Does the level of Corporate Governance predicts the financial performance of the company? Evidence from the Brazilian market through artificial neural networks
DOI:
https://doi.org/10.16930/2237-766220192796Keywords:
Financial Performance, Corporate Governance, Artificial Neural Networks.Abstract
This study aimed to develop models of artificial neural networks to predict the financial performance of companies in and outside the governance levels of the B3 (before, BM&FBovespa). The models were developed using data from companies in the Brazilian stock market from 2005 to 2017. For these models, it was stipulated as a hypothesis that the models, which corresponded to the companies in the governance segments, would be most successful in predicting the performance of companies than those, which represent the ones that were not in the levels of governance. The results confirmed the hypothesis. In addition, they indicated that the performance of not belonging to corporate governance levels companies is more sensitive to oscillations in the external environment, making it difficult to forecast this indicator. This work was the first in Brazil to construct models through techniques of artificial intelligence – more specifically neural backpropagation network – to predict the performance of the company by relating it to aspects of corporate governance.References
Alencar, R. C. D. (2007). Nível de disclosure e custo de capital próprio no mercado Brasileiro. Tese de doutorado. Universidade de São Paulo, São Paulo, SP, Brasil.
Almeida, M. A., & Santos, J. D. (2008). Relação entre variáveis endógenas e a qualidade das práticas de governança corporativa das empresas brasileiras de capital aberto não listadas em bolsa. Revista de Informação Contábil, 2(4), 17-37.
Almeida, J. E. F., & Dalmácio, F. Z. (2015). The effects of corporate governance and product market competition on analysts' forecasts: evidence from the Brazilian capital market. The International Journal of Accounting, 50(3), 316-339. DOI: https://doi.org/10.1016/j.intacc.2015.07.007
Ariff, A. M., Ibrahim, M. K., & Othman, R. (2007). Determinants of firm level governance: Malaysian evidence. Corporate Governance: The international journal of business in society, 7(5), 562-573. DOI: https://doi.org/10.1108/14720700710827158
Badele, C. S., & Fundeanu, D. (2014). Policy's Beneficiaries of Corporate Governance and Diversification Strategy. Procedia-Social and Behavioral Sciences, 124, 468-477. DOI: https://doi.org/10.1016/j.sbspro.2014.02.509
Black, B. S., Carvalho, A. G., & Sampaio, J. O. (2014). The evolution of corporate governance in Brazil. Emerging Markets Review, 20, 176-195. DOI: https://doi.org/10.1016/j.ememar.2014.04.004
BM&FBovespa (2019). Sobre segmentos de listagem. Segmentos de listagem. Recuperado em 27 maio, 2019, de http://www.bmfbovespa.com.br/pt_br/listagem/acoes/segmentos-de-listagem/sobre-segmentos-de-listagem/
Bodyanskiy, Y., & Popov, S. (2006). Neural network approach to forecasting of quasiperiodic financial time series. European Journal of Operational Research, 175(3), 1357-1366. DOI: https://doi.org/10.1016/j.ejor.2005.02.012
Burrell, P. R., & Folarin, B. O. (1997). The impact of neural networks in finance. Neural Computing & Applications, 6(4), 193-200. DOI: https://doi.org/10.1007/BF01501506
Catapan, A., & Colauto, R. D. (2014). Governança corporativa: uma análise de sua relação com o desempenho econômico-financeiro de empresas cotadas no Brasil nos anos de 2010–2012. Contaduría y Administración, 59(3), 137-164. DOI: https://doi.org/10.1016/S0186-1042(14)71268-9
Chen, H. J., Huang, S. Y., & Kuo, C. L. (2009). Using the artificial neural network to predict fraud litigation: Some empirical evidence from emerging markets. Expert Systems with Applications, 36(2), 1478-1484. DOI: https://doi.org/10.1016/j.eswa.2007.11.030
Chi, L. C. (2009). Do transparency and disclosure predict firm performance? Evidence from the Taiwan market. Expert Systems with Applications, 36(8), 11198-11203. DOI: https://doi.org/10.1016/j.eswa.2009.02.099
Chung, K. H., & Pruitt, S. W. (1994). A simple approximation of Tobin's q. Financial management, 70-74. DOI: https://doi.org/10.2307/3665623
Costa, A. P. P. D., & Wood Jr, T. (2012). Corporate frauds. Revista de Administração de Empresas, 52(4), 464-472. DOI: https://doi.org/10.1590/S0034-75902012000400008
Duan, W. Q., & Stanley, H. E. (2011). Cross-correlation and the predictability of financial return series. Physica A: Statistical Mechanics and its Applications, 390(2), 290-296. DOI: https://doi.org/10.1016/j.physa.2010.09.013
Egeli B., Ozturan, M., & Badur, B. (2003). “Stock market prediction using artificial neural networks”. Proceeding of the Hawaii International Conference on Business, Honolulu, Hawaii, USA, 3.
