Determinants of Going Concern in the Banking Sector: Evidence from Ghana and Nigeria

References

• Abdallah, S. (2018). External auditor type, discretionary accruals and investors’ reactions. Journal of Accounting in Emerging Economies, 8(3), 352–368. https://doi.org/10.1108/JAEE-10-2017-0098
   Web of Science ®Google Scholar
• Aboagye, A. Q. (2020). Ghanaian banking crisis of 2017-2019 and related party transactions. African Journal of Management Research, 27(1), 2–19. https://doi.org/10.4314/ajmr.v27i1.1
   Google Scholar
• Ainsworth, P., & Deines, D. (2019). Introduction to accounting: An integrated approach. John Wiley & Sons.
   Google Scholar
• Akamah, H. T., Czerney, K., & Omer, T. C. (2019). The effects of rule-based versus principle-based accounting estimates on auditors’ going concern assessments. Social Science Research Network Electronic Journal, Available at SSRN 3414626. https://doi.org/10.2139/ssrn.3414626
   Google Scholar
• Al-Rawi, K., Kiani, R., & Vedd, R. R. (2008). The use of Altman equation for bankruptcy prediction in an industrial firm (case study). International Business & Economics Research Journal (IBER), 7(7). https://doi.org/10.19030/iber.v7i7.3276
   Google Scholar
• Altman, E. I. (1968). Financial ratios, discriminant analysis and the prediction of corporate bankruptcy. The Journal of Finance, 23(4), 589–609. https://doi.org/10.1111/j.1540-6261.1968.tb00843.x
   Web of Science ®Google Scholar
• Altman, E. I. (2013). Predicting financial distress of companies: revisiting the Z-score and ZETA® models. In Handbook of research methods and applications in empirical finance (pp. 428–456). Edward Elgar Publishing.
   Google Scholar
• Altman, E. I. (2018). A fifty-year retrospective on credit risk models, the Altman Z-score family of models and their applications to financial markets and managerial strategies. The Journal of Credit Risk, 14(4), 1–34. https://doi.org/10.21314/JCR.2018.243
   Web of Science ®Google Scholar
• Altman, E., Hartzell, J., & Peck, M. (1995). Emerging markets corporate bonds: A scoring system. Wiley and Sons.
   Google Scholar
• Appiah, K. O., Chizema, A., & Arthur, J. (2015). Predicting corporate failure: A systematic literature review of methodological issues. International Journal of Law and Management, 57(5), 461–485. https://doi.org/10.1108/IJLMA-04-2014-0032
   Web of Science ®Google Scholar
• Averio, T. (2020). The analysis of influencing factors on the going concern audit opinion–a study in manufacturing firms in Indonesia. Asian Journal of Accounting Research, 6(2), 152–164. https://doi.org/10.1108/AJAR-09-2020-0078
   Google Scholar
• Aziz, M. A., & Dar, H. A. (2006). Predicting corporate bankruptcy: Where we stand? Corporate Governance the International Journal of Business in Society, 6(1), 18–33. https://doi.org/10.1108/14720700610649436
   Google Scholar
• Balcaen, S., & Ooghe, H. (2006). 35 years of studies on business failure: An overview of the classic statistical methodologies and their related problems. The British Accounting Review, 38(1), 63–93. https://doi.org/10.1016/j.bar.2005.09.001
   Google Scholar
• Beaver, W. H. (1966). Financial ratios as predictors of failure. Journal of Accounting Research, 4, 71–111. https://doi.org/10.2307/2490171
   Web of Science ®Google Scholar
• Deakin, E. B. (1972). A discriminant analysis of predictors of business failure. Journal of Accounting Research, 10(1), 167–179. https://doi.org/10.2307/2490225
   Web of Science ®Google Scholar
• Dewi, N., Azam, S., & Yusoff, S. (2019). Factors influencing the information quality of local government financial statement and financial accountability. Management Science Letters, 9(9), 1373–1384. https://doi.org/10.5267/j.msl.2019.5.013
   Google Scholar
• Du, S., Bhattacharya, C. B., & Sen, S. (2010). Maximizing business returns to corporate social responsibility (CSR): The role of CSR communication. International Journal of Management Reviews, 12(1), 8–19. https://doi.org/10.1111/j.1468-2370.2009.00276.x
   Web of Science ®Google Scholar
• Effendi, S. A., & Fatmawati, D. S. A. (2021). Fair value: Is really more relevance and less reliability? International Journal of Multicultural and Multireligious Understanding, 8(11), 581–593.
   Google Scholar
• Egboro, E. M. (2016). The 2008/2009 banking crisis in Nigeria: The hidden trigger of the financial crash. British Journal of Economics, Management & Trade, 12(2), 7. https://doi.org/10.9734/BJEMT/2016/23656
   Google Scholar
• Enkhbold, E. (2019). The role of the ortoq in the Mongol Empire in forming business partnerships. Central Asian Survey, 38(4), 531–547. https://doi.org/10.1080/02634937.2019.1652799
   Web of Science ®Google Scholar
• Fabio, B. (2020). Distressed firm valuation: A scenario discounted cash flow approach. Journal of Business Valuation and Economic Loss Analysis, 15(1). https://doi.org/10.1515/jbvela-2020-0002
   Google Scholar
• Flower, J., & Ebbers, G. (2018). Global financial reporting. Macmillan International Higher Education.
