Corporate Financial Distress Diagnosis in China

Ling Zhang, College of Business Administration Hunan University (South Campus) Changsha Hunan, P.R.China, 410082 E-mail: jenyling@163.com or emba@hnu.cn Shou Chen, Vice President Hunan University (South Campus) Changsha Hunan, P.R.China, 410082 E-mail: chenshou@hnu.cn

Jerome Yen, Professor, Cathay Financial Holdings, Taipei, Taiwan, and Dept. of Finance, Hong Kong University of Science and Technology Clearwater Bay, Kowloon, Hong Kong SAR E-mail: jyen@ust.hk Edward I.Altman, Max L.Heine Professor of Finance Salomon center, New York University 44 West Fourth Street, Suit 9-160 New York, N.Y. 10012-1126 E-mail: ealtman@stern.nyu.edu

The authors would like to acknowledge the useful comments of Professor Bilderbeek Jan from Twenty University and also the financial assistance of Natural Science Foundation of China (NSFC). Data assistance from Xiongwei Wu, Xin Zhang, Yanchun Cao and Shenzhen GuoTai’An Information Technology (GTA) is also acknowledged.

Corporate Financial Distress Diagnosis in China*

Abstract With the enforcement of the removal system for “distress firms” in China’s securities market in 2001, the development of the bankruptcy process for firms in China did create huge impacts to the community. Therefore, identification of potential business failures and offering early warnings for the impending financial crises became very important to analysts, practitioners and regulators. There are very distinct differences in the nature of the firms, the accounting procedures, the quality or trustworthiness of financial documents, and corporate governance between the firms in China and in the western world. Therefore, it may not be practical to directly apply the models or methodologies that developed in the western world to support the identification of such potential distress. In our research, we developed a model called ZChinaScore to support the identification of potential distress firms. We applied the model to China’s securities market for distress diagnosis. The study achieved a 98.8 percent accuracy in classifying distress firms for the original samples and 94 percent accuracy for holdout samples. The early validation test showed that the discriminant-ratio model constructed provided an early warning capacity up to four years prior to financial distress.

Key word financial distress, discriminant analysis China’s stock market

1. Introduction

Over the past twenty years, China has achieved great success in the economic development that the annual GDP growth maintained above an astonishing average of eight percent. Also, China has become the No. two country in attracting overseas investment and also the No. two in foreign currency reserve in 2004. However, such wonderful achievement was not reflected in the performance of the securities market that Shanghai and Shenzhen A-shares plunged over 40 percent since 2001. Many researchers believed that the sentiment of the market was so pessimistic was caused by the inadequate market transparency, poor market regulation from government, as well as lack of sound and reliable models to support the assessment of firms’ financial situation and identification of potential distress. The security market in China is quite different from those in the western world due to historical background. For example, many listed firms are originally the state-owned enterprises (SOEs) and also a certain percentage of shares in such companies are not tradable. Therefore, the actual financial situation of these firms might not totally be reflected in their stock prices. To avoid invest on such potential distress firms is very important to the investors. Also, the investment strategies of majority of investors in China are more favor of long-term holding, which might * This paper is supported by Natural Science Foundation of China (NSFC)-Project (70172018).

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cover several years. Therefore, to have a sound and reliable model to predict the firms’ survivability with longer horizon, say few years, has become extremely important. Also, with the Basel II new Economy Accord introduced in 2001, most of the active banks in the western countries will use new rules in Capital Adequacy Requirement (CAR) to estimate the needed capital. If they opted to use Internal Ratings Based (IRB) approaches, either foundation or advanced, they have to prove that they have the capability to develop sound and robust credit rating and default prediction models. Based on the historical data captured, such models can be used to support the estimation of probability of default (PD), which is the most important in estimating the potential losses in credit risk. Although China government indicated that they will not adopt Pillar One - minimum capital requirement, they indicated that they will follow Pillar Two and Pillar Three to improve the internal management and public disclosure of banks after end of 2006. However, in order to stimulate banks to improve their capability in measurement and management of credit risks, China government still encouraged banks to develop their own default (distress) prediction models. Over the past ten years, many researchers from the western countries and China tried to develop models to evaluate the performance or predict distress of Chinese firms. Some of such studies are summarized in Section 2 – Literature Review. However, we found some weaknesses in those studies. The first weakness is the lack of a solid theoretical basis or a deep understanding of the China accounting procedures and what factors that really affected the firm performance in their selection of variables for model development. Most researchers selected variables based on the prior studies that conducted in the western countries. However, the situations of both places were quite different, especially the accounting rules, quality of data, due diligence, equity structure, and the factors that affected the performance of firms. Some researchers applied directly those ratios that familiar by western scholars into the model construction for Chinese companies. The results were quite obvious that such models might not truly reflect the actual situation of the Chinese firms. The second weakness is the lack of cash flow variables in the initial process of ratio selection. Many studies in western countries[5] indicated that cash flow information were very important and useful in evaluating the financial healthiness by parameters, such as, liquidity, financial flexibility, profitability, and risk of entity. Such information is very important in supporting the classification of failed and non-failed firms. Before 1998, the listing firms in China were not required to disclose cash flow information on their financial statements. Which restricted the availability of data [5] Largay and Stickney,1980; Gambola and Ketz,1983; Casey and Bartczak,1985; Gentry, Newbold, and Whitford,1984,1985a&b,1987; Banson,1987,Lau and Lau,1989; Gilbert, Menon, and Schwartz,1990,etc.

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in supporting the use of such western models in prior studies and researchers were forced to develop models that lacked of such data. Therefore, models that were developed prior to that are no longer valid or useful today. The third weakness of most of the prior studies is the insufficiency of distress firms to support independent holdout tests or follow-up predictive studies. This has been a major challenge to the researchers who wanted to study distress or bankruptcy prediction of Chinese firms. Unlike Moody’s or Standard & Pools, database of each company contains information of over tens of thousands healthy and unhealthy firms over the past 30 years. However, data about Chinese firms is limited, especially the distress firms. Based on the above discussion and to overcome the weaknesses, we set the objectives of our study to be: (1) Improve the weakness that the parameters used in the earlier studies might not

truly reflect the special condition of China’s accounting procedure and corporate governance. In our study, we considered a broader coverage of financial ratios (see Table 1 in Section 3) and five cash flow variables were first introduced in our model. They include: net cash from operation activities (defined as the net worth of operating cash inflow and operating cash outflow) to total outstanding number of shares (X6), working capital to total assets (X10), working capital to main business revenue (X11), net cash from operation activities to current liabilities (X12), and net cash from operation activities to financial expenses (X13).

(2) We also considered the unique structure of equity components[6] of Chinese share-holding companies, we calculated two ratios for market capitalization: market value of total shares (including trading and non-trading shares) to total liabilities (X30) and market value of trading shares to total liabilities (X31) in order to capture the impact of the difference.

