Bankruptcy Prediction for Chinese Firms: Comparing Data Mining Tools With Logit Analysis

Transcription

1 Journal of Modern Accounting and Auditing, ISSN October 2014, Vol. 10, No. 10, D DAVID PUBLISHING Bankruptcy Prediction for Chinese Firms: Comparing Data Mining Tools With Logit Analysis Wikil Kwak, Xiaoyan Cheng, Jinlan Ni University of Nebraska, Omaha, USA Yong Shi Graduate University of the Chinese Academy of Science, Beijing, China University of Nebraska, Omaha, USA Guan Gong Shanghai University of Finance and Economics, Shanghai, China Nian Yan Nebraska Furniture Market, Omaha, USA China s capital market is different from that of the US in economic, political, and socio-cultural ways. China s dynamic and fast growing economy for the past decade entails some structural changes and weaknesses and as a consequence, there are some business failures. We propose bankruptcy prediction models using Chinese firm data via several data mining tools and traditional logit analysis. We used Chinese firm data one year prior to bankruptcy and our results suggest that the financial variables developed by Altman (1968) and Ohlson (1980) perform reasonably well in determining business failures of Chinese firms, but the overall prediction rate is low compared with those of the US or other countries studies. The reasons for this low prediction rate may be structural weaknesses resulting from China s fast growth and immature capital market. Keywords: China, bankruptcy, data mining, logit analysis Introduction China s capital markets have grown rapidly since the commencement of economic reform. Capital markets promote the development and allocation of market-oriented economic resources, resulting in increased business competition. The intense competition in China s capital markets inevitably entails some business failures. Past research (Kwak, Shi, & Cheh, 2006; Mansi, Maxwell, & Zhang, 2012) documents business failures in the US quite some time, but the numbers of academic research on business failures in China are relatively small. In this study, we employ both data mining and logit model approaches to investigate bankruptcy predictions Wikil Kwak, professor, Department of Accounting, College of Business Administration, University of Nebraska. wkwak@unomaha.edu. Xiaoyan Cheng, assistant professor, Department of Accounting, College of Business Administration, University of Nebraska. Jinlan Ni, associate professor, Department of Economics, University of Nebraska. Yong Shi, director, Graduate University of the Chinese Academy of Science; College of Information Science and Technology, University of Nebraska. Guan Gong, professor, School of Economics, Shanghai University of Finance and Economics. Nian Yan, manager, Nebraska Furniture Market.

2 BANKRUPTCY PREDICTION FOR CHINESE FIRMS 1031 using Chinese financial data. We also investigate the validity of US bankruptcy models in different socio-economic contexts. The usefulness of financial statement data in predicting bankruptcy has been examined in different countries, since corporate bankruptcy can be financially devastating for investors and therefore, stakeholders such as bankers, investors, and regulators are interested in assessing bankruptcy risk via bankruptcy prediction models. Like most Asian firms such as Korean and Japanese firms, Chinese firms have a low bankruptcy rate, because the government usually is heavily involved in bankruptcy processes and the bankruptcy law in China encourages negotiations among involved parties instead of promoting liquidation as a way to resolve financial distress (Fan, Huang, & Zhu, 2008). In addition, local government protection leads to fewer bankruptcy cases in China (Huang & Wang, 2009). This study uses Chinese firm data to predict bankruptcy in China s capital market that may not be the same as that of the US capital market due to the socio-economic or political environment. Our data mining and logit analysis results show that overall prediction rates are lower than those in studies of the US and other countries. We need to investigate the lower prediction rates of Chinese bankruptcy firms in a future study. However, our overall results suggest that financial variables used in previous US bankruptcy studies are still valid in the context of the Chinese business environment. Our paper is organized as follows. The next section reviews previous bankruptcy studies and relevant data mining and logit studies. The third section describes data collection processes. The following section presents our results. The last section summarizes and concludes our paper with limitations of our paper and future research avenues. Literature Review Data mining is a tool that can be used to predict business problems, such as bankruptcy, credit card default, and consumer purchase behavior based on past data (Chan & Lewis, 2002). Recently, Song, Ding, Huang, and Ge (2010) proposed a generic algorithm-based (GA) approach and statistical filter approaches for the best features for the support vector machine in a case of Chinese bankruptcy study. Chen and Hu (2011) employed preference ranking organization methods with Altman s (1968) five ratios in predicting bankruptcy in Taiwan. Peat and Jones (2012) introduced a neural net model to predict bankruptcy, and Sung, Chang, and Lee (1999) used the decision tree approach for bankruptcy prediction. In addition, Alali and Romero (2013) performed a traditional survival analysis by applying 24 factors to predict US commercial bank failure. Altman (1968) used multiple discriminant analysis (MDA) to predict bankruptcy based on financial ratios. Altman, Haldeman, and Narayanan (1977) later proposed the zeta model, but their assumptions of data being normally distributed can be a problem when applying this model. Ohlson (1980) used a logit model that does not require any assumptions about the prior probability of bankruptcy or the distribution of predictor variables. Assumptions about the knowledge of prior probabilities and group distributions make both of the above techniques very restrictive in bankruptcy prediction studies. Grice and Ingram (2001) empirically replicated Altman s (1968) study using a small sample of 33 manufacturing firms with an equal number of control firms and reported 83.5% overall accuracy. However, Altman s model using the test period showed that the overall correct classification rate dropped to 57.8%. Begley, Ming, and Watts (1996) also re-estimated both Altman s (1968) and Ohlson s (1980) models using 1980 s data and reported that Ohlson s model showed a Type I error rate of 29.2% and a Type II error rate of 14.9% at the cutoff point of Here, the Type I error refers to the false rejection error. For a

