An econometric analysis of financial distress determinants from an emerging economy governance perspective

Figures & data

Table 1. Financial distress models

Author	Method	Description
Beaver (1968)	Univariate model	Cash flow to total debt to be the best failure predictor out of thirty financial ratios.
Altman (1968; 1993)	Z-score model	Using MDA^4 established Z-score model comprises of liquidity, profitability, leverage and solvency categories. Z-scores between 1,81^5 and 2,67 is the “ignorance zone”.
De la Rey (1981)	K-score model	Established K-score model using Altman’s MDA method. K-scores between—0,19 to + 0,2 is the ignorance zone and companies with scores below −0,19 are distressed, while scores above + 0,2 are healthy.
Zmijewski (1984)	Probit-based model	This model is comprised of financial ratios measuring a firm’s performance, leverage and liquidity.
Altman (2005)	EMS Z model	EMS (Emerging market Z-score) model was developed as an alternative to the original Z-score, which was for publicly traded manufacturing companies. The EMS Z-model was tested on Mexican companies before and after the Mexican Peso crisis of 1994 and other emerging economies such as Argentina, Brazil and Southeast Asian countries.

Table 2. Variable definitions

Dependent variable
FDistr	Financial distress	K = −0.01662 (total external financing/total assets x 100) + 0.0111 (EBIT/ total assets x 100) + 0.0529 (total current assets and listed investments/total current liabilities) + 0.86 (income after tax/average total assets x 100) + 0.0174 (net cash flow/average total assets x 100) + 0.01071 (stock/inflation-adjusted total assets x 100)—0.068881	De la Rey, 1981; Sewpersadh 2020
Independent variables
MajorS	Major Shareholders	Cumulative percentage of the shares held by major shareholders (5 percent or more of shares)	M. C. Daily & Dalton, 1994a; Abdullah, 2006; Mokarami & Motefares, 2013; Miglani et al., 2015; Shahwan, 2015; Manzaneque et al., 2016
DShare	Director shareholding	Cumulative percentage of directors’ shareholding in the firm	M. C. Daily & Dalton, 1994a; Abdullah, 2006; Miglani et al., 2015; Manzaneque et al., 2016
CeoD	CEO duality	“1” if the board chairman and CEO roles are combined, “0” otherwise	M. C. Daily & Dalton, 1994a; Elloumi & Gueyié, 2001; Abdullah, 2006; Lakshan & Wijekoon, 2012; Miglani et al., 2015; Darrat et al., 2016; Manzaneque et al., 2016
CeoTen	CEO tenure	Number of years served as CEO	Mokarami & Motefares, 2013; Darrat et al., 2016
DRem	Director remuneration	Director’s emoluments as a ratio of profit before tax	Lakshan & Wijekoon, 2012
BQual	Board Qualifications	Percentage of post graduate board members
BNet	Board Networks	Percentage of board members serving on other boards	M. C. Daily & Dalton, 1994a
BIndep	Board independence	Percentage of independent directors on the board	Elloumi & Gueyié, 2001; Abdullah, 2006; Mokarami & Motefares, 2013; Lakshan & Wijekoon, 2012; Miglani et al., 2015; Manzaneque et al., 2016; Bhabra & Eissa, 2017
ACInd	Audit committee independence	“1” if all audit committee members are independent directors; “0” otherwise	Elloumi & Gueyié, 2001; Abdullah, 2006; Rahmat et al., 2009
ACSize	Audit committee Size	Total number of committee members	Rahmat et al., 2009
Control variables (cvit)
Industry	Industry	Type of industry
Bsize	Board size	Total number of board members
EntVal	Enterprise Value	(Market capitalization—cash and cash equivalents+ preferred stock +debt)/Share
Growth	Cash reinvestment Ratio	(Cash from operating activities/ fixed Assets +intangible assets + Net Current Assets) x100

