STAR Model: Predicting ST* Companies in China STAR Model... · Bankruptcy & Reorganization –...

Bankruptcy & Reorganization – Professor Altman 12/15/2014

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STAR Model: Predicting ST* Companies in China Pushkar Bodas, Shyam Lingam, Shuyu Wang, Wendy Yang

Introduction

China’s securities market is an attractive investment opportunity. According to World

Federation of Exchanges, Shanghai and Shenzhen stock exchanges are ranked among the top 10

in terms of market cap and trade volume. Previously, foreign investors had minimal access due

to China’s regulations limiting inflows of foreign capital. Institutional investors were subject to

strict government-set quotas, and retail investors were excluded. Shanghai-Hong Kong Stock

Connect was established on 17 November 2014 and allows institutional and retail investors to

directly trade China-listed stocks, which caps at $2.11 billion per day. This represents a much

more open trading platform, available to international investors all over the world including

investors investing in Chinese distress stocks.

Companies are more likely to be distressed due to GDP deceleration and a decrease in

China’s economic growth. Companies in various industries, such as four solar-cell companies

between 2013 and 2014, have been listed in other countries as filing for bankruptcy due to

anemic domestic demands, and the Chinese government had to bail out several public companies

as a result of bond defaults. State-owned banks have increasingly high debt ratios at levels

reminiscent of the country’s last major debt crisis. As of 5 December 2014, China has 35

companies designated as Special Treatment, or ST, an identification of financial distress. ST

companies represent 1.38% of all the listed companies in the Shanghai and Shenzhen stock

exchanges and change as current ST companies recover and new companies become financially

distressed.

Every major stock exchange has its own set of delisting rules, which is a fundamental

component of a well-functioning securities market. When a public firm becomes financially

distressed, delisting rules ensure that the market recycles and redirects capital into more

promising companies and improve operating efficiencies. Corporate governance of public firms

better protect investors, as regulators are able to weed out poor performing companies and limit

systematic risks. The Chinese government is embracing long-overdue economic reforms; it

recently announced commitment to establish a deposit insurance system and frequently

emphasized the significance of easing regulations to file for bankruptcy in the nation’s economic

agenda.

Regulations of distressed firms and delisting standards have changed since trading began

on the Shanghai and Shenzhen Stock Exchanges in 1990. ST designation was established in

March 1998, the first law regulating distressed firms. Companies are classified as ST stock if (1)

the firm had negative earnings over the past two accounting years, or if (2) the firm’s

shareholders equity is lower than its registered capital in the most recent accounting year1. In

1999 and 2001, the government defined PT designation, a transfer warning, for ST companies

that did not have financial improvement in year one. A formal delisting process will begin for

ST companies that do not have financial improvement in year two2. In 2001, China had its first

delisted company. China Securities Regulatory Commission (CSRC), the governmental agency

responsible for regulating the exchanges, publically solicited advice to reform current delisting

rules in July 2014.

1 http://www.szse.cn/szseWeb/FrontController.szse?ACTIONID=15&ARTICLEID=883&TYPE=0 2 Altman


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Chinese regulations changed in 2003, and the previous ST and PT standards were

eliminated. The publication of “Delisting Risk Warning Rules” stipulates that companies at risk

of being delisted from the Shanghai and Shenzhen Stock Exchanges will be designated as ST*,

or delisting risk warning special treatment, as a warning to potential investors. According to the

Shanghai Stock Exchanges, companies are designated as Special Treatment, or ST, if (1) the firm

had negative shareholder’s equity in its latest financial year or if (2) the firm’s accountant

expresses negative opinion in its auditing report, and other non-financial events3. ST designation

signifies primary level of financial distress. Companies are designated as ST* if (1) the firm had

negative earnings over the past two years or if (2) the firm has significant accounting fraud or

failed to disclose annual or half-yearly financial statements, as well as other non-financial

events4. ST* designation implies that firms are in a worse financial position and at higher risk of

delisting than ST firms are. It is important to note that a firm may skip ST and have ST*

designation. Table 1 provides historic ST* data, which indicates that, since 2003, ST*

companies increased over the subsequent years. From 2007 onward, we noticed a significant

drop in ST* companies until 2014. As a result of stricter enforcement and execution standard, no

public company were delisted5.