(3) Answer the question such as “Can financial distresses be predicted in China based on the published financial information as emerging market under current accounting system?” Financial analysts and other users of financial statements have long recognized the intricacy that financial statements be masked and doubted the reliability of the financial information. Events of financial crises such as Enron, Merck, Xerox, and Worldcom occurred recently in U.S. increased the concerns on the creditworthiness of the disclosed financial data. Under this background, many practitioners, academicians and even market agencies tended to express a negative attitude toward the trustworthiness of accounting information in China. However, for the listed firms, because they were under close monitoring of regulators, the trustworthiness of their financial statements

[6] Four categories on the basis of investing entities and ownership: state shares (non-tradable), legal person shares (non-tradable), internal/employee holdings and public/individual shares. About more than half percent shares are not tradable in Chinese Stock Exchanges.

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was higher and we will search for the answer for the stated question. (4) To test the robustness of discriminant-ratio model when using the Chinese data.

Discriminant-ratio models have been used extensively in the western world, which was simple and easy to apply, so that even forty years passed since the pioneering work done by Altman (1968), it is still the most powerful model. We would like to test the performance of such model in predicting the performance or distress of Chinese firms.

(5) To provide some insightful views about the financial risks in China’s stock market. We would like to see if our research work could provide stakeholders a useful analytical tool to classify and predict financial distress and to assess performance of firms. Based on the new parameters been introduced, we would like to see if any new insights about how to distinguish healthy firms from non-healthy firms that were never reported in prior studies.

(6) To see if such model could be used by government, such as, China's Securities Regulatory Commission (CSRC), as a thermometer in measuring the financial health condition of each sector. So that proper action can be taken to keep economics under control.

The rest of the paper will be organized as following. Section two provides literature review about the distress prediction research in the western world and in China. Section three provides a discussion about the development of the MDA model. Section 4 discusses a two-stage validation test. In this section, we will provide a discussion about the follow-up study about the prediction on large population Section five summarizes the conclusions and provides some discussion for future research.

2．Literature Review

In this section, we will first discuss the previous research in the western countries that discriminated failing firms from surviving firms, then in the second subsection for the similar research in China. 2.1 Research in the Western Countries The development of empirical models that successfully discriminated between failing firms and the surviving firms started in the mid 1960s. The pioneer research included Beaver (1966) and Edward I. Altman's (1968) work in business failure classification by the use of the univariate and multivariate discriminant analysis (MDA). The essence of this technique is an issue of classification under several assumptions. It assigns a score (the Z score) to each of the observed firms in a sample. The Z score is a linear combination of several independent variables. Based on the sample results, a cutoff score is established that divides the firms into two different groups based on the score. The general MDA form is expressed as:

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∑=

+=k

jjtj XZ

1βα (1)

Where, Z is the score used to classify the object, α is the constant term, βj are the discriminant coefficients or weights, Xjt are discriminant variables. Since then, extensive research has been conducted to evaluate the usefulness of the financial ratios that constructed from the financial statements in discriminating and predicting financial distress firms. Over the past two decades, more sophisticated methodologies have been developed to support corporate failure assessment, which included gambler’s ruin approach and option pricing model (OPM), which was built with a strong theoretical foundation about “How a firm gets bankrupted when the market (liquidation) value of its assets falls below its debt obligations". Such model was developed and modified by Wilcox (1972), Merton (1974), and Scott (1981). The commercial usages of such model included the famous KMV model (1995). Second group of models include those that seek to impute implied probabilities of default (PD) from the term structure of yield spreads between default free and risky corporate securities. Third class of models are the capital market based models, which include the mortality rate model of Altman (1988, 1989) and the aging approach of Asquith, Mullins and Wolff (1989). The latest approaches include the application of neural network to support the risk classification. Despite of the variety of approaches used in failure prediction, statistical methods, like discriminant analysis, still dominate due to their simplicity (Matthias Kerling), and remain the main stream of the descriptive school of thought[1]. In general, these models have been featured with high classification accuracy, cost and time saving, as well as convenience in application in solving real world problem. Any way, the methodologies mentioned above have been replicated and improved to support failure prediction around the globe. Financial distress has been examined by many researchers. Guthmann and Dougall (1952) defined three stages of financial difficulty: temporary or technical insolvency, debt burden unsupportable and reorganization, as well as dissolvency. Dewing (1952) identified four causes of business failures: excessive competition, unprofitable expansion, cessation of public demand for the firm’s products or services, and excessive payment of capital charges. Beaver (1966) defined failure as bankruptcy, bond default - an overdrawn of bank account, or nonpayment of a dividend on preferred stock. Altman (1968) defined failure as a company that had filed a bankruptcy petition under Chapter X of the Bankruptcy Act of 1898. Donaldson’s (1969) concept of “financial flexibility”. Newton (1975) perceived that firms in financial distress passed through four stages of deterioration before declaring [1] Altman (1968); Altman, Haldeman and Narayanan (1977); Blum (1974); Dearkin (1972); Edminster (1972); Elam (1975); Moyer (1977); Norton and Smith (1979); Casey and Bartczak (1985); Gentry, Newbold, and Whitford(1985)

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bankruptcy: incubation, cash shortage, financial insolvency and total insolvency. More modern concepts about distress include the following: Lau (1987) defined financial distress as a three stage process: incubation, deficit funds-flow, and financial distress or recovery. Gilbert, Menon, and Schwartz (1990) defined distress firms as firms that declared bankruptcy and firms that had negative cumulative earnings over three consecutive years. Stephen A. Ross (1996) stated that “Financial distress is a situation where a firm’s operating cash flows are not sufficient to satisfy current obligations and the firm is forced to take corrective action”. It is difficult to define precisely what distress or bankruptcy is due to the variety of accounting procedures or rules in different countries or at different time spots as well as various events that sent firms into financial distress. However, for most studies been conducted in US, they used the criterion that firms filed for protection under either Chapter X of the Bankruptcy Act of 1898 or Chapter XI of the 1978 Bankruptcy code. Also, most researchers used the contents of the financial statements and based on the prediction model to discriminate the failed firms from those financially healthy firms. Based on the above discussion, financial distress studies in western countries are equivalent to the bankruptcy prediction. 2.2 Research in China Research on financial distress and bankruptcy prediction is still at the beginning stage in China. However, for special reasons, such as, the poor performance of the China equity market, such research has attracted great attention from both the practitioners and the government agencies. Before going to the details concerning China’s special case, a discussion about the criterion that defines firms as being in a state of financial distress is required. In China, Bankruptcy Law came into effect on Nov.11, 1988. Over the past decade, many firms, especially those non-listed firms, declared bankruptcy or liquidated under such Law. But due to the fact the process might go at different levels of courts, for example, at the county, at the municipal, or at the provincial level, it is virtually impossible to retrieve those companies’ financial records. Therefore, researchers in China were difficult to obtain sufficient bankrupt cases to support analysis. Also, quality of data was also a big question to those non-listed firms that due diligence has always been a major issue that challenged the accountants and auditors. Therefore, academicians are unable to obtain enough bankrupt firms for analysis as most of the studies in such field by western scholars. As for the listed firms, bankruptcy process so far has not been applied due to many reasons at the transition period of China’s economy. Among the reasons, the major one is to strike a balance between the level that the society can afford to let companies go bankrupt and the degree in which it can develop a social security system to avoid huge social problem. The reason behind it was that most of such