Table 3. Descriptive summary of variables

	Descriptive statistics						Collinearity diagnostics before regression				After regression
Variable	Obs	Mean	Median	Std Dev	Min	Max	VIF	Sort VIF	Tolerance	R-Squared	VIF	1/VIF
K score	661	0.1705	0.15	1.057	−4.43	3.57	1.22	1.1	0.8222	0.1778	n/a	n/a
Major shareholders	661	45.2691	44.5	21.579	0	93	1.32	1.15	0.7585	0.2415	1.24	0.8086
Director shareholding	661	14.3177	4.55	19.136	0	74.71	1.34	1.16	0.7446	0.2554	1.34	0.7447
CEO duality	661	0.0242	0	0.154	0	1	1.11	1.05	0.9027	0.0973	1.1	0.9053
CEO tenure	661	8.3873	6	8.786	0	41	1.1	1.05	0.9050	0.0950	1.1	0.9111
Director remuneration	661	30.8592	4.0	564.850	−941.63	14,464.84	1.08	1.04	0.9300	0.0700	1.07	0.9312
Board qualifications	661	72.5747	75	17.361	18.2	100	1.28	1.13	0.7822	0.2178	1.27	0.7899
Board networks	661	58.9068	57.14	18.832	12.5	100	1.41	1.19	0.7068	0.2932	1.41	0.7071
Board independence	661	51.6899	50	13.063	14.29	91.67	1.49	1.22	0.6730	0.3270	1.47	0.6791
Audit committee size	661	3.3858	3	0.673	2	6	1.43	1.2	0.7001	0.2999	1.43	0.7015
Audit committee independence	661	0.8593	1	0.348	0	1	1.29	1.14	0.7760	0.2240	1.29	0.7762
Industry	661	2.8534	3	1.329	1	6	1.27	1.13	0.7880	0.2120	1.16	0.8604
Board size	661	10.0484	10	2.816	4	25	1.54	1.24	0.6478	0.3522	1.54	0.6492
Enterprise value	661	71.1650	27	147.843	−102.56	2155.85	1.29	1.13	0.7773	0.2227	1.28	0.7808
Growth	661	10.0643	10.31	45.285	−536.66	789.47	1.03	1.01	0.9739	0.0261	1.03	0.9741

Table 4. Pearson’s correlation

	FDistr	MajorS	DShare	CeoD	CeoTen	DQual	DRem	BNet	BIndep	AcSize	AcInd	Industry	BSize	EntVal	Growth
FDistr	1
MajorS	0.1520***	1
	0.0001
DShare	0.0127	0.2098***	1
	0.7443	0
CeoD	−0.031	0.0749*	−0.0022	1
	0.4259	0.0542	0.7553
CeoTen	0.0782**	0.0909**	0.2250***	0.0637	1
	0.0445	0.0194	0	0.1019
DQual	0.1060***	−0.0881**	−0.1209***	−0.0491	0.0872**	1
	0.0064	0.0235	0.0018	0.2071	0.0249
DRem	0.0158	0.0425	0.0464	0.2453***	−0.024	−0.0015	1
	0.6852	0.2749	0.2339	0	0.5381	0.6696
BNet	−0.1446***	0.0023	−0.2224***	0.0802**	−0.0527	0.3740***	−0.0034	1
	−0.0002	0.7527	0	0.0393	0.1763	0	0.931
BIndep	−0.0740*	−0.1900***	−0.3131***	−0.0691*	−0.1551***	0.2756***	−0.0325	0.2948***	1
	0.0574	0	0	0.076	0.0001	0	0.4045	0
AcSize	0.0751*	0.1382***	0.1923***	−0.0025	−0.1089***	0.0536	−0.03	0.0523	0.2698***	1
	0.0537	0.0004	0	0.7483	0.0051	0.1691	0.4411	0.1792	0
AcInd	0.0104	0.0780**	−0.1939***	−0.0212	0.0312	0.2252***	0.0138	0.2654***	0.2955***	−0.1497***	1
	0.7889	0.0449	0	0.5864	0.4228	0	0.7232	0	0	0.0001
Industry	−0.2312***	−0.1000**	0.1185***	−0.0387	0.0581	−0.1149***	0.0097	0.1137***	−0.1673***	−0.0607	−0.0693*	1
	0	0.0101	0.0023	0.3202	0.1354	0.0031	0.7036	0.0034	0	0.119	0.0748
BSize	−0.0386	−0.2464***	−0.3507***	−0.0412	−0.1071**	0.1317***	−0.052	0.1441***	0.1638***	0.4276***	0.1090***	−0.1021***	1
	0.3215	0	0	0.2903	0.0058	0.0007	0.1816	0.0002	0	0	0.005	0.0086
EntVal	−0.0274	−0.2945***	−0.1959***	−0.0143	−0.0078	0.1612***	−0.0248	0.1631***	0.2087***	0.2090***	0.1166***	−0.1008***	0.3690***	1
	0.4818	0	0	0.7128	0.63421	0	0.5251	0	0	0	0.0027	0.0095	0
Growth	0.0472	−0.0933**	−0.0905**	0.0105	0.012	−0.0206	0.0051	−0.0292	0.0067	0.0146	−0.01	−0.0123	0.0753*	0.012	1
	0.2255	0.0165	0.0199	0.7884	0.7572	0.5975	0.6953	0.453	0.7631	0.7078	0.7968	0.7518	0.053	0.756

