Purpose

The 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/approach

In 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.

Findings

The 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 implications

The 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/value

This 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.

中文翻译：

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印度企业财务困境的决定因素与预测

目的

该研究的主要目的是确定财务困境的一些关键微观经济决定因素，并为印度这样的新兴经济体设计简约的困境预测模型。在此过程中，作者还尝试比较其他遇险预测技术的预测准确性。

设计/方法/方法

在这项研究中，作者使用两种基于会计信息的财务困境定义来构建财务困境的衡量标准。然后，作者使用二项式 logit 模型和其他两种流行的基于机器学习的模型，即人工神经网络和支持向量机，来比较这些替代技术对印度企业部门的遇险预测准确率。

发现

该研究的实证结果表明，五个财务比率，即已使用资本回报率、现金流与总负债、资产周转率、固定资产与总资产、债务与权益比率以及衡量公司规模（对数总资产）在遇险预测中具有非常重要的作用。该研究的结果表明，与简单的二项式 logit 模型相比，基于机器学习的模型，即支持向量机 (SVM) 和人工神经网络 (ANN)，在预测精度方面更为出色。结果还表明，提前一年的预测相对好于提前两年的预测。

实际影响

研究结果对债权人、政策制定者、监管机构和其他利益相关者具有重要的实际意义。首先，不是监控和收集关于一系列预测变量的信息，而是仅监控六个最重要的会计比率来跟踪健康公司向财务困境的转变。其次，我们的六因素模型可用于设计完善的企业财务困境预警系统。三、基于机器学习的遇险预测模型在预测精度上优于现有文献中常用的时间序列模型，用于涉及二元因变量的遇险预测。

原创性/价值

本研究是为目前正面临高度企业财务困境的新兴印度经济调查和设计简约困境预测模型的首次全面尝试之一。与之前的研究不同，作者使用两种不同的基于会计信息的财务困境衡量标准，以确定衡量财务困境的有效方法。本研究中确定的一些财务困境的决定因素与文献中使用的流行的困境预测模型不同。我们的遇险预测模型可用于其他新兴市场的遇险预测。