Does the threshold matter? The impact of the monitoring activity on non-performing loans

Abstract

Purpose The purpose of this paper is to investigate whether the quality of the credit process is sensitive to reaching a particular threshold level of non-performing loans (NPLs) and, more importantly, whether higher NPLs ratios could make the monitoring activity ineffective. Design/methodology/approach The empirical design is composed of two steps: in the first step, the authors introduce a monitoring performance indicator (MPI) of the credit process by combining the non-parametric technique Data Envelopment Analysis with some financial ratios adopted as input and output variables. As second step, the authors apply a threshold panel regression model to a sample of 298 Italian banks, over the time period 2006–2014, and the authors investigate whether the quality of the credit process is sensitive to reaching a particular threshold level of NPLs. Findings This paper finds that, first, when the NPLs ratio remains below the threshold value estimated endogenously, an increase in the quality of monitoring has a positive impact on the NPLs ratio. Second, if the NPLs ratio exceeds the estimated threshold, the relationship between the NPLs ratio and quality of monitoring assumes a positive value and is statistically significant. Research limitations/implications Due to the lack of data, the investigation of NPLs in the Italian industry across loan types combined with the monitoring effort by banks management was not possible. The authors plan to investigate this topic in future studies. Practical implications The identification of the threshold has a double operational valence. The first regards the Supervisory Authority, the threshold approach could be used as an early warning in order to introduce active control strategies based on the additional information requested or by on-site inspections. The second implication is highlighted in relation to the individual banks, the monitoring of credit control quality, if objective and comparable, could facilitate the emergence of best practices among banks. Social implications A high NPLs ratio requires greater loan provisions, which reduces capital resources available for lending, and dents bank profitability. Moreover, structural weaknesses on banks’ balance sheets still persist particularly in relation to the inadequate internal governance structures. This means that bank management must able to recognise in advance early warning signals by providing prudent measurement together with an in-depth valuation of loans portfolio. Originality/value The originality of the paper is twofold: the authors introduce a new proxy of credit monitoring, called MPI; the authors provide an empirical proof of the Diamond’s (1991) economic intuition: for riskier borrowers, the monitoring activity is an inappropriate instrument depending on the bad reputational quality of borrowers.

Determinants of nonperforming loans after recapitalization in the Nigerian banking industry: Does efficiency matter?

“…The respective empirical studies have focused their investigations on cross‐country evidence or individual countries banking industry. The works of Kjosevski and Petkovski (2017) and Alandejani and Asutay (2017) investigate the panel data of several countries while studies of Atoi (2019), Piatti and Cincinelli (2019), Kjosevski et al (2019), Yurttadur et al (2019), Ünvan and Yakubu (2020), and Bolarinwa et al (2012) focus their investigations on single banking industries. The empirical results of these works vary given differences in datasets, periods, environments, and individual countries.…”

“…The method fails to account for managerial expertise, human resources, training, marketing, and other attributes of firm managerial efficiency. Few studies that adopt standard measure have only been limited to the data envelope analysis (DEA) method (Alandejani & Asutay, 2017; Piatti & Cincinelli, 2019). This work unlike extant studies adopts the stochastic frontier analysis (SFA) method.…”

“…Recent works have also examined the threshold in the relationship; however, these studies are scarce (Bolarinwa et al, 2012; Piatti & Cincinelli, 2019). In the analysis of Nigerian banks, Bolarinwa et al (2012) examine the threshold effect in the nonperforming loans‐profitability nexus and conclude that 3.5% nonperforming loan is the adequate level that ensures equilibrium profitability in the banking industry.…”

“…In the analysis of Nigerian banks, Bolarinwa et al (2012) examine the threshold effect in the nonperforming loans‐profitability nexus and conclude that 3.5% nonperforming loan is the adequate level that ensures equilibrium profitability in the banking industry. Similarly, Piatti and Cincinelli (2019) examine the threshold in the relationship between Italian banks to determine whether the quality of bank credit is sensitive to reaching a particular threshold level. The work confirms the sensitivity of bank loan quality to a particular threshold.…”

“…For instance, managerial skills, productivity, expertise and productivity are not incorporated into these single measures (Bolarinwa, Obembe, & Olaniyi, 2019). Few studies adopt a robust method of data envelope analysis (DEA) (Alandejani & Asutay, 2017; Piatti & Cincinelli, 2019). However, the method fails to account for the error term in modeling efficiency thus they tend to report biased efficiency estimates especially under economic uncertainties (Fries & Taci, 2005).…”