Does the Quality of Director Fusion Raise the Risk of Corporate Debt Default?
Abstract
:1. Introduction
2. Literature Review
2.1. Corporate Debt Default
2.2. Director Change and Diversity
2.3. Nodule
3. Theoretical Analysis and Research Hypothesis
4. Study Design
4.1. Data Source
4.2. Variable Design
- Corporate debt default risk (CDDR). The data on corporate debt default risk comes from the CRI (Credit Research Initiative) database of the Risk Management Institute of the National University of Singapore. The data on the expected corporate default rate of debt in the next year is selected to analyze the potential default risk of corporates from a prior perspective. The larger the indicator, the greater the risk of corporate debt default.
- Rate of new directors (RND). Learn from the method of Wang and Zatzick. First, the directors are divided into new and old directors using the data of the start and end date of the director’s tenure [22]. Taking 2010 as an example, if the term of office begins in 2010, it will be set as a new director; if the term of office starts before 2010, it will be recognized as an old director, and so on for other years. Then, calculate the ratio of new directors to all directors based on the number of new directors each year, combined with the total number of board members.
- 3.
- Control variables. Referring to the relevant literature the following control variables were introduced: (1) Return on assets (roa, net profit to total assets), (2) gearing ratio (lev, total liabilities to total assets), (3) operating efficiency (trover, ratio of operating income to total assets), and (4) cash flow ratio (cf, cash flow from operating activities to total assets) [20,31].
4.3. Model Setting
5. Study Results
5.1. Descriptive Statistics
5.2. Threshold Regression Results of the Percentage of New Directors and Corporate Debt Default Risk
6. Further Research
6.1. Role Matching and Threshold Regression Results of Corporate Debt Default Risk
6.2. Threshold Regression Results of Group Matching Corporate Debt Default Risk
6.3. The Threshold Regression Results of the Interaction between Role Matching and Group Matching on the Default Risk of Corporate Debt
7. Research Conclusions and Insights
7.1. Research Conclusions
7.2. Management Enlightenment and Policy Suggestions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
- Maretno, A.H.; Wang, Y. Board of directors network centrality and environmental, social and governance (ESG) performance. Corp. Gov. 2020, 20, 965–985. [Google Scholar]
- Switzer, L.N.; Wang, J.; Zhang, Y. Effect of corporate governance on default risk in financial versus nonfinancial firms: Canadian evidence. Can. J. Adm. Sci. 2018, 35, 313–328. [Google Scholar] [CrossRef]
- Chowdhury, E.K.; Dhar, B.K.; Stasi, A. Volatility of the US stock market and business strategy during COVID-19. Bus. Strategy Dev. 2022, 5, 350–360. [Google Scholar] [CrossRef]
- George, M.E. The danger of not “changing” the board of directors. Board Dir. 2014, 117, 86–87. [Google Scholar]
- Elms, N.; Nicholson, G.; Pugliese, A. The importance of group-fit in new director selection. Manag. Decis. 2015, 53, 1312–1328. [Google Scholar] [CrossRef] [Green Version]
- Balagopal, G.; Sanket, M. Insolvency regimes and firms’ default risk under economic uncertainty and shocks. Econ. Model. 2020, 91, 180–197. [Google Scholar]
- Mohamed, A.; Mathieu, G.; Kuntara, P. Corporate social responsibility and M&A uncertainty. J. Corp. Financ. 2019, 56, 176–198. [Google Scholar]
- Li, Y.; Lu, R.; Srinivasan, A. Relationship bank behavior during borrower distress. J. Financ. Quant. Anal. 2019, 54, 1231–1262. [Google Scholar] [CrossRef]
