A Forecasting Model of Financial Risk for Manufacturing Listed Companies in China

Transcription

1 A Forecasting Model of Financial Risk for Manufacturing Listed Companies in China XIAO Xiang,BAI Xue School of management of Beijing jiaotong university Abstract: Manufacturing industry plays an important role in every country. At present, most of the ST companies in Shanghai and Shenzhen Stock Exchange are manufacturing companies. Therefore, it s very necessary to work out suitable prediction method. Selecting 27 specially treated manufacturing listed companies which are newly added in 2006 and their 27 corresponding manufacturing listed companies which are in normal financial conditions as the comparative samples, the present article makes use of 22 financial ratios based on public accounting data from 2003 to 2005 to analyze the most important financial ratios for forecasting the financial risk of manufacturing companies. After a series of tests such as K-S test, T test, M-W-W test, and collinearity diagnostics, a logistic regression model is set up, on the basis of which the article finally concludes that five financial ratios such as growth rate of net assets and net assets per share, etc, have very significant effects on judging whether a manufacturing listed company will plunge into financial risk, among them growth rate of net assets is the most significant one. Key words: manufacturing company; financial risk; forecasting; Multi-logistic regression 1. Introduction 1.1. Research background As the most important backbone industry in the national economy, manufacturing industry often plays a key role in nearly every country. For example, 68.1% of the wealth in United States comes from the manufacturing industry, and 49% of the Japanese gross domestic product is provided by the manufacturing industry. In the early 1990s, China's manufacturing industry already accounted for more than 40% in the gross industrial output value. At present, most of the Special Treatment (ST) companies in Shanghai and Shenzhen Stock Exchange are manufacturing companies. However, many manufacturing enterprises in China have lower economic efficiency and higher rate of debts. As a result, they fall into a simple reproduction process and are unable to develop new products or improve their product quality, leading to a serious shortage of innovation capability which turns out to be a heavy burden, and an increasing number of manufacturing enterprises are therefore facing a financial crisis. In view of this, it is very necessary for manufacturing enterprises to select and establish a suitable forecasting model that could give them early warnings to do something so as to avoid financial crisis Literature Review The study of forecasting model of financial risk has a long history in the world, Generally, this kind of model falls into two categories: statistical and non-statistical The study of forecasting model of financial risk abroad (1) The statistic methods a. Unit-discriminance Beaver (1966) makes a study on financial ratios of the 79 failed companies between 1954 and 1964, which was the earliest prediction of financial crisis. The research shows that the ratio of cash flow to total debt is best, with a successful rate of 87% in the previous year before the companies failed. Later, other unit-variable models such as assets and liabilities decomposition model, gamblers theory, etc, came into being. b. Multiple linear discriminance For the first time, Altman (1968) introduces multiple linear discriminance to the financial crisis 32

2 forecasting. He does a research on 33 companies that put forward a petition of bankruptcy from 1946 to 1965, and on the non-bankrupt companies with same number. He chooses five financial ratios: working capital to total assets, retained surplus to total assets, profit before tax and interest to total assets, market value of shares to the book value of debt, and sales to total assets. A total value Z is obtained through multiple discriminance, which is used for judgment. This research shows that the accuracy rate of this model is as high as 90%. It is called Z Model. In 1977, Altman, Haldeman & Narayanan put forward a new forecasting model of financial risk that is more accurate, which contains seven financial ratios. c. Multiple logistic regression Ohlson (1980) analyzes non-matching samples that are composed of 105 companies of bankruptcy from 1970 to 1976 and 2058 normal companies using multiple logistic regression. He finds that the accuracy rate of financial risk forecasting is 96.12% with the ratios such as the size of companies, capital structure, performance and the current liquidity. Bartczak and Norman (1985) use multi-determination analysis and stepwise regression analysis to do a research on 60 companies that put forward petition in bankruptcy between 1971 