Standard errors in parentheses *** p < 0.01, ** p < 0.05, * p < 0.1

Table 5. GMM estimation results. Dependent variable: financial distress: K-Score ($FDistrit$)

	Model 1		Model 2		Model 3		Model 4		Model 5
Variables	FD-GMM	SYS-GMM	FD-GMM	SYS-GMM	FD-GMM	SYS-GMM	FD-GMM	SYS-GMM	FD-GMM	SYS-GMM
Shareholding
Major shareholders	0.0101*	0.00914*	0.0127***	0.00905**	0.0126***	0.00936**	0.0138***	0.00939**	0.0138***	0.00975**
	0.00406	0.00355	0.00448	0.00399	0.00446	0.00421	0.00476	0.00471	0.00446	0.00438
Director shareholdings	0.0105	0.00435	0.00521*	0.00305*	0.00529*	0.00233*	0.00532**	0.00241**	0.00501**	0.00242**
	0.0117	0.00455	0.0106	0.00485	−0.0109	0.0052	−0.0112	−0.00545	−0.0109	−0.0052
CEO characteristics
CEO duality	−0.323*	−0.312*	−0.643**	−0.408**	−0.677**	−0.450**	−0.745**	−0.460**	−0.668**	−0.554**
	−0.237	−0.163	−0.247	−0.167	−0.263	−0.2	−0.304	−0.194	−0.262	−0.214
CEO tenure	0.0105	0.0102	0.0136*	0.0139**	0.0143*	0.0137**	0.0157**	0.0165**	0.0140*	0.0151**
	0.00782	0.00729	0.00743	0.00617	0.00755	0.0061	0.0079	0.00736	0.0073	0.00651
Board characteristics
Director remuneration	0.00122***	0.00101***	0.00132***	0.00104***	0.00135***	0.00105***	0.00142***	0.00113***	0.00133***	0.00109***
	0.0000	0.0000	0.0000	0.0000	0.0000	0.0000	0.0000	0.0000	0.0000	0.0000
Board qualifications	0.00565	0.00324	0.0107	0.00484	0.0115*	0.00721*	0.0131*	0.00745*	0.0109*	0.00748*
	0.00596	0.0129	0.00717	0.012	0.00693	0.0138	0.00705	0.0136	0.00654	0.0137
Board Networks	−0.0304**	−0.00573*	0.0216*	−0.00532*	0.0230*	−0.00634*	0.0289**	−0.00553*	0.0233**	−0.00520*
	−0.0124	−0.00915	−0.0124	−0.0104	−0.0123	−0.0115	−0.0121	−0.0122	−0.0107	−0.0118
Board independence	0.00822	0.00404	−0.00176	0.00067	−0.00467	0.00157	−0.00655	−0.000722	−0.00475	−0.000593
	−0.0081	−0.0071	−0.00829	−0.00722	−0.00862	−0.0075	−0.00857	−0.0079	−0.00843	−0.00788
Audit committee
Audit committee size	0.293***	0.232***	0.299***	0.220**	0.267**	0.259**	0.264**	0.325**	0.258**	0.285**
	0.125	0.0747	0.107	0.0878	0.115	0.127	0.123	0.129	0.113	0.129
Audit committee independence	1.543**	0.878*	1.638**	1.002**	1.506**	1.043**	1.558**	1.005**	1.487**	1.008**
	0.69	0.456	0.633	0.484	0.631	0.504	0.663	0.516	0.595	0.498
Control variables
Industry			−0.309***	−0.286	−0.311***	−0.292	−0.333***	−0.388*	−0.315***	−0.387*
			−0.0819	−0.212	−0.082	−0.22	−0.0832	−0.232	−0.0772	−0.205
Board size					−0.0168	0.0224	−0.0145	0.0694	−0.0101	0.051
					−0.0341	−0.0567	−0.0365	−0.0585	−0.0351	−0.0571
Enterprise Value							−0.000585*	−0.000811**	−0.000599**	−0.000736**
							−0.00031	−0.00037	−0.00029	−0.00034
Growth									0.00151	0.00189
