May board committees reduce the probability of financial distress? A survival analysis on Italian listed companies
Introduction
Financial distress prediction is a popular topic among academics and practitioners. Over the years, scholars have developed models based on different methodologies to help firms avoid potential bankruptcy (Shi and Li, 2019). However, the debate is ongoing, and researchers are continuing to develop models with greater prediction accuracy (Habib, Costa, Huang, Bhuiyan, and Sun, 2020) (even in the context of the COVID-19 pandemic), and regulators, governments and supranational entities are concerned about the potential risk of financial distress for firms. The limits of traditional rating models have also become apparent (mainly based on financial ratios and accounting data).
Most studies of default-prediction modeling are elaborated on the basis of information gathered from balance sheets (among others, Altman, 1968; Beaver, 1967; Ohlson, 1980; Nam and Jinn, 2000; Pindado, Rodrigues, and De la Torre, 2008; Lizares and Bautista, 2021) or from initial public offerings prospects (e.g., Platt and Platt, 1990). Other studies (among others, Norden and Weber, 2010; Bartoli, Ferri, Murro, and Rotondi, 2013; Bergerès, d'Astous, and Dionne, 2015) have instead considered data relating to the relationship with banks, suggesting that adding this information improves the predictive power of models. Some studies (among others, Wilson, Wright, and Altanlar, 2014; Li, Crook, Andreeva, and Tang, 2021) have analyzed the effect of corporate governance indicators and board member characteristics on failure by demonstrating that adding corporate governance variables to financial ratios significantly improves default-prediction accuracy rates (Altman, Iwanicz-Drozdowska, Laitinen, and Suvas, 2015). However, these studies mainly focused on the characteristics of boards and CEOs, neglecting the role of board committees in financial distress. In general, few papers have focused on the composition and functioning of board committees and the relation between these and financial distress. Instead, the presence of board risk committees is associated with a reduced risk of financial constraints (Malik, Nowland, and Buckby, 2021), and the composition and functioning of audit, remuneration and nomination committees are linked with the monitoring power, the mitigation of potential agency problems, stakeholders' protection and firm performance. Therefore, the influence of these on financial distress is worthy of investigation (Cao, Leng, Feroz, and Davalos, 2015; Udin, Khan, and Javid, 2017).
With this purpose, we conducted a survival analysis (e.g., exponential, Weibull and Cox regression models) of 273 Italian listed companies for the period 2004–2017. We controlled the robustness of our investigation by employing different statistical tests, and we also checked for differences and similarities between financial and non-financial companies.
The main findings suggest that the presence of non-executive members on remuneration committees may enhance firms' stability. Furthermore, audit and remuneration committees meeting more frequently may help firms avoid financial distress. In contrast, excessive nomination committee meetings seem to be positively related to the probability of financial distress, thus negatively affecting the stability of firms.
Accordingly, our research contributes to the literature on financial distress-prediction models by furthering understanding of the importance of the composition and functioning of board committees, which has been neglected in previous studies. In doing so, we can also make practical suggestions about the best composition and functioning of board committees to promote companies' financial stability.
This paper is organized as follows: Section 2 reviews the literature on default-prediction models, including corporate governance variables and the effect of board composition and functioning on defaults; Section 3 describes the data and methodology; Section 4 details the results and robustness checks; Section 5 discusses the main findings and presents our concluding remarks.
Section snippets
Default-prediction models, including corporate governance variables: A review of the literature
The role of corporate governance variables in predicting financial distress has gained increased attention in recent decades (among others, see Liang, Lu, Tsai, and Shih, 2016; Li et al., 2021). Based on the large volume of published studies on the subject, it is now clear that early warning systems based on accounting measures can benefit from including corporate governance variables (Aziz and Dar, 2006; Ciampi, 2015; Lee and Yeh, 2004).
Chaganti, Mahajan, and Sharma (1985) conducted a seminal
Data
Our sample was composed of 273 Italian companies listed on Euronext Milan (which includes the most capitalized companies in Italy) for the period 2004–2017. We used Bureau Van Dijk for retrieving financial data, and we manually collected corporate governance information. Table 1 shows the descriptive statistics for the selected variables, which are further illustrated. Financial variables were lagged by three years in order to capture the effect of corporate governance, which may not be
Findings
Table 3 shows the results obtained when we ran the three models ((a) exponential, (b) Weibull and (c) Cox) over the full sample. The data indicated the hazard rates (the probability of distress happening). If distress had not occurred, the case was censored. Censored cases were not used for estimation of the regression coefficients but were used to calculate the baseline hazard.
The results showed some differences related to the composition and functioning of the different committees. The
Discussion and conclusion
In this study, we sought to shed light on the role of corporate governance in firms' financial distress. We employed different survival model specifications (e.g., exponential, Weibull and Cox regressions), focusing on the composition and functioning of board committees and, at the same time, considering the conjoint effect of CG and financial indicators on adverse financial conditions. Our analysis of Italian listed companies for the period 2004–2017 indicated that the composition and
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