- Azizpour, S.; Giesecke, K.; Schwenkler, G. Exploring the sources of default clustering. J. Financ. Econ. 2018, 129, 154–183. [Google Scholar] [CrossRef]
- Ali, S.; Liu, B.; Su, J.J. Does corporate governance quality affect default risk? The role of growth opportunities and stock liquidity. Int. Rev. Econ. Financ. 2018, 58, 422–448. [Google Scholar] [CrossRef]
- Li, X.; Zhou, Y.L.; Liu, S.; Ge, X.Y. The characteristics of local government debt governance: Evidence from qualitative and social network analysis of Chinese policy texts. Econ. Res.-Ekon. Istraž. 2022, 35, 6037–6066. [Google Scholar] [CrossRef]
- Milidonis, A.; Nishikawa, T.; Shim, J. CEO Inside Debt and Risk Taking: Evidence from Property–Liability Insurance Firms. J. Risk Insur. 2017, 86, 451–477. [Google Scholar] [CrossRef]
- Ghouma, H. How does managerial opportunism affect the cost of debt financing? Res. Int. Bus. Financ. 2017, 39, 13–29. [Google Scholar] [CrossRef]
- Peters, F.S.; Wagner, A.F. The executive turnover risk premium. J. Financ. 2014, 69, 1529–1563. [Google Scholar] [CrossRef]
- Intintoli, V.J.; Serfling, M.; Shaikh, S. CEO turnovers and disruptions in customer–supplier relationships. J. Financ. Quant. Anal. 2017, 52, 2565–2610. [Google Scholar] [CrossRef]
- Harjoto, M.A.; Laksmana, I.; Yang, Y. Board diversity and corporate investment oversight. J. Bus. Res. 2018, 90, 40–47. [Google Scholar] [CrossRef]
- Adams, R.B.; Akyol, A.C.; Verwijmeren, P. Director skill sets. J. Financ. Econ. 2018, 130, 641–662. [Google Scholar] [CrossRef]
- Giannetti, M.; Zhao, M. Board Ancestral Diversity and Firm-Performance Volatility. J. Financ. Quant. Anal. 2019, 54, 1117–1155. [Google Scholar] [CrossRef] [Green Version]
- Fu, X.; Zhang, Z. CFO cultural background and stock price crash risk. J. Int. Financ. Mark. Inst. Money 2019, 62, 74–93. [Google Scholar] [CrossRef]
- Du, X. What’s in a Surname? The Effect of Auditor-CEO Surname Sharing on Financial Misstatement. J. Bus. Ethics 2019, 158, 849–874. [Google Scholar] [CrossRef]
- Pfeffer, J.; Salancik, G.R. The External Control of Organizations: A Resource Dependence Perspective. Am. J. Sociol. 1981, 87, 757–759. [Google Scholar]
- Wang, T.; Zatzick, C.D. Human Capital Acquisition and Organizational Innovation: A Temporal Perspective. Acad. Manag. J. 2019, 62, 99–116. [Google Scholar] [CrossRef]
- Kaufman, A.; Englander, E. A team production model of corporate governance. Acad. Manag. Perspect. 2005, 19, 9–22. [Google Scholar] [CrossRef]
- Boone, A.L.; Field, L.C.; Karpoff, J.M. The determinants of corporate board size and composition: An empirical analysis. J. Financ. Econ. 2007, 85, 66–101. [Google Scholar] [CrossRef]
- Schmidt, S.L.; Brauer, M. Strategic governance: How to assess board effectiveness in guiding strategy execution. Corp. Gov. Int. Rev. 2006, 14, 13–22. [Google Scholar] [CrossRef]
- Switzer, L.N.; Wang, J. Default risk estimation, bank credit risk, and corporate governance. Financ. Mark. Inst. Instrum. 2013, 22, 91–112. [Google Scholar] [CrossRef]
- Hillman, A.J.; Dalziel, T. Boards of directors and firm performance: Integrating agency and resource dependence perspectives. Acad. Manag. Rev. 2003, 28, 383–396. [Google Scholar] [CrossRef]
- Cohen, W.M.; Levinthal, D.A. Innovation and learning: The two faces of R&D. Econ. J. 1989, 99, 569–596. [Google Scholar]
- Granovetter, M. Economic action and social structure: The problem of embeddedness. Am. J. Sociol. 1985, 91, 481–510. [Google Scholar] [CrossRef]
- Haitian, L.; Wang, B.; Wang, H.Z.; Zhao, T.Y. Does social capital matter for peer-to-peer-lending? Empirical evidence. Pac.-Basin Financ. J. 2020, 61, 101–338. [Google Scholar]