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firms are the backbone in their industry sector in China. The top priority of such firms may not be profit making, but just keep employees employed. Yet, in some industries the situation just gets worse and worse. For example, many traded firms exhibit a deteriorating trend in financial situation that it was difficult to pull themselves out from such trap. To protect the interests of investors, as a message that investors should be aware of default risk, China's Securities Regulatory Commission (CSRC) decided in March, 1998 to differentiate those firms in financial difficulties by launching new policy to offer “special treatment”[2] (hereinafter called “ST” firms) to such firms. These “ST” firms include: (1) Companies that had negative cumulative earnings over two consecutive years or

net asset value (NAV) per share is below its par value (book value). (2) Companies that had a negative earning for one year, but the current year

shareholders’ equities are below its registered capital.[3] (3) Companies that received the auditors’ “going concern opinions”.

The “ST” firms are pressed to improve their financial situation by efforts such as reorganization, mergers, etc. Those exhibit no sign of financial improvement in the following year will receive “particular transfer” warning (hereinafter called “PT” firms) given by CSRC. If the “PT” firms are still unable to revitalize in the following year, they will be deleted from stock exchanges – the action of delisting. In this regard, authors in this paper define these “ST” and “PT” companies as financial distress firms. The states of Chinese “ST” and “PT” firms are very close as perceived by Newton (1975) and by Lau (1982). As defined by Gilbert, Menon and Schwartz (1990), that these “ST” or “PT” firms in general will go through “three stages” or “four stages”: omit or reduce the stage of annual dividend payments due to cash shortage, default on loan payment leading to law suit, reorganization or taken over, delete from stock exchange and transfer to Asset Management Companies[4] for disposal. Several Chinese researchers studied the corporate financial distress classification problem. Qing Chen (1999) was the first to use the financial data of 27 “ST” and non-ST firms to construct a MDA model with an overall predictive accuracy of 92.6 percent. But regret to say, no holdout samples were used for the test purpose. Xiao Chen and Zhihong Chen (2000) established a logit model with an overall predictive

[2] Special treatment is a daily price limitation of 5% applied to those firms in financial difficulties. [3] Registered capital equals to the share capital initially committed in accordance with the Article of Association. [4] In 1999, Chinese government set up four asset management corp. to purchase and deal with those non-performance loans or bad debts transferred by state banks. The four corporations include: China Greatwall Asset Management Corp., China Orient Asset Management Corp., China Huarong Asset Management Corp. and China Cinda Asset Management Corp.

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accuracy of 86.5 percent. Ling Zhang (2000) developed a 4-variable discriminant model out of 11 ratios and found it had a predictive ability of up to four years prior to “ST”. Shilong Wu and Xianyi Lu (2001) conducted a study by using of LPM, Logistic and MDA, the results indicated a predictive accuracy of 72 percent four years prior to “ST”. The work above formed a base to support further studies in such field in China.

3. Development of the MDA Model In this section, we will discuss our construction of the multiple discriminant analysis (MDA) model by using 32 financial ratios of 164 listed companies in China. The validation process will be discussed in Section 4. 3.1 Sample selection and variables definition As mentioned earlier, two of the objectives of our study are to include more parameters or ratios that reflect the special condition of Chinese firms, particularly those related to cash flow, and also to include ratios that uniquely tell the structure of equity components of Chinese firms. In this study, five cash flow variables were first included in the model development: net cash from operation activities (defined as the net worth of operating cash inflow and operating cash outflow) to total outstanding number of shares, working capital to total assets, working capital to main business revenue, net cash from operation activities to current liabilities, and net cash from operation activities to financial expenses. In addition, we consider the unique structure of equity components of Chinese share-holding companies that some shares are not tradable. We calculate two ratios for market capitalization: market value of total shares (including trading and non-trading shares) to total liabilities and market value of trading shares to total liabilities in order to capture the impact of the difference. The samples consist of 164 traded companies that covering 10 industries: 74 manufacturers, 39 comprehensive firms, 15 wholesale & retail firms, 12 in IT & telecommunications, 10 in trade & services (excluding banking and real estate), 5 in energy & water supply, 5 in transport & warehousing, 3 in architecture and one from median. The samples are divided into original (or estimation) samples and test samples. The original samples consist of 86 companies with 43 "ST" or “PT” companies defined as financial distress companies in this paper and 43 non-distress companies (refer to Appendix 1). The test sample consists of 78 companies with 40 distress companies and 38 randomly selected non-distress companies (refer to Appendix 2). Three financial statements[7], i.e. balance sheet, income statement and cash flow statement were collected and processed. [7] All the financial data are derived from China Stock Market Accounting Research(CSMAR) database prepared by Shenzhen GuoTaiAn Information Technology (GTA)

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Data for distress companies were derived from statements one year before the "ST" announcement, similarly, those of non-distress companies were obtained from the same year. As to the selection of variables, we take into consideration those ratios that widely accepted in China[8] as well as those contributive in previous studies. Therefore, totally, 32 financial ratios were selected and calculated from the aspects of profitability, liquidity, asset management efficiency, sustainable growth and leverage[9], see Table 1.

Table 1. Initial Financial Ratio Set for Model Development Financial dimension Ratios Expected Sign

Ratios X1 (ROA)=Net profit/total assets + X2（ROI）=EBIT/total assets + X3 (ROE)=Net profit/average shareholders’ equity + X4 =net profit/main business revenue + X5 =(Main business revenue-main business cost)/main business cost +

Profitability

X6 = Net cash from operating activities/total outstanding number of shares（A-shares，excluding B-shares and H-shares）(New) +

X7 (EPS)=Net profit/ total outstanding number of shares + X8 (CR)=Current asset/current liabilities + X9 (acid ratio)=quick assets/current liabilities + X10 =Working capital/total assets (New) + X11 =Working capital/main business revenue (New) + X12 = Net cash from operating activities /current liabilities (New) +

Liquidity

X13= Net cash from operating activities /financial expenses (New) + X14 (Interest cover) =EBIT/financial expenses + X15 (Asset turnover)=Main business revenue/ average total assets + X16 =Main business cost/average inventory + X17 =Main business revenue/average accounts receivable +

Asset

management efficiency

X18 (Fixed asset turnover)= Main business revenue /fixed asset + X19 = Financial expenses /average liabilities - X20 =Net profit growth + X21 =LOG fixed asset + X22 =Total asset growth + X23 =Main business revenue growth + X24 =Retained earnings /net profit +

Sustainable growth

X25 =Retained earnings /total asset + X26 =Net asset / total number of shares + X27 =Total liabilities /total asset - X28= Shareholders’ equity /total liabilities + X29 =Total liabilities / Total share capital book value - X30 = Market value of total shares* /total liabilities (New) +

Leverage

X31 =Market value of trading shares /total liabilities (New) + X32 = Book value of total share capital /Market value of total shares -

*Total shares include tradable shares and non-tradable shares (state-shares, legal representative shares)

3.2 Model construction To support discriminant analysis, we code the distress companies as group 1, the healthy companies as group 0. In order to reduce the number of input variables as well as to avoid the problem of multicollinearty, a correlation matrix was calculated

[8] Refer to Performance Assessment Rules on State Capital jointly promulgated by Ministry of Finance, National Commission of Economics and Trade, Ministry of Personnel and National Development Planning Commission and the Financial Indicator System for Corporate Performance Assessment [9] Accounting system after 1995 has adapted to International Accounting system.