3 1032 BANKRUPTCY PREDICTION FOR CHINESE FIRMS bankruptcy prediction model, we reject a firm as a non-bankruptcy firm even though this firm is actually a bankruptcy firm. In bankruptcy cases, this cost is higher than Type II error costs. They suggested that both models accuracy rates drop as they are applied in different time periods, but Ohlson s model is a preferred one. Besides, Altman s (1968) model may have an upward bias by selecting an equal number of matching control firms. Shumway (2001) proposed a survival model using time-series data, but in most bankruptcy cases, the firm s financial data and other variables are not available near bankrupt time periods. Therefore, the logit model is the preferred choice in most accounting or finance literature. Pompe and Feelders (1997) compared machine learning, neural networks, and statistics using experiments to predict bankruptcy. However, their results are not conclusive as to which method outperforms the other methods. Shin and Lee (2002) proposed a GA in bankruptcy prediction modeling and their approach is capable of extracting rules that are easy to understand for users like expert systems. Their accuracy rate of bankruptcy prediction is 80.8% of both training and hold-out samples. Park and Han (2002) proposed a case-based reasoning (CBR) with the feature weights derived by an analytic hierarchy process (AHP) for bankruptcy prediction since AHP incorporates both financial ratios and non-financial variables into the model. They reported an accuracy rate of 84.52%. However, we cannot generalize this study to firms in the US or in other countries, because Korea went through an economic turmoil in McKee (2000) suggested a rough set theory to develop a bankruptcy prediction model. Neural networks can be characterized as a black box model that decision makers may not understand. The rough set model, however, is easily understandable and supported by a set of real examples. Rough set analysis provides better results when the attributes are continuous variables. In this case, non-financial variables are not easy to incorporate. In addition, McKee s study assumed equal costs for both bankruptcy and non-bankruptcy misclassifications. As suggested by Nanda and Pendharkar (2001), GA provides the best performance in terms of reducing Type I error costs. They reported that goal programming and GA had the highest correct classification rate using a decision maker explicitly incorporating costs of Type I and Type II errors. This is a more realistic assumption. In the real world, Type I error costs are much higher than Type II error costs in bankruptcy cases. The purpose of this study is to examine the effectiveness of a set of financial variables used previously by Altman (1968) and Ohlson (1980) in predicting Chinese business failure by comparing data mining tools with logit analysis. We use Chinese financial data. The Chinese capital market may not be the same as that of the US market because of social, cultural, and political differences. The Chinese capital market has experienced a major transition from a planned economy to a market-orientated economy. The privatization of Chinese firms does not have a positive effect on Chinese firms (Wai-Ming & Lam, 2004). Chinese culture leads people to save more for future uncertainty and thus a large amount of money could be invested. So far, much of the investment is being performed by the state-owned banking system. The banking system was controlled by the central bank but now has more independent control over its management decisions. A recent capital market reform Guiding Principles for the Healthy Development of Capital Markets by the State Council is intended to make China s capital markets much more diverse, structured, and transparent. This study uses survey data. Our financial data are one year before the firm declares bankruptcy as in most prior studies. We extend the literature by empirically testing whether our models of logit and data mining tools using Chinese financial data perform as effectively as they did using US financial data.

