Skip to main content
SpringerLink
Log in

A search for macroeconomic determinants of corporate financial distress

  • Article
  • Published:
Indian Economic Review Aims and scope Submit manuscript

Abstract

The aim of this paper is to explore the macroeconomic determinants of corporate financial distress using a data from the Indian corporate sector. We also consider a set of quasi-macro variables to develop a hybrid model of financial distress. For this purpose, we use accounting information-based three-factor criteria to construct a series for probability of financial distress of firms. We initially use a systematic variable selection approach to develop alternative models of financial distress and then apply the bounds test to establish a long-run equilibrium relationship between financial distress of firms and its determinants. We find that macroeconomic factors play crucial role in determining financial distress. Results suggest that aggregate output, flow of international funds, international demand, and corporate profitability are negatively associated with probability of financial distress. However, periods of high inflation may not be beneficial as the results suggest that a high inflation leads to high financial distress. The findings are important in the sense that movement in these macroeconomic and quasi-macro factors can be useful in monitoring the buildup of risk or financial distress in the balance sheet of firms over time. The set of indicators identified in the study can be used to develop an efficient distress prediction models.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Similar content being viewed by others

Notes

  1. In simple words, ‘twin balance sheet problem’ is explained as the condition in which a high level of financial distress in one sector of the economy leads to high levels on non-performing assets in banking sector. Indian economy followed a slightly different path of twin balance sheet problem. The non-food bank credit doubled within a span of 3 years (2004–05 to 2008–09) leading to a credit boom and increased the risk appetite of firms. Overtime, this excessive risk taking by firms started to move in wrong direction. Among others, costs increased and it became difficult to obtain land and environmental clearances. Rapidly rising costs, decline in revenues and increase in financing-related issues restricted the cash flows to firms leading to low-interest coverage ratios (mainly for firm in infrastructure, power generation, and metals’ sector) and difficulty in servicing of outstanding debt (See Economic Survey, 2016–17 for a detailed discussion).

  2. See also some recent studies such as Bhattacharjee and Han (2014), Tinoco and Wilson (2013), Mselmi et al. (2017), Charalambakis and Garrett (2019), and references therein, for further discussion on the role and impact of firm-specific factors on the financial distress of firms.

  3. We construct these variables by averaging and not by aggregating for the reason that the number of firms in our sample varies every year, and hence, the aggregate numbers shall not be comparable over time.

  4. See Sehgal et al. (2021) for a detailed discussion on the role of these factors in the determination and prediction of financial distress in the Indian corporate sector.

  5. Unlike a calendar year (January to December), financial year in India takes into account the time period between 1st April and 31st March for financial accounting and taxation.

  6. The first case under the IBC was admitted by the National Company Law Appellate Tribunal (NCLT) on January 17, 2017 and the first insolvency resolution plan was approved on August 2, 2017. It is noteworthy that by February 2019, that is, within 27 months of the implementation of the IBC, as many as 14,000 applications had been filed for initiation of corporate insolvency resolution process (CIRPs) (see Economic Survey, 2018–19).

  7. Interest Coverage Ratio (ICR) that is defined as the EBITDA (Earnings before Interest, Tax, Depreciation and Amortization) divided by the Interest Charge. In the literature, interest cover is a frequently employed as a measure of financial distress and an important determinant of bankruptcy (see, for example, Asquith et al., (1994) and DeAngelo et al., (2002), and Kam et al. (2008).

  8. It is noteworthy that during our preliminary variables’ selection analysis using bivariate regression, we observed that FDI has a highly significant impact on probability of financial distress. Hence, we include FDI in our model.

  9. See Johansen (1988, 1995) and Johansen and Juselius (1990) for further discussion.

  10. See also Pesaran and Pesaran (1997), Pesaran and Shin (1999), Pesaran et al., (2001) for further discussion.

  11. It may be noted that null of no cointegration for the final hybrid model is rejected at 10% level for model–6 using the Narayan (2005) critical values.

  12. Pesaran and Shin (1999) show that the SBC method is superior to the AIC method for the selection of ARDL model. Hence, we consider using the SBC criterion for lag selection.

References

  • Acharya, V. V. (2020). Quest for restoring financial stability in India. SAGE Publications India Pvt Ltd.