- Jaffe, A. Technological Opportunity and Spillovers of R&D: Evidence from Firm’s Patents, Profits and Market Value. Am. Econ. Rev. 1986, 76, 984–1001. [Google Scholar]
- Hansen, B.E. Threshold effects in non-dynamic panels: Estimation, testing and inference. J. Econom. 1999, 93, 345–368. [Google Scholar] [CrossRef] [Green Version]
- Jiang, G.; Lee, C.M.C.; Zhang, Y. Information uncertainty and expected returns. Rev. Account. Stud. 2005, 10, 185–221. [Google Scholar] [CrossRef]
ai | ai Method of Determining Numerical Values |
---|---|
Part-time Executives | 1 = yes, 0 = no |
Gender | 1 = Male, 0 = Female |
Academic qualifications | 0 = unknown, 1 = Secondary school and below, 2 = College, 3 = Bachelor, 4 = Master, 5 = PhD, 6 = Other (degree published in other forms, such as honorary doctorate, correspondence, etc.), 7 = MBA/EMBA. |
Age | Actual age of directors |
Term of office | Counting of directors’ terms by month |
Part Time Company | Number of directors holding part-time positions in other companies |
Academic Background | Director’s experience in teaching at a university, serving at a research institution, or conducting research at an association. 0 = no academic experience, 1 = one of the above three experiences, 2 = two of the above three experiences, 3 = all three of the above experiences |
Overseas Background | Director studied and worked overseas. 0 = no overseas experience, 1 = studied or worked overseas, 2 = studied and worked overseas |
Philosophy | Determined according to the professional knowledge background of the director, the classification is based on the 13 majors identified in the Catalogue of Undergraduate Majors in General Higher Education Institutions (2012). Philosophy, economics, law, education, literature, history, science, engineering, agriculture, medicine, management, art, military science. 1 = Philosophy as director’s major, 0 = other as director’s major or unknown |
Economics | 1 = Director specializing in economics, 0 = Director specializing in other or unknown |
Legal Studies | 1 = Director’s major is law, 0 = Director’s major is other or unknown |
Pedagogy | 1 = Director’s major is education, 0 = Director’s major is other or unknown |
Literature | 1 = Director’s major is literature, 0 = Director’s major is other or unknown |
Historiography | 1 = Director’s major is history, 0 = Director’s major is other or unknown |
Science | 1 = Director’s major is science, 0 = Director’s major is other or unknown |
Engineering | 1 = Director’s major is engineering, 0 = Director’s major is other or unknown |
Agronomy | 1 = Director’s major is Agronomy, 0 = Director’s major is other or unknown |
Medicine | 1 = Director’s specialty is medicine, 0 = Director’s specialty is other or unknown |
Management | 1 = Director’s major is management, 0 = Director’s major is other or unknown |
Artistic Studies | 1 = Director’s major is art, 0 = Director’s major is other or unknown |
Military Science Production | 1 = Director’s major is military science, 0 = Director’s major is other or unknown According to CSMAR’s classification of directors’ professional backgrounds: production, R&D, design, human resources, management, marketing, finance, legal, government, finance. 1 = director has a professional background in production, 0 = director’s professional background is other or unknown |
R&D | 1 = Director has a professional background in R&D, 0 = Director’s professional background is other or unknown |
Design | 1 = Director has a professional background in design, 0 = Director’s professional background is other or unknown |
Human Resources | 1 = Director has a professional background in human resources, 0 = Director’s professional background is other or unknown |
