On the determinants and prediction of corporate financial distress in India

Abstract

PurposeThe main aim of the study is to identify some critical microeconomic determinants of financial distress and to design a parsimonious distress prediction model for an emerging economy like India. In doing so, the authors also attempt to compare the forecasting accuracy of alternative distress prediction techniques.Design/methodology/approachIn this study, the authors use two alternatives accounting information-based definitions of financial distress to construct a measure of financial distress. The authors then use the binomial logit model and two other popular machine learning–based models, namely artificial neural network and support vector machine, to compare the distress prediction accuracy rate of these alternative techniques for the Indian corporate sector.FindingsThe study’s empirical results suggest that five financial ratios, namely return on capital employed, cash flows to total liability, asset turnover ratio, fixed assets to total assets, debt to equity ratio and a measure of firm size (log total assets), play a highly significant role in distress prediction. The study’s findings suggest that machine learning-based models, namely support vector machine (SVM) and artificial neural network (ANN), are superior in terms of their prediction accuracy compared to the simple binomial logit model. Results also suggest that one-year-ahead forecasts are relatively better than the two-year-ahead forecasts.Practical implicationsThe findings of the study have some important practical implications for creditors, policymakers, regulators and other stakeholders. First, rather than monitoring and collecting information on a list of predictor variables, only six most important accounting ratios may be monitored to track the transition of a healthy firm into financial distress. Second, our six-factor model can be used to devise a sound early warning system for corporate financial distress. Three, machine learning–based distress prediction models have prediction accuracy superiority over the commonly used time series model in the available literature for distress prediction involving a binary dependent variable.Originality/valueThis study is one of the first comprehensive attempts to investigate and design a parsimonious distress prediction model for the emerging Indian economy which is currently facing high levels of corporate financial distress. Unlike the previous studies, the authors use two different accounting information-based measures of financial distress in order to identify an effective way of measuring financial distress. Some of the determinants of financial distress identified in this study are different from the popular distress prediction models used in the literature. Our distress prediction model can be useful for the other emerging markets for distress prediction.

Solvency Risk and Corporate Performance: A Case Study on European Retailers

“…There is consistent research that concludes that machine learning techniques can provide more accurate predictions than the standard empirical methods, because they use the data-driven process, and can deal with high-dimensional datasets. Nevertheless, the most important aspect of using the machine learning techniques is that they can analyze the unbalanced datasets and obtain helpful information inside the dataset (Vieira et al 2009;Liang et al 2018;Sehgal et al 2021;Alessi and Savona 2021;Malakauskas and Lakštutien ė 2021). More research, on the risk of default using machine learning based random forest models, was conducted in seven EU countries (Germany, Spain, Portugal, France, Finland, Italy, and United Kingdom), in a sample of 945,062 companies in 2010 and 1,019,312 firms in 2011 (Behr and Weinblat 2017a), showing that the most important variable for default prediction is the rate of return on assets, the rate of return on sales, dynamic gearing ratio, and debt ratio.…”

A search for macroeconomic determinants of corporate financial distress

“…Shumway (2001) and Hillegeist et al (2004) used both accounting and market variables for distress prediction. Furthermore, in a recent study, Sehgal et al (2021) have highlighted the role of accounting ratios in explaining distress at microlevel for firms in the Indian corporate sector. Based on these findings, we believe that the firm-specific or accounting ratios will also display significant ability to explain financial distress even at the macro level.…”

“…Existence of financial distress in one or more sectors when the overall economy is growing at a relatively faster rate is suggestive of some deeper trouble which will only deteriorate further in the absence of timely identification and policy intervention. The recent slowdown of economic activity and demand will tighten the cash flows, profit conditions, and the debt servicing ability of firms in the near future and will further aggravate the financial health of firms and lending institution (see Sehgal et al, 2021). A clear understanding of factors that determine the financial distress of firms in the Indian corporate sector will help in monitoring the transition of firms from a financially healthy unit to a distressed and loss making production unit.…”

“…First, to the best of our knowledge, this is one of the first attempts to investigate role of macroeconomic factors for the Indian corporate sector. In a most recent study, Sehgal et al (2021) examined financial distress of firms but from a microeconomic or firms specific perspective. Second, previous studies have highlighted the role of firm-specific factors or accounting ratios in distress prediction (see Altman, 1968;Ohlson, 1980;Zmijewski, 1984;Shumway, 2001 andHillegeist et al, 2004) and confirmed significant explanatory power of these factors.…”

“…Therefore, we attempt to design a parsimonious hybrid model by combining macroeconomic and aggregate corporate characteristics that determine financial distress. Although these variables are not truly macroeconomic variables but they do possess the ability to influence financial distress of firms, hence, at aggregate level, they behave as quasi-macrovariables (see Sehgal et al, 2021). Three, unlike a legal outcome-based definition of distress or firm's failure, we use an accounting-based definitions involving three variables for classifying firms as healthy or distressed.…”