4 BANKRUPTCY PREDICTION FOR CHINESE FIRMS 1033 Data Collection and Research Design Our dataset comes from the National Bureau of Statistics (NBS). The Annual Survey of Industrial Production includes all state-owned firms and private firms with at least 5 million Yuan in annual sales from 1999 to For each year, we looked for the operational status indicating bankruptcy to identify bankrupt firms. We deleted observations in years 2001 and 2004, because our survey data labeled cancellation combined bankrupt firms with other exit firms in those years. We checked data consistency and excluded observations with missing information. We also excluded outliers by deleting the lower and upper 1% of financial variables, given that results may be misleading due to the excessive influence by outliers. Our final sample consists of 394 bankrupt firms and 726 matching control firms based on size and industry from 1999 to Empirical Results Tables 1 and 2 present the descriptive statistics of Chinese bankrupt and non-bankrupt firms between 1999 and Turning first to the measures of liquidity, the means of total liabilities/total assets (TL_TA) and total current liabilities/current assets (CL_CA) in bankrupt firms are significantly higher than in non-bankrupt firms. The mean of working capital/total assets in bankrupt firms (WCA_TA) is much lower than that of non-bankrupt firms. Second, compared with non-bankrupt control firms, bankrupt firms have worse ratios of firm profitability in terms of net income (NI_TA), retained earnings (RE_TA), and sales (SALE_TA). In sum, our results indicate that bankrupt firms have liquidity and financial performance problems. Our findings are consistent with the literature documented in previous studies (Ni, Kwak, Cheng, & Gong, 2014). Table 1 Descriptive Statistics of Chinese Bankrupt Firms Between 1999 and 2007 Variable No. Mean Std. dev. Min. Max. t-value Total capital , Size TL_TA *** WCA_TA *** CL_CA *** OENEG *** NI_TA *** INTWO ** RE_TA *** BV_TD SALE_TA ** CHGIN *** TP_TL * Notes. * : p < 0.10; ** : p < 0.05; and *** : p < Size = Total assets/gross domestic products; TL_TA = Total liabilities/total assets; WCA_TA = Working capital divided by total assets; CL_CA = Total current liabilities/total current assets; OENEG = If TL_TA > 1, then OENEG = 1, otherwise OENEG = 0; NI_TA = Net income/total assets; INTWO = If net income < 0 or lag (net income) < 0, then INTWO = 1, otherwise INTWO = 0; RE_TA = Retained earnings/total assets; BV_TD = Market value of equity/book value of total debt; SALE_TA = Sales/total assets; CHGIN = (Net income lag (net income))/[absolute (net income) + absolute (lag net income)]; and TP_TL = Earnings before interest and taxes/total liabilities.

5 1034 BANKRUPTCY PREDICTION FOR CHINESE FIRMS Table 2 Descriptive Statistics of Chinese Non-bankrupt Firms Between 1999 and 2007 Variable No. Mean Std. dev. Min. Max. Total capital , Size TL_TA WCA_TA CL_CA OENEG NI_TA INTWO RE_TA BV_TD SALE_TA CHGIN TP_TL Tables 3-8 present the results of the logit model with defined financial variables (from 1999 to 2007). Table 3 reports the results using Ohlson s eight variables. As shown in Table 3, the coefficients on variables CL_CA, OENEG, and CHGIN are significant and the overall prediction rate is 66.69% (see Table 4). This rate is lower than that of most US studies. Table 5 shows the results using Altman s five variables and variables RE_TA and SALE_TA are significant. We find that, as expected, the overall prediction rate is 65.35% in this model (see Table 6). Table 7 presents the results with the combined Ohlson and Altman variables and all of the abovementioned variables are significant except for RE_TA. This variable may be correlated with variable SALE_TA. The overall prediction rate is 67.23% in this combined model (see Table 8). Table 3 Logit Regression Using Ohlson s Eight Variables Analysis of maximum likelihood estimates Parameter Estimate Std. dev. Wald Chi-square Pr. > Chi-square Intercept WCA_TA Size TL_TA CL_CA NI_TA INTWO OENEG CHGIN Max-rescaled R-square No. of observations 1,120 Table 4 Overall Prediction Rate Using Ohlson s Eight Variables Correct Incorrect Total Bankruptcy Non-bankruptcy Overall prediction rate

