THE BANKRUPTCY RISK IN INFRASTRUCTURE SECTORS: AN ANALYSIS FROM 2006 TO 2018

DELAPEDRA-SILVA, VANDERSON A.

doi:10.1590/1678-6971/eRAMF210104

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Strategic Finance • RAM, Rev. Adm. Mackenzie 22 (4) • 2021 • https://doi.org/10.1590/1678-6971/eRAMF210104 copy

THE BANKRUPTCY RISK IN INFRASTRUCTURE SECTORS: AN ANALYSIS FROM 2006 TO 2018

Authorship SCIMAGO INSTITUTIONS RANKINGS

ABSTRACT

Purpose:

This research aims to identify the probability of default of infrastructure companies considering the sector specificities of their activities. In addition, the work seeks to identify the application of structural variables of probability of default in a model in a reduced way in order to identify the significance of its use. For this purpose, we investigated 1,520 North American companies from six different sectors linked to infrastructure.

Originality/value:

The analyzes carried out to identify the probability of a company going bankrupt hardly consider its sectorial particularity. Although most models bring important inputs for risk assessment, most of them do not consider this sectoral view. Then, this work has as value and originality the contribution to fill this gap and identify the existence of sectorial differences in the analysis of default risk in infrastructure companies in the North American market in the period between 2006 and 2018.

Design/methodology/approach:

The study performed a logistic regression (logit model) using 11 model variables established in calculating the probability of default. It also used the variable distance to default as an explanatory variable in order to identify its ability to explain the researched phenomenon.

Findings:

The study identified that, in addition to the size of the companies, the distance to default variable is the only variable that can be applied with significance in all the analyzed sectors. In addition, it was identified that companies in the oil and gas sector have less sensitivity to this variable than companies in the other sectors.

KEYWORDS
Sectoral risk; Distance to default; Infrastructure; Merton model; Credit risk

Name	Source	Variable
Size	Altman (1968)Altman, E. (1968). Financial ratios, discriminant analysis and the prediction of corporate bankruptcy. The Journal of Finance, 23(4), 589-609. Recuperado de https://www.jstor.org/stable/2978933%0D https://www.jstor.org/stable/2978933%0D...	SZ
Working capital/total assets	Altman (1968)Altman, E. (1968). Financial ratios, discriminant analysis and the prediction of corporate bankruptcy. The Journal of Finance, 23(4), 589-609. Recuperado de https://www.jstor.org/stable/2978933%0D https://www.jstor.org/stable/2978933%0D...	WK
Retained earnings/total assets	Altman (1968)Altman, E. (1968). Financial ratios, discriminant analysis and the prediction of corporate bankruptcy. The Journal of Finance, 23(4), 589-609. Recuperado de https://www.jstor.org/stable/2978933%0D https://www.jstor.org/stable/2978933%0D...	RE
Ebit/total assets	Altman (1968)Altman, E. (1968). Financial ratios, discriminant analysis and the prediction of corporate bankruptcy. The Journal of Finance, 23(4), 589-609. Recuperado de https://www.jstor.org/stable/2978933%0D https://www.jstor.org/stable/2978933%0D...	EB
MKT value of equity/total liabilities	Altman (1968)Altman, E. (1968). Financial ratios, discriminant analysis and the prediction of corporate bankruptcy. The Journal of Finance, 23(4), 589-609. Recuperado de https://www.jstor.org/stable/2978933%0D https://www.jstor.org/stable/2978933%0D...	EQ
Sales/total assets	Altman (1968)Altman, E. (1968). Financial ratios, discriminant analysis and the prediction of corporate bankruptcy. The Journal of Finance, 23(4), 589-609. Recuperado de https://www.jstor.org/stable/2978933%0D https://www.jstor.org/stable/2978933%0D...	SL
Net income/total assets	Zmijewski (1984)Zmijewski, M. E. (1984). Methodological issues related to the estimation of financial distress prediction models. Journal of Accounting Research, 22(suppl.), 59-82.	NI
Total liabilities/total assets	Zmijewski (1984)Zmijewski, M. E. (1984). Methodological issues related to the estimation of financial distress prediction models. Journal of Accounting Research, 22(suppl.), 59-82.	LA
Current assets/current liabilities	Zmijewski (1984)Zmijewski, M. E. (1984). Methodological issues related to the estimation of financial distress prediction models. Journal of Accounting Research, 22(suppl.), 59-82.	LI
Cash flow/total liabilities	Beaver (1967)Beaver, W. (1967). Financial ratio as predictors of failure, empirical research in accounting: selected studies 1966. Journal of Accounting Research, 4, 71-111.	CF
Distance to default	Duffie et al. (2007)Duffie, D., Saita, L., & Wang, K. (2007). Multi-period corporate default prediction with stochastic covariates. Journal of Financial Economics, 83(3), 635-665.	DD

	Variables	N	%
Sector	RNE	50	4.69%
	LOG	98	9.19%
	PET	842	78.99%
	TEL	76	7.13%
Default	Yes	100	9.38%
Default	No	966	90.62%

