Gestión de resultados en instituciones financieras en Brasil

Bortoluzzo, Adriana Bruscato; Sheng, Hsia Hua; Gomes, Ana Luiza Porto

doi:10.5700/rausp1233

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FINANCES & ACCOUNTING • Rev. Adm. (São Paulo) 51 (2) • Apr-Jun 2016 • https://doi.org/10.5700/rausp1233 copiar

Gestión de resultados en instituciones financieras en Brasil

Autoría SCIMAGO INSTITUTIONS RANKINGS

RESUMEN

El objetivo en este trabajo es estudiar la gestión de resultados en una muestra representativa de 123 bancos en el mercado brasileño, entre 2001 y 2012. Teniendo en cuenta el importante papel que desempeñan los bancos en la economía de un país, es necesario entender que hay factores discrecionales implicados en la información sobre la rentabilidad de una institución financiera. Normas de asignación de crédito para las instituciones financieras brasileñas se describen en la Resolución 2682/99 del Consejo Monetario Nacional. Debido a la discrecionalidad permitida en la resolución, la provisión para pérdidas de crédito se utiliza como herramienta de gestión de resultados, lo que no es una práctica ilegal, sin embargo, este comportamiento afecta a la percepción de riesgo de los agentes y analistas, que deben ser conscientes de eso y entenderlo. Los resultados muestran que la asignación de crédito se utiliza como un mecanismo de gestión de resultados para suavizar el beneficio neto de las instituciones financieras brasileñas. Los bancos tratan de evitar no sólo el beneficio neto antes de gastos con provisión e impuestos negativo, sino también el beneficio neto antes de gastos con provisión e impuestos inferior en relación con el período anterior. A diferencia de lo que indican estudios anteriores, no está claro si los bancos buscan evitar el beneficio neto inferior a un determinado grupo de pares.

Palabras clave:
instituciones financieras brasileñas; gestión de resultados; asignación de crédito; regresión con datos de panel

Period	Cooperatives		Automaker		Public		Subsidiaries		Retail		Total
Period	No.	Row %	No.	Row %	No.	Row %	No.	Row %	No.	Row %	No.	Row %
2001	2	1.9	13	12.0	9	8.3	29	26.9	55	50.9	108	7.9
2002	2	1.8	13	11.9	9	8.3	30	27.5	55	50.5	109	7.9
2003	2	1.9	13	12.0	9	8.3	29	26.9	55	50.9	108	7.9
2004	2	1.9	13	12.0	9	8.3	29	26.9	55	50.9	108	7.9
2005	2	1.8	14	12.7	9	8.2	29	26.4	56	50.9	110	8.0
2006	2	1.8	14	12.5	9	8.0	31	27.7	56	50.0	112	8.2
2007	2	1.8	14	12.3	9	7.9	31	27.2	58	50.9	114	8.3
2008	2	1.7	15	12.8	9	7.7	33	28.2	58	49.6	117	8.5
2009	2	1.7	16	13.4	9	7.6	35	29.4	57	47.9	119	8.7
2010	2	1.6	17	13.8	9	7.3	36	29.3	59	48.0	123	9.0
2011	2	1.6	17	13.9	9	7.4	36	29.5	58	47.5	122	8.9
2012	2	1.6	17	13.8	9	7.3	36	29.3	59	48.0	123	9.0
	24	1.7	176	12.8	108	7.9	384	28.0	681	49.6	1,373	100.0

Period	Only Minimal Provision		Excess Provision		Total
Period	No.	%	No.	%	No.	Row %
2001	62	71.3	25	28.7	87	7.3
2002	58	63.7	33	36.3	91	7.6
2003	60	65.2	32	34.8	92	7.7
2004	61	64.2	34	35.8	95	7.9
2005	55	58.8	39	41.5	94	7.9
2006	54	54.5	45	45.5	99	8.3
2007	56	56.6	43	43.4	99	8.3
2008	50	49.0	52	51.0	102	8.5
2009	49	45.4	59	54.6	108	9.0
2010	49	45.8	58	54.2	107	8.9
2011	49	44.1	62	55.9	111	9.3
2012	51	45.9	60	54.1	111	9.3
	654	54.7	542	45.3	1,196	100.0