6 BANKRUPTCY PREDICTION FOR CHINESE FIRMS 1035 Table 5 Logit Regression Using Altman s Five Variables Parameter Estimate Std. dev. Wald Chi-square Pr. > Chi-square Intercept < WCA_TA RE_TA TP_TL BV_TD SALE_TA Max-rescaled R-square No. of observations 1,120 Table 6 Overall Prediction Rate Using Altman s Five Variables Correct Incorrect Total Bankruptcy Non-bankruptcy Overall prediction rate Table 7 Logit Regression Using Combined Altman and Ohlson Variables Parameter Estimate Std. dev. Wald Chi-square Pr. > Chi-square Intercept WCA_TA RE_TA TP_TL BV_TD SALE_TA Size TL_TA CL_CA NI_TA INTWO OENEG CHGIN Max-rescaled R-square No. of observations 1,120 Table 8 Overall Prediction Rate Using Combined Altman and Ohlson Variables Correct Incorrect Total Bankruptcy Non-bankruptcy Overall prediction rate Table 9 shows prediction rates of data mining models. Decision tree, Naive Bayes, simple logistic, neural networks, and nearest neighbor show overall prediction rates of 65.18%, 63.66%, 68.04%, 65.89%, and 62.95%, respectively. Nearest neighbor has the worst prediction rate. As we can see from these results, bankruptcy

7 1036 BANKRUPTCY PREDICTION FOR CHINESE FIRMS prediction models of Chinese firms using data mining are similar to those of traditional logit models. Overall prediction rates are lower than those of other studies because of China s macro-economic factors. From our empirical results, the bankruptcy prediction models for Chinese firms are as effective as our traditional logit analysis data mining models. Usually, from data mining models outputs, we cannot tell which factor contributed to the prediction rates due to the black box process. However, the logit analysis can show which factors are contributing to improving the prediction rates and it is easy for managers or other decision makers to focus on their companies financial factors. The same will be true for the survival models if longitudinal data are available. Table 9 Prediction Rates for Data Mining Models Method Accuracy Confusion matrix Decision tree 65.18% 600, 126 a = Non-bankrupt 264, 130 b = Bankrupt Naive Bayes 63.66% 622, 104 a = Non-bankrupt 303, 91 b = Bankrupt Simple logistic 68.04% 676, 50 a = Non-bankrupt 308, 86 b = Bankrupt Neural networks 65.89% 569, 157 a = Non-bankrupt 225, 169 b = Bankrupt Nearest neighbor 62.95% 556, 170 a = Non-bankrupt 245, 149 b = Bankrupt Notes. All the variables are normalized with min-max normalization: x' = (x-min)/(max-min). So all the values are between [0,1]. Attached are the normalized data: There are 726 non-bankruptcy and 394 bankruptcy cases in the training set. The 10-fold cross-validation was used for evaluation. Summary and Conclusions In this paper, we adopt the logit and data mining models to predict bankruptcy in China. Using Chinese bankruptcy data, our results suggest that Chinese firms with current liquidity problems and firms experiencing a decline in profits are more likely to file for bankruptcy. Overall prediction rates are lower than those of previous US, Japanese, or Korean bankruptcy studies as shown in studies of Kwak, Shi, Eldridge, and Kou (2006) and Kwak, Shi, and Kou (2012). However, the financial variables developed by Altman (1968) and Ohlson (1980) perform reasonably well in determining business failures of Chinese firms. In addition, the traditional logit model is as good as other data mining models in the context of the Chinese business environment. China s capital market has become more dynamic and diverse. We may need to incorporate productivity enhancement and effective strategy implementation for Chinese firms to improve bankruptcy prediction rates (Bryan, Fernando, & Tripathy, 2013). In addition, we may use time-series data as suggested by Shumway (2001) and macroeconomic factors to improve our bankruptcy model (Nam, Kim, Park, & Lee, 2008). Limitations of our study include the use of a subset of data from 1999 to Observations in years 2001 and 2004 were deleted to ensure a clean sample for testing. Our results may be caused by a selection bias. As a result, our results should be interpreted with appropriate caution. Future studies may use recent data ( ) to investigate bankruptcy in China.

