Financial Distress Prediction Models of Listed Companies by Using Non-financial Determinants in

Bayesian Criterion

Zhaoyuan Geng,Lan Tan1, Xiaoli Gao, Yining Ma, Lufeng Feng, Jiaying Zhu Department of Applied Economics,

Business School of Zhejiang University City College Hangzhou, 310015, China

E-mail: gengzy@zucc.edu.cn

Abstract—Based on Bayesian Criterion, non-financial indicators were introduced in this paper to establish an early warning model for financial failures． Studies were carried out over 61 companies actually under Special Treatment (ST) and indicated that 54 of the companies were correctly judged (accuracy: 88.5%). The results proved good prediction effectiveness of the financial distress prediction model.

Keywords-Bayesian Criterion ， Non-financial indicators ，

Financial Distress, Prediction Model，Financial crisis，Business performance

I. INTRODUCTION According to previous researches, non-financial indicators

play an important role in enterprise long-term forecast and have been proved applicative and effective [1] [2]. Compared with frequency method, Bayesian Criterion can quantitatively analyze results of hypothesis testing and estimation problems. Thus, in this paper, based on Bayesian Criterion, non-financial indicators were introduced to build an early warning model for financial failures [3].

II. BAYESIAN METHODOLOGY Known as prior probability，Bayes' theorem is based on

the assumption that objectives had been enough recognized [4].

Assume there are k overalls，G1,G2,G3,….., Gk， with the prior probability of q1,q2,q3,…..,qk. (they can be given from experience or estimation). The density function of each

overall was 1 2( ), ( ), , ( )kf x f x f x⋅ ⋅ ⋅ . In discrete situation it is probability function.

With the sample X observed, the posterior probability can be calculated by the Bayes' formula (compared to the prior probability, it was called posterior probability). See (1).

（1） And While

（2） Then judge that X was from h.

Sometimes，the concept of misjudgment of minimum loss can be used as a judgment function. Then, the error X is the average loss of h. See (3).

（3） L(h/g) is called loss function, which represent the loss

caused when g is misjudged as h. Obviously, Formula (3) is the weighted average of loss function determined by probability or is termed misjudged average loss. While h=g, L(h/g)=0. And if h≠g, L(h/g)＞0. See (4).

（4） Then, judge that the sample is from h.

In principle, it makes the model more reasonable to put loss function under consideration. However, L(h/g) was not easy to be determined in practical application. Therefore, in mathematic models, misjudge losses are often directly considered equal. See (5).

（5） Thus, determine h to maximize posterior probability and to

minimize average misjudge losses are of equal value [3]. See (6).

(6)

1 Corresponding Author

This paper is supported by the construct program of the key laboratory in Hangzhou.

978-1-4244-6581-1/11/$26.00 ©2011 IEEE

III. EMPIRICAL RESEARCH

A．SAMPLING In this study, 61 companies under Special Treatment (ST)

were selected as research objects. For simplicity, the author define ST companies as financial anomalies ones and non-ST companies as non- financial anomalies ones. As most of the ST companies experienced loss in successive two years, the author selected the annual financial reports of 2008-2009 as his sample.

B．DATA SOURCES Most of the data came from the information, including

annual reports of individual listed companies in Tonghuashun securities software and from other sources such as www.Jrj.com.cn and Sina Finance (www.sina.com ). The market price of every share was its Dec. 31 close price of that year.

C．FINANCIAL INDEX According to modern financial management theory, the

financial status of an enterprise depends upon the solvency,

profitability, and cash-flow of the enterprise. With the research experiences of the scholars home and abroad, and in accordance with the <Enterprises Performance Evaluation Operating Rules (Revised)> and the requirements of China Securities Regulatory Commission over listed company information disclosure, the author chose financial indices that could reflect enterprises’ solvency, operation ability, profitability, growth ability and cash flow [5]. The variables and formulas are listed in the Table Ⅰ.

D．FINANCIAL INDEX NON-FINANCIAL INDEX Not only financial activities, but also non-financial

activities can cause financial crisis. The following non-financial indices were chosen， reflecting enterprises’ stock structure, annual report disclosure, management,

geographic factors, and capital scale [5]. The variables and formulas are listed in the TableⅡ.

TABLE I. TABLE TYPE STYLES

Financial Indexes Variables Formulas

Gross profit margin X1 Gross Profit/ Sales

ROA X2 Earnings before taxes/ Total assets

ROE X3 Earnings before taxes/ Total owners’ equity

Sales growth rate X4 （Sales at current period-Sales at prior period）/ Sales at prior period

Net profit growth rate X5 （Net profit at current period- Net profit at prior period）/ Net profit at prior period

Acid-test ratio X6 Liquid capital/ Current liabilities

Debt ratio X7 Total liabilities/ Total assets

Long-term debt to equity X8 Long-term liabilities/ Total assets

Current ratio X9 Current assets/ Current liabilities

Interest coverage ratio X10 EBIT/ Interests

Receivables turnover ratio X11 Sales/ Accounts receivables

Current assets turnover- ratio X12 Current assets/ Total assets

Inventory turnover ratio X13 COGS/ Inventory

Asset turnover ratio X14 Sales/ Total assets

Equity to debt ratio X15 Total owners’ equity/ Total liabilities

Cash flow ratio X16 Operating cash flows/ Current liabilities

Total assets growth rate X17 (Total assets at the end- Total assets at the beginning)/ Total assets at the beginning

Equity growth rate X18 (Total equities at the end- Total equities at the beginning)/ Total equities at the beginning

Equity ratio X19 Total owners’ equity/ Total assets

TABLE II. NON-FINANCIAL INDEX

Items Variables Formula

Net cash flow per share X20 Net operating cash flow /Total shares

Cash-Debt ratio X21 Operating cash flow/Total liabilities

Biggest shareholders’ holding ratio X22 Biggest shareholders holding shares/Total shares

