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Research on Financial Risk Assessment of Metal Packaging Enterprises:

Case Study on China Aluminum Cans Shareholding Limited Company

Ya-lan Luo

Siam University

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Abstract

This paper reviews and analyzes the domestic and international research about

financial risk assessment. Then, this proposed research determines the financial risk

evaluation index according to the results of literature research that selects the financial

data of 18 metal packaging listed companies as study samples for the past four years.

Next, the author sets up financial risk evaluation system for metal packaging

enterprises via the descriptive statistical analysis and factor analysis of SPSS. Finally,

taking China Aluminum Cans Shareholding Limited Company for case study, this

study will analyze its financial risk status and build the financial risk evaluation

system that practices in the company and results in financial risk assessment to draw

conclusions of the company's financial risk strategies and recommendations.

Keywords: Metal packaging, financial risk assessment, factor analysis

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Chapter one Introduction

Due to the food and beverage industries develop rapidly recent years, metal

packaging with its excellent metal texture and easy to store characteristics of food

flavor advantages in the food packaging industry which occupies an irreplaceable

position. As the low barriers of competition, the packaging industry has a large

number of small and medium-sized enterprises, resulting in duplication, low-end

products and overcapacity. Therefore, metal packaging industry is also constantly

adjusted to form a consolidation trend. Meanwhile, with the gradual emergence of

large metal packaging groups, the metal packaging industry requires higher level of

financial risk management. This study explores how to identify financial risks

scientifically and rationally and evaluate financial risks so that adopt effective

measures to deal with the financial risks and improve financial risk management of

enterprises in order to narrow the gap with the international advanced level, enhance

the leading brand value, form technological advantages and economies of scale, and

improve the competitiveness of metal packaging enterprises which play a decisive

role in the market.

Chapter Two Literature Review

2.1 Definition of Metal Packaging

Metal packaging refers to the use of amount of sheet metal materials on a variety

of containers according to their use of different forms of packaging. It is a major

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category in Chinese packaging industry and formed a complete packaging system

which includes cans, aluminum cans, and others. Furthermore, the types of end

products are also very rich which cover all fields. In the manufacturing field, the

metal packaging industry plays a very important part of the total industrial and output

value in the proportion is about ten percent. Compared with other packaging material,

it has a unique packaging method which has a good seal and increases the aesthetics

through the decoration. Therefore, it has been used in a wide range of applications

such as food, medicine and various fields.

2.2 Definition of Financial Risk

The introduction of the risk management which is from the Western countries

initially established a professional theoretical system in the 1950s. Risk management

has been widely used in some relatively large enterprises, and the related research

theories have become more perfect. Due to the relevant research was late in China and

the theoretical basis is also at the initial stage which the information is severely

limited. Moreover, foreign experience cannot fully meet the Chinese companies’

requirement in this field of research. Hence, our scholars should carry on concrete

research analysis to the circumstance. Tang and Liu (1989) point out that the so-called

financial risk is there is no way to predict it and not easy to control the impact of

factors when the enterprise runs a business. It is easy to cause the financial status

deviates from the target goal, and even directly affects the company's expecting

earnings or economic losses. Financial risk has a strong duality: On the one hand it

may cause damage to the economic interests of enterprises; but on the other hand

companies may also find more profit because of the risk points.

2.3 Financial risk assessment

Financial risk assessment refers to the analysis of the financial statements of

relevant enterprises, and the data is derived from the analysis of specific financial

indicators. Fitzpartrick (1932) adopted a single financial ratio as the basis and selected

19 companies which were divided into the bankruptcy group and non-bankruptcy

groups, and then determined the financial risk assessment to show the significant ratio

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of assets capacity and debt asset. Martin (1977) conducted a financial risk assessment

and forecasted for the US banks, and resulted in the accurate risk judgment on

Logistic was far more than Z-Score. Mokhatab Rafii (2011) used artificial neural

network, multiple regression method and other methods to practice financial risk

analysis of early warning during business process. He believed that the predictive

accuracy of artificial neural network model was higher to other models, and therefore

it has a wider range of usability. Using Logistic and artificial neural network method

has the advantage that the data can be seen behind the natural business, combined

with the environment, industry characteristics, economic conditions and other

dimensions of analysis. But its drawback is that the current researches often use

traditional data and simple application model without the combination of the actual

situation. Wang (2016) pointed out that the current studies exploit overly on data

mining, large data technology, cloud computing and other methods, and ignore the

traditional analytical methods such as DuPont model and financial statements analysis

which can bring the certain information. In the mathematical application of these

models, the researchers have not modified the traditional indicators according to the

actual developing situation; the method is new, but the results may not be any

different from traditional analytical methods. Based on the research status quo, the

data analysis will adopt the traditional financial indicators, combining with the overall

financial situation of the metal packaging industry and then screen the indicators.

