The current article aims to identify important macroeconomic variables that could indicate the likelihood of financial distress among 294 Indian firms included in the BSE 500 index. Twelve years of financial data and macroeconomic indicators were analysed through a logit model. The study applied a synthetic measure of financial distress adopted from Bhattacharjee and Han (2014, China Economic Review, 30, 244–262.) which is based on the interest coverage ratio. A significant negative relationship is reported between the likelihood of financial distress among firms and three macroeconomic indicators, namely, the index of industrial production (IIP), exchange rate and a 10-year bond yield. Accordingly, the classification accuracy reported for the full, training and testing samples was 75.51, 79.49 and 70 per cent, respectively. With respect to theoretical contribution, the study has underlined the difference between financial distress and bankruptcy. An additional classification test was conducted wherein the harmonic mean based on the current dataset characteristics was the discriminating score. A prediction accuracy of 76.92 and 70 per cent with respect to the training and testing sample was reported.
AgrawalK. & MaheshwariY. (2014). Default risk modelling using macroeconomic variables. Journal of Indian Business Research, 6(4), 270–285.
2.
AltmanE. I. (1968). Financial ratios, discriminant analysis and the prediction of corporate bankruptcy. The Journal of Finance, 23(4), 589–609.
3.
AzizM. A. & DarH. A. (2006). Predicting corporate bankruptcy: Where we stand?Corporate Governance: The International Journal of Business in Society, 6(1), 18–33.
4.
BalcaenS. & OogheH. (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.
5.
BandyopadhyayA. (2006). Predicting probability of default of Indian corporate bonds: Logistic and Z-score model approaches. The Journal of Risk Finance, 7(3), 255–272.
6.
BeaverW. H. (1966). Financial ratios as predictors of failure. Journal of Accounting Research, 4, 71–111.
7.
BeaverW. H. (1968). The information content of annual earnings announcements. Journal of Accounting Research, 6(Suppl.), 67–92.
8.
BeaverW. H.McNicholsM. F. & RhieJ. W. (2005). Have financial statements become less informative? Evidence from the ability of financial ratios to predict bankruptcy. Review of Accounting Studies, 10(1), 93–122.
9.
BhattacharjeeA. & HanJ. (2014). Financial distress of Chinese firms: Microeconomic, macroeconomic and institutional influences. China Economic Review, 30, 244–262.
10.
BhuniaA. & SarkarR. (2011). A study of financial distress based on MDA. Journal of Management Research, 3(2), 1–11.
11.
CarlingK.JacobsonT.LindéJ. & RoszbachK. (2007). Corporate credit risk modeling and the macroeconomy. Journal of Banking & Finance, 31(3), 845–868.
12.
CavesR. E. (1998). Industrial organization and new findings on the turnover and mobility of firms. Journal of Economic Literature, 36(4), 1947–1982.
13.
CrutzenN. & Van CaillieD. (2008). The business failure process: an integrative model of the literature. Review of Business and Economics, 53(3), 287–316.
14.
DashP. (2017). Predicting financial crises: A study of Asian economies. Global Business Review, 18(5), 1262–1277.
15.
FiglewskiS.FrydmanH. & LiangW. (2012). Modeling the effect of macroeconomic factors on corporate default and credit rating transitions. International Review of Economics & Finance, 21(1), 87–105.
16.
FilipeS. F.GrammatikosT. & MichalaD. (2016). Forecasting distress in European SME portfolios. Journal of Banking & Finance, 64, 112–135.
17.
FrydmanH.AltmanE. I. & KaoD. L. (1985). Introducing recursive partitioning for financial classification: The case of financial distress. The Journal of Finance, 40(1), 269–291.
18.
FungL. (2008). Large real exchange rate movements, firm dynamics, and productivity growth. Canadian Journal of Economics/Revue canadienne d’économique, 41(2), 391–424.
19.
GieseckeK.LongstaffF. A.SchaeferS. & StrebulaevI. (2011). Corporate bond default risk: A 150-year perspective. Journal of Financial Economics, 102(2), 233–250.
20.
GuZ. (2002). Analyzing bankruptcy in the restaurant industry: A multiple discriminant model. International Journal of Hospitality Management, 21(1), 25–42.
21.
GudmundssonS. V. (1999). Airline failure and distress prediction: A comparison of quantitative and qualitative models. Transportation Research Part E: Logistics and Transportation Review, 35(3), 155–182.
22.
HudsonJ. (1986). An analysis of company liquidations. Applied Economics, 18(2), 219–235.
23.
JacobsenD. H. & KlosterT. B. (2005). What influences the number of bankruptcies?Norges Bank. Economic Bulletin, 76(4), 191.
24.
JenkinsD. S.KaneG. D. & VeluryU. (2009). Earnings conservatism and value relevance across the business cycle. Journal of Business Finance & Accounting, 36(9–10), 1041–1058.
25.
KamA.CitronD. & MuradogluG. (2008). Distress and restructuring in China: Does ownership matter?China Economic Review, 19(4), 567–579.