Management | 1 = Director has a professional background in management, 0 = Director’s professional background is other or unknown |
Market | 1 = Director has a professional background in marketing, 0 = Director’s professional background is other or unknown |
Finance | 1 = Director has a professional background in finance, 0 = Director’s professional background is other or unknown |
Legal | 1 = Director has a legal background, 0 = Director has other or unknown professional background |
Government Background | Determined by the category of the director’s government service, 0 = director’s professional background is other or unknown, 1 = director has held or is holding a government position in one category of organization, 2 = director has held or is holding a government position in two categories of organization, and so on |
Financial Background | According to the classification of financial background by Guotaian (CSMAR): regulatory department, policy bank, commercial bank, insurance company, securities company, fund management company, securities registration and settlement company, futures company, investment bank, trust company, investment management company, exchange company. 0 = director has no financial background, 1 = director has one of the above financial backgrounds, 2 = director has two of the above financial backgrounds 2 = director has two of the above financial backgrounds, and so on. |
Mean | sd | CDDR | RND | DRM | DGM | lev | roa | cf | trover | |
---|---|---|---|---|---|---|---|---|---|---|
CDDR | 0.0037 | 0.0042 | 1 | |||||||
RND | 0.0875 | 0.1140 | 0.0942 * | 1 | ||||||
DRM | 0.780 | 0.0971 | −0.0514 * | −0.00140 | 1 | |||||
DGM | 0.0348 | 0.0586 | 0.0523 * | 0.0828 * | 0.0436 * | 1 | ||||
lev | 0.2510 | 0.1640 | 0.6461 * | 0.0574 * | −0.0857 * | 0.00630 | 1 | |||
roa | 0.0307 | 0.0342 | −0.3726 * | −0.0775 * | 0.0733 * | −0.0406 * | −0.2327 * | 1 | ||
cf | 0.0201 | 0.0728 | −0.1727 * | −0.0269 | −0.0226 | −0.0286 | −0.1609 * | 0.2814 * | 1 | |
trover | 0.3020 | 0.2540 | 0.0147 | −0.0388 * | 0.0505 * | −0.0275 | 0.0970 * | 0.1770 * | 0.0493 * | 1 |
Models | F-Value | p-Value | Number of BS | Threshold Value | ||
---|---|---|---|---|---|---|
1% | 5% | 10% | ||||
Single Threshold | 4.304 ** | 0.025 | 1000 | 6.367 | 3.428 | 2.535 |
Double Threshold | 5.163 ** | 0.044 | 500 | 8.077 | 4.357 | 3.188 |
Three-fold threshold | 0.000 *** | 0.002 | 500 | 0.000 | 0.000 | 0.000 |
Threshold Estimates | 95% Confidence Interval | |
---|---|---|
Single threshold model (g1) | 0.083 | (0.083, 0.364) |
Double threshold model (g2) | ||
Ito1 (g1) | 0.231 | (0.083, 0.364) |
Ito1 (g2) | 0.083 | (0.083, 0.364) |
Triple threshold model (g3) | 0.097 | (0.091, 0.200) |
RND Interval | 2011 | 2012 | 2013 | 2014 | 2015 | 2016 | 2017 | 2018 |
---|---|---|---|---|---|---|---|---|
[0, 0.083) | 136 | 155 | 146 | 96 | 109 | 123 | 121 | 136 |
[0.083, 0.231) | 83 | 72 | 63 | 81 | 80 | 64 | 86 | 74 |
[0.231, 1) | 14 | 6 | 24 | 56 | 44 | 46 | 26 | 23 |
Variables | CDDR | |
---|---|---|
Threshold Return | FE | |
RND | 0.00244 *** | 0.00102 ** |
(3.572) | (2.404) | |
lev | 0.0126 *** | 0.0132 *** |
(8.910) | (9.980) | |
roa | −0.0196 *** | −0.0188 *** |
(−6.487) | (−8.534) | |
cf | −0.00165 *** | −0.00154 ** |
(−3.128) | (−2.198) | |
trover | −0.000369 *** | −0.000497 *** |
(−3.920) | (−3.042) | |
RND × d1 | 0.00933 *** | |
(3.113) | ||
RND × d2 | −0.00151 * | |
(−1.912) | ||
Constant | 0.000948 * | 0.00110 *** |
(1.772) | (2.638) | |
Observations | 1757 | 3473 |
Number of groups | 233 | 615 |
Models | F-Value | p-Value | Number of BS | Threshold Value | ||
---|---|---|---|---|---|---|
1% | 5% | 10% | ||||
Single Threshold | 0.000 *** | 0.000 | 300 | 0.000 | 0.000 | 0.000 |