State-owned shareholding ratio X23 State-owned shares/Total shares

Controlling shareholders exist or not X24 Virtual variable 1

The audit opinion type X25 Virtual variable 2

The announcement day are delay or not X26 Virtual variable 3

The independent directors’ proportion X27 The amount independent directors/The amount of directors

The chairman and manager are the same person or not X28 Virtual variable 4

Notes Virtual variable 1： If the Biggest shareholders holding ratio is larger than the sum of latter nine big shareholders holding ratio, the digit is 1，otherwise is 0. Virtual variable 2： When nonstandard audit report issued，the digit is 1 ，otherwise is 0. Virtual variable 3： If announcement day is later than the booking disclosure day, the digit is 1，otherwise is 0. Virtual variable 4, If the chairman and manager are the same person, the digit is 1 ，otherwise is 0.

The listed indexes above have high correlations. In order to avoid overlap information and complex process，the author factor analyzed these indexes, and obtained the matrix below.

In Table Ⅱ, 19 financial indexes and 9 non-financial are divided into three parts:

Part A (X6,X9,X10,X11,X12,X13) reflect enterprises profitability. Part B(X3 ，

X1,X5,X7,X8,X15,X20,X21,X22,X23,X24,X25,X26) reflect Operation Capability. Part C (X1,X2,X3) reflected liquidity ratios. See Table Ⅲ.

Finance (www.sina.com ), by using SPSS statistics program and Bayes’s methodology, the author analyzed the three factors and sample data, and got the Bayes’s discriminant functions below [3].

From table Ⅳ, two discriminant functions can be obtained as below:

A：See（7）

（7）

B：See（8）

（8）

Substute independent variables into the two Bayes’s discriminant functions and get out two values. Compare the two values, and classify samples on the basis of which value is bigger, and put the bigger one’s sample into the category.

In 61 truly ST companies, 54 were correctly judged. The high accuracy indicates the model is credible.

TABLE III. FACTOR MATRIX

Items Component

1 2 3

Gross profit margin .856 -.139 .065

ROA .831 .384 .218 ROE .828 .287 .133 Sales growth rate .763 .577 .182 Net profit growth rate .752 .616 .167

Acid-test ratio .692 .622 .144 Debt ratio .130 .811 .065 Long-term debt to equity .124 .788 -.124 Current ratio .214 .710 .229

Interest coverage ratio .215 .113 .943 Receivables turnover ratio .053 -.071 .93 Current assets turnover- ratio .165 .156 .895 Inventory turnover ratio .098 .076 .150

Asset turnover ratio .561 -.042 .167 Equity to debt ratio -.689 -.025 .253 Cash flow ratio -.031 -.032 .276 Total assets growth rate .674 .015 .376

Equity growth rate .348 .014 .834 Equity ratio .875 .045 .765 Net cash flow per share -.403 -.278 -.071 Cash-Debt ratio -141 .320 -.050

Biggest shareholders’ holding ratio .242 -.046 -.012 State-owned shareholding ratio .155 .075 -.026 Controlling shareholders exist or not .312 .061 .057 The audit opinion type .016 .356 .342

The announcement day are delay or not .093 -.111 -.028 The independent directors’ proportion .251 .112 .129 The chairman and manager are the same or not .252 -.070 -.021

TABLE IV. BAYES’S DISCRIMINANT FUNCTION

ST or not

Non-ST ST

Profitablity factors 13.215 -9.118

Operating capacity factors 4.609 2.797

Solvency factors 17.150 22.353

(Constant) -5.699 -7.323

TABLE V. ERROR MATRIX

ST or not Predicted Group Membership Total

Non-ST ST

Original Count ST 7 54 61

% ST 11.5% 88.5% 100.0%

E． RESULT ANALYSIS

The author summarized 61 actually been ST companies to carry out this studies. The results showed that 54 of them were correctly judged，the accuracy is 88.5%．

The empirical analysis result shows:

1）The predictive accuracy of the model is rather high.

2）Financial indicators can predict the financial situation of listed companies.

3） on-financial indicators introduced the model can enhance the accuracy of early warning model for financial failures.

IV. SUGGESTION

The author recommends introducing more non-financial factors into financial distress prediction model, so to realize the combination of quantitative and qualitative analyses. It may be an improvement of the Bayesian Criterion model to add some arguments into the model.

As an early warning model, Bayesian Criterion is useful for financial failure prediction.

V. CONCLUSION

The model proves some applicability in financial distress prediction for China’s ST listed companies. However, in practical applications there are some limitations in the model, e.g. the selection of non-financial index and quantitative analyses.

The result above shows that the main problem of the Bayesian Criterion is the selection of reasonable prior distribution. It is necessary to put comprehensive factors into account in the study of enterprises’ financial crises, so that

financial distress prediction model has broad prospects .

REFERENCES [1] Dr. Mijntje Lückerath-Rovers. Financial Distress Prediction And

Operating Leases[EB/OL]．http://ssrn.com/abstract=1411691,May 29, 2009.

[2] John Y. Campbell, Jens Hilscher， and Jan Szilagyil．Predicting Financial Distress and the Performance of Distressed Stocks[J]．Journal of Investment Management, January,2010

[3] ZhangLe based on Bayesian Criterion method of listed company financial prewarning model [J].journal of southern metal, 2009,167.

[4] HeQiuLu, wen YongJun non-financial indicators in the application of financial analysis [J].j commercial modernization, 2009, (564).

[5] 2001 introducing non-financial indicators of financial crisis prewarning model [J].journal of yiwu fang-tsang commerce vocational technical college journal, 2007,5 (2).