135–138. https://doi.org/10.1016/j.jfs.2008.01.005
41.
RajaramanI., BhaumikS., & BhatiaN. (1999). NPA variations across Indian commercial banks: Some findings. Economic & Political Weekly, 34(3/4), 161–168. http://www.jstor.org/stable/4407577
42.
RegehrK., & SenguptaR. (2016). Has the relationship between bank size and profitability changed?Economic Review, 49–72. https://www.communitybanking.org/~/media/files/communitybanking/2016/session2_paper3_sengupta.pdf
43.
Reserve Bank of India (RBI). (1995, November). Report of the Working Group to review the system of on-site supervision over banks. https://rbidocs.rbi.org.in/rdocs/PublicationReport/Pdfs/CR360_199510450D92D3884B8FB3C51A89B9CF20D4.PDF
44.
RBI. (2001, August). Master Circular—Prudential norms on income recognition, asset classification and provisioning pertaining to advances. https://www.rbi.org.in/scripts/BS_ViewMasterCirculars.aspx?Id=449&Mode=0
45.
RBI. (2009, September). Prudential norms on income recognition, asset classification and provisioning pertaining to advances—Computation of NPA levels. https://www.rbi.org.in/scripts/NotificationUser.aspx?Id=5290&Mode=0
46.
RBI. (2015a, July). Master circular—Prudential norms on income recognition, asset classification and provisioning pertaining to advances. https://www.rbi.org.in/scripts/BS_ViewMasCirculardetails.aspx?id=9908
47.
RBI. (2015b, September). Report of the Committee on differential premium system for banks in India. https://www.rbi.org.in/scripts/PublicationReportDetails.aspx?UrlPage=&ID=825
48.
RBI. (2022a, April). Master circular—Prudential norms on income recognition, asset classification and provisioning pertaining to advances. https://www.rbi.org.in/Scripts/BS_ViewMasCirculardetails.aspx?id=12281
RBI. (n.d.-a). Database on Indian economy (DBIE). Retrieved, 20April2024https://dbie.rbi.org.in/DBIE/dbie.rbi?site=publications#!4
51.
RBI. (n.d.-b). Statistical Tables: Components of gross domestic product at constant prices. Economic Surveyof India (2022-23). https://www.indiabudget.gov.in/budget2023-24/economicsurvey/doc/Statistical-Appendix-in-English.pdf
52.
RoodmanD. (2009). How to do xtabond2: An introduction to difference and system GMM in Stata. The Stata Journal, 9(1), 86–136. https://journals.sagepub.com/doi/pdf/10.1177/1536867x0900900106
53.
RuddyJ. A. (2018). Using a U.S. bank regulatory approach to explain bank financial strength ratings. The Journal of Corporate Accounting & Finance, 29(3), 70–82. https://doi.org/10.1002/jcaf.22345
54.
SamantarayaA. (2016). Procyclical credit growth and bank NPAs in India. Economic & Political Weekly, 51(12), 112–119. https://www.jstor.org/stable/44004144
55.
SatpathyA., BeheraS. R., & DigalS. K. (2015). Macroeconomic factors affecting the NPAs in the Indian banking system: An empirical assessment. The IUP Journal of Bank Management, 14(1), 57–74. https://ssrn.com/abstract=2664008
56.
ShaddadyA., & MooreT. (2019). Investigation of the effects of financial regulation and supervision on bank stability: The application of CAMELS-DEA to quantile regressions. Journal of International Financial Markets, Institutions & Money, 58, 96–116. https://doi.org/10.1016/j.intfin.2018.09.006
57.
SwamyV. (2013). Determinants of bank asset quality and profitability—An empirical assessment. Munich Personal RePEc Archive. https://mpra.ub.uni-muenchen.de/47513/
58.
SyedA., & TripathiR. (2020). Macroeconomic vulnerabilities and its effect on nonperforming loans in Indian commercial banks. Indian Journal of Finance, 14(2), 34–49. https://doi.org/10.17010/ijf/2020/v14i2/150555
59.
The Global Economy.com. (n.d.). Non-performing loans by country, around the world. Retrieved, 20May2023, from https://www.theglobaleconomy.com/rankings/nonperforming_loans/
60.
UdegbunamR. I. (2004). Asset portfolio composition, size, and bank stock risk: Evidence from Nigerian Commercial Banks. African Review of Money Finance and Banking, 5-30. http://www.jstor.org/stable/23026291
61.
UNDP. (2010). Human development report 2010: 20th anniversary edition; the real wealth of nations: Pathways to human development. United Nation Developmental Programme. https://ssrn.com/abstract=2294686
62.
Wikipedia Contributors. (2024, September12). List of Indian general elections. Wikipedia. https://en.wikipedia.org/wiki/List_of_Indian_general_elections
63.
WooldridgeJ. M. (2010). Econometric analysis of cross section and panel data. MIT Press.
64.
WooldridgeJ. M. (2013). Introductory econometrics: A modern approach (5th ed.). South-Western Pub.
Supplementary Material
Please find the following supplemental material available below.
For Open Access articles published under a Creative Commons License, all supplemental material carries the same license as the article it is associated with.
For non-Open Access articles published, all supplemental material carries a non-exclusive license, and permission requests for re-use of supplemental material or any part of supplemental material shall be sent directly to the copyright owner as specified in the copyright notice associated with the article.