Finally, the research will construct the financial risk evaluation index system which is

suitable for the metal packaging industry.

Chapter Three Selection of financial risk evaluation index for metal

packaging industry

3.1 Sample selection

Metal packaging is a sub-industry under the packaging industry, and there are not

many from the listed companies. The author selects the 18 well-known listed

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companies in the industry as a sample through the Chinese packaging network and the

Chinese metal packaging industry websites, shown on Table 3-3 (O.R.G Packaging,

MYS Group Co., Ltd., Sheng Xing Group Co., Ltd., Baosteel Metal Co., Ltd., Lipeng

Co., Ltd., Zijiang Enterprise, Zhuhai Zhongfu Enterprise Co., Ltd., Hycan Holdings,

Haishun New Pharmaceutical Packaging Co., Ltd., Global Printing, Shandong

Huapeng Glass Co., Ltd., HXPP, HuangShan Novel Co., Ltd., Shandong

Pharmaceutical Glass Co., Ltd., ShenZhen Beauty Star Co., Ltd.,

Shanghai Luxin Packing Materials Science and Technology Co., Ltd., Prince New

Materials Co., Ltd., Xintonglian Packing).

3.2 Index selection

Existing research on financial risk indicators does not have a unified standard,

and if it combines with non-financial indicators, it becomes very difficult. Metal

packaging industry is manufacturing, so the financial indicators in the main reference

is based on the criteria of the manufacturing industry. Therefore, the author selects

operating capacity (X1 Inventory turnover, X2 Current assets turnover, X3 fixed

assets turnover, X4 total asset turnover, X5 Receivable turnover ratio), solvency (X6

current ratio, X7 cash ratio, X8 quick ratio, X9 cash flow ratio, X10 operating profit

as a percentage of liabilities, X11 assets and liabilities, X12 equity ratio), profitability

(X13 gross profit margin%, X14 sales net profit margin%, X15 ROE%, X16 total

assets net profit margin%, X17 return on capital invested%), growth capacity (X18

gross revenue growth%, X19 Net cash flow from operating activities YoY growth%,

X20 fixed asset investment expansion ratio%, X21 return on equity [diluted] YoY

growth%, X22 monetary capital growth rate%, X23 capital project scale maintenance

rate%, X24 net assets per share relative to the beginning of the year growth rate%)

and cash flow (X25 main business income ratio, X26 net cash flow from operating

activities, X27 cash meets the investment ratio, X28 cash recoveries of all assets, X29

cash operations index) five aspects.

3.3 Model selection

This research selects the 29 indicators for the 29 metal packaging companies,

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descriptive statistical analyzes the data from 2012 to 2015 individually and

standardizes the unified data to eliminate the impact of dimensions. The author

removes the indicators which are not very high related to the overall financial risk

indicators through the reliability analysis. The results are as the following:

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Table 1 The descriptive analysis of the financial data and the total correlation of the corrected items