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for these 32 ratios (refer to Appendix 3). We arbitrarily selected a critical value of the correlation coefficients to be 0.6 to eliminate the variables from inclusion. We first eliminated the ratios: X2 , X5 , and X7 . Then, the remaining 29 ratios were used as the inputs for model development. Forward stepwise procedures were used with the discriminant analysis to further reduce the number of interaction variables and identify the most significant indicators that indicate financial distress. The evolution of the model was done by the Discriminant Analysis tool in SPSS software (version 10.0). After 7 stepwise trials, a 7-ratio model was detected as the best fit. The results are presented in Table 2.

Table 2 Forward Stepwise Statistics (Relative contribution of variables in the distress model)

Step entered Wilks’ Lambda df1 df2 df3 F值 df1 df2 Sig. 1 X1 .349 1 1 84.000 156.602 1 84.000 .000 2 X32 .273 2 1 84.000 110.281 2 83.000 .000 3 X21 .220 3 1 84.000 95.546 3 82.000 .000 4 X23 .206 4 1 84.000 77.860 4 81.000 .000 5 X24 .190 5 1 84.000 68.237 5 80.000 .000 6 X6 .181 6 1 84.000 59.626 6 79.000 .000 7 X31 .171 7 1 84.000 54.016 7 78.000 .000

The canonical discriminant function can be expressed as follows:

ZChinaScore = −8. 751+6.3X1 +0.761X6 +1.295X 21+0.412 X23+0.015X24+0.105X31 −21.164X32 (2)

ZChinaScore –Overall score (Chinese model)

X1-Return on total asset (ROA) is one of the major indicators to measure firms’ profitability, which is the number 1 target to most of the firms. However, firms often face a trade-off between the sales-to-asset ratio (turnover ratio) and the profit margin if ROA is fixed by competition. That’s why we often found labor-intensive firms with quick turnover also tended to operate on low profit margins; while capital-intensive firms have relatively high margins, but this is offset by lower turnover ratios. Therefore, competition is also explicitly considered. Of the seven ratios, ROA contributes most to the overall discriminating ability of the model. Inclusion of this variable is consistent with several past MDA studies (Beaver, 1967; Altman, 1968 and 1977).

X6-Net cash from operating activities to total number of shares outstanding (New) measures the cash generating capacity against unit equity capital invested. Which is one of the five parameters to measure cash flow that first introduced on the study of Chinese firms. Cash flow from operations (OCF) is the most important cash source in a firm. It reflects the liquidity, financial flexibility, profitability and risk of an entity. It’s considered to be one of the best yardstick to test financial distress (Hong Chi Lin, 1999).

X21 –LOG fixed asset measures the size effect of a business. A logarithmic

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transformation was applied to keep the figure at the same number scale. This size indicator ranks third in its contribution.

X23-Main business revenue growth measures the profitability and sustainable growth obtainable on their “cash cow” business and core competences. At present, many listing firms seek blindly the diversified business strategy that doesn’t bring about much promising effect. Instead, diversification put many firms in blind investment. Inclusion of this variable captures the information of the reality of Chinese firms.

X24-Retained earnings to net income is a measure of the sustainable growth and reinvest ability of a firm.

X31-Market value of outstanding trading shares to total liabilities (new). The difference between ratio X30 and X31 lies in the numerator. That for ratio X30 uses the market value of total shares which includes the market capitalization of nontradable shares that were calculated at the market price (China Securities, 2000). Obviously, it is overvalued for China’s case since about 60 percent shares are non-tradable shares. Entry of ratio X31 to the model proves to be more effective predicator than X30. This reflects Chinese characteristics in the stock market.

X32-Book value of total shares to market value of total shares. This is the reversion of the market-to-book ratio and measures the valuation generating ability of firms. If the ratio is less than one, indicating the market values of the firms are well in excess of their book values. This variable is also found to be significant in several previous studies (e.g., Altman, 1968). In this study, it ranks second in its contribution to the distress model.

With respect to the ratio variables constructed in the model, four factors (X1, X21, X31, X32) are the same with those found to be significant in several prior studies (Beaver, 1967; Altman, 1968 and 1977). The other three factors (X6, X23, X24) though with some minor modification, are also similar to those ratios found in prior studies. One point to be noticed is that ratios X23, X24, and X31 reflect the particular situation of Chinese firms. In addition, it is impressive to see that each sign of the ratio predicator is in agreement with its logic economic meaning. This indicates that published financial data in China, when properly interpreted and analyzed, does indeed possess significant and useful information for assessing firm’s performance and possibility of failure.

It is important to note that two new variables X6 (Net cash from operating activities to total number of shares outstanding) and X31 (Market value of outstanding trading shares to total liabilities) were the first to be included in such study and also they were among the seven factors that survived for building the seven-factor model. Combine together with the discussion above, it is obvious that there is still a quite significant difference between the set of factors that affected Chinese listed firms and those that affected the western firms.

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3.3 Statistical Test of the Function The goodness of fit of the discriminant function is to estimate the difference between the groups and within the group. The null hypothesis is that the means of the groups are equal, or the covariance matrices are the same. The statistic test results are summarized in Table 3 and Table 4. Which indicate statistically significant differences exist in these variables between groups and reject null hypothesis that

these two groups have equal mean. The null hypothesis that the covariance matrices were the same was supported at the significance level of 0.000. Table 3. Function Statistic Test Results (1)

Function Eigen value Wilks’ ambda Chi-square df Sig. 1 4.848 .171 142.165 7 .000

a. Fisrt 1 canonical discriminant function were used in the analysis

Table 4. Function Statistic Test Results (2) Test of Equal Covariance Matrices Functions at group Centroids

Box’s M F Approx. df1 df2 Sig.

256.791 8.355 28 24587.151 .000

Y 0 1

Function 1 2.176 -2.176

3.4 The Cutoff Point and Classification Accuracy The cutoff point (M) of the standardized form is determined by means of the Z-score

( 21, gg ZZ ) of the two groups that based on the symmetry rule, for example,

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02/176.2176.22/21 =+−=+= gg ZZM .