Table 1 Year 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011

Total # of

Listed

Companies

1086 1160 1235 1296 1384 1387 1440 1530 1604 1700 2063 2342

Total # of

Delisted

Companies

3 7 4 8 12 4 6 1 0 0 0

% of Total

Companies 0.26% 0.57% 0.31% 0.58% 0.87% 0.28% 0.39% 0.06% 0.00% 0.00% 0.00%

Total # of

ST

Companies

15 12 38 44 34 31 50 46 19 24 37 15

% of Total

Companies 1.38% 1.03% 3.08% 3.40% 2.46% 2.24% 3.47% 3.01% 1.18% 1.41% 1.79% 0.64%

Total # of

ST*

Companies

34 28 26 48 43 19 20 35 12

% of Total

Companies 2.62% 2.02% 1.87% 3.33% 2.81% 1.18% 1.18% 1.70% 0.51%

% of Total

ST

Companies

77.27% 82.35% 83.87% 96.00% 93.48% 100.00% 83.33% 94.59% 80.00%

For our research paper, we created the STAR model that predicts the probability of

Chinese companies that will be in financial distress. Of the two designations, we decided to

focus on the predictability of ST* companies because the financial analysis and accounting ratios

3 http://www.htsc.com.cn/browser/view/investorsReadingNews.jsp?nodeName=tzzjy2&docId=4583704&cur=default 4 http://www.htsc.com.cn/browser/view/investorsReadingNews.jsp?nodeName=tzzjy2&docId=4583704&cur=default 5 http://www.csrc.gov.cn/pub/zjhpublic/zjh/201410/P020141017502705934937.pdf


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can better predict the scenarios that result in ST* designation. Our research is important for

investors investing in Chinese distressed stocks, regulators, and researchers. We noticed that the

stock price fall a month ahead of the delisting which will allow investors to adjust their strategies

and limit their investment exposure. Regulators are able to weed out poor performing companies

and limit systematic risks. And for academics, the STAR model provides the foundation for

further research.

Literature Review

Numerous scholars have researched the relationship between corporate governance and

financial distress. Based on data on 96 financially distressed companies and 96 healthy

companies, Wang and Deng (2006) 6 analyzed the influence of governance structure and

managerial agency cost factors on a company’s probability of financial distress. They found that

a higher percentage of independent directors siting in a company’s board, large shareholder and

state ownership, and lower managerial agency costs, as measured by administrative expense ratio,

tend to decrease the likelihood that a company will be in financial distress. Meanwhile,

managerial ownership, board size, and CEO longevity have no significant influence on a

company’s financial performance.

Kam, Citron, and Muradoglu’s research (2007)7 focused on the survival of distressed

firms designated as ST or ST* and found that merger & acquisitions might not be ideal. Their

research concluded that private companies, rather than state-owned companies, are more likely to

gain financial strength by doing M&A. However, state-owned firms tend to lose value when

controlling ownership is transferred. Furthermore, markets perceive asset sales as a negative

sign of a company’s financial health for both competitive and state-owned firms.

Zhang, Wu, and Yan (2012) 8 used key ratios of a company’s financial structure,

specifically assets, to predict a company’s stock price based on a sample of ST companies from

2007 and 2009. Based on those companies’ financial performance after their ST designation,

their research concluded that higher current asset to total asset ratio and collateralizable assets to

total asset ratio predict better stock price performance, while equity per share of the company

does not affect the predictability.

Z-China Model

The Z-Score China model uses a multivariate discriminant analysis to classify whether a

company is ST* or not. Despite volatile market dynamics and various new techniques such as

neural networks,, this model has been very popular as it is easy to understand and replicate and

has consistent success of predictability. The Z-score model used to identify distressed companies

in China uses four independent variables in a linear combination as follows:

Z = 0.0517 + 0.460X1+9.320X2+0.388X3+1.158X4

where,

X1= asset liability ratio (total liabilities/total assets);

6 Wang, Zong-Jun, and Xiao-Lan Deng. "Corporate governance and financial distress: Evidence from Chinese listed

companies." Chinese Economy 39, no. 5 (2006): 5-27. 7 Kam, Amy, David Citron, and Gulnur Muradoglu. "Distress and restructuring in China: Does ownership

matter?." China Economic Review 19, no. 4 (2008): 567-579. 8 DONG, Nan-yan, Zong-wu JIA, and Jun-rui ZHANG. "Research on the Value Relevance of Asset Structure for

Financially Distressed Listed Companies on China Security Market." In Statistics & Information Forum, vol. 5, p. 010. 2012.


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X2 = rate of return on total assets (net profit/average total assets);

X3= working capital to total asset ratio (working capital/total assets), where working

capital equals current assets minus current liabilities;

X4= retained earnings to total assets ratio (retained earnings/total assets).