to 1982 and on 230 non-bankrupt companies of the same period. The result shows that the data of operating cash flow could not increase the accuracy rate as the financial ratios are based on the accrual basis. d. Survival analysis Using survival analysis, Chen and Lee (1993) make an investigation into the oil and gas industry on the 1980s, whose samples consist of 175 companies from 1980 to Their study shows that liquidity ratio, financial leverage, operating cash flow, the success rate of mining, corporate history and the scale could make enormous impacts on the companies. (2) Other methods In recent years, many researchers have tried various forecasting models. Altman (1985) provides recursive partitioning for financial analysis and makes a comparison with discriminant analysis. Coats (1991) discusses neural network model. Kim and McLeod Jr (1999) use expert decision-making method to construct linear and nonlinear bankruptcy prediction model. Charitou and Trigeorgis (2000) use option pricing model. Some researchers try to integrate various methods (e.g. statistical, financial, and economic methods) so as to promote the research. As different industries have different characteristics, it is of great value to make study in terms of different industries Domestic study on forecasting model of financial risk Although empirical study of company's financial crisis in China has just begun, domestic scholars have made some valuable exploration. Jing Chen (1999) selects 27 ST listed companies and 27 normal listed companies, and then chooses four financial ratios for unit-variable analysis. The result shows that the accuracy rates are 92.6%, 85.2, and 79.2% from one year to three years before the financial difficulties of business. Ling Zhang (2000) selects 120 listed companies of 14 trades from Shanghai and Shenzhen Stock Exchange. 4 ratios such as solvency, profitability, etc are chosen filtering from 15 related financial ratios and form four discriminant model. Xiao Chen and Zhi-hong Chen (2000) predict the financial crisis of listed companies in China by using multiple logistic regression model and publicly available financial data. Shi-nong Wu & Xian-yi Lu (2001) select 70 companies in a financial crisis and the normal 70 financial companies as a sample. They use uni-variable analysis and section analysis to confirm six targets for forecasting the financial risk. Xiu-hua Jiang and Sun Zheng (2001) select 42 ST listed companies in Shanghai and Shenzhen Stock Exchange, then 42 paired companies selected randomly from all non-st companies on the reference point of Nov. 20, From 13 original financial ratios, they filter gross profit rate, the ratio of accounts receivable-others to total assets, the ratio of short loan to total assets and equity concentration coefficient to build a logistic discriminant model Basic evaluation of previous studies At present, the mainstream method is statistic method despite the fact that there are many predicting models of financial risk. As the research history of the other methods is short, their stability need to be further tested. Among statistic methods, unit-variable analysis is simple and feasible, but it has obvious shortcomings: 1 using any single financial indicator will largely exclude the role of other indicators; 33

3 2 it can not reflect the overall distribution of financial data accurately. Multiple linear discriminant method is an improvement upon unit-variable analysis, but the premises that group distribution approximates normal distribution and two covariance matrix equal are hard to be satisfied in reality. Furthermore, standards matching the financial crisis group and non-financial crisis group are difficult to be established, and the relative importance of variables is also difficult to be explained. Multiple logistic regression overcomes these limitations, and the problem has been simplified to calculate the possibility of financial crisis of a firm with certain financial features. If the calculated conclusion is greater than setting split point, the company will be in a financial crisis. The method does not require the normal distribution of data, so the parameter estimation of multiple logistic regression is more stable than discriminant analysis. Selecting Chinese manufacturing listed companies with their industry characteristics taken into account, the present paper adopts multiple logistic regression model which is more scientific and practical in an attempt to construct the forecasting modes of financial risk. It is hoped that the study may provide some beneficial advice to the financial forecast and control for manufacturing industry in China. 