   Google Scholar
• Garrow, N. S., Awolowo, I. F., & Growe, G. A. (2019). Annual reports: Fact or fiction? Are there governance implications? Journal of Leadership, Accountability and Ethics, 16(1), 21–26.
   Google Scholar
• Gerantonis, N., Vergos, K., & Christopoulos, A. G. (2009). Can Altman Z-score models predict business failures in Greece? Research Journal of International Studies, 12(21), 21–28.
   Google Scholar
• Gkouma, O., Filos, J., & Chytis, E. (2018). Financial crisis and corporate failure: The going concern assumption findings from Athens stock exchange. Journal of Risk & Control, 5(1), 141–170.
   Google Scholar
• Haber, J. R. (2005). Assessing how bankruptcy prediction models are evaluated. Journal of Business & Economics Research (JBER), 3(1). https://doi.org/10.19030/jber.v3i1.2739
   Google Scholar
• Hosaka, T. (2019). Bankruptcy prediction using imaged financial ratios and convolutional neural networks. Expert Systems with Applications, 117, 287–299. https://doi.org/10.1016/j.eswa.2018.09.039
   Web of Science ®Google Scholar
• Islamiati, R., Julianto, W., & Maulana, A. (2021). The determination of the acceptance of going concern audit opinion: Financial distress, institutional ownership, and auditor reputation. Accounting Analysis Journal, 10(3), 206–212.
   Google Scholar
• Javaid, M. I., & Javid, A. Y. (2018). Efficacy of going concern prediction model for creditor oriented regime via liquidation: A MDA approach. Journal of Applied Accounting Research, 19(4), 552–573. https://doi.org/10.1108/JAAR-07-2017-0070
   Web of Science ®Google Scholar
• Kałużny, R. (2018). Should creditors believe in auditors’ opinions? Auditors’ independence, going-concern warnings and credit risk assessment. Bezpieczny Bank, 2(71), 137–156.
   Google Scholar
• Kumar, P. R., & Ravi, V. (2007). Bankruptcy prediction in banks and firms via statistical and intelligent techniques–A review. European Journal of Operational Research, 180(1), 1–28.
   Web of Science ®Google Scholar
• Li, B., Martin, E. B., & Morris, A. J. (2001). Box–tidwell transformation based partial least squares regression. Computers & Chemical Engineering, 25(9–10), 1219–1233. https://doi.org/10.1016/S0098-1354(01)00696-2
   Web of Science ®Google Scholar
• Lombardi, R. (2021). The going-concern in accounting research. In The going-concern-principle in non-financial disclosure. SIDREA Series in Accounting and Business Administration (pp. 1–29). Springer, Cham. https://doi.org/10.1007/978-3-030-81127-3_1
   Google Scholar
• Manni, F., & Faccia, A. (2020). The business going concern: Financial return and social expectations. In M. Mateev, & J. Nightingale (Eds.), Sustainable development and social responsibility (Vol. 1, pp. 201–213). Springer. https://doi.org/10.1007/978-3-030-32922-8_20
   Google Scholar
• McKee, T. (1976). Discriminant prediction of going concern status: A model for auditors. In Selected Papers of the AAA Annual Meeting.
   Google Scholar
• Menard, S. (2002). Applied logistic regression analysis (Vol. 106). Sage.
   Google Scholar
• Musah, A., & Agyirakwah, J. A. (2019). Application of Altman Bankruptcy prediction model in Ghana. Jurnal Perspektif Pembiayaan dan Pembangunan Daerah, 7(1), 63–72. https://doi.org/10.22437/ppd.v7i1.7558
   Google Scholar
• Myers, W. R., Brenneman, W. A., & Myers, R. H. (2005). A dual-response approach to robust parameter design for a generalized linear model. Journal of Quality Technology, 37(2), 130–138. https://doi.org/10.1080/00224065.2005.11980311
   Web of Science ®Google Scholar
• Ohlson, J. A. (1980). Financial ratios and the probabilistic prediction of bankruptcy. Journal of Accounting Research, 18(1), 109–131. https://doi.org/10.2307/2490395
   Web of Science ®Google Scholar
• Penrose, E., & Penrose, E. T. (2009). The theory of the growth of the firm. Oxford university press.
   Google Scholar
• Poche’moret, R. (2021). Analysis of the effects of adjusting for binary non-confounders in a logistic regression model: A simulation study. Louisiana State University Health Sciences Center].