									−0.00154	−0.00209
Constant	2.515***	2.057**	3.545***	2.824***	3.672***	2.742**	3.726***	2.495**	3.604***	2.721***
	0.872	0.837	0.79	0.997	0.83	1.049	0.862	0.915	0.789	0.876
Observations	661	661	661	661	661	661	661	661	661	661
Number of id	116	116	116	116	116	116	116	116	116	116
Hansen	46.61	60.19	44.59	60.61	44.46	59.27	42.53	57.07	43.23	56.13
Hansen p-value	0.571	0.709	0.613	0.664	0.578	0.677	0.618	0.718	0.547	0.717
AR(2)	0.384	0.202	0.437	0.241	0.387	0.246	0.486	0.326	0.448	0.324
AR(2) p-value	0.701	0.84	0.662	0.81	0.711	0.829	0.627	0.886	0.654	0.801
Standard errors in parentheses *** p < 0.01, ** p < 0.05, * p < 0.1.

NB: Statistically, the K-score measures financial distress inversely, thus the smaller the K-score values, the higher the risk of financial distress. Accordingly, for the hypotheses results, a positive correlation will have increases in the explanatory variable with increases in the response variable, K-score that results in decreases to financial distress, and vice versa for the negative correlation.

Table 6. Summary of hypotheses findings

Hypothesis	Theory	Expected sign	Actual sign	Supported/Not
H1. Firms with major shareholders are associated with a lower probability of financial distress.	Agency; Shareholder activism	(+)^7	(+)***	Supported
H2. Increases in director shareholding are associated with a lower probability of financial distress.	Agency; Stewardship	(+)	(+)**	Supported
H3. The presence of CEO duality in firms is associated with a higher probability of financial distress.	Agency	(-)	(-)**	Supported
H4.CEO tenure in a firm is associated with a lower probability of financial distress.	Upper echelons; Power	(+)	(+)**	Supported
H5. Director’s remuneration is associated with a lower probability of financial distress.	Agency	(+)	(+)***	Supported
H6. A higher percentage of postgraduate board members are associated with a lower probability of financial distress.	Resource dependence; Human capital	(+)	(+)*	Supported
H7. Firms with board networks are associated with a higher probability of financial distress.	Resource dependence; Relational capital	(-)	(-)*	Supported
H8. Firms with a high proportion of independent board members are associated with a higher probability of financial distress.	Management hegemony; Information asymmetry	(-)	(-)	Not Supported
H9. Audit committee size is associated with a lower probability of financial distress	Resource dependency	(+)	(+)**	Supported
H10. Audit committees comprised solely of independent directors are associated with a lower probability of financial distress	Agency	(+)	(+)**	Supported

