Purpose
The study aims to explore the impact of ownership concentration (OC) on bank financial distress (FD). Furthermore, the bank’s financial stability levels determine the association between the two.
Design/methodology/approach
Bank data of 33 Indian commercial banks are procured for ten years (2013–2022). The panel data econometrics is applied for empirical estimations. The quantile regression approach is used to determine the association between OC and FD at different quantiles of the FD. Non-normalcy of the data is checked and ensured before applying the quantile regression.
Findings
Surprisingly, it is found that promoters have a nonlinear impact on the firm’s stability. The inverted U-shape result implies that as promoters cross a threshold level, the benefit of increasing promoters’ stake takes a beating and a further increase in promoters’ stakes adversely impacts the stability of the banks. Moreover, this threshold value increases while moving from low to high levels of stability in a quantile regression application.
Research limitations/implications
This study uses promoters as the proxy for OC. Other existing definitions of OC are not used in the study, which can further improve the robustness of the results. Additionally, the use of the type of ownership (private, public or foreign) is also not adopted in the present study. Both the limitations can be the study’s future scope on the topic.
Practical implications
The high OC is supposed to influence corporate governance adversely. Therefore, policymakers recommend low OC for better governance. However, the present study finds evidence that a higher OC (high threshold of OC as the stability increases) would be better for financial stability. This situation demands a trade-off between governance and financial stability regarding OC.
Originality/value
The authors do not observe any study having the nonlinear impact of OC on financial stability (opposite of FD). Moreover, the threshold of OC for the optimum level of financial stability increases as stability goes high. This evidence using quantile regression and finding the turning point using a quadratic equation is also not seen in the literature.