This means that firms with a score (Z score) above zero are classified as non-distress firms (Group 0) and with a probability of classification of failure less than 0.5. Otherwise, they are classified as distress firms (Group 1) with a probability less than 0.5 for potential failure. Table 5 presents the classification results for both original and holdout samples. From Table 5, we can see a 100-percentage classification accuracy for distress companies (Group I) and 97.7 percentage classification accuracy for non-distress companies (Group 0) at the zero cutoff point. The overall accuracy is up to 98.8 percent. Holdout sample test indicates 95 percent classification accuracy for distress companies (Group I) and 92 percent classification accuracy for non-distress companies (group 0). The overall accuracy is 94 percent. Figure 1 shows the actual ZChina -score distribution of both initial and holdout samples. We can see that the interval of classification error is relatively small (-0.5 to 0.71), which indicates that the discriminant power of the developed model is good.

Table 5. Classification Results Original samples Holdout samples

Predicated Group Membership Total Predicated Group

Membership Total Y 0 1 0 1

0 42 1 43 35 3 38 Original count 1 0 43 43 2 38 40

0 97.7 2.3 100.0 92.1 7.9 100.0 % 1 .0 100.0 100.0 5.0 95.0 100.0

a 98.8% of original grouped cases correctly classified.

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4. Validation of MDA Model After model been developed, we validated the model by historical data. The validation process consists of two stages. In this section, we first discuss the first stage that the model constructed was validated by examining the classification accuracy for both initial and holdout samples. Then, we test the early prediction capability of the model that covers a five-year period (1993-1997) prior to distress (“ST”). The second stage is a follow-up prediction test that based on a larger number of firms that drawn from 1998 to 2001 to further test the suspecting non-stationerity over time. 4.1 Early Classification Accuracy (1993-1997) General speaking, usually there was a certain period of time that a firm deteriorated financially till its eventual failure. Such period sometimes lasted for few months or, in some cases, it might last for several years. Therefore, earlier accurate predication would be valuable to the investors, particularly those shareholders who intended to hold for long time. To determine the predictive accuracy of the model that covers a longer period, say few years, prior to financial distress, we apply the equation (2) that constructed earlier to process all the samples (both initial and holdout samples) to make a prediction for a five-year period prior to the "ST" penalty. Table 6 summarizes the predictive accuracy and error for the five-year period.

Table 6 Predictive Results of the Model (Five-year prior to distress)

Year before distress (“ST”) Cutoff Type I

Error

Type II

Error

Overall

Error(%)

Overall

Accuracy(%)

1 (Group1 n=83, Group 0 n=81) 0 2 (2.4%) 4 (4.9%) 3.65 96.35

2 ( Group1 n= 83, Group0 n =81) 0 12 (14.8%) 14 (17.3%) 16.15 83.85

3 (Group1 n =77, Group0 n =81) 0 13 (16.8%) 22 (27.1%) 21.95 78.05

4 (Group1 n =66, Group0 n =81) 0 21 (31.8%) 21 (25.9%) 28.85 71.15

5 (Group1 n =50, Group0 n =80) 0 25 (50%) 31 (38.75%) 44.4 55.6

Results indicate that this model has an early warning capability up to four years prior to financial distress – the period that with overall accuracy higher than 70 percent. In terms of error, type I error (identify a failed firm as healthy company) is lower than type II error (the wrong prediction of a healthy company as failed) throughout the five-year period, which indicate an overall predicative accuracy of 96.35 percent, 83.85 percent, 78.05 percent, 71.15 percent and 55.6 percent respectively. As we mentioned earlier, the "ST" penalty was given by CSRC to those companies who suffered losses for two consecutive years. An effective prediction period of four years provides two years for observation before the first loss occurred. In other

17

words, the results suggest that the model provides an effective warning for a period of up to four years or it is an accurate forecaster of failure of up to two years before any sign of potential losses appeared. 4.2 Follow-up Prediction on Large Population Data set of 1998-2001 In this subsection, we will discuss a follow-up prediction test that was conducted with a large sample of firms that drawn from both Shanghai and Shenzhen stock markets from 1998 to 2001 to test for the existence of any non-stationarity. We applied the developed MDA model to a total population of 1001 listed companies that cover ten sectors. Based on such large sample size and time span, it is possible to test the stability of the model. Once the model proved to be stable, it can be applied to predict or identify the potential distress firms. Also such study should provide some insights about how to assess the financial situation of the traded companies in China’s equity market as well as providing some guidelines for market participants as we did at the end of Section 3.2. Previous subsection is a backward validation test. In this subsection, a follow-up prediction was conducted based on a large sample of 714 firms[11] that drawn from 1998 to 2001 to identify the potential failure firms as well as to study the suspecting non-stationarity over time. First, we calculated the ZChina-scores for all the 714 firms by using the data that drawn from financial statements of the 1998 fiscal year. Based on our model, we found 184 firms with high potential that will be distressed, that is, with negative ZChina-scores, which accounted for 25 percent of the total sample. According to the predictive ability of the seven-factor model that covered a period of up to four years, 1998 was set as the base year for our study and our analysis covered the data till 2001. We observed and tracked the change of the financial status of the 184 suspecting firms in the following three years. That is, how many firms out of the predicted potential distress firms would eventually become actual distressed firms (ST firms). Table 7 summarizes the results.

Table 7. Follow-up Prediction (1998-2001) Items

Year Predicted potential risk firms

ST Announcement (distress firms)

Percentage of 1998 predicated firms

Taken over/ reorganized firms

1998 (base year) 184 1999 35 19% NA 2000 45 24% 16 (8.7%) 2001 59 32% NA Cumulative 139 75%

From Table 7, we can see that during the following three years of observation since 1998, there were 139 firms, out of the predicted 184 firms, fell into the “ST” family. The cumulative accuracy reached 75 percent, which was very close to our model’s

[11] Trading firms(ex.banking, real estate) with data available in both Shangshai and Shenzhen stock exchanges.

17

prediction ability (78 percent, see Table 6). In addition, a number of these negative ZChina-score firms were subsequently taken over or forced to be reorganized or restructured. 4.3 Overall Diagnoses by Sectors in China’s Stock Market (2001) In order to have an overall diagnosis of the potential financial distress firms or studying the soundness of the traded companies in China’s stock market, we made a broad study across the two stock exchanges by applying the Equation (2) to 1001 traded companies that classified into 11 sectors[12] (exclude banking, finance and real estate). Before doing so, a slight adjustment was made to the zero cutoff point that assigned by the computer. By observing the ZChina-score distribution of both initial and holdout samples (see Table 5), we found that there were only two distress firms and four non-distress firms misclassified. The overlap zone is between –0.5 and 0.71 (See Figure 1), which can be defined as the “gray area” because of the existence of misclassifications. Based on this, we deduced the following rules of thumb:

(1) Firms with a ZChina-score less than -0.5 (Z<-0.5) are classified as distress companies. (or “ST” companies.).

(2) Firms with a ZChina-score between- 0.5 and 0.71 (-0.5< Z<0.71) are classified as non-distress companies (or non-ST companies). But need to keep close watch.

(3) Firms with a ZChina-score greater than 0.71 (Z>0.71) are classified as financially healthy companies.