Table

No		Author	Title of paper	Methodology	Financial distress indicator	Corporate governance variables	Main findings
1		D. C. Hambrick & D’Aveni, 1992	Top team deterioration as part of the downward spiral of large corporate bankruptcies	Manova and logit analysis	Filed under Chapter 11 of bankruptcy code	Team size, expertise, tenure, CEO dominance, compensation and independent directors	As company declines top management teams are lost and there are fewer outside directors, lower team compensation, shorter tenures, and greater CEO dominance as company declines.
2		M. C. Daily & Dalton, 1994a	Corporate governance and the bankrupt firm: An empirical assessment	Logistic regression analysis	Filed under Chapter 11 of bankruptcy code	CEO duality and independent directors	Firms with CEO duality and lower proportions of independent directors are associated with bankruptcy.
3		C. M. Daily & Dalton, 1994b	Bankruptcy and corporate governance: the impact of board composition and structure	Logistic regression analysis	Filed under Chapter 11 of bankruptcy code	CEO duality and board composition	During the time of crisis, CEO duality does not deal with the decline as effectively when CEO duality is absent. In times of firm decline, stronger boards can formulate suitable strategies to address firm’s decline.
4		Elloumi & Gueyié, 2001	Financial distress and corporate governance: an empirical analysis	Logistic regression analysis	Negative earnings per share during the last five years.	Independent directors, CEO duality, CEO turnover, and audit committee.	Findings were that independent outside directors enhance firms’ financial conditions and financially distressed firms are less likely to have audit committees composed solely of outsiders. Failed to detect any significance of CEO duality in financially distressed or healthy firms.
5		Abdullah, 2006	Board structure and ownership in Malaysia: the case of distressed listed companies	Pooled logistic regression analysis	An affected listed issuer as classified in the Bursa Malaysia Practice Note 4.	Board independence, CEO duality and ownership structure	Insignificant findings on board independence and CEO duality with financial distress. Whereas non-executive directors and outside blockholders are negatively associated with financial distress
6		Li et al., 2008	Ownership, independent directors, agency costs and financial distress: evidence from Chinese listed companies	Binary logistic analysis	Special Treatment as per Chinese Securities Regulatory Commission	Ownership structure, independent directors, managerial agency costs and audit’s opinion	Large shareholders’ ownership and the state ownership have negative effects on the probability of distress whereas managerial ownership is unrelated with the distress status. Managerial agency costs was positively associated with the probability of financial distress but auditors’ opinion was negatively related to the likelihood of financial distress
7		Rahmat et al., 2009	Audit committee characteristics in financially distressed and non-distressed companies	Logistic regression analysis	Suspended from Bursa Malaysia Practice Note 4.	Audit committee size, its independence, activity and accounting knowledge	The findings show that financial distress has a significantly negative association with financial literacy of the audit committee and the quality of external audit. However, there were insignificant findings between financial distress and the three other independent variables, size, composition and the frequency of meetings of an audit committee.
8		Lakshan & Wijekoon, 2012	Corporate governance and corporate failure	Logistic regression analysis	Either companies had been incurring losses OR had negative cash flow for 3 years continuously or more.	Board size, CEO duality, outside directors, outsider’s ownership, audit opinion, audit committee existence and board members remuneration	The findings support that outside director ratio/board independence whereas audit committee existence and board members remuneration were negatively associated with financial distress. While board size, auditor’s opinion and outside ownership are not significant determinants of failure. The existence of a separate audit committee was associated with a lower likelihood of financial distress, but CEO duality is positively related with the likelihood of corporate failure.
9		Mokarami & Motefares, 2013	Corporate governance and predicting bankruptcy of firms using survival analysis	Survival analysis, longitudinal data and Cox Regression	Applied a definition of financial distress	Board size, ratio of non-executive directors, CEO change and major ownership	Findings were that a significant relationship exists between CEO turnovers and bankruptcy. However, insignificant relationships were found between bankruptcy and the other variables i.e. percentage of non-executive directors, board size, major ownership.
