RAKESH SHARMA, Neba Bhalla

Enlightening the Critical Factors affecting the Solvency of Indian Construction Industry: An Empirical Analysis using Multivariate Discriminant Analysis and Logistic Regression

Introduction

Enlightening the critical factors affecting the solvency of indian construction industry: an empirical analysis using multivariate discriminant analysis and logistic regression . Empirically analyze critical financial factors impacting the solvency of the Indian construction industry. Uses MDA & logistic regression to predict insolvency, aiding policymakers.

Abstract

The present research work's purpose is to examine the vital factors that affect the solvency of the Indian construction sector. The two different parameters of solvency, namely debt to total assets (DTA) and cash flow to total liabilities (CFTL), are used in the present study. These two solvency indicators have been categorized using zero and one numerical values. One indicates financially sound companies, and zero indicates weak companies with poor solvency ratios. The different financial ratios, namely, profitability, liquidity, leverages, and turnovers, are used as predictors or explanatory variables of insolvency of Indian construction companies. The study employs multivariate discriminant analysis (MDA) and binary logistic regression to predict the factors accountable for the insolvency of the Indian construction sector. The empirical findings of MDA and logistic regression show significant discrimination in the solvency position of construction companies according to their different financial performance parameters, namely, profitability, liquidity, leverage, and management efficiency. Since there are two categories of companies as per their solvency position. So one discriminant function is created and found significant at 5% levels for two different solvency parameters. In the case of the first measure of solvency (DTA), turnover liquidity leverage ratios are the critical indicators for predicting the solvency of the Indian construction industry. Findings of MDA indicate that in the case of the second parameter of solvency (CFTL), profitability and management efficiency significantly discriminate between solvent and solvent companies. The logistic regression findings show that In the case of the first measure (DTA), leverage, liquidity, and management efficiency significantly distinct two sets of construction companies. In the case of the second measure of solvency (CFTL), profitability, management efficiency significantly discriminate solvency of two sets of companies. Overall the findings of MDA and logistic regression are consistent with each other. The outcomes of the study will be helpful to policymakers' different stakeholders. Society will also be benefitted by knowing the critical factors responsible for companies that are likely to become insolvent. Accordingly, the policymakers may issue financial assistance, subsidies, and advice to concerned companies.

Review

This research aims to critically examine the vital factors influencing the solvency of the Indian construction sector, a timely and significant topic given the industry's economic importance. The study's methodology appears robust, employing both Multivariate Discriminant Analysis (MDA) and binary logistic regression to predict insolvency. A notable strength is the adoption of two distinct solvency parameters—Debt to Total Assets (DTA) and Cash Flow to Total Liabilities (CFTL)—which provides a more comprehensive understanding than relying on a single measure. The categorization of companies into financially sound and weak based on these parameters, using a range of predictor variables encompassing profitability, liquidity, leverage, and turnovers, sets a solid foundation for empirical analysis. The abstract effectively communicates the core findings, highlighting a significant discrimination in the solvency position of construction companies across different financial performance parameters. Both MDA and logistic regression consistently identify critical indicators, though these vary depending on the solvency measure. For DTA, turnover, liquidity, and leverage ratios emerge as crucial predictors, while for CFTL, profitability and management efficiency are key discriminators. The development of a significant discriminant function at the 5% level further underscores the analytical rigor. The consistency between the MDA and logistic regression findings adds credibility to the results, reinforcing the identified financial performance drivers of solvency within the Indian construction industry. The study's potential implications are clearly articulated, suggesting its utility for policymakers, various stakeholders, and society at large. By identifying companies prone to insolvency, the research can inform targeted financial assistance, subsidies, and advisory services, ultimately contributing to the stability and health of the construction sector. To further enhance its contribution, the full paper would benefit from detailing the data source, sample size, and period of study, as well as providing specific definitions of the financial ratios used. A more in-depth discussion of the practical application of these findings, perhaps through case studies or scenarios, could also strengthen its impact and provide actionable insights for practitioners. Overall, this research presents a well-conceived approach to a critical issue with promising empirical findings.

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