Estadão (2015). Contrato da Petrobrás com a Odebrech é investigado por superfaturamento. Recuperado em 29 junho, 2018, de http://economia.estadao.com.br/noticias/negocios,contrato-da-petrobras-com-a-odebrecht-e-investigado-por-superfaturamento,169716e
Ferreira, R. N., Santos, A. C., Lopes, A. L. M., Fonseca, R. A., & Nazareth, L. G. C. (2013). Governança corporativa, eficiência, produtividade e desempenho. Revista de Administração Mackenzie, 14(4). DOI: https://doi.org/10.1590/S1678-69712013000400006
Haydar, A., Agdelen, Z., & Özbeseker, P. (2006). The use of backpropagation algorithm in the estimation of firm performance. Working paper, Istanbul Ticaret Üniversitesi.
Haykin, S. S. (2001). Redes neurais. Bookman.
Hecht-Nielsen, R. (1990). On the algebraic structure of feedforward network weight spaces. Advanced Neural Computers, 129-135. DOI: https://doi.org/10.1016/B978-0-444-88400-8.50019-4
Huang, M. C., Cheng, H. L., & Tseng, C. Y. (2014). Reexamining the direct and interactive effects of governance mechanisms upon buyer–supplier cooperative performance. Industrial Marketing Management, 43(4), 704-716. DOI: https://doi.org/10.1016/j.indmarman.2014.02.001
Instituto Brasileiro de Governança Corporativa – IBGC (2015). Recuperado em 29 junho, 2018, de http://www.ibgc.org.br/inter.php?id=18161/governanca-corporativa
Kolarik, T., & Rudorfer, G. (1994). Time series forecasting using neural networks. ACM Sigapl Apl Quote Quad, 25(1), 86-94. DOI: https://doi.org/10.1145/190468.190290
Laboissiere, L. A., Fernandes, R. A., & Lage, G. G. (2015). Maximum and minimum stock price forecasting of Brazilian power distribution companies based on artificial neural networks. Applied Soft Computing, 35, 66-74. DOI: https://doi.org/10.1016/j.asoc.2015.06.005
La Porta, R., Lopez-de-Silanes, F., Shleifer, A., & Vishny, R. W. (2002). Investor protection and corporate valuation. The Journal of Finance, 57(3), 1147-1170. DOI: https://doi.org/10.1111/1540-6261.00457
Leal, R. P. (2004). Governance practices and corporate value: a recent literature survey. Revista de Administração de Empresas da USP, 39(4), 327-337.
Lei n. 11.638/07. (2007). Altera e revoga dispositivos da Lei no 6.404, de 15 de dezembro de 1976, e da Lei no 6.385, de 7 de dezembro de 1976, e estende às sociedades de grande porte disposições relativas à elaboração e divulgação de demonstrações financeiras. Brasília, 2007. Recuperado em 28 abril, 2019, de http://www.planalto.gov.br/ccivil_03/_ato2007-2010/2007/lei/l11638.htm
Leshno, M., & Spector, Y. (1996). Neural network prediction analysis: The bankruptcy case. Neurocomputing, 10(2), 125-147. DOI: https://doi.org/10.1016/0925-2312(94)00060-3
Malacrida, M. J. C., & Yamamoto, M. M. (2006). Governança corporativa: nível de evidenciação das informações e sua relação com a volatilidade das ações do Ibovespa. Revista contabilidade e finanças, 17, 65-79. DOI: https://doi.org/10.1590/S1519-70772006000400006
Macedo, M. A. D. S., & Corrar, L. J. (2012). Análise comparativa do desempenho contábil-financeiro de empresas com boas práticas de governança corporativa no Brasil. Revista Contabilidade e Controladoria, 4(1). DOI: https://doi.org/10.4013/base.2012.91.02
Maestri, C. O. N. M., Teruel, R. L. H., & Ribeiro, K. C. S. (2017). Governança Corporativa e o Impacto no Custo de Capital Próprio das Empresas Brasileiras de Capital Aberto. Revista de Finanças Aplicadas, 7(4), 1-17.
Nguyen, T., Locke, S., & Reddy, K. (2015). Ownership concentration and corporate performance from a dynamic perspective: Does national governance quality matter? International Review of Financial Analysis, 41, 148-161. DOI: https://doi.org/10.1016/j.irfa.2015.06.005
Okimura, R. T., Silveira, A. D. M. D., & Rocha, K. C. (2007). Estrutura de propriedade e desempenho corporativo no Brasil. RAC eletrônica, 1(1), 119-135.