Based on the above rules, ZChina-scores for the 1001 firms were calculated by using the data drawn from financial statements of 2000 and 2001 fiscal years. Table 8 gives a statistical summary of the companies by sectors.

Table 8. ZChina- Scores Distribution by Sectors in China's Stock Market (2001)

Z>0.71 (financially safe)

-0.5 ≤ Z ≤ 0.71 (need close watch)

Z<-0.5 (financial distressed)

Score Industry

No.of corps.

(%) No.of corps.

(%) No.of corps. (%)

Mean of Z score ( )*

1. Mining, n=11 firms 9 81.81 2 18.1 0 0 1.99 (1.12)

2. Energy & electric power, n=40 firms

34 87.18 3 7.69 2 6.13 1. 62 (1.33)

3.Transport & communi- cations, n=37 firms

26 70.27 11 29.73 0 0 1. 46 (1.02)

4. Information technology, N=53 firms

35 66.04 7 13.21 11 20.75 1.10 (1.58)

5. Manufactures, n=595 299 50.25 198 33.28 98 16.47 0.592(3.11) 6. Architecture, n=15 firms 6 40.00 4 26.67 5 33.33 0.55(1.41) 7. Agriculture, n=27 firms 11 40.74 14 51.85 2 7.41 0.48(0.76) 8. Media, n=10 firms 5 50.00 1 10.00 4 40.00 0.43(1.65) [12] China Stock Market Accounting Research (CSMAR) database by Shenzhen GuoTai Information Technology (GTA) is classified into 13 industry sectors.

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9. Services, n=39 firms 24 61.54 10 25.64 5 12.21 0.39(3.60) 10. Wholesale & Retail, N=96 firms

48 50.00 30 31.25 11 11.46 0.21(2.11)

11. Comprehensive, n=78 firms

32 41.03 24 30.77 22 28.21 -0.19 (2.62)

Total n=1001 firms 529 52.84 304 30.37 160 15.98

*Figure in the brackets indicates standard deviation

Table 8 provides many useful information that the financial health condition of the eleven sectors can be easily seen. If measured by industry average ZChina-score, only four (4) sectors out of the eleven (11) stand above the safe line, they are mining, energy and electric power, transportation and communications, as well as information technology. These sectors were capital-intensive and enjoyed a natural monopolistic position, which contributed much to their above-average performance. Sectors, such as, manufactures, architectures, agriculture, media, services, wholesale and retail were in the “gray area” that need to be closely watched. The comprehensive sector, with negative ZChina-score, was in financial difficulty. In terms of the percentage of healthy companies by sector, eight (8) sectors out of the eleven (11) had half or more than half healthy companies. Among which, energy & electric power took the highest percentage (87 percent), followed by mining sector (82 percent), transportation & communications (70 percent), information (66 percent), services (61 percent), wholesale & retail and median (50 percent). As to the percentage of financial distress firms by sectors, media was the highest percentage (40 percent), followed by architecture (33 percent), comprehensive industry (28 percent), and information (21 percent) sector. Taking the market as a whole, there were about 16 percent[13] (160 firms) were highly risky or with high possibility of distress (e.g., ZChina-score less than –0.5), about 30 percent (304 firms) were in “gray area” which have demonstrated distress symptoms and need close watching, and finally, 53 percent (529 firms) were financially sound companies. 5. Discussion and Future Research With the enforcement of the delisting system in China’s stock market that began in 2001, the progress toward the bankruptcy process for traded firms has aroused great attention among market participants. Concern with business failures and their impacts to the society is unquestionably sincere. Therefore, business failure identification and early warnings of impending financial crises will become more and more important to analysts, practitioners as well as regulators who are interested or have a stake in China’s capital market. The analyses and results obtained indicated that the discriminant-ratio model

[13] Experience in the U.S with Zeta model, is 12-15 % of the population are at-risk; H.Y.IZAN (1984) results shows approximately 10% were at-risk; About 9% of the population of Japanese firms had Z-score below the cutoff-score (Nikkei Business,1978)

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developed in this study was robust and useful in assessing the performance and prediction of potential distress firms. Based on the data from 1993 to 2001, our seven-factor model demonstrated an early warning capability of up to four years prior to financial distress. The model reached 100 percent accuracy in classifying distress firms (ST firms) of the original sample and 97.7 per cent for the non-distress firms, resulted in an overall accuracy of 98.8 percent. Holdout sample indicated a 95 percentage classification accuracy for distress firms and 92.1 percentage classification accuracy for non-distress firms, with an overall accuracy of 94 percent. The early validation test showed an overall predictive accuracy of 96 percent (1-year prior), 84 percent (2-year prior), 78 percent (3-year prior), and 71 percent (4-year prior), respectively. The follow-up validation test based on a large sample size provided a cumulative accuracy of 75 percent for a 3-year period, which was very close to 78 percent in the early validation test for the original and holdout samples. With respect to the variables that used to construct the model, four factors (X1, X21, X31, X32) were the same with those found to be significant in several earlier studies (Beaver, 1967; Altman, 1968 and 1977). The other three factors (X6, X23, X24), though with some minor modifications in definition, were also similar to those ratios that found in earlier studies. One point to be noticed is that ratios X23, X24, and X31

reflected the particular financial situation of Chinese firms. In addition, it is impressive to see that all the signs of variables were in agreement with their logical economic meaning. This indicated that the published financial data in China, when properly interpreted and analyzed, does indeed possess useful and significant information for investors to assess firm’s performance and to failure potential.

It is also important to note that two new variables X6 (Net cash from operating activities to total number of shares outstanding) and X31 (Market value of outstanding trading shares to total liabilities) were the first to be included in model development and also among the seven factors that survived for building the seven-factor model. Combine together with the discussion in the previous paragraph, it is obvious that there is a significant difference between the factors that affected China listed firms and those that affected the western firms.

As mentioned before that due to the historical background, structure of equity (e.g., tradable and non-tradable shares), corporate governance, etc., without proper adjustment, the models that developed in the western world might not be applicable to China market. Therefore, the important lesson we learned in this study was that the research to develop models that truly reflect the particular situation of Chinese firms should be encouraged.