Variable	Expected Sign	Definition	References
% expense with credit provisions/total assets [llp]	---	Expense with credit provisions in the period	*Anandarajan et al. (2005)Anandarajan, A., Hasan, I., & Vivas, A. L. (2005). Loan loss provision decisions: An empirical analysis of the Spanish depository institutions. Journal of International Accounting, Auditing and Taxation, 14(1), 55-77. Bornemann et al. (2012)Bornemann, S., Kick, T., Memmel, C., & Pfingsten, A. (2012). Are banks using hidden reserves to beat earnings benchmarks? Evidence from Germany. Journal of Banking & Finance, 36(8), 2403-2415. El Sood (2012)El Sood, H. A. (2012). Loan loss provisioning and income smoothing in US banks pre and post the financial crisis. International Review of Financial Analysis, 25, 64-72. Fonseca and González (2008)*Fonseca, A. R., & González, F. (2008). Cross-country determinants of bank income smoothing by managing loan-loss provisions. Journal of Banking & Finance, 32(2), 217-228.
% expense with credit provisions/total assets of previous period [llp(-1)]	(+)	Expense with credit provisions in the previous period	*Bornemann et al. (2012)*Bornemann, S., Kick, T., Memmel, C., & Pfingsten, A. (2012). Are banks using hidden reserves to beat earnings benchmarks? Evidence from Germany. Journal of Banking & Finance, 36(8), 2403-2415.
% net income/total assets [ni_assets]	(+)	Accounting net income in the period	*Anandarajan et al. (2005)Anandarajan, A., Hasan, I., & Vivas, A. L. (2005). Loan loss provision decisions: An empirical analysis of the Spanish depository institutions. Journal of International Accounting, Auditing and Taxation, 14(1), 55-77. Bornemann et al. (2012)Bornemann, S., Kick, T., Memmel, C., & Pfingsten, A. (2012). Are banks using hidden reserves to beat earnings benchmarks? Evidence from Germany. Journal of Banking & Finance, 36(8), 2403-2415. El Sood (2012)El Sood, H. A. (2012). Loan loss provisioning and income smoothing in US banks pre and post the financial crisis. International Review of Financial Analysis, 25, 64-72. Fonseca and González (2008)*Fonseca, A. R., & González, F. (2008). Cross-country determinants of bank income smoothing by managing loan-loss provisions. Journal of Banking & Finance, 32(2), 217-228.
1= publicly traded [d_pubtrad]	(+) or (-)	May have different incentives for earnings management in publicly traded banks	*Anandarajan et al. (2005)Anandarajan, A., Hasan, I., & Vivas, A. L. (2005). Loan loss provision decisions: An empirical analysis of the Spanish depository institutions. Journal of International Accounting, Auditing and Taxation, 14(1), 55-77. Balboa et al. (2013)Balboa, M., López-Espinosa, G., & Rubia, A. (2013). Nonlinear dynamics in discretionary accruals: An analysis of bank loan-loss provisions. Journal of Banking & Finance, 37(12), 5186-5207. Fonseca and Gonzales (2008)*Fonseca, A. R., & González, F. (2008). Cross-country determinants of bank income smoothing by managing loan-loss provisions. Journal of Banking & Finance, 32(2), 217-228.
% Services income/total assets [serv_assets]	(?)	Indicates the dependence of the bank in relation to credit activities	*Anandarajan et al. (2005)*Anandarajan, A., Hasan, I., & Vivas, A. L. (2005). Loan loss provision decisions: An empirical analysis of the Spanish depository institutions. Journal of International Accounting, Auditing and Taxation, 14(1), 55-77.
1= bank has provisions in excess regarding the regulatory minimum [d_prov_excess]	(+)	Indicates banks that use discretion in credit provisioning	Resolução 2682, December 21, 1999
% Value of the provisions excess/total assets [excess_prov]	(?)	Provision excess regarding the required regulatory minimum	Resolução 2682, December 21, 1999
% GDP growth [GDP]	(-)	Macroeconomic environment factors influence the behavior of the bank	*Balboa et al. (2013)Balboa, M., López-Espinosa, G., & Rubia, A. (2013). Nonlinear dynamics in discretionary accruals: An analysis of bank loan-loss provisions. Journal of Banking & Finance, 37(12), 5186-5207. El Sood (2012)El Sood, H. A. (2012). Loan loss provisioning and income smoothing in US banks pre and post the financial crisis. International Review of Financial Analysis, 25, 64-72. Fonseca and Gonzalez (2008)*Fonseca, A. R., & González, F. (2008). Cross-country determinants of bank income smoothing by managing loan-loss provisions. Journal of Banking & Finance, 32(2), 217-228.
% Unemployment [unempl]	(+)	Macroeconomic environment factors influence the behavior of the bank	*Anandarajan et al. (2003)Anandarajan, A., Hasan, I., & Vivas, A. L. (2003). The role of loan loss provisions in earnings management, capital management, and signaling: The Spanish experience. Advances in International Accounting, 16, 45-65. Bornemann et al. (2012)*Bornemann, S., Kick, T., Memmel, C., & Pfingsten, A. (2012). Are banks using hidden reserves to beat earnings benchmarks? Evidence from Germany. Journal of Banking & Finance, 36(8), 2403-2415.
1= years of crisis (2007-2009) [d_crisis]	(?)	Aims to investigate behavior changes in periods of crisis	El Sood (2012)El Sood, H. A. (2012). Loan loss provisioning and income smoothing in US banks pre and post the financial crisis. International Review of Financial Analysis, 25, 64-72.
1= net income below zero in the period [d_loss (H1)]	(-)	H1: Investigates if banks seek to avoid net income below zero.	*Bornemann et al. (2012)*Bornemann, S., Kick, T., Memmel, C., & Pfingsten, A. (2012). Are banks using hidden reserves to beat earnings benchmarks? Evidence from Germany. Journal of Banking & Finance, 36(8), 2403-2415.
1= net income below the net income in the previous period [d_prev_year (H2)]	(-)	H2: Investigates if banks seek to avoid decreases in net income in relation to the previous period	Bornemann et al. (2012)
1= net income below the net income in the previous period of the respective peer group [d_peer (H3)]	(-)	H3: Investigates if banks seek to avoid decreases in the net income with credit provisions and taxes in relation to the previous period of a determined peer group	*Bornemann et al. (2012)*Bornemann, S., Kick, T., Memmel, C., & Pfingsten, A. (2012). Are banks using hidden reserves to beat earnings benchmarks? Evidence from Germany. Journal of Banking & Finance, 36(8), 2403-2415.