Oliveira, F. A., Nobre, C. N., & Zárate, L. E. (2013). Applying Artificial Neural Networks to prediction of stock price and improvement of the directional prediction index–Case study of PETR4, Petrobras, Brazil. Expert systems with applications, 40(18), 7596-7606. DOI: https://doi.org/10.1016/j.eswa.2013.06.071
Olson, D., & Mossman, C. (2003). Neural network of Canadian stock returns using accounting ratios. International Journal of Forecasting, 19, 453-465. DOI: https://doi.org/10.1016/S0169-2070(02)00058-4
Pao, H. T. (2008). A comparison of neural network and multiple regression analysis in modeling capital structure. Expert Systems with Applications, 35(3), 720-727. DOI: https://doi.org/10.1016/j.eswa.2007.07.018
Rodrigues, R. L., & Ambrozini, M. A. (2015). Teoria de agência, política de dividendos e governança corporativa: evidências nas empresas brasileiras de capital aberto no período de 2000 a 2013. Anais do SEMEAD, São Paulo, SP, Brasil, 18.
Shleifer, A., & Vishny, R. W. (1997). A survey of corporate governance. The journal of finance, 52(2), 737-783. DOI: https://doi.org/10.1111/j.1540-6261.1997.tb04820.x
Silva, A. L. C. (2004). Governança corporativa, valor, alavancagem e política de dividendos das empresas brasileiras. Revista de Administração da Universidade de São Paulo, 39(4).
Silveira, A. D. M. D., Leal, R. P. C., Carvalhal-da-Silva, A. L., & Barros, L. A. B. D. C. (2010). Endogeneity of Brazilian corporate governance quality determinants. Corporate Governance: International Journal of Business in Society, 10(2), 191-202. DOI: https://doi.org/10.1108/14720701011035701
Song, Z., Liu, D., & Chen, S. (2012). A decision engineering method to identify the competitive effects of working capital: A neural network model. Systems Engineering Procedia, 5, 326-333. DOI: https://doi.org/10.1016/j.sepro.2012.04.051
Srour, G. (2005). Práticas diferenciadas de governança corporativa: um estudo sobre a conduta e a performance das firmas brasileiras. Revista Brasileira de Economia, 59(4), 635-674. DOI: https://doi.org/10.1590/S0034-71402005000400006
Tang, T. C. (2010). Effects of announcements of reorganization outcome. Applied Economics, 42(9), 1113-1124. DOI: https://doi.org/10.1080/00036840701721174
Tavares, V. B., & Penedo, A. S. T. (2018). Níveis de governança corporativa da B3: interesse e desempenho das empresas ? uma análise por meio de redes neurais artificiais. Revista Contabilidade, Gestão e Governança, 21(1), 40-62. DOI: https://doi.org/10.21714/1984-3925_2018v21n1a3
Torres Jr, R. G., Machado, M. A. S., & Souza, R. C. (2005). Previsão de séries temporais de falhas em Manutenção industrial usando redes neurais. Engevista, 7(2), 4-18. DOI: https://doi.org/10.22409/engevista.v7i2.163
Valor Econômico. (2015). Polícia Federal investiga Eike Batista por três crimes contra o mercado. Recuperado em 29 junho, 2018, de http://www.valor.com.br/empresas/3520984/policia-federal-investiga-eike-batista-por-tres-crimes-contra-o-mercad
Zahedi, J., & Rounaghi, M. M. (2015). Application of artificial neural network models and principal component analysis method in predicting stock prices on Tehran Stock Exchange. Physica A: Statistical Mechanics and its Applications, 438, 178-187. DOI: https://doi.org/10.1016/j.physa.2015.06.033
Watts, R. L., & Zimmerman, J. L. (1990). Positive accounting theory: A ten year perspective. The Accounting Review, 65(1), 131–156.
Published
How to Cite
Issue
Section
License
The copyright for articles published in this journal belongs to the author (s), with first publication rights assigned to Revista Catarinense da Ciência Contábil. Due to appearing in this publicly accessible journal, articles are free to use, with mandatory recognition of the original authorship and initial publication in this magazine and for educational and non-commercial applications. The magazine chose to use published works for non-commercial purposes, including the right to submit or work for publicly accessible databases. The content of published articles is the sole and exclusive responsibility of the authors. - The author (s) authorize (s) a publication of the article in the journal; - The author (s) guarantee (s) that a contribution is original and unpublished and that it is not being evaluated in another magazine (s); - A magazine is not responsible for the opinions, ideas and concepts emitted in the texts, for the full responsibility of the author (s); - It is reserved to the editors or the right to make textual adjustments and to adjust the article to the publication rules.
This work is licensed under a Creative Commons Atribuição-NãoComercial-CompartilhaIgual 4.0 Internacional.