2. The samples and indicators of manufacturing forecasting model of financial risk in China 2.1. Sample design and selection Financial risk is also called financial distress. So far the academic circles have different views on its definition in the world. For example, it is defined as enterprises in legal bankruptcy process by Altman (1968). Deakin (1972) believes it is the company of bankruptcy and insolvency or the company has been liquidated to the interests of the creditors. Beaver (1996) considers financial risk as bankruptcy, preference dividend and debt in arrears. Ross et al. ( ) considers it is because the operating cash flow can t satisfy existing debt maturity. In addition, Qi Gu & Shu-lian Liu (1999) define it as a kind of economic phenomenon that the enterprise is insolvent, unable to pay the debt maturity or costs. On Mar 16, 1998, China Securities Regulatory Commission (CSRC) promulgated "The Notice of Stock Special Treatment in the Abnormal Period of Listed Companies, which states that, when the financial or other situations are so abnormal that it is difficult for the investors to determine prospects of the company and the investors rights and interests are likely to be hurt, stock exchange will make a special treatment to the stock trading. Abnormal financial situation involves two cases: one is that the net profits of recent two fiscal years are both negative;the other is that the equity is less than the registered capital by recently auditing. Other situations means the production and business activity is suspended as the result of natural disasters and serious accident, and the company is likely to be involved in the litigation which compensation is more than the net assets and so on. The present paper holds it appropriate to considers ST and *ST manufacturing listed companies as a financial crisis enterprises for the following reasons: First, the enterprise in financial crisis is defined as insolvent enterprise abroad, but there are less companies of bankruptcy in China currently. Secondly, ST and *ST listed companies comply with the definition of financial crisis-enterprises. Thirdly, *ST listed company appeared in China from 2003 on. Sample selection in the present paper is guided by the following principles: A: Satisfying the requirement of comparability which means of the same industry, period and size. According to The Industry Classification Guidelines of Listed Company issued by CSRC, enterprises selected from the same industry are matching samples; the financial indicators of ST companies are consistent with the matching non-st companies in time; the difference of total assets in the end of the recent year between ST company and matching non-st company is no more than 10%. B: Satisfying the time requirement of samples The author selects the time scope is three years before ST companies have been treated specially. This requires the samples must have a certain degree of time continuity and comparability. We select 27 ST manufacturing listed companies which are specially treated as the result of 34

4 abnormal financial situation as samples, and their 27 matching samples which are in normal financial conditions according to the requirements of A and B, forming a total sample of 54 companies Selection of financial indicators Generally speaking, financial indicators selected should comply with five principles which are mensurability, predictability, importance, comparability, and industry characteristic principle. On the basis of the empirical research results at domestic and abroad with actual financial management in China taken into account, the present study selects 22 initial variables for investigating the financial situation of Chinese listed manufacturing companies from following six aspects: 1Profitability ratios, which include return on total assets, main operating profit rate, and EPS. 2Solvency ratios, which mainly include current ratio, quick ratio, the ratio of operating capital to total assets, asset-liability ratio. 3Viability ratios, which mainly include total assets turnover ratio, accounts receivable turnover ratio,inventory turnover rate and current assets turnover ratio. 4Development capacity ratios, which include total assets growth ratio, net assets growth, net profit growth and main business revenue growth. However, the four categories above may not fully reflect the characteristics of the manufacturing listed companies. Therefore, this study will further consider the ratios that could reflect expansion capability of equity, and cash flow status. 1Expansion capability of equity ratios, which specifically include net assets per share, retained profits per share, capital fund per share, the ratio of retained earnings to total assets. 