Variable	N	Mean	Std. Dev.	Min	Max
llp	1213	1.80	2.91	0.00	31.99
ni_assets	1361	4.00	6.25	-87.30	40.53
serv_assets	1263	2.97	6.81	0.00	74.80
GDP	1375	11.75	3.13	6.26	16.38
unempl	1500	7.83	2.18	4.60	10.90
excess_prov	1196	0.17	0.45	0.00	5.38

Variable	Category	n (%)	% Expense With Credit Provisions
Variable	Category	n (%)	Mean	Std. Dev.	Min	Max
d_pubtrad	0	1031 (85.07)	1.83	3.07	0.00	31.99
	1	181 (14.93)	1.56	1.76	0.21	16.63
d_prov_excess	0	638 (54.86)	1.68	2.96	0.00	31.99
	1	525 (45.14)	2.03	2.95	0.00	22.05
d_crisis	0	899 (74.17)	1.79	2.99	0.00	31.99
	1	313 (25.83)	1.72	2.67	0.00	19.56
d_loss	0	1090 (89.93)	1.72	2.90	0.00	31.99
	1	122 (10.07)	1.34	3.03	0.00	16.63
d_prev_year	0	707 (63.18)	2.04	3.02	0.00	22.05
	1	412 (36.82)	1.42	2.79	0.00	31.99
d_peer	0	219 (19.57)	1.70	2.41	0.00	15.75
	1	900 (80.43)	1.84	3.07	0.00	31.99