10		Miglani et al., 2015	Voluntary corporate governance structure and financial distress: evidence from Australia	Logit regression	Zmijewski financial score	Board independence, blockholder ownership, CEO duality, director ownership, audit committee and audit opinion	Findings show greater levels of blockholder ownership, director ownership and audit committee existence are associated with lower likelihood of financial distress. The findings for CEO duality, audit opinion and board independence were inconsistent between the different statistical analyses in this study. This study found causal evidence that adopting corporate governance structures voluntarily leads to lower levels of financial distress, rather than financial distress detection leading to corporate governance structural reform.
11		Shahwan, 2015	The effects of corporate governance on financial performance and financial distress: evidence fromEgypt	LAV regression model and Logistic regression model	Altman’s Z-score	Constructed a corporate governance index (CGI) with governance disclosure and transparency; board composition; shareholders’ rights and investor relations; and ownership and control structure	The study found that the overall score of the CGI, on average, suggests that the quality of CG practices within Egyptian-listed firms was relatively low. The results did not support the positive association between corporate governance practices and financial performance and there was an insignificantly negative relationship between corporate governance practices and the likelihood of financial distress.
12		Darrat et al., 2016	Corporate governance and bankruptcy risk.	Logit model	Filed under Chapter 11 of bankruptcy code	CEO duality, CEO shareholding, CEO tenure, CEO turnover, CEO age, CEO compensation, insider holding, institutional holding, audit committee independence.	The findings were that having larger boards reduces the risk of bankruptcy only for complex firms. The proportion of inside directors on the board is inversely associated with the risk of bankruptcy in firms that require more specialist knowledge and that the reverse is true in technically unsophisticated firms. Bankruptcy is more likely when the CEO serves as board chairman. CEO turnover, bankruptcy is less likely when the CEO has been replaced within the previous 3 years and that bankruptcy is negatively related to CEO tenure.
13		Manzaneque et al., 2016	Corporate governance effect on financial distress likelihood: evidence from Spain	Conditional logistic regression	Binary variable, 1 if either EBITDA is lower than its financial expenses for two consecutive years; and/or a fall in its market value occurs between two consecutive periods or 0.	Ownership concentration, large directors’ ownership, CEO duality and large board size.	The findings reveal that board ownership, proportion of independent directors and board size decrease probability of financial distress. However, the findings were insignificant regarding ownership concentration, institutional or non-institutional large shareholders and CEO duality with the probability of financial distress. The results show a negative relationship between large board size and the probability of financial distress.
14		Parkinson, 2016	Corporate governance during financial distress – an empirical analysis	Case study approach	In a formal business rescue proceeding	Detailed analysis of the decisions taken by directors, creditors and shareholders.	The findings reveal that in times of financial distress, directors’ attention shifts away from shareholders’ interests to those of creditors’ interests.
15		Mariano et al. (2021)	Can we predict the likelihood of financial distress in companies from their corporate governance and borrowing?	A binomial logistic model	Dummy variable 1 when companies are considered financially distressed and 0 otherwise	Ownership concentration, independence indicators, CEO duality, director remuneration and corporate loans are considered, as well as the UK Corporate Governance Code.	The results indicate that companies with low ownership concentration and a low degree of independence are more likely to incur financial distress. Larger boards and better director remuneration can reduce likelihood OF financial distress and the existence of corporate loans can increase this likelihood. Empirical consideration of corporate borrowing is a new contribution to the literature.

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Availability of data and material

The data is freely available from the IRESS database.