2Cash flow ratios, which mainly include net operating cash flow to net profit, operating cash flow per share, net cash flow to current liability. Net cash flow includes net cash flows from operating activities, cash received from profit and share dividends and cash received from the interests of the debt. 3. Forecasting model of financial risk for manufacturing listed companies in China 3.1. Normality test of financial ratios The present paper adopts K-S test to validate whether the financial ratios are consistent with Normal Distribution. The statistics equation is as follow: D= max S n(x)-f 0 (X) Sn(X) is the cumulative probability of the actual measurement data of samples; F0(X) is the cumulative probability of a particular distribution. Hypothesis H1 is accepted when D is greater than Dα; otherwise, accept the assumption H0. In general, when Dα>0.05, we think the financial ratios are consistent with Normal Distribution. Otherwise, they are not. One year before the financial risk happened, 7 of the 22 tested financial ratios were consistent with Normal Distribution, which are main operating profit rate, asset-liability ratio, operating capital to total assets, total assets turnover ratio, total assets growth ratio, net assets per share, capital fund per share. We also test the ratios 2 years and 3 years before the financial risk happened, and find that nine and ten financial ratios are consistent with Normal Distribution. The above results show that, on the whole the financial ratios in China do not accord with Normal Distribution of assumption, which is in line with the most empirical conclusions of previous scholars abroad Test of the Variance between the financial ratio means 35

5 We employ T-test for the financial ratios that accord with Normal Distribution, and employ M-W-W test of non-parametric test for the non-normal-distributioned financial ratios, and it shows that one year before risk, there are three rations which are inventory turnover ratio, net cash flow to current liability and capital fund per share that failed to pass the significance test, and four ratios that failed to pass the examination two years before, and twelve ratios three years before. So we can see that the closer the date of the financial crisis, the more significant the variance between the financial ratios, and the more deteriorative of the financial ratios. The above result shows that there is a significant difference between the ratios of enterprise in financial risk and those normal comparative enterprises. Therefore, it is feasible to establish a forecasting model of financial risk on the basis of an appropriate statistical method Diagnosis of financial ratio multicollinearity The present study finds that the VIF of variance expansion factor of 8 ratios is over 10, which are return on total assets, EPS, asset-liability ratio, liquidity ratio, quick ratio, the ratio of operating capital to total assets, retained profit per share and the ratio of retained profit to total assets. This means that there is multicollinearity among these variables, which therefore should be removed Logistic regression analysis Logistic model According to previous studies, it is normal that financial ratios fluctuate within a certain range and will not lead to a significant increase of crisis probability. It is only when the ratio exceeds a certain critical value that the deterioration of ratios may lead to the significant increase of probability of financial risk. So the probability between financial risk and ratios is nonlinear, and logistic model should be adopted. On the determination and prediction of the financial risk, the logistic model is as follows: Y i =β 0 +β 1 x 1i + +βx ki P i =1/ (1+e -yi ) Y i = 1 if the company is in the financial risk, otherwise Y i =0. X ki means the i company, the k ratio. P i is estimated probability of a financial risk Logistic Model Construction Having obtained the 5 ratios after multicollinearity test above, we select independent variable using backward method of regression in SPSS, and establish the coefficient and related parameters by the financial ratios one year before financial crisis, as is shown in Table 1. variable Table 1: Parameters table of logistic model one year before financial risk Coeffi cient standard deviation Waldstatistic degree of freedom conspicuous level partial correlation coefficient Current assets turnover ratio x Total assets growth x Net assets growth x net assets per share x Operating cash flow per share x Constant Models established according to Table 1: P=1/(1+e-( x1-0.10x2-0.15x3-1.36x4-2.68x5)) In the model, the coefficients of the ratios are all negative, which shows the greater the coefficients, the smaller probability of a financial crisis, and two ratios are significant at the 10% level. Net assets growth makes the largest contributions to P as is seen from Wald-statistic. As Table 2 shows, the value 36