   Google Scholar
• Salehi, M., Tarighi, H., & Sahebkar, H. (2018). The impact of auditor conservatism on accruals and going concern opinion: Iranian angle. International Journal of Islamic and Middle Eastern Finance and Management, 11(4), 650–666. https://doi.org/10.1108/IMEFM-12-2015-0158
   Web of Science ®Google Scholar
• Sanusi, L. S. (2010). The Nigerian banking industry: What went wrong and the way forward. delivered at annual convocation ceremony of Bayero University. Kano Held On, 3(1), 2010.
   Google Scholar
• Senaviratna, N., & Cooray, T. (2019). Diagnosing multicollinearity of logistic regression model. Asian Journal of Probability and Statistics, 5(2), 1–9. https://doi.org/10.9734/ajpas/2019/v5i230132
   Google Scholar
• Shi, Y., & Li, X. (2019). A bibliometric study on intelligent techniques of bankruptcy prediction for corporate firms. Heliyon, 5(12), e02997. https://doi.org/10.1016/j.heliyon.2019.e02997
   PubMed Web of Science ®Google Scholar
• Shrestha, N. (2019). Application of binary logistic regression model to assess the likelihood of overweight. American Journal of Theoretical and Applied Statistics, 8(1), 18–25. https://doi.org/10.11648/j.ajtas.20190801.13
   Google Scholar
• Sperandei, S. (2014). Understanding logistic regression analysis. Biochemia Medica, 24(1), 12–18. https://doi.org/10.11613/BM.2014.003
   PubMed Web of Science ®Google Scholar
• Sun, J., Liu, X., Ai, W., & Tian, Q. (2021). Dynamic financial distress prediction based on class-imbalanced data batches. International Journal of Financial Engineering, 08(03), 2150026. https://doi.org/10.1142/S2424786321500262
   Google Scholar
• Taffler, R. J. (1983). The z-score approach to measuring company solvency. The Accountant’s Magazine, 87(921), 91–96.
   Google Scholar
• Thornton, J. (1998). Africa and Africans in the Making of the Atlantic World, 1400-1800. Cambridge University Press.
   Google Scholar
• Tkachuk, N. V. (2019). Historical cost and fair value: Advantages, disadvantages, application. Journal of History Culture and Art Research, 8(1), 173–182. https://doi.org/10.7596/taksad.v8i1.2052
   Google Scholar
• Wang, X., Pan, J., Ren, Z., Zhai, M., Zhang, Z., Ren, H., Song, W., He, Y., Li, C., Yang, X., Li, M. (2021). Application of a novel hybrid algorithm of Bayesian network in the study of hyperlipidemia related factors: A cross-sectional study. BioMed Central Public Health, 21(1), 1–14. https://doi.org/10.1186/s12889-021-11412-5
   PubMed Web of Science ®Google Scholar
• Wójcik-Jurkiewicz, M., & Karczewska, M. (2019). Assessment of the going concern value of a business entity by means of selected discriminative models. ASEJ Scientific Journal of Bielsko-Biala School of Finance and Law, 23(1), 53–60. https://doi.org/10.5604/01.3001.0013.1644
   Google Scholar
• Wulandari, K. M., & Muliartha, K. (2019). Good corporate governance sebagai pemoderasi pengaruh financial distress pada opini audit going concern. E-Jurnal Akuntansi, 28(2), 1170–1199. https://doi.org/10.24843/EJA.2019.v28.i02.p15
   Google Scholar
• Yadav, B. (2014). Creative accounting: An empirical study from professional prospective. International Journal of Management and Social Sciences Research, 3(1), 38–53.
   Google Scholar
• Yin, H., & Zhang, R. N. (2020). The effect of maturity mismatch between investing and financing on audit pricing. The Journal of Asian Finance, Economics and Business, 7(9), 51–61. https://doi.org/10.13106/jafeb.2020.vol7.no9.051
   Web of Science ®Google Scholar
• Yousaf, U. B., Jebran, K., & Wang, M. (2021). Can board diversity predict the risk of financial distress?. Corporate Governance. The International Journal of Business in Society, 21(4), 663–684.
   Web of Science ®Google Scholar
• Yusuf, B. R., & Tijani, J. O. (2019). An evaluation of financial health of Nigerian deposit money banks using CAMELS rating model. Annals of the University Dunarea de Jos of Galati: Fascicle: I Economics & Applied Informatics, 25(1), 48–61. https://doi.org/10.35219/eai158404097
   Google Scholar
• Yu, W., Xu, W., & Zhu, L. (2017). A modified Hosmer–Lemeshow test for large data sets. Communications in Statistics - Theory and Methods, 46(23), 11813–11825. https://doi.org/10.1080/03610926.2017.1285922
   Web of Science ®Google Scholar
• Zahedi, J., Salehi, M., & Moradi, M. (2021). Identifying and classifying the contributing factors to financial resilience. Foresight, 24(2), 177–194. https://doi.org/10.1108/FS-10-2020-0102
   Web of Science ®Google Scholar
• Zéman, Z., & Lentner, C. (2018). The changing role of going concern assumption supporting management decisions after financial crisis. Polish Journal of Management Studies, 18(1), 428–441. https://doi.org/10.17512/pjms.2018.18.1.32
   Web of Science ®Google Scholar