Abstract
This study discusses the key factors of financial distress warning models for companies using corporate governance variables and financial ratios as the research variables, sieving out influential variables based on the attribute simplification process of rough set theory (RST). Then, we construct some classification models for diagnosing enterprise distress based on RST, using a data mining technique of decision trees with the selected indicators and variables. The empirical results obtained from analysis of enterprise distress indicators, show that financial distress is not only affected by the traditional financial ratios, but also by corporate governance variables. In addition, enterprise distress diagnosis models constructed based on RST and decision trees can effectively diagnose firms in times of crisis. In particular, the RST models are more accurate. This study provides a reference for better understanding the symptoms that might lead to a company’s financial crisis in advance and thus provide a valuable reference for investment decision making by stakeholders.
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References
Beaver, W.H.: Financial Ratios as Predictors of Failure. Journal of Accounting Research 4, 71–111 (1966)
Altman, E.I.: Financial Ratios Discriminate Analysis and the Prediction of Corporate Bankruptcy. Journal of Finance 23(4), 589–609 (1968)
Ohlson, J.: Financial Ratios and the Probabilistic Prediction of Bankruptcy. Journal of Accounting Research 18(1), 109–131 (1980)
Rajan, R.G., Zingales, L.: Which Capitalism? Lessons from the East Asian Crisis. Journal of Applied Corporate Finance 11(3), 40–48 (1998)
Prowse, S.: Corporate Governance: Emerging Issues and Lessons from East Asia, Responding to the Global Financial Crisis–World Bank Mimeo (1998)
Sueyoshi, T., Goto, M., Omi, Y.: Corporate Governance and Firm Performance: Evidence From Japanese Manufacturing Industries after the Lost Decade. European Journal of Operational Research 203(3), 724–736 (2010)
Morellec, E., Nikolov, B., Schürhoff, N.: Corporate Governance and Capital Structure Dynamics. Journal of Finance 67(3), 803–848 (2012)
Chen, W.S., Du, Y.K.: Using Neural Networks and Data Mining Techniques for the Financial Distress Prediction Model. Expert Systems with Applications 36(2), 4075–4086 (2009)
Cohen, S., Doumpos, M., Neofytou, E., Zopounidis, C.: Assessing Financial Distress where Bankruptcy is not an Option: An Alternative Approach for Local Municipalities. European Journal of Operational Research 218(1), 270–279 (2012)
Claessens, S., Fan, P.H.J., Djankov, S., Lang, H.P.L.: On Expropriation of Minority Shareholders: Evidence from East Asia. Available at SSRN 202390 (1999)
La Porta, R., Lopez-de-Silanes, F., Shleifer, A., Vishny, R.: The quality of government. Journal of Law, Economics, and organization 15(1), 222–279 (1999)
Altman, E.L., Edward, I., Haldeman, R., Narayanan, P.: Zetatm Analysis a new Model to Identify Bankruptcy Risk of Corporations. Journal of Banking and Finance 23(1), 29–54 (1977)
Johnson, S., Boone, P., Breach, A., Friedman, E.: Corporate Governance in the Asian Financial Crisis. Journal of Financial Economics 58(1-2), 141–186 (2000)
Aziz, M.A., Dar, H.A.: Predicting Corporate Bankruptcy: Where We Stand? Corporate Governance 6(1), 18–33 (2006)
Odom, M.D., Sharda, R.: A Neural Networks Model for Bankruptcy Prediction. In: IEEE International Joint Conference on Neural Networks, California, San Diego, pp. 163–167 (1990)
Tam, K.Y., Kiang, M.Y.: Managerial Applications of Neural Networks: The Case of Bank Failure Prediction. Management Science 38(7), 926–947 (1992)
Altman, E.I., Marco, G., Varetto, F.: Corporate Distress Diagnosis: Comparisons Using Linear Discriminant Analysis and Neural Networks (the Italian Experience). Journal of Banking and Finance 18, 505–529 (1994)
Kotsiantis, S., Koumanakos, E., Tzelepis, D., Tampakas, V.: Forecasting Fraudulent Financial Statements Using Data Mining. International Journal of Computational Intelligence 3(2), 104–110 (2006)