We collected data on 204 companies, half were ST* and half were not. Table 2 sums up the

accuracy in predicting ST* companies. The Z-China model resulted in (4+3)/102, or 7% Type 1

error, which refers to the actual ST* companies that the model had predicted as non ST*. It also

resulted in (18+26)/102, or 18% Type 2 error, which refers to the actual non ST* companies that

the model had predicted as ST*. Type 2 error is higher than we expected. Graph A is a plot

showing the distribution of our analysis using the Z-China model. The x-axis lists the

companies, and the y-axis is the z score. If Z<0.5, the firm is distressed. If 0.5<Z<0.9, the firm

is potentially distressed. If Z>0.9, the firm is financially healthy.

Table 2

Actual ST Actual Non ST*

Predicted Good 3 58

Predicted Maybe 4 26

Predicted Bad 95 18

Total 102 102

Graph A

O-Score model

The Ohlson’s model builds on existing MDA models but uses probabilistic model to

interpret the results, easily differentiating companies in financial distress from companies that


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are financially healthy. To improve accuracy, Ohlson uses nine independent variables including

two binary ones. Most of these variables have appeared in prior research and were used based on

their frequency of appearance.

O-score = -1.32 – 0.407*log (TA) + 6.03*(TL/TA) – 1.43*(WC/TA) + 0.757*(CL/CA) –

2.37*(NI/TA) – 1.83*(FFO/TL) -1.72*X + 0.285*Y – 0.521*(NIt-NI t-1/

|NIt|+|NIt-1|)

where,

TA = total assets;

TL = total liabilities;

WC = working capital;

CL = current liabilities;

CA = current assets;

X = 1 if TL > TA, 0 otherwise;

NI = net income;

FFO = funds from operations;

Y = 1 if a net loss for the last two years, 0 otherwise

Using logistic regression, the O-score is converted to predict the probability that a

Chinese company will be designated as ST* as follows:

Probability of Financial distress (P) = exp (O-score)/1+exp (O-score)

Using the same data on 204 companies, Table 3 sums up the accuracy in predicting ST*

companies. The O-Score model resulted in 35/102, or 35% Type 1 error and 30/102, or 30%

Type 2 error. Graph B is a plot showing the distribution of our analysis using the O-Score

model. The x-axis lists the companies, and the y-axis is the predicted probability of ST*. If the

probability is greater than 50%, the firm is distressed. If the probability is less than 50%, the

firm is financially healthy.

Table 3

Actual ST* Actual Non ST*

Predicted ST* 67 30

Predicted non ST* 35 72

Total 102 102


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Graph B

STAR Model

Using the Z-China and O-score models as benchmark, we came up with the STAR model.

In addition to the four variables from the Z-China model, we added the percentage of cash in

total assets, an indicator variable for change in cash level, and the net profit over average total

assets. The following are detailed descriptions of each variable analyzed, including the ones that

we eliminated for the STAR model.

Following previous studies linking ownership structure and number of independent

directors to the predicted probability that a company will be designated as ST*, we analyzed and

concluded that management ownership, employee ownership, and state ownership are not

relevant. There was no significant information on the managerial and employee ownership one

year before a company was designated as ST*, and there were no reliable data for the number of

independent directors for our sample set of companies. Reduction in state ownership provided

some meaningful information and was a leading indicator in most cases. However, there was no

pattern in the reduction of state ownership.

We analyzed and concluded that higher inventory to total assets ratio is not relevant to

predicting financial distress. A company might be facing financial issues because not everything

that they produce might be consumed and hence they might have a higher inventory than the

companies that are not facing financial problems. However the correlation of net inventory to

total assets ratio the year before with the designation of ST* the following year is only -0.0544

for our sample set.

We analyzed and concluded that higher available-for-sale financial assets to total assets

ratio is not relevant. If a company encounters financial problems, it might sell assets to generate

cash flow to continue operation resulting in higher available-for-sale financial assets to total


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assets ratio. However, the correlation was only -0.0997 for companies the year before their ST*

designation with the following year.

We analyzed and concluded that long-term investments are not relevant. We tested

whether long-term investments of firms will reduce one year from two years before their ST*

designation. However, the correlation is -0.0174.

We analyzed and concluded that higher DE ratio is not relevant. Financial distress could

result from poor management decisions in allocating capital structure and debt overhang problem.

