CEO Overconfidence, Loan-Loss Provisions, and the Effect of Country Corruption: An International Investigation

ABSTRACT

This study examines the relationship between CEO overconfidence and bank recognition of loan-loss provisions (LLPs) and the effect of country corruption level on this relationship by analyzing cross-country data. We use a sample of 3,047 financial institutions in 53 different countries over 2013–2018. The results reveal that overconfident CEOs are more likely to recognize lower LLPs, suggesting that they overestimate (underestimate) favorable (unfavorable) outcomes of loan collection. Furthermore, we find that lower country-level corruption attenuates this relationship. We further show that overconfident CEOs reduce only the recognition of discretionary components of LLPs.

KEYWORDS:

• Loan-loss provisions
• CEO overconfidence
• corruption
• international investigation
• CEO characteristics

JEL:

• G34
• G38
• M41

Disclosure Statement

No potential conflict of interest was reported by the authors.

Supplementary Material

Supplemental data for this article can be accessed online at https://doi.org/10.1080/1540496X.2022.2153591

Notes

1. Details of the 13 data sources can be found in Appendix B.

2. Of the 13 data sources, three are related to country governance (African Development Bank Country Policy and Institutional Assessment, Bertelsmann Stiftung Sustainable Governance Indicators, and Bertelsmann Stiftung Transformation Index), three to country risk (Economist Intelligence Unit Country Risk Service, Global Insight Country Risk Ratings, and the PRS Group International Country Risk Guide), two to competitiveness (IMD World Competitiveness Center World Competitiveness Yearbook Executive Opinion Survey and World Economic Forum Executive Opinion Survey), and two to country transparency (World Bank Country Policy and Institutional Assessment and World Justice Project Rule of Law Index Expert Survey).

3. Data on Z-score were collected from World Bank Doing Business databases. According to the World Bank, Z-score captures the probability of default of a country’s banking system, calculated as a weighted average of the z-scores of a country’s individual banks (the weights are based on the individual banks’ total assets). Z-score compares a bank’s buffers (capitalization and returns) with the volatility of those returns. A higher Z-score represents lower insolvency risks or higher stability.

4. Sargan test confirms the overall validity of the instrument, and it enables us to confirm that there was no correlation between the instruments and the error term.

5. The null hypothesis (no second-order autocorrelation) is not rejected (AR2(p) > 0.05). Moreover, the consistency of the GMM was confirmed by the Sargan test, which found no evidence of over-identifying restriction.

6. We also applied GMM estimation methods for additional analyses (Appendix D1 and D2 in supplementary material). The untabulated results using the GMM were qualitatively consistent with those in Appendix D1 and D2 in supplementary material.

7. Data on rule of law, government effectiveness, and control of corruption were collected from World Bank Doing Business databases. They capture (1) perceptions of the extent to which agents have confidence in and abide by the rules of society, in particular, the quality of contract enforcement, property rights, the police, and the courts, as well as the likelihood of crime and violence, (2) perceptions of the quality of public services, civil services, and degree of its independence from political pressures, the quality of policy formulation and implementation, and the credibility of the government’s commitment to such policies, and (3) perceptions of the extent to which public power is exercised for private gain, respectively (Kaufmann, Kraay, and Mastruzzi 2011).