Kim, S.Y., Upneja, A.: Predicting Restaurant Financial Distress Using Decision Tree and AdaBoosted Decision Tree Models. Economic Modelling 36, 354–362 (2014)
Kirkos, E., Spathis, C., Manolopoulos, Y.: Data Mining Techniques for the Detection of Fraudulent Financial Statements. Expert Systems with Applications 32(4), 995–1003 (2007)
Bose, I.: Deciding the Financial Health of Dot-coms Using Rough Sets. Information and Management 43(7), 835–846 (2006)
Slowinski, R., Zopounidis, C.: Application of the Rough Set Approach to Evaluation of Bankruptcy Risk. International Journal of Intelligent Systems in Accounting. Finance and Management. 4, 27–41 (1995)
Lau, A.H.L.: A Five-state Financial Distress Prediction Model. Journal of Accounting Research 25(1), 127–138 (1987)
Ward, T.J., Foster, B.P.: An Empirical Analysis of Thomas’s Financial Accounting Allocation Fallacy Theory in a Financial Distress Context. Accounting and Business Research 26(2), 137–152 (1996)
Pindado, J., Rodrigues, L., De la Torre, C.: Estimating Financial Distress Likelihood. Journal of Business Research 61, 995–1003 (2008)
Ericsson, J., Jacobs, C., Oviedo, R.: The Determinants of Credit Default Swap Premia. Journal of Financial and Quantitative Analysis 44, 109–132 (2009)
Salzman, J.: Decentralized Administrative Law in the Organization for Economic Cooperation and Development. Law and Contemporary Problems 68(3/4), 189–224 (2005)
Claessens, S.S., Djankov, S.J., Fan, P.H., Lang, L.H.P.: Corporate Diversification in East Asia: The Role of Ultimate Ownership Group Affiliation, World Bank, Research Paper 2089 (1999)
Yeh, Y.H., Lee, T.S., Ko, C.E.: Corporate Governance and Rating System. Sunbright Culture Eds, Taipei (2002)
Hsu, S.N., Ouyang, H., Chen, C.F.: Corporate Governance, Earnings Management, and the Construction of Financial Warning Models. Journal of Accounting and Corporate Governance 4(1), 85–121 (2007)
Kim, E.H., Lu, Y.: Corporate Governance Reforms around the World and Cross-border Acquisitions. Journal of Corporate Finance 22, 236–253 (2013)
Deakin, E.B.: A Discriminant Analysis of Predictors of Business Failure. Journal of Accounting Research, pp. 167–179 (1972)
Xiao, Z., Yang, X., Pang, Y., Dang, X.: The Prediction for Listed Companies’ Financial Distress by Using Multiple Prediction Methods with Rough Set and Dempster–Shafer Evidence Theory. Knowledge-Based Systems 26, 196–206 (2012)
Lin, F., Liang, D., Yeh, C.C., Huang, J.C.: Novel Feature Selection Methods to Financial Distress Prediction. Expert Systems with Applications 41(5), 2472–2483 (2014)
Pawlak, Z.: Rough Sets. International Journal of Information and Computer Secience 11, 341–356 (1982)
Pawlak, Z., Slowinski, R.: Rough Set Approach to Multiattribute Decision Analysis. European Journal of Operational Research 72, 443–459 (1994)
Kusiak, A.: Rough Set Theory: a Data Mining Tool for Semiconductor Manufacturing. IEEE Transactions on Electronics Packaging Manufacturing 24(1), 44–50 (2001)
Kirkos, E., Spathis, C., Manolopoulos, Y.: Audit‐firm Group Appointment: an Artificial Intelligence Approach. Intelligent Systems in Accounting, Finance and Management 17(1), 1–17 (2010)
Stone, M.: Cross-validatory Choice and Assessment of Statistical Predictions. Journal of the Royal Statistical Society 36, 111–147 (1974)
Zhang, G., Hu, M.Y., Patuwo, B.E., Indro, D.C.: Artificial Neural Networks in Bankruptcy Prediction: General Framework and Cross-validation Analysis. European Journal of Operational Research 116(1), 16–32 (1999)
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Chen, F.H., Chi, DJ., Kuo, CY. (2014). Using Rough Set Theory and Decision Trees to Diagnose Enterprise Distress – Consideration of Corporate Governance Variables. In: Huang, DS., Jo, KH., Wang, L. (eds) Intelligent Computing Methodologies. ICIC 2014. Lecture Notes in Computer Science(), vol 8589. Springer, Cham. https://doi.org/10.1007/978-3-319-09339-0_20
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DOI: https://doi.org/10.1007/978-3-319-09339-0_20
Publisher Name: Springer, Cham
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