As for the application of seven-factor model, it can be applied to serve for a wide range of purposes. First of all, like many prior studies, it is a useful extension of the distress-predication model in credit evaluation for business loans in the banking industry. It also can serve as a model to support the government in their assessment of financial condition in each sector before forming any policy for macro

20

management of economics. Since the financial reform that began in 1995, the four big specialized banks[14] in China have transformed into commercial banks. This means they will be responsible for their own business operation and bear the loss and profit occurred. Credit evaluation, used to be ignored, became extremely important in loan risk control over the past few years. In order to avoid granting loans to firms with high potential of distress and accumulation of non-performing loans, it is important to develop sound and robust credit rating or distress prediction models, especially after Basel II been introduced in 2001. In order to be qualified for using Internal Ratings Based (IRB) approaches, either foundation or advanced, banks need to provide the evidence that they have the capability to develop such credit rating models. Based on the historical data captured, such models can be used to support the estimation of probability of default (PD). The seven-factor model is a promising model for serving such purpose. In this regard, the ZChina-score can be used as a guide to classify the loan applications into different categories, so that the bank can focus on those firms which are either unhealthy or need to be closely monitored. Secondly, this model can be a useful tool to support corporate internal control. The predication ability of the discriminant model enables management to identify potential problems early enough that before any signs of potential losses alert messages can be generated to prevent more critical situations. Third, creditors, fund managers and stockholders can use the predictor to screen out undesirable investments, or reduce losses by withdrawing investment or collect receivables from those unhealthy firms to avoid real losses. Fourth, government and the market authorities can use the predicator as a guideline to increase the transparency of regulatory objectives. Or it can be used as barometer to measure the economic condition of firms in different sectors. Government then based on the results making decisions what actions to take to keep the economy under control. Reference Altman, E.I. Financial Ratios, Discriminant Analysis and the Prediction of Corporate

Bankruptcy, Journal of Finance 1968 (23) pp.189-209 Altman, E.I. 1989. Measuring Corporate Bond Mortality and Performance. Journal

of Finance eptember, pp.909-922 Altman, E.I., 1984. The success of the business failure prediction models: An

international survey. Journal of Banking and Finance 8, pp.171-198 Altman, E.I., 1997. Anthony Saunders. Credit risk measurement: development over

the last 20 years, working paper. [14] They are Bank of China, Commercial and Industrial Bank of China, Agriculture Bank of China, and Communication Bank of China.

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Asquith,Paul,David W.Mullins Jr.and E.D.Woff.1989. Original Issue High Yield Bonds: Aging Analysis of Defaults, Exchanges and Calls” Journal of Finance. pp.923-953

Aoxiang Pan, 1999. Financial Statement Analysis. Economic Science Press, Beijing, China,

Beaver,W 1967.Financial Ratios as Predictors of Failure, Journal of Accounting Research 4 (suppl.) pp.71-111

Chen,Qing.1999. Evidence Analysis on Financial Distress of Listing Companies. Accounting Research 4, pp.31-38. (Chinese Journal)

Chen, Xiao. Chen, Zhihong. 2000. Corporate Financial Distress theory, methodology and Application. Investment Study.2, pp.125-126. (Chinese Journal)

China Securities. Varies issues in 2000 (Chinese Journal) Dewing, H.S. 1952. The Financial Policy of Corporations. New York: Ronald Press. Donaldson, G.1969. Strategy for Financial Mobility. Boston, Mass.: Harvard

University. Gilbert,L.R., K.Menon, and K.B.Schwartz. 1990. Predicting Bankruptcy for Firms in

Financial Distress. Journal of Business Finance and Accounting. Spring.pp.161-171.

Gao, Shangquan and Chi, Fulin 1996. The Chinese Securities Market. Foreign Languages Press, Beijing.

Guthman,H.G and H.E. Dougall. 1952. Corporate Financial Policy. New York: Prentice-Hall, Inc.

H.Y. IZAN. 1984. Corporate distress in Australia, Journal of Banking and Finance. 8, pp.303-320.

James SCOTT. 1981, The probability of bankruptcy-a comparison of empirical predictions and theoretical models, Journal of Banking and Finance 5, pp.317-344

KMV Corporation 1995. Introducing Credit monitor, Version 4.San Francisco: KMV Corporation

Lau, A. 1987. A Five-state Financial Distress Prediction Model.Journal of Accounting Research, Spring, pp.127-138.

Lin, Hong-Chi 1997, A study of the ability of cash flow data in predicting financial distress for Taiwan’s food processing industry, Dissertation, Nova Southeastern University.

Newton, G.W. 1975. Bankruptcy and Insolvency Accounting. New York:The Ronald Press.

Ross, Stephen A. West3erfield, Randolph W. Jaffe, Jeffrey F. 1996. Corporate Finance. 4th edition, McGraw-Hill Companies.

Wilcox Wilcox. 1972. A Prediction of Business Failure Using Accounting Data. Journal of Accounting Research. Vol.ii

Wu, Shilong and Lu, Xianyi. 2001. Financial Distress Prediction Model for Chinese Trading Firms. Economic Research Journal. 6, pp.46-55. (Chinese Journal)

Zhang, Ling. 2000. Financial Distress Early Warning Model. Quantitative and Technical Economics. (3) pp.49-51 (Chinese Journal)

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Appendix 1： Initial Samples (Estimation Samples)

Financial Distress Firms Date of financial statements Non-Distress Firms Stock code* Date of “ST” Stock code

000003 29/04/2000 12/31/1999 000005 000004 24/04/1999 12/31/1998 000008 000009 29/04/1999 12/31/1998 000012 000010 26/04/2000 12/31/1999 000016 000011 29/04/2000 12/31/1999 000019 000014 12/06/1998 12/31/1997 000021 000015 30/04/1998 12/31/1997 000024 000017 30/04/1999 12/31/1998 000027 000020 24/04/1999 12/31/1998 000032 000025 18/04/2000 12/31/1999 000035 000030 12/06/1998 12/31/1997 000037 000034 29/04/1999 12/31/1998 000039 000038 11/04/2000 12/31/1999 000040 000049 09/06/1998 12/31/1997 000043 000411 06/04/2000 12/31/1999 000045 000413 30/04/1998 12/31/1997 000501 000430 26/04/2001 12/31/2000 000509 000502 30/04/1999 12/31/1998 000517 000503 12/06/1998 12/31/1997 000520 000504 18/04/2000 12/31/1999 000524 000506 29/04/1998 12/31/1997 000527 000507 21/04/1999 12/31/1998 000528 000511 27/04/1998 12/31/1997 000533 000515 29/04/1998 12/31/1997 000537 000518 30/04/1998 12/31/1997 000539 000522 24/04/1999 12/31/1998 000541 000526 16/04/1999 12/31/1998 000543 000536 28/04/1999 12/31/1998 000549 000546 20/04/2000 12/31/1999 000554 000548 28/04/1998 12/31/1997 000559 000550 02/06/2000 12/31/1999 000561 000555 28/04/1999 12/31/1998 000564 000556 30/04/1999 12/31/1998 000568 000558 28/04/1998 12/31/1997 000570 000566 28/04/1999 12/31/1998 000576 000569 08/06/1998 12/31/1997 000578 000585 29/04/2000 12/31/1999 000582 000588 30/04/1999 12/31/1998 000584 000592 29/04/2000 12/31/1999 600600 000602 10/05/2000 12/31/1999 600602 000607 21/04/1999 12/31/1998 600605 000613 30/04/1999 12/31/1998 600607 000639 29/04/1999 12/31/1998 600608

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Appendix 2: Holdout Samples

Financial Distress Firms Non-Distress Firms Stock Code Date of “ST” Date of financial statements Stock code