Explanatory Variable	Dependent Variable: % Expense With Credit Provisions/Total Assets
Explanatory Variable	Model 1	Model 2	Model 3	Model 4	Model 5
llp (-1)	0.5931^* ***** Significant at 1%	0.5127^* ***** Significant at 1%	0.5891^* ***** Significant at 1%	0.5924^* ***** Significant at 1%	0.5640^* ***** Significant at 1%
	(0.0681)	(0.0375)	(0.0283)	(0.0292)	(0.0311)
ni_assets	0.1030^* ***** Significant at 1%	0.1456^* ***** Significant at 1%	0.1097^* ***** Significant at 1%	0.1026^* ***** Significant at 1%	0.1377^* ***** Significant at 1%
	(0.0359)	(0.0385)	(0.0237)	(0.0255)	(0.0287)
d_pubtrad	-0.2475^ Significant at 5%,	-0.1286	-0.2866	-0.2213	-0.1689^ Significant at 5%,
	(0.1044)	(0.2197)	(0.1800)	(0.1815)	(0.1827)
serv_assets	-0.0010	-0.0012	-0.0017	-0.0015	-0.0045
	(0.0043)	(0.0141)	(0.0118)	(0.0119)	(0.0118)
GDP	-0.0872^* ***** Significant at 1%	-0.0827^* ***** Significant at 1%	-0.0826^* ***** Significant at 1%	-0.0877^* ***** Significant at 1%	-0.0789^* ***** Significant at 1%
	(0.0175)	(0.0224)	(0.0215)	(0.0215)	(0.0213)
Unempl	0.0244	0.0168	0.0125	0.0278	0.0099
	(0.0325)	(0.0383)	(0.0347)	(0.0356)	(0.0347)
d_prov_excess	0.2673^* * Significant at 10%,	0.3023	0.2414	0.2850^* * Significant at 10%,	0.2858^* * Significant at 10%,
	(0.1533)	(0.1908)	(0.1602)	(0.1620)	(0.1593)
excess_prov	-0.1107	-0.0410	-0.0666	-0.1102	-0.0793
	(0.2395)	(0.3078)	(0.2435)	(0.2460)	(0.2406)
d_crisis	-0.1287	-0.1278	-0.1340	-0.1298	-0.1339
	(0.2342)	(0.1474)	(0.1408)	(0.1411)	(0.1391)
d_loss (H₁)	---	-0.8974^ Significant at 5%,	---	---	-1.0252^* ***** Significant at 1%
	---	(0.4097)	---	---	(0.3174)
d_prev_year (H₂)	---	---	-0.3256^ Significant at 5%,	---	-0.4198^* ***** Significant at 1%
	---	---	(0.1546)	---	(0.1503)
d_peer (H₃)	---	---	---	0.1430	0.1626
	---	---	---	(0.1616)	(0.1617)
Constant	1.1872^* ***** Significant at 1%	1.0621^* ***** Significant at 1%	1.3331^* ***** Significant at 1%	1.0454^* ***** Significant at 1%	1.0235^* ***** Significant at 1%
	(0.2538)	(0.3309)	(0.3079)	(0.3410)	(0.3415)
R² overall	0.5109	0.5203	0.5158	0.5111	0.5271
Adjusted R²	0.5064	0.5090	0.5108	0.5061	0.5211

Departamento de Administração da Faculdade de Economia, Administração e Contabilidade da Universidade de São Paulo Avenida Professor Luciano Gualberto, 908, sala F184, 05508-900 São Paulo / SP Brasil, Tel./Fax 55 11 3818-4002 - São Paulo - SP - Brazil
E-mail: rausp@usp.br

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[1] Adriana Bruscato Bortoluzzo, Professora Assistente do Insper - Instituto de Ensino e Pesquisa (CEP 04546-042 - São Paulo/SP, Brasil). E-mail: AdrianaB@insper.edu.br Address: Insper - Instituto de Ensino e Pesquisa, Rua Quatá, 300, 04546-042 - São Paulo, SP - Brasil