Corporate Performance during Business Cycles: Evidence from Indian Manufacturing Firms

Abstract

The present study is an attempt to assess the ‘probability of incurring loss’ of manufacturing firms in India during different phases of business cycles. We use data on a sample of 87 manufacturing companies for the period from 2002 to 2014 (comprising 1131 firm years). We use the panel logit model with the dependent variable derived from the return on assets to empirically test the hypothesis. Besides, we use firm-specific variables and macroeconomic variables as independent variables in the model. Firm-specific variables, namely size of the firm and interest coverage ratio and macroeconomic variables namely exchange rate, bank credit, inflation, interest rate and index of industrial production are statistically significant in predicting the probability of incurring loss of the firms during the study period. The results are important for investors, corporate houses, managers, lenders, policymakers and the research community as business cycles have a visible impact on all functional areas of an organization. Our study assumes significance because of the importance of macroeconomic variables in the strategic decision-making of the corporate sector in general and manufacturing firms in particular.

Political environment, employee tenure security and firm performance in middle-income economies

“…(2014) and Nakano and Nguyen (2013) but disagrees with Asiedu and Freeman (2009). Similarly, the sector influence concurs with Lagesh et al . (2018) and Mukherjee (2018).…”

Trade Credit as a Determinant of Firm’s Financial Performance: Moderating Role of Bank Financing

“…Similarly, high lending IR deteriorates the corporate financial efficiency as it causes additional financing cost. It makes leveraging firms susceptible towards any economic shock and reduces the investment confidence, which results in low financial performance (Farooq et al, 2021;Lagesh et al, 2018). Another macroeconomic variable, that is, GDP growth rate has a positive role in corporate financial output.…”

Looking Beyond the Financial Numbers: The Relationship Between Macroeconomic Indicators and the Likelihood of Financial Distress

“…Prior research have considered a default cut-off probability of 0.50 (Kim & Gu, 2006; Lenard & Alam, 2009; Tseng & Hu, 2010). Accordingly, firms with financial distress likelihood greater than 0.50 are sorted into the financially distressed group, while firms having likelihood lower than 0.50 are classified as healthy.…”

“…These include univariate analysis, multiple discriminant analysis (MDA), linear probability model, cumulative sums procedure, partial adjustment processes and risk index models. Frydman, Altman, and Kao (1985) introduced techniques of artificial intelligence systems to the current theme and recursively partitioned decision trees, neural networks, genetic algorithms and rough sets model were among the most popular ones (Li & Sun, 2009; Liang, Tsai, & Wu, 2015; Zhou, Lai, & Yen, 2012). Overall, the logit model and MDA have been extensively used, despite the availability of several other techniques.…”