Double Threshold | 27.281 *** | 0.000 | 300 | 16.859 | 11.265 | 9.159 |
Three-fold threshold | 0.000 *** | 0.005 | 200 | 0.000 | 0.000 | 0.000 |
Threshold Estimates | 95% Confidence Interval | |
---|---|---|
Single threshold model (g1) | 0.986 | (0.654, 0.986) |
Double threshold model (g2) | ||
Ito1 (g1) | 0.785 | (0.593, 0.986) |
Ito1 (g2) | 0.782 | (0.781, 0.940) |
Triple threshold model (g3) | 0.785 | (0.783, 0.785) |
Variables | CDDR | |
---|---|---|
Threshold Return | FE | |
DRM | −0.00167 *** | −0.00451 *** |
(−3.035) | (−5.132) | |
lev | 0.0122 *** | 0.0123 *** |
(7.279) | (8.459) | |
roa | −0.0174 *** | −0.0178 *** |
(−6.596) | (−7.464) | |
cf | −0.000680 | −0.00111 |
(−0.663) | (−1.465) | |
trover | −0.000345 *** | −0.000489 *** |
(−7.088) | (−3.280) | |
DRM × d1 | −0.00197 ** | |
(−2.572) | ||
DRMl × d2 | −0.00223 *** | |
(−2.669) | ||
Constant | 0.00420 *** | 0.00500 *** |
(6.607) | (7.205) | |
Observations | 2143 | 2883 |
Number of groups | 349 | 589 |
Models | F-Value | p-Value | Number of BS | Threshold | ||
---|---|---|---|---|---|---|
1% | 5% | 10% | ||||
Single Threshold | 4.500 | 0.218 | 500 | 16.862 | 10.147 | 8.062 |
Double Threshold | 2.909 | 0.176 | 500 | 9.306 | 5.190 | 3.982 |
Three-fold threshold | −3.464 | 0.612 | 500 | 10.541 | 5.456 | 3.633 |
CDDR | |
---|---|
Variables | Group Matching |
DGM | 0.00205 *** |
(5.578) | |
DGM × DRM | |
lev | 0.0131 *** |
(9.008) | |
roa | −0.0196 *** |
(−8.309) | |
cf | −0.00110 |
(−1.445) | |
trover | −0.000545 *** |
(−3.339) | |
Constant | 0.00125 *** |
(2.916) | |
Observations | 2820 |
Number of groups | 581 |
Models | F-Value | p-Value | Number of BS | Threshold Value | ||
---|---|---|---|---|---|---|
1% | 5% | 10% | ||||
Single Threshold | 3.628 * | 0.088 | 2000 | 9.351 | 4.870 | 3.447 |
Double Threshold | 9.819 ** | 0.019 | 1000 | 13.854 | 6.710 | 4.810 |
Three-fold threshold | −1.852 | 0.524 | 500 | 5.750 | 3.000 | 1.373 |
Threshold Estimates | 95% Confidence Interval | |
---|---|---|
Single threshold model (g1) | 0.126 | (0.036, 0.188) |
Double threshold model (g2) | ||
Ito1 (g1) | 0.083 | (0.036, 0.188) |
Ito1 (g2) | 0.092 | (0.036, 0.188) |
Triple threshold model (g3) | 0.126 | (0.085, 0.188) |
DGM × DRM Interval | 2013 | 2014 | 2015 | 2016 | 2017 | 2018 |
---|---|---|---|---|---|---|
[0, 0.083) | 212 | 212 | 206 | 209 | 204 | 198 |
[0.083, 0.092) | 6 | 4 | 3 | 3 | 5 | 11 |
[0.092, 1) | 22 | 24 | 31 | 28 | 31 | 31 |
Variables | CDDR | |
---|---|---|
Threshold Return | FE | |
DGM × DRM | −0.00927 | 0.00200 *** |
(−1.021) | (3.992) | |
lev | 0.0128 *** | 0.0129 *** |
(6.696) | (8.250) | |
roa | −0.0191 *** | −0.0193 *** |
(−12.46) | (−9.651) | |
cf | 0.00149 | −0.00104 |
(0.951) | (−1.131) | |
trover | −0.000411 *** | −0.000470 *** |
(−4.425) | (−3.229) | |
DGMDRM × d1 | 0.00818 | |
(0.894) | ||
DGMDRM × d1 | 0.0116 | |
(1.202) | ||
Constant | 0.00144 ** | 0.00133 *** |
(1.972) | (2.747) | |
Observations | 1418 | 2704 |
Number of groups | 240 | 577 |
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content. |
© 2023 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
Share and Cite
Yu, W.; Zhang, Y.; Du, K.; Wu, Y. Does the Quality of Director Fusion Raise the Risk of Corporate Debt Default? Sustainability 2023, 15, 1698. https://doi.org/10.3390/su15021698
Yu W, Zhang Y, Du K, Wu Y. Does the Quality of Director Fusion Raise the Risk of Corporate Debt Default? Sustainability. 2023; 15(2):1698. https://doi.org/10.3390/su15021698
Chicago/Turabian StyleYu, Wencheng, Yikang Zhang, Kun Du, and Yanzhou Wu. 2023. "Does the Quality of Director Fusion Raise the Risk of Corporate Debt Default?" Sustainability 15, no. 2: 1698. https://doi.org/10.3390/su15021698