6 of intercept model-2ll is 74.86,Coefficient model-2ll is 23.72,and Chi-square test is 0.00,which shows the five ratios selected by stepwise method can provide a good explanation for financial crisis. Table 2: The moderation test of Logistic model one year before financial risk item value chi square test Intercept model -2LL Chi-Square Coefficient model -2LL Degree of freedom 5 Goodness of Fit Significant level 0.00 From the table 3, we can see error rates of ClassⅠand Ⅱ before the financial crisis happens are both 3.7%, among which ClassⅠmeans that the enterprise in financial risk is considered as non-financial risk enterprise; and Class Ⅱ means that the non-financial risk enterprise is considered as enterprise in financial risk, and the total accuracy rate is 96.3%. Table 3: Interactive table of Logistic model one year before financial risk Forecast value Non-financial risk Financial risk Total Accuracy rate Error rate Measured value 0 1 Non-financial risk % 3.70% Financial risk % 3.70% Total % 3.70% The results of two years before the financial crisis Forecasting are as Table 4, and the result for three years before nearly have no forecasting capacity. Table 4: Determination and forecasting table of Logistic model two years before financial risk Forecast value Non-financial risk Financial risk Total Accuracy rate Error rate Measured value 0 1 Non-financial risk % 25.93% Financial risk % 7.41% Total % 16.67% 4. Conclusion 1Most of the financial ratios of manufacturing listed companies are not satisfied with normal distribution, so it is improper to construct a model on the basis of multivariate discriminant method. 2There are significant differences for most financial ratios between the normal firms and company having financial risk two years later. So when most of the financial ratios show remarkable changes, managerial department should pay much attention to it, and find the causes and take some measures to avoid financial risk. 3Five ratios play a significant role of judgment, which are current assets turnover ratio, total assets growth, net assets growth, net assets per share, and operating cash flow per share, among which net assets growth is the most important one. The greater they are, the smaller possibility of financial crisis for manufacturing listed company. 4The Logistic model for manufacturing listed company we construct is applied to short-time forecasting but not long-term. In spite of the above conclusions, some problems still need to be further explored due to the limitations of the present study, in which the samples size, for example, is limited; and in the process of 37

7 selecting variables, the ratio is removed directly when it is abnormal which may be efficient for forecasting. For all the limitations mentioned above, the forecasting of financial risk is effective to some extent, and is therefore of practical significance. In order to reduce investment risk, forecasting the financial failure accurately is in urgent need, and the study of this field is sure to receive more and more attention. Main References [1]Jing Chen. Forecasting of Financial deterioration for listed companies Empirical Analysis. Accounting Research (4). [2]Tao Cheng. Research on the Forecasting Model of Financial Risk for Company. Financial Monthly ) [3]Yang Zhang.Rong-qiu Chen. Changes of Forecasting Model of Financial Crisis. Science and Technology Journal of Huazhong University (2) [4]Shou-hua Zhou.Zheng-fei Lu.Gu-liang Tang. Forefront of Modern Financial Theory. Finance and Economics of Northeast China University Press. Harbin.2000 [5]Pei-ye Gao. Discriminant of enterprises failure Theoretical and Empirical Study. Doctoral Dissertation;.1999 [6]Kai Hu.ChaoKang. Forecasting of Enterprise financial crisis from a theoretical perspective. Financial study (6) [7]Ru-yin Hu. Empirical Research on the success or failure of the listed company in China. Fudan University Press.ShangHai.2003 [8]Shan-dong Huang. Forecasting system of Financial Risk for the Listed Companies in China in urgent Need. Financial Study (10) [9]Shing-nong Wu.Xian-yi Lu. Forecasting Model of Financial Plight for the Listed Company in China. Economic Research (6). [10]Beaver W.H.. Financial Rations as Predictors of Failure. Journal of Accounting Research (supplement).1966 [11]Edward I. Altman. Financial Ratios Discriminant Analysis and the Prediction of Corporate Bankruptcy. Journal of Finance (9) [12]Edward I. Altman. R.C. Haldeman and P. Narayanan Zeta Analysis: A New Model to Identify Bankruptcy Risk of Corporation. Journal of Banking and Finance.1977 [13]Edward I. Altman. Corporate Financial Distress: A Complete Guide to Predicting: Avoiding and Dealing with Bankruptcy. Journal of Finance.1983 [14]Jason Kingdom. Intelligent Systems and Financial Forecasting. Journal of Finance.1998 [15]Richard Morris. Early Warning Indicators of Corporate Failure A critical review of previous research and further empirical evidence. Ashgate Publishing Ltd.1997 Xiang Xiao :(doctor, associate professor) Xue Bai : (graduate) 38