Higher interest payments resulting from more debt might cause a company to be designated as

ST*. We compared DE ratio with the industry average DE ratio in the year before the ST*

designation as DE ratios varied by industries. (For example, the railroad industry capital

intensive.) We assigned an indicator variable of 1 if the ratio is higher and 0 if the ratio is lower.

We also computed the DE ratio for all the companies in the sector from CSMAR [R] and took an

average for the industry average DE ratio. However, the correlation is 0.1815.

We analyzed and concluded that the percentage of cash in total assets and an indicator variable

for change in cash level are relevant. If a company is close to financial problems, its Cash/Total

Assets ratio will fall as it burns through cash. A correlation of -0.26679 validated our hypothesis.

Also, if the Cash/Total Assets increased from two years to one year before the designation of ST,

there is a lower chance that the company will face financial distress, again validating our

hypothesis.

The last variable that we added is the Net Profit over Average Total Assets, which is 6-

level categorical variable, in which a value is assigned to each of the six scenarios based on

NP/ATA at 2 years and 1 year before the ST* designation and the change in NP/ATA between

year 2 and year 1. This flow information is significant since it can determine if a company is

doing well or not. In Table 4, if the company had a positive NP/ATA two years before and if it

increased one year before ST*, we denoted the scenario as direction 1.

Table 4

NP/ATA

(2 years before ST*)

NP/ATA

(1 year before ST*) NP/ATA Change Direction

Positive Positive Increased 1

Positive Positive Decreased 2

Positive Negative Decreased 3

Negative Positive Increased 4

Negative Negative Increased 5

Negative Negative Decreased 6

Table 5 sums up the correlations of the independent and dependent variables. The NP/ATA

independent variable one year before the ST* designation had a very high correlation of

0.6382914.


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Table 5

X1-X2 X1-X3 X1-X4 X1-X5 X1-X6 X1-X7 X1-ST*

-0.122753 -0.77376 -0.670092 -0.213532 0.0232682 0.1859236 0.2527391

X2-X3 X2-X4 X2-X5 X2-X6 X2-X7 X2-ST*

0.1791181 -0.123929 0.3244134 0.1365146 -0.611659 -0.591269

X3-X4 X3-X5 X3-X6 X3-X7 X3-ST*

0.5554479 0.3929681 0.0622433 -0.212798 -0.318324

X4-X5 X4-X6 X4-X7 X4-ST*

0.0140018 -0.058825 -0.14593 -0.174492

X5-X6 X5-X7 X5-ST*

0.2794833 -0.171598 -0.26679

X6-X7 X6-ST*

-0.015604 -0.11767

X7-ST*

0.6382914

From our sample set, we decided to keep the 25 ST* and 25 non ST* companies from

2014 as a test set. We used the remaining 77 ST* and 77 non ST* companies as a training set to

come up with the STAR model. We used logistic regression to come up with a STAR score and

ran our data using WEKA to come up with suitable coefficients. (Please note that logistic

regression was best as we also tested using Naïve Bayes and Neural Networks.) Based on the

models generated, we tweaked the model’s co-efficients for accuracy and to ensure that the

model is not constrained to the training set. The STAR score is as follows and Table 6 lists

explains the STAR variables:

STAR Score = -3.7937 -1.0651 * X1 + 15.7617 * X2 + 2.9307 * X3 + 5.6007 * X4 -

4.4805 * X5 + 1.5383 * X6 + X7


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Table 6

Variable Value

X1 Total Liabilities/Total Assets

X2 Net Profit/Avg total Assets [NP/ATA]t

X3 Working Capital/Total Assets

X4 Retained Earnings/Total Assets

X5 Cash/Total Assets

X6 If Cash/Total Assets increased from last year, 1 else 0

X7 Value Condition

6.50 [NP/ATA]t > 0; [NP/ATA]t-1 > 0; [NP/ATA]t > [NP/ATA]t-1

4.00 [NP/ATA]t > 0; [NP/ATA]t-1 > 0; [NP/ATA]t < [NP/ATA]t-1

2.25 [NP/ATA]t < 0; [NP/ATA]t-1 > 0;

4.50 [NP/ATA]t > 0; [NP/ATA]t-1 < 0;

1.00 [NP/ATA]t < 0; [NP/ATA]t-1 < 0; [NP/ATA]t > [NP/ATA]t-1

-20.00 [NP/ATA]t < 0; [NP/ATA]t-1 < 0; [NP/ATA]t < [NP/ATA]t-1

Based on the STAR score, we came up with the probability for predicting the probability

that a company will be designated as ST* in the following year:

P (ST*) = 1/ (1+EXP (STAR Score))

Using the training set, Table 7 shows that the STAR model had a 92% accuracy rate and

resulted in 5/77, or 6% Type 1 error and 8/77, or 10% Type 2 error.