000653 18/02/2000 31/12/1999 600611 000658 09/05/2000 31/12/1999 600612 000669 15/05/1998 31/12/1997 600615 000689 27/04/2000 31/12/1999 600619 000696 25/04/2000 31/12/1999 600623 600083 30/04/1998 31/12/1997 600628 600097 14/07/2000 31/12/1999 600630 600137 25/04/2000 31/12/1999 600631 600606 28/05/1998 31/12/1997 600637 600610 26/04/1999 31/12/1998 600644 600625 30/04/1999 31/12/1998 600645 600629 27/04/2000 31/12/1999 600650 600633 30/04/1998 31/12/1997 600655 600647 28/04/1998 31/12/1997 600657 600658 17/04/2000 31/12/1999 600660 600670 04/05/1998 31/12/1997 600664 600683 12/04/2000 31/12/1999 600669 600691 28/04/1999 31/12/1998 600671 600696 08/05/2000 31/12/1999 600673 600715 28/04/1999 31/12/1998 600677 600721 06/04/2000 31/12/1999 600680 600758 30/04/1999 31/12/1998 600682 600759 30/04/1999 31/12/1997 600687 600762 25/04/2000 31/12/1999 600690 600775 25/05/1999 31/12/1998 600695 600806 12/04/2000 31/12/1999 600698 600813 30/04/1998 31/12/1997 600800 600818 28/04/1999 31/12/1998 600805 600831 28/04/1998 31/12/1997 600808 600833 15/05/1999 31/12/1998 600812 600837 30/04/1998 31/12/1997 600817 600845 27/04/2000 31/12/1999 600820 600847 22/04/1999 31/12/1998 600825 600852 04/06/1998 31/12/1997 600839 600855 09/06/1998 31/12/1997 600849 600862 04/06/1998 31/12/1997 600854 600874 17/05/1999 31/12/1998 600868 600876 28/04/1999 31/12/1998 600292 600892 05/06/1998 31/12/1997 600898 26/04/1999 31/12/1998

Appendix 3 32 Ratios Correlation Matrix

X1 X2 X3 X4 X5 X6 X7 X8 X9 X10 X11 X12 X13 X14 X15 X16 X17 X18 X19 X20 X21 X22 X23 X24 X25 X26 X27 X28 X29 X30 X31 X32

X1 1.000

X2 .910 1.000

X3 .681 .544 1.000

X4 .459 .542 .270 1.000

X5 .070 .055 .082 -.079 1.000

X6 .044 .027 -.003 -.022 -.095 1.000

X7 .741 .588 .670 .292 .057 .197 1.000

X8 .302 .206 .247 .104 .249 -.079 .271 1.000

X9 .281 .185 .207 .027 .334 -.104 .231 .866 1.000

X10 .453 .351 .499 .246 .145 -.092 .392 .755 .645 1.000

X11 .395 .312 .497 .429 .253 -.071 .373 .622 .459 .793 1.000

.045 .035 -.032 .054 -.029 .644 .022 -.060 -.039 -.070 -.102 1.000

X13 -.036 -.026 -.001 -.019 .092 .098 -.015 -.003 -.055 -.001 -.023 -.017 1.000

X14 .019 .030 .038 -.003 .061 .028 .028 .009 -.081 .038 .034 -.092 .973 1.000

X15 .021 .035 .055 .085 -.197 .204 .170 -.108 -.153 .090 -.025 -.061 .108 .124 1.000

X16 .020 -.003 .084 -.030 -.013 .006 .018 .111 .222 .111 .086 -.033 -.055 -.087 -.030 1.000

X17 -.069 -.039 -.029 -.014 -.044 .021 -.079 -.125 -.117 -.197 -.054 -.014 .017 .020 .040 .002 1.000

X18 -.102 -.104 -.003 -.067 -.157 .193 .018 -.071 -.132 .129 -.081 -.040 .034 .051 .695 .002 -.043 1.000

X19 -.279 -.095 -.252 -.069 -.054 .175 -.156 -.545 -.505 -.382 -.337 .130 .079 .086 -.052 -.050 .021 .090 1.000

X20 .070 .013 .013 .056 .014 .021 .134 -.051 -.055 -.058 -.033 .013 -.005 -.009 .020 -.184 -.001 .027 -.090 1.000

X21 .122 .197 .058 .031 .000 .170 .078 -.029 -.043 -.078 -.045 .232 .024 .008 .009 -.143 .118 -.397 -.118 -.042 1.000

X22 .343 .340 .136 .177 .013 .147 .270 .234 .318 .295 .228 .042 .046 .108 .155 -.060 -.053 .102 .120 .008 .016 1.000

X23 .005 -.028 .053 -.110 -.085 .056 .107 -.032 -.010 -.099 -.150 -.074 .028 .056 .221 .006 -.015 .473 .126 -.008 -.282 .259 1.000

X24 -.125 -.088 -.067 -.013 .051 -.121 -.137 -.043 -.063 .003 .076 -.083 .012 .019 -.191 -.043 -.027 -.127 .033 -.007 .020 -.162 -.097 1.000

X25 .511 .545 .418 .227 .273 -.103 .314 .317 .357 .504 .461 -.081 -.070 -.034 .142 -.042 -.041 -.157 -.277 -.158 .292 .265 -.200 .041 1.000

X26 .210 .148 .264 .062 -.015 -.013 .328 .356 .363 .447 .298 -.022 -.022 .018 .099 .026 -.060 .046 -.189 -.038 .233 .501 -.008 -.152 .301 1.000

X27 -.338 -.185 -.515 -.056 -.292 .108 -.369 -.700 -.657 -.611 -.551 .061 .126 .127 .254 -.163 .077 .273 .494 -.005 .005 -.074 .111 .037 -.397 -.318 1.000

X28 .193 .106 .185 .021 .306 -.122 .169 .843 .781 .483 .438 -.184 -.172 -.207 -.228 .162 -.074 -.220 -.572 -.004 -.019 .028 -.046 -.054 .262 .193 -.776 1.000

X29 -.006 .096 -.190 .048 -.176 .080 -.037 -.317 -.330 -.172 -.209 -.034 .084 .114 .401 -.124 .024 .276 .128 -.045 .267 .169 .015 -.084 .009 .331 .622 -.466 1.000

X30 .191 .048 .206 -.017 .342 -.165 .196 .654 .597 .381 .387 -.217 -.082 -.088 -.241 .124 -.111 -.181 -.411 .021 -.279 .003 -.022 -.092 .143 -.053 -.735 .799 -.598 1.000

X31 .133 .018 .111 .002 .144 -.173 .108 .600 .612 .330 .235 -.291 .066 .022 -.141 .205 -.086 -.065 -.348 -.011 -.303 .079 .043 -.071 .059 -.012 -.542 .733 -.466 0.812 1.000

X32 -.095 -.016 -.050 .023 -.039 .164 -.180 -.039 -.060 -.056 -.026 .173 -.052 -.105 -.102 -.081 .124 -.174 .030 -.221 .519 -.210 -.104 .181 .041 -.154 .093 .040 -.023 -0.231 -0.101 1.000