    Book  Google Scholar 

  • Altman, E. I. (1968). Financial ratios, discriminant analysis and the prediction corporate bankruptcy. Journal of Finance, 23(4), 589–609.

    Article  Google Scholar 

  • Altman, E. I. (1983). Why business fail. Journal of Business Strategy, 3(4), 15–21.

    Article  Google Scholar 

  • Altman, E. I., & Hotchkiss, E. (2006). Corporate financial distress and bankruptcy: Predict and avoid bankruptcy, analyze and invest in distressed debt (3rd edn.). Hoboken, New Jersey: John Wiley & Sons Inc.

    Google Scholar 

  • Asis, G., Chari, A., & Haas, A. (2021). In search of distress risk in emerging markets. Journal of International Economics, 131, 103463.

    Article  Google Scholar 

  • Asquith, P., Gertner, R., & Scharfstein, D. (1994). Anatomy of financial distress: an examination of junk-bond issuers. Quarterly Journal of Economics, 109, 625–658.

    Article  Google Scholar 

  • Banerjee, A., Dolado, J., Galbraith, J. W., & Hendry, D. F. (1993). Co-integration. Oxford University Press.

    Book  Google Scholar 

  • Banerjee, A., Dolado, J., & Mestre, R. (1998). Error-correction mechanism tests for cointegration in a single equation framework. Journal of Time Series Analysis, 19, 267–283.

    Article  Google Scholar 

  • Beaver, W. H. (1966). Financial ratios as predictors of failure. Journal of Accounting Research, 4, 71–111.

    Article  Google Scholar 

  • Beaver, W. (1968). Market prices, financial ratios, and the prediction of failure. Journal of Accounting Research, 6(2), 179–192.

    Article  Google Scholar 

  • Bhattacharjee, A., & Han, J. (2014). Financial distress of Chinese firms: microeconomic, macroeconomic and institutional influences. China Economic Review, 30(2014), 244–262.

    Article  Google Scholar 

  • Bhattacharjee, A., & Han, J. (2014). Financial distress of Chinese firms: microeconomic, macroeconomic and institutional influences. China Economic Review, 30, 244–262.

    Article  Google Scholar 

  • Bhattacharjee, C., Higson, S. H., & Kattuman, P. (2009). Macroeconomic instability and business exit: determinants of failures and acquisitions of UK firms. Economica, 2009(76), 108–131.

    Article  Google Scholar 

  • Carpenter, R., Fazzari, S., & Petersen, B. (1994). Inventory investment, internal finance fluctuation, and the business cycles. Brookings Papers on Economic Activity, 2, 75–122.

    Article  Google Scholar 

  • Charalambakis, E. C., & Garrett, I. (2019). On corporate financial distress prediction: What can we learn from private firms in a developing economy? Evidence from Greece. Rev Quant FinanAcc, 52, 467–491.

    Article  Google Scholar 

  • Deakin, E. B. (1972). A discriminant analysis of predictors of failure. Journal of Accounting Research, 10, 167–179.

    Article  Google Scholar 

  • DeAngelo, H., DeAngelo, L., & Wruck, K. H. (2002). Asset liquidity, debt covenants, and managerial discretion in financial distress: the collapse of L.A Gear. Journal of Financial Economics, 64, 3–34.

    Article  Google Scholar 

  • Desai, M., & Montes, A. (1982). A macroeconomic model of bankruptcies in the British economy. British Review of Economic Issues, 4, 1–14.

    Google Scholar 

  • Economic Survey (2016–17), Government of India, Ministry of Finance, Department of Economic Affairs, Economic Division, New Delhi.

  • Economic Survey (2018–19), Government of India, Ministry of Finance, Department of Economic Affairs, Economic Division, New Delhi.

  • Enders, W. (1995). Applied econometric time series. John Wiley & Sons.

    Google Scholar 

  • Engle, R. F., & Granger, C. W. J. (1987). Cointegration and error correction representation: estimation and testing. Econometrica, 55(2), 251–276.

    Article  Google Scholar 

  • Fazzari, S. R., Hubbard, G., & Peterson, B. (1988). Financing constraints and corporate investment. Brookings Papers on Economic Activity, 1, 141–195.

