Review of Corporate Governance Practices and Financial Distress Prediction

  • Authors

    • Syed Muhammad Hassan Gillani Ahmad
    • Suresh Ramakrishnan
    • Hamad Raza
    • Humara Ahmad
    2018-11-30
    https://doi.org/10.14419/ijet.v7i4.28.22385
  • Good corporate governance practices play an import role in increasing the firm value. Based on the agency theory related to corporate governance, if an agent (management) does not protect interest of principal (shareholders) then, agency cost is occurred and this creates a bad impact on the corporate performance. Therefore, it is necessary to address weak corporate governance practices in early stages otherwise firms can go in financial distress and eventually become bankrupt. The objective of this current study is to conduct a nonsystematic review of literature on theories and models related to corporate governance and financial distress. In the light of thorough review of literature, it is found that corporate governance variables (i.e. ownership concentration, board size, board composition, CEO duality, level of independence of board from management and managerial ownership) are good predictors for predicting financial distress. Moreover, it is also found that these corporate governance variables were not only used separately for predicting financial distress but also used along with others variables (firm level and country level) for the purpose of enhancing quality of financial distress models.

  • References

    1. [1] Beaver, W. H. (1966). Financial ratios as predictors of failure. Journal of accounting research, 71-111.

      [2] Altman, E. I. (1968). Financial ratios, discriminant analysis and the prediction of corporate bankruptcy. The journal of finance. 23(4), pp. 589-609.

      [3] Norton, C. L. and Smith, R. E. (1979). A comparison of general price level and historical cost financial statements in the prediction of bankruptcy. Accounting Review. pp. 72-87.

      [4] Ohlson, J. A. (1980). Financial ratios and the probabilistic prediction of bankruptcy. Journal of accounting research. pp. 109-131.

      [5] Mensah, Y. M. (1984). An examination of the stationarity of multivariate bankruptcy prediction models: A methodological study. Journal of Accounting Research. pp. 380-395.

      [6] Frydman, H., Altman, E. I. and KAO, D. L. (1985). Introducing recursive partitioning for financial classification: the case of financial distress. The Journal of Finance. 40(1), pp. 269-291.

      [7] Dimitras, A., Slowinski, R., Susmaga, R. and Zopounidis, C. (1999). Business failure prediction using rough sets. European Journal of Operational Research. 114(2), pp. 263-280.

      [8] Chen, H.-H. (2008). The timescale effects of corporate governance measure on predicting financial distress. Review of Pacific Basin Financial Markets and Policies. 11(01), pp. 35-46.

      [9] Ooghe, H. and Balcaen, S. (2015). Are failure prediction models widely usable? An empirical study using a Belgian dataset.

      [10] Ramakrishnan, S., Nabi, A. A. and Anuar, M. A. (2016). Default Prediction in Pakistan using Financial Ratios and Sector Level Variables. International journal of economics and Financial Issues. 6(3S).

      [11] Butt, S. A. and Hasan, A. (2009). Impact of ownership structure and corporate governance on capital structure of Pakistani listed companies.

      [12] Chaganti, R. S., Mahajan, V. and Sharma, S. (1985). Corporate board size, composition and corporate failures in retailing industry [1]. Journal of Management Studies. 22(4), pp. 400-417.

      [13] Daily, C. M. and Dalton, D. R. (1994). Bankruptcy and corporate governance: The impact of board composition and structure. Academy of Management journal. 37(6), pp. 1603-1617.

      [14] Elloumi, F. and Gueyie, J.-P. (2001). Financial distress and corporate governance: an empirical analysis. Corporate Governance: The international journal of business in society. 1(1), pp. 15-23.

      [15] Lee, T. S. and Yeh, Y. H. (2004). Corporate governance and financial distress: Evidence from Taiwan. Corporate governance: An international review. 12(3), pp. 378-388.

      [16] Wang, Z.-J. and Deng, X.-L. (2006). Corporate governance and financial distress: Evidence from Chinese listed companies. Chinese Economy. 39(5), pp. 5-27.

      [17] Sami, A., Jusoh, A., & Qureshi, M. I. (2016). Does Ethical Leadership Create Public Value? Empirical Evidences from Banking Sector of Pakistan. International Review of Management and Marketing, 6(4S).

      [18] Polsiri, P. and Sookhanaphibarn, K. (2009). Corporate distress prediction models using governance and financial variables: evidence from Thai listed firms during the East Asian economic crisis. Journal of Economics and Management. 5(2), pp. 273-304.

      [19] Ciampi, F. and Gordini, N. (2013). The Potential of Corporate Governance Variables for Small Enterprise Default Prediction Modeling. Statistical Evidence from Italian Manufacturing Firms. Preliminary Findings. Proceedings of the 2013 Cambridge business and economics conference proceedings. pp. 1-19.

      [20] Manzaneque, M., Priego, A. M. and Merino, E. (2016). Corporate governance effect on financial distress likelihood: Evidence from Spain. Revista de Contabilidad. 19(1), pp. 111-121.

      [21] Merton, R. C. (1974). On the pricing of corporate debt: The risk structure of interest rates. The Journal of finance, 29(2), 449-470.

      [22] Modigliani, F. and Miller, M. H. (1958). The cost of capital, corporation finance and the theory of investment. The American economic review. 48(3), pp. 261-297.

      [23] Kester, W. C. (1986). Capital and ownership structure: A comparison of United States and Japanese manufacturing corporations. Financial management. pp. 5-16.

      [24] Ross, S. A. (1977). The determination of financial structure: the incentive-signalling approach. The bell journal of economics. pp. 23-40.