Variables	N	Average	SD	Min.	1º Q	2º Q	3º Q	Max.
SZ	1,066	1.86	1.55	-1.81	0.66	2.2	3.13	4.68
WK	1,066	-19.03	111.2	-934.39	-0.33	-0.01	0.11	0.58
RE	1,066	-163.92	910.56	-7571.15	-5.19	-0.46	0	0.66
EB	1,066	-1.96	10.4	-83.31	-0.12	-0.01	0.01	0.09
SL	1,066	0.2	0.4	0	0.03	0.08	0.19	2.93
NI	1,066	-2.53	13.54	-109.04	-0.15	-0.02	0.01	0.39
LA	1,066	20.79	113.75	0.07	0.4	0.59	1.02	938.85
LI	1,066	3.83	8.16	0.04	0.85	1.51	3.01	57.29
CF	1,066	-1.69	8.53	-68.1	-0.28	0.05	0.21	2.26
DD	1,066	7,860.62	60,177.4	6.37	85.68	357.71	915.83	567,420.4
EQ	1,066	10,662.05	76,237.76	0.12	1.82	5.91	55.06	691,468.2

Default
Sectors	No		Yes		p-value
Sectors	N	%	N	%	p-value
PET	757	89.90%	85	10.10%	0.46
ENR	48	96.00%	2	4.00%
LOG	90	91.80%	8	8.20%
TEL	71	93.40%	5	6.60%

Variable	Initial model
Variable	β	IC	OR	p-value
(Intercept)	-2.405	[-2.92; -1.92]	-	0
SZ	0.346	[0.16; 0.54]	1.413	0
Sector = PET	-	-	-	-
Sector = ENR	-1.005	[-2.85; 0.23]	0.366	0.178
Sector = LOG	-0.429	[-1.28; 0.3]	0.651	0.281
Sector = TEL	-0.559	[-1.66; 0.32]	0.572	0.259
WK	0.006	[-0.01; 0.02]	1.006	0.4
RE	0	[0.00; 0.00]	1	0.547
EB	-0.066	[-0.19; 0.00]	0.936	0.109
SL	0.188	[-0.44; 0.72]	1.207	0.52
NI	0.043	[-0.01; 0.15]	1.043	0.244
LA	0.005	[-0.01; 0.02]	1.005	0.513
LI	-0.047	[-0.12; 0.00]	0.954	0.111
CF	0.01	[-0.04; 0.06]	1.01	0.686
DD/100	-0.069	[-0.12; -0.02]	0.934	0.007
EQ	0	[0.00; 0.00]	1	0.445
Hosmer-Lemeshow			0.364
Bigger VIF			48.358
AUC			0.695

Variable	Final model
Variable	β	IC	OR	p-value
(Intercept)	-2.721	[-3.16; -2.32]	-	0
SZ	0.384	[0.20; 0.57]	1.468	0
Sector = PET
Sector = ENR
Sector = LOG
Sector = TEL
WK
RE
EB	-0.024	[-0.04; 0.00]	0.977	0.012
SL
NI
LA
LI
CF
DD/100	-0.067	[-0.12; -0.02]	0.935	0.008
EQ
Hosmer-Lemeshow	0.361
Bigger VIF	1.729
AUC	0.67

Variable	Oil companies				Non-oil companies
Variable	β	IC	OR	p-value	β	IC	OR	p-value
(Intercept)	-2.729	[-3.22; -2.29]	-	0	-2,713	[-3.87; -1.79]	-	0
SZ	0.403	[0.20; 0.62]	1.497	0	0,445	[0.02; 0.89]	1.561	0.043
EB	-0.022	[-0.04; 0.00]	0.978	0.048	-0,028	[-0.06; 0.01]	0.973	0.111
DD/100	-0.054	[-0.11; -0.01]	0.948	0.038	-0,203	[-0.42; -0.02]	0.816	0.036
Hosmer-Lemeshow			0.299				0.247
Bigger VIF			1.688				1.806
AUC			0.667				0.714

Editora Mackenzie; Universidade Presbiteriana Mackenzie Rua da Consolação, 896, Edifício Rev. Modesto Carvalhosa, Térreo - Coordenação da RAM, Consolação - São Paulo - SP - Brasil - cep 01302-907 - São Paulo - SP - Brazil
E-mail: revista.adm@mackenzie.br

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[1] Correspondence concerning this article should be addressed to Vanderson A. Delapedra-Silva, Saun, s/n, Quadra 5, Lote B, Edifício Banco do Brasil, Asa Norte, Brasília, Distrito Federal, Brazil, CEP 70040-912. E-mail: vanderson.economia@gmail.com

	SZ
SZ	1.00	WK
WK	0.31	1.00	RE
RE	0.32	0.90	1.00	EB
EB	0.34	0.74	0.73	1.00	SL
SL	-0.08	-0.16	-0.19	-0.29	1.00	NI
NI	0.34	0.80	0.77	0.93	-0.23	1.00	LA
LA	-0.32	-0.99	-0.91	-0.76	0.16	-0.82	1.00	LI
LI	-0.14	0.01	-0.01	0.06	-0.11	0.06	-0.01	1.00	CF
CF	0.35	0.50	0.56	0.60	-0.14	0.58	-0.52	-0.02	1.00	DD
DD	-0.16	-0.23	-0.20	-0.20	0.07	-0.20	0.22	0.08	-0.12	1.00	EQ
EQ	-0.17	-0.09	-0.08	-0.13	0.00	-0.10	0.10	0.00	-0.23	0.07	1.00