    Article  Google Scholar 

  • Gilchrist, S., & Himmelberg, C. P. (1995). Evidence on the role of cash flow for investment. Journal of Monetary Economics, 36(3), 541–572.

    Article  Google Scholar 

  • Granger, C. W. J. (1986). Developments in the study of co-integrated economic variables. Oxford Bulletin of Economics and Statistics., 48, 213–228.

    Article  Google Scholar 

  • Harada, N., & Kageyama, N. (2011). Bankruptcy dynamics in Japan. Japan and the World Economy, 23, 119–128.

    Article  Google Scholar 

  • Hillegeist, S. A., Keating, E. K., Cram, D. P., & Lunstedt, K. G. (2004). Assessing the probability of bankruptcy. Review of Accounting Studies, 9, 5–34.

    Article  Google Scholar 

  • Johansen, S. (1988). Statistical analysis of cointegration vectors. Journal of Economic Dynamics and Control, 12, 231–254.

    Article  Google Scholar 

  • Johansen, S. (1995). Likelihood-based inference in cointegrated vector autoregressive models. Oxford Univ. Press.

    Book  Google Scholar 

  • Johansen, S., & Juselius, K. (1990). Maximum likelihood estimation and inference on cointegration with applications to the demand for money. Oxford Bulletin of Economics and Statistics, 52(2), 169–210.

    Article  Google Scholar 

  • Kam, A., Citron, D., & Muradoglu, G. (2008). Distress and restructuring in China: does ownership matter? China Economic Review, 19, 567–579.

    Article  Google Scholar 

  • Kao, C., & Chiang, M. H. (2000). On the estimation and inference of a cointegrated regression in panel data. In B. H. Baltagi, T. B. Fomby, & R. Carter Hill (Eds.), Nonstationary panels, panel cointegration, and dynamic panels, Advances in Econometrics, vol. 15. (pp. 179–222). Bingley: Emerald Group Publishing Limited.

    Chapter  Google Scholar 

  • Liu, J. (2004). Macroeconomic determinants of corporate failures: evidence from the UK. Applied Economics, 36(9), 939–945.

    Article  Google Scholar 

  • Liu, J. (2009). Business failures and macroeconomic factors in the UK. Bulletin of Economic Research, 61(1), 0307–3378.

    Article  Google Scholar 

  • Mahtania, U. S., & Garg, C. P. (2018). An analysis of key factors of financial distress in airline companies in India using fuzzy AHP framework. Transportation Research Part-A., 117, 87–102.

    Google Scholar 

  • Mare, D. S. (2015). Contribution of macroeconomic factors to the prediction of small bank failures. Journal of International Financial Markets, Institutions and Money, 39, 25–39.

    Article  Google Scholar 

  • Mslemi, N., Lahiani, A., & Hamza, T. (2017). Financial Distress prediction: the case of French small and medium-sized firms’. International Review of Financial Analysis, 50, 67–80.

    Article  Google Scholar 

  • Narayan, P. K. (2005). The saving and investment nexus for China: evidence from cointegration tests. Applied Economics, 37, 1979–1990.

    Article  Google Scholar 

  • Narayan, P. K. (2006). Determinants of female fertility in Taiwan, 1966–2001: empirical evidence from cointegration and variance decomposition analysis. Asian Economic Journal, 20(4), 33–407.

    Article  Google Scholar 

  • Narayan, P. K., & Smyth, R. (2006). Higher education, real income and real investment in China: Evidence from granger causality tests. Education Economics, 14(1), 107–125.

    Article  Google Scholar 

  • Ohlson, J. (1980). Financial ratios and the probabilistic prediction of bankruptcy. Journal of Accounting Research., 18(1), 109–131.

    Article  Google Scholar 

  • Pesaran, H. M., & Pesaran, B. (1997). Microfit 4.0. Oxford Univ. Press.

    Google Scholar 

  • Pesaran, H. M., Shin, Y., & Smith, R. (1996). Testing the existence of a long-run relationship DAE Working paper Series No. 9622. Department of Applied Economics, University of Cambridge.

    Google Scholar 

  • Pesaran, M. H., & Shin, Y. (1999). An autoregressive distributed lag modeling approach to cointegration analysis. In S. Storm (Ed.), Econometrics and economic theory in the 20th century: the ragnar frisch centennial symposium (pp. 1–31). Cambridge Univ. Press.