Table 7

Actual ST* Actual non ST* Total

Predicted ST* 72 8 80

Predicted non ST* 5 69 74

Total 77 77

Using the test set from 2014, Table 8 shows that the STAR model had an accuracy of 88%

with 2/25, or 8% Type 1 error and 4/25, or 16% Type 2 error.

Table 8

Actual ST* Actual non ST* Total

Predicted ST* 23 4 27

Predicted non ST* 2 21 23

Total 25 25

For the overall data, the STAR model had 7/102, or 7% Type 1 error and 12/102 Type 2

error. We plotted the results to show the distribution for the training set in Graph C. The x-axis

lists the companies, and the y-axis is the predicted probability of ST*. If the probability is


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greater than 50%, the firm is distressed. If the probability is less than 50%, the firm is

financially healthy.

Graph C

Comparison of Results

We compared the probability of ST* for all three models using our 2014 test data. For

meaningful comparison, we used the same training set as the STAR model for uniformity and

created a logistic regression model for the Z-China model using the four variables from the

original paper as follows:

Z = 1.3741 -1.2491X1 + 30.1693X2 + 1.9696X3+ 7.4372X4

Where,

X1= asset liability ratio (total liabilities/total assets)

X2 = rate of return on total assets (net profit/average total assets)

X3= working capital to total asset ratio (working capital/total assets), where working

capital equals current assets minus current liabilities

X4= retained earnings to total assets ratio (retained earnings/total assets)


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We used the same probability formula from STAR model as follows:

P (ST*) = 1/ (1+EXP (STAR Score))

Table 9 sums up the accuracy in predicting ST* companies. For Z-China Logistic model,

Type 1 error is 1/25, or 4%, and Type 2 error is 11/25, or 44%. For Ohlson’s model, Type 1

error is 6/25, or 24%, and Type 2 error is 11/25, or 44%. For STAR model, Type 1 error is 2/25,

or 8%, and Type 2 error is 4/25, or 16%. Graph D is a plot showing the distribution of our

analysis using the Z-China Logistic model for 2014 test data. Graph E is a plot showing the

distribution of our analysis using the Ohlson’s model for 2014 test data. Graph F is a plot

showing the distribution of our analysis using the STAR model for 2014 test data. The x-axis

lists the companies, and the y-axis is the predicted probability of ST*. If the probability is

greater than 50%, the firm is distressed. If the probability is less than 50%, the firm is

financially healthy.

Table 9

Z-China (Logistic)

model

Ohlson’s model STAR model

Actual

ST*

Actual

Non ST*

Actual

ST*

Actual

Non ST*

Actual

ST*

Actual

Non ST*

Predicted

ST* 24 11

19 11 23 4

Predicted

Non ST* 1 14

6 14 2 21

Total 25 25 25 25 25 25


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Graph D

Graph E

Pro

ba

bil

ity

of

ST

*

Company

Z-China Logistic Model for 2014 Data SetP

rob

ab

ilit

y o

f S

T*

Company

O-Score Model for 2014 Data Set

ST*

Non ST*

Y = 0.5


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Graph F

Conclusion

Of the two designations, we decided to focus on the predictability of ST* companies in

China because the financial analysis and accounting ratios can better predict the scenarios that

result in ST* designation. Our research is important for investors investing in Chinese distressed

stocks, regulators, and researchers. China has a 5% gain or loss limit per day. It caps the move

at intraday prices. We noticed that over a period of a month, the companies that are designated

as ST* started losing a significant portion of their market value, which will allow investors to

adjust their strategies and limit their investment exposure. Regulators are able to weed out poor

performing companies and limit systematic risks. And for academics, the STAR model provides

the foundation for further research.

Our conclusion is that the STAR model reduced Type 2 error significantly while

maintaining Type 1 error. This improved predictability allows investors to better invest in or

steer clear of distressed Chinese companies and allows for regulators to identify distressed

companies early on to properly allocate public capital. Of our data set, only 1 company was

delisted from the stock exchange; therefore, it is not a predictor of whether a company will be

delisted if it transitions from ST* to ST.

Pro

ba

bil

ity

of

ST

*

Company

STAR Model for 2014 Data Set

ST*

Non ST*

Y = 0.5

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