26.
KasilingamR. & RamasundaramG. (2012). Predicting solvency of non-banking financial institutions in India using Fulmer and Springate model. Journal of Services Research, 12(1), 65.
27.
KimH. & GuZ. (2006). Predicting restaurant bankruptcy: A logit model in comparison with a discriminant model. Journal of Hospitality & Tourism Research, 30(4), 474–493.
28.
LageshM. A.SrikanthM. & AcharyaD. (2018). Corporate performance during business cycles: Evidence from Indian manufacturing firms. Global Business Review. Retrieved from https://doi.org/10.1177/0972150918788740
29.
LenardM. J. & AlamP. (2009). An historical perspective on fraud detection: From bankruptcy models to most effective indicators of fraud in recent incidents. Journal of Forensic & Investigative Accounting, 1(1), 1–27.
30.
LennoxC. (1999). Identifying failing companies: A re-evaluation of the logit, probit and DA approaches. Journal of Economics and Business, 51(4), 347–364.
31.
LiH. & SunJ. (2009). Gaussian case-based reasoning for business failure prediction with empirical data in China. Information Sciences, 179(1–2), 89–108.
32.
LiangD.TsaiC. F. & WuH. T. (2015). The effect of feature selection on financial distress prediction. Knowledge-Based Systems, 73(1), 289–297.
33.
LiuJ. (2009). Business failures and macroeconomic factors in the UK. Bulletin of Economic Research, 61(1), 47–72.
34.
NamC. W.KimT. S.ParkN. J. & LeeH. K. (2008). Bankruptcy prediction using a discrete-time duration model incorporating temporal and macroeconomic dependencies. Journal of Forecasting, 27(6), 493–506.
35.
NouriB. A. & SoltaniM. (2016). Designing a bankruptcy prediction model based on account, market and macroeconomic, variables (Case study: Cyprus Stock Exchange). Iranian Journal of Management Studies, 9(1), 125–147.
36.
OhlsonJ. A. (1980). Financial ratios and the probabilistic prediction of bankruptcy. Journal of Accounting Research, 18(1), 109–131.
37.
OogheH. & De PrijckerS. (2008). Failure processes and causes of company bankruptcy: A typology. Management Decision, 46(2), 223–242.
38.
Reserve Bank of India (2017a). In database on Indian economy. Retrieved from https://dbie.rbi.org.in/DBIE/dbie.rbi?site=publications
39.
Reserve Bank of India (2017b, June13). RBI identifies accounts for reference by banks under the Insolvency and Bankruptcy, Code (IBC). Retrieved from https://rbidocs.rbi.org.in/rdocs/PressRelease/PDFs/PR3363482A1FF9229F4B9A92EA0090D5D71518.PDF
40.
RobsonM. T. (1996). Macroeconomic factors in the birth and death of UK firms: Evidence from quarterly VAT registrations. The Manchester School, 64(2), 170–188.
41.
ScottJ. (1981). The probability of bankruptcy: A comparison of empirical predictions and theoretical models. Journal of Banking & Finance, 5(3), 317–344.
42.
The Companies Act (2013). Retrieved from http://www.mca.gov.in/Ministry/pdf/CompaniesAct2013.pdf
43.
TirapatS. & NittayagasetwatA. (1999). An investigation of Thai listed firms’ financial distress using macro and micro variables. Multinational Finance Journal, 3(2), 103–125.
44.
TsaiB. H.LeeC. F. & SunL. (2009). The impact of auditors’ opinions, macroeconomic and industry factors on financial distress prediction: An empirical investigation. Review of Pacific Basin Financial Markets and Policies, 12(3), 417–454.
45.
TsengF. M. & HuY. C. (2010). Comparing four bankruptcy prediction models: Logit, quadratic interval logit, neural and fuzzy neural networks. Expert Systems with Applications, 37(3), 1846–1853.
46.
United Nations (2018). World economic situation and prospects 2018. Sales No. E. 18.II.C.2. Retrieved from https://www.un.org/development/desa/dpad/wp-content/uploads/sites/45/publication/WESP2018_Full_Web-1.pdf
47.
WuY.GauntC. & GrayS. (2010). A comparison of alternative bankruptcy prediction models. Journal of Contemporary Accounting & Economics, 6(1), 34–45.
48.
ZavgrenC. V. (1985). Assessing the vulnerability to failure of American industrial firms: A logistic analysis. Journal of Business Finance & Accounting, 12(1), 19–45.
49.
ZhouL.LaiK. K. & YenJ. (2010, November). Bankruptcy prediction incorporating macroeconomic variables using neural network. Presented at 2010 International Conference on Technologies and Applications of Artificial Intelligence, Georgia, USA.
50.
ZhouL.LaiK. K. & YenJ. (2012). Empirical models based on features ranking techniques for corporate financial distress prediction. Computers & Mathematics with Applications, 64(8), 2484–2496.