      [25] Jensen, M. C. and Meckling, W. H. (1976). Theory of the firm: Managerial behavior, agency costs and ownership structure. Journal of financial economics. 3(4), pp. 305-360.

      [26] Jensen, M. C. (1986). Agency costs of free cash flow, corporate finance, and takeovers. The American economic review. 76(2), pp. 323-329.

      [27] Hernandez, M. (2012). Toward an understanding of the psychology of stewardship. Academy of Management Review. 37(2), pp. 172-193.

      [28] Hannan, M. T. and Freeman, J. (1984). Structural inertia and organizational change. American sociological review. pp. 149-164.

      [29] Clarkson, P. M., Li, Y., Richardson, G. D. and Vasvari, F. P. (2011). Does it really pay to be green? Determinants and consequences of proactive environmental strategies. Journal of Accounting and Public Policy. 30(2), pp. 122-144.

      [30] Aziz, M. A. and Dar, H. A. (2004). Predicting Corporate Bankruptcy: whither do we stand?

      [31] FitzPatrick, P. J. (1932). A comparison of the ratios of successful industrial enterprises with those of failed companies.

      [32] Cascio, W. F., Young, C. E. and Morris, J. R. (1997). Financial consequences of employment-change decisions in major US corporations. Academy of management Journal. 40(5), pp. 1175-1189.

      [33] Edmister, R. O. (1972). An empirical test of financial ratio analysis for small business failure prediction. Journal of Financial and Quantitative analysis. 7(2), pp. 1477-1493.

      [34] Richardson, F. M. and Davidson, L. F. (1984). On linear discrimination with accounting ratios. Journal of Business Finance & Accounting. 11(4), pp. 511-525.

      [35] Eisenbeis, R. A. and Avery, R. B. (1972). Discriminant analysis and classification procedures: theory and applications.

      [36] Deakin, E. B. (1976). Distributions of financial accounting ratios: some empirical evidence. The Accounting Review. 51(1), pp. 90-96.

      [37] Mcleay, S. and Omar, A. (2000). The sensitivity of prediction models to the non-normality of bounded and unbounded financial ratios. The British Accounting Review. 32(2), pp. 213-230.

      [38] Javid, A. Y., & Iqbal, R. (2008). Ownership concentration, corporate governance and firm performance: Evidence from Pakistan. The Pakistan Development Review, 643-659.

      [39] Moyer, R. C. (1977). Forecasting financial failure: a re-examination. Financial Management (pre-1986), 6(1), 11.

      [40] Booth, P. J. (1983). Decomposition measures and the prediction of financial failure. Journal of Business Finance & Accounting. 10(1), pp. 67-82.

      [41] Laitinen, E. K. and Laitinen, T. (1998). Cash management behavior and failure prediction. Journal of Business Finance & Accounting. 25(7â€8), pp. 893-919.

      [42] Tam, K. Y. and Kiang, M. Y. (1992). Managerial applications of neural networks: the case of bank failure predictions. Management science. 38(7), pp. 926-947.

      [43] Martens, D., Baesens, B., Van Gestel, T. and Vanthienen, J. (2007). Comprehensible credit scoring models using rule extraction from support vector machines. European journal of operational research. 183(3), pp. 1466-1476.

      [44] Carter, C. and Catlett, J. (1987). Assessing credit card applications using machine learning. IEEE expert. 2(3), pp. 71-79.

      [45] Messier Jr, W. F. and Hansen, J. V. (1988). Inducing rules for expert system development: an example using default and bankruptcy data. Management Science. 34(12), pp. 1403-1415.

      [46] Pompe, P. and Feelders, A. (1997). Using machine learning, neural networks, and statistics to predict corporate bankruptcy. Computerâ€Aided Civil and Infrastructure Engineering. 12(4), pp. 267-276.

      [47] Gepp, A., Kumar, K. and Bhattacharya, S. (2010). Business failure prediction using decision trees. Journal of forecasting. 29(6), pp. 536-555.

      [48] Breiman, L., Friedman, J., Olshen, R. and Stone, C. (1984). Classification and regression tree. Wadsworth International Group, Belmont, CA.

      [49] Hand, D. J., Mannila, H. and Smyth, P. (2001). Principles of data mining. MIT press.

      [50] Balcaen, S. and Ooghe, H. (2006). 35 years of studies on business failure: an overview of the classic statistical methodologies and their related problems. The British Accounting Review. 38(1), pp. 63-93.

      [51] Shachmurove, Y. (2002). Applying artificial neural networks to business, economics and finance. University of Pennsylvania, Center for Analytic Research in Economics and the Social Sciences.

      [52] Härdle, W., Moro, R. and Schäfer, D. (2005). Predicting bankruptcy with support vector machines. In Statistical Tools for Finance and Insurance. (pp. 225-248). Springer.

      [53] Chen, S., Härdle, W. and Moro, R. (2011). Modeling default risk with support vector machines. Quantitative Finance. 11(1), pp. 135-154.

      [54] Fich, E. M. and Slezak, S. L. (2008). Can corporate governance save distressed firms from bankruptcy? An empirical analysis. Review of Quantitative Finance and Accounting. 30(2), pp. 225-251.

  • Downloads

  • How to Cite

    Ahmad, S. M. H. G., Ramakrishnan, S., Raza, H., & Ahmad, H. (2018). Review of Corporate Governance Practices and Financial Distress Prediction. International Journal of Engineering & Technology, 7(4.28), 30-33. https://doi.org/10.14419/ijet.v7i4.28.22385