8 BANKRUPTCY PREDICTION FOR CHINESE FIRMS 1037 References Alali, F., & Romero, S. (2013). Characteristics of failed US commercial banks: An exploratory study. Accounting and Finance, 53(4), Altman, E., Haldeman, R. G., & Narayanan, P. (1977). ZETA analysis: A new model to identify bankruptcy risk of corporations. Journal of Banking and Finance, 1(1), Altman, E. (1968). Financial ratios, discriminant analysis, and the prediction of corporate bankruptcy. The Journal of Finance, 23(3), Begley, J., Ming, J., & Watts, S. (1996). Bankruptcy classification errors in the 1980s: An empirical analysis of Altman s and Ohlson s models. Review of Accounting Studies, 1(4), Bryan, D., Fernando, G. D., & Tripathy, A. (2013). Bankruptcy risk, productivity, and firm strategy. Review of Accounting and Finance, 12(4), Chan, C., & Lewis, B. (2002). A basic primer on data mining. Information Systems Management, 19(4), Chen, H., & Hu, Y. (2011). Single-layer perception with non-additive preference indices and its application to bankruptcy prediction. International Journal of Uncertainty, Fuzziness, and Knowledge-Based Systems, 19(5), Fan, J., Huang, J., & Zhu, N. (2008). Distress without bankruptcy: An emerging market perspective. Working Paper 6/2005, Hong Kong Institute of Monetary Research. Grice, J. S., & Ingram, R. W. (2001). Tests of generalizability of Altman s bankruptcy prediction model. Journal of Business Research, 54(1), Huang, J., & Wang, H. (2009). Government protection and corporation risk management in China. Chinese Economy, 42(2), Kwak, W., Shi, Y., & Cheh, J. (2006). Firm bankruptcy prediction using multiple criteria linear programming data mining approach. Advances in Investment Analysis and Portfolio Management, 2, Kwak, W., Shi, Y., & Kou, G. (2012). Bankruptcy prediction for Korean firms after the 1997 financial crisis: Using a multiple criteria linear programming data mining approach. Review of Quantitative Finance and Accounting, 38(4), Kwak, W., Shi, Y., Eldridge, S. W., & Kou, G. (2006). Bankruptcy prediction for Japanese firms: Using multiple criteria linear programming data mining approach. International Journal of Business Intelligence and Data Mining, 1(4), Mansi, S. A., Maxwell, W. F., & Zhang, A. J. (2012). Bankruptcy prediction models and the cost of debt. Journal of Fixed Income, 21(4), McKee, T. E. (2000). Developing a bankruptcy prediction model via rough sets theory. Intelligent Systems in Accounting, Finance, and Management, 9(3), Nam, C., Kim, T., Park, N., & Lee, H. (2008). Bankruptcy prediction using a discrete-time duration model incorporating temporal and macroeconomic dependencies. Journal of Forecasting, 27(6), Nanda, S., & Pendharkar, P. (2001). Linear models for minimizing misclassification costs in bankruptcy prediction. Intelligent Systems in Accounting, Finance, and Management, 10(3), Ni, J., Kwak, W., Cheng, X., & Gong, G. (2014). The determinants of bankruptcy for Chinese firms. Review of Pacific Basin Financial Markets and Policies, 17(2), Ohlson, J. (1980). Financial ratios and the probabilistic prediction of bankruptcy. Journal of Accounting Research, 18(1), Park, C. S., & Han, I. (2002). A case-based reasoning with the feature weights derived by analytic hierarchy process for bankruptcy prediction. Expert Systems With Applications, 23(3), Peat, M., & Jones, S. (2012). Using neural nets to combine information sets in corporate bankruptcy prediction. Intelligent Systems in Accounting, Finance, and Management, 19(2), Pompe, P. P. M., & Feelders, J. (1997). Using machine learning, neural networks, and statistics to predict corporate bankruptcy. Microcomputers in Civil Engineering, 12(4), Shin, K. S., & Lee, Y. J. (2002). A genetic algorithm application in bankruptcy prediction modeling. Expert Systems With Applications, 23(3), Shumway, T. (2001). Forecasting bankruptcy more accurately: A simple hazard model. Journal of Business, 74(1), Song, X., Ding, Y., Huang, J., & Ge, Y. (2010). Feature selection for support vector machine in financial crisis prediction: A case study in China. Expert Systems, 27(4), Sung, T. K., Chang, N., & Lee, G. (1999). Dynamics of modeling in data mining: Interpretive approach to bankruptcy prediction. Journal of Management Information Systems, 16(1), Wai-Ming, F., & Lam, K. C. K. (2004). Privatization and performance: The experience of firms in China. Chinese Economy, 37(4), 5-27.