    Google Scholar 

  • Pesaran, M. H., Shin, Y., & Smith, R. J. (2001). Bounds testing approaches to the analysis of level relationships. Journal of Applied Econometrics, 16, 289–326.

    Article  Google Scholar 

  • Phillips, P. C. B., & Hansen, B. E. (1990). Statistical inference in instrumental variable regression with I(1) processes. Review of Economic Studies, 57, 99–125.

    Article  Google Scholar 

  • Pindado, J., Rodrigues, L., & De la Torre, C. (2008). Estimating financial distress likelihood. Journal of Business Research, 61, 995–1003.

    Article  Google Scholar 

  • Sehgal, S., Mishra, R. M., Deisting, F., & Vashisht, R. (2021). On the determinants and prediction of corporate financial distress in India. Managerial Finance, 47(10), 1428–1447.

    Article  Google Scholar 

  • Shan, J. (2002). A VAR approach to the economics of FDI in China. Applied Economics, 34, 885–893.

    Article  Google Scholar 

  • Shumway, T. (2001). Forecasting bankruptcy more accurately: A simple hazard model. Journal of Business, 74, 101–124.

    Article  Google Scholar 

  • Taffler, R. J. (1982). Forecasting company failure in the UK using discriminant analysis and financial ratio data. Journal of the Royal Statistical Society. Series A (general), 145, 342–358.

    Article  Google Scholar 

  • Tinoco, M. H., & Wilson, N. (2013). Financial distress and bankruptcy prediction among listed companies using accounting, market and macroeconomic variables. International Review of Financial Analysis, 30, 394–419.

    Article  Google Scholar 

  • Turner, P., & CottsBowden, A. S. (1992). The effect of the Thatcher government on company liquidation: an econometric study. Applied Economics, 24(8), 935–943.

    Article  Google Scholar 

  • Wadhwani, S. B. (1986). Inflation, bankruptcy, default premia and the stock market’. Economic Journal, 96(381), 120–138.

    Article  Google Scholar 

  • Wu, Y., Gaunt, C., & Gray, S. (2010). A comparison of alternative bankruptcy prediction models. Journal of Contemporary Accounting & Economics, 6, 34–45.

    Article  Google Scholar 

  • Zhang, J., Bessler, D. A., & Leatham, D. J. (2013). Aggregate business failures and macroeconomic conditions: A var look at the U.S. between 1980 and 2004. Journal of Applied Economics, 16(1), 179–202.

    Article  Google Scholar 

  • Zmijewski, M. (1984). Methodological issues related to the estimation of financial distress prediction models. Journal of Accounting Research, 22, 59–82.

    Article  Google Scholar 

Download references

Acknowledgements

The authors wish to acknowledge, with thanks, helpful suggestions and comments received from the participants of ‘Financial Distress, Bankruptcy, and Corporate Finance conference’ in IIM Ahmedabad. Author Ritesh K. Mishra is grateful to Prof. Chandan Sharma and Prof. Wasim Ahmed for this help and encouragements. We also acknowledge the comments and suggestions of the anonymous referee that helped us in improving the paper.

Funding

None.

Author information

Authors and Affiliations

  1. Department of Finance and Business Economics, University of Delhi, New Delhi, 110021, India

    Sanjay Sehgal & Ritesh Kumar Mishra

  2. Shaheed Sukhdev College of Business Studies, University of Delhi, New Delhi, India

    Ajay Jaisawal

Authors
  1. Sanjay Sehgal
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Ritesh Kumar Mishra
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Ajay Jaisawal
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Ritesh Kumar Mishra.

Ethics declarations

Conflict of interest

No conflict of interest to report.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Appendix-A

Appendix-A

1.1 Table of results

See Tables 1, 2, 3, 4, 5, 6, 7, 8 and 9.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Sehgal, S., Mishra, R.K. & Jaisawal, A. A search for macroeconomic determinants of corporate financial distress. Ind. Econ. Rev. 56, 435–461 (2021). https://doi.org/10.1007/s41775-021-00119-4

Download citation

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s41775-021-00119-4

Keywords

JEL Classification

Search

Navigation