Debt covenants and classification shifting: moderating role of audit quality

Abstract

PurposeThe study aims to investigates which form of classification shifting is preferred by firms to avoid the violation of debt covenants and whether the higher-audit quality constraints the shifting practices of firms incentivized to avoid covenant violations or not.Design/methodology/approachA sample of 1,644 Bombay Stock Exchange (BSE)-listed firms during the period 2009–2021 has been used in this study and tested through panel data regression models. Two forms of classification shifting, namely expense shifting and revenue shifting have been taken into account. The findings are validated through the propensity-score matching technique.FindingsThe findings deduced from the empirical evidence demonstrate that firms prefer revenue shifting over expense shifting to avoid covenant violations, consistent with the notion of the ease-need-advantage-based shifting framework, where firms are found to prefer a shifting tool with greater relative advantage. Further, the author finds that superior audit quality has a constraining effect on expense shifting, but not on revenue shifting, indicating the partial effectiveness of high-quality auditors in curbing the corporate misfeasance of classification shifting. These results are robust to the problem of endogeneity and self-selection bias.Originality/valueThe paper provides new evidence on debt market incentives behind classification shifting, where firms are found to substitute classification shifting forms to avoid covenant violations. Further, the study is among pioneering attempts to investigate the impact of audit quality on revenue shifting and document the non-constraining effect.

Culture clash or collaboration? Unraveling the role of hierarchical organizational culture in International Financial Reporting Standards adoption

“…Consistent with many prior studies under Indian institutional settings (for instance, Bansal, 2022aBansal, , 2023aBansal, , 2023bBansal et al, 2022Thomas et al, 2023;, we run Equations ( 1)-( 6) cross-sectionally for each industry year having a minimum of 15 observations per industry year. Two-digit Standard Industrial Classification code has been used to identify industry.…”