Financial indicator

2012 2013 2014 2015

N

Maxim

um M

inimum

CITC

N

Maxim

um M

inimum

CITC N

Maxim

um M

inimum

CITC N

Maxim

um M

inimum

CITC

X1 18 2.5 10.070.555 1

82.36 6.85

0.45818 1.2 6.61

0.69918 1.38 7.53

0.681

X2 18 0.86 1.870.103 1

81.01 1.99

0.17218 0.7 2.15

0.2518 0.85 1.93

-0.02

X3 18 0.92 9.380.386 1

80.97 7.48

0.31118 0.97 6.34

0.39718 0.87 5.25

0.565

X4 18 0.5 1.320.44 1

80.53 1.27

0.42618 0.34 1.23

0.60218 0.4 1.04

0.733

X5 18 3.09 21.39-0.131 1

82.81 26.25

-0.061

18 2.42 24.580.511

18 2.2423.4

70.128

X6 18 0.68 3.390.465 1

80.61 3.64

0.19718 0.55 4.08

0.57618 0.53 5.25

0.592

X7 18 0.08 1.960.469 1

80.09 1.61

0.00118 0.07 1.55

0.48518 0.08 1.9

0.659

X8 18 0.38 2.830.509 1

80.38 2.95

0.22818 0.35 3.55

0.63718 0.35 4.69

0.644

X9 18 0.03 0.550.769 1

80.01 0.53

0.57218 0.1 1.3

0.85518 -0.17 0.88

0.718

X10 18 0.03 0.760.747 1

8-0.03 0.89

0.57118 -0.07 1.09

0.84718 0 0.93

0.786

X11 18 16.41 62.63-0.438 1

820.51 75.35

-0.286

18 19.41 69.67-0.463

18 14.9866.8

9-0.519

X12 18 0.2 1.68-0.47 1

80.26 3.06

-0.243

18 0.24 2.3-0.441

18 0.18 2.02-0.491

X13 18 13.59 33.710.447 1

812.18 34.07

0.20918 12.55 34.86

0.35818 15.97

36.47

0.385

X14 18 -6.58 16.230.719 1

8-42.7 16.09

0.45118 -3.4 18.79

0.71918 -3.85

20.09

0.509

X15 18 -8.18 340.699 1

8-69.47 25.72

0.59818 -2.91 25.52

0.80918 -6.12

25.08

0.623

X16 18 -3.05 18.410.774 1

8-21.16 18.63

0.60418 -1.83 19.89

0.90218 -1.92

18.75

0.736

X17 18 -4 24.570.801 1

8-27.18 20.81

0.58818 -2.13 23.88

0.88318 -2.08

24.85

0.752

X18 18 -13.78 41.10.458 1

8-10.21 35.77

0.25218 -15.2 22.09

0.11918 -20.9 93.4

-0.462

9

X19 18 -89.62565.4

40.083 1

8-94.24 967.1

-0.031

18 -94.24 157.30.382

18 -39154.0

50.535

X20 18 -4.39192.8

20.036 1

8-26.02 134.6

0.08318 -10.15 56.45

-0.17218 -9.33

32.07

0.181

X21 18 -537 99.07-0.088 1

7-59.49 49.56

0.04318 -161.5

103.63

0.41418 -270 358

-0.384

X22 18 -38.56531.6

5-0.008 1

8-58.66 159.6

-0.174

18 -51.31229.4

30.434

18 -69.7496.

20.209

X23 17 -37.72880.2

20.164 1

8-262.9 734.6

0.09618 -103.7

410.69

-0.23318 -88.5

316.1

0.071

X24 18 -31.98123.6

20.357 1

8-49.12 41.55

0.06718 -55.86 34.02

0.02818 -45.9

53.73

0.088

X25 18 0.02 0.210.551 1

80.01 0.21

018 0.08 0.25

0.38618 -0.21 0.23

0.493

X26 17 0.19 5.25-0.129 1

70.04 4.03

-0.3116 0.57 5.31

-0.44718 -2.2

46.05

-0.216

X27 18 0.06 1.520.545 1

80.02 4.38

0.35118 0.32 5.1

0.65418 -0.95 2.67

0.385

X28 18 1.09 13.880.799 1

80.49 15.18

0.51518 3.39 21.8

0.87318 -7.5

15.72

0.675

X29 18 0.11 1.510.494 1

80.04 1.7

0.1118 0.59 1.54

-0.02618 -1.22 1.51

0.454

The analysis result from Table 1, the author deletes the missing values of the

variables which are X21, X23 and X26 individually. Next, the research selects the

appropriate indicators according to the corrected item total correlation (hereafter refer

to CITC value) and CITC value should be greater than 0.4. If it is smaller than 0.4, we

consider the project is less relevant to the population; therefore it should be deleted

for projects by CITC values below 0.4. The analysis of the results from 2012 to 2015,

the indexes of CITC values higher than 0.4 are selected, and delete X1, X4, X7, X9,

X10, X14, X15, X16, X17 and X28. These ten indicators have no the growth capacity

of the index which means the growth ability has no strong relevance with other

financial indicators. Meanwhile, comparing to other several capacity indicators, the

ability to reflect the risk is relatively weak.

Chapter Four Results

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In order to get better financial risk assessment of metal packaging industry, this

research combines the industry characteristics and makes the analysis which is

suitable for the industry. This study adopts factor analysis and explores the financial

data of 18 listed companies of the metal packaging industry in 2012, 2013, 2014 and

2015. As a result of involving panel data, we use Dong, Tan and Zhou’s method

(2009) to analyze the data for each section from 2012 to 2015 by using SPSS. Finally,

the variance contribution rate is adopted as the weight, and then weighted average

synthetic synthesis is scored.

4.1 Validity test

Table 2 KMO and Bartlett's spherical test results

KMO and Bartlett Test 2012 2013 2014 2015The Kaiser-Meyer-Olkin

metric for sampling adequacy.656 .500 .681 .678

Bartlett's Sphericity Test

Approximate chi-square

236.823 279.562 270.238 245.871

Df 45 45 45 45Sig. .000 .000 .000 .000

According to the results above analysis of the validity we can see that the data

from 2012 to 2015 validity test is significant. KMO value is generally considered to

be greater than 0.6 that factor analysis can be done. The KMO value is 0.5 in 2013, so

the data of 2013 is not adopted in the exploratory factor analysis below. Finally, the

model is validated by the data of 2013 after the model is constructed.

4.2 Factor analysis results

Table 3 Rotation component matrix

Index2012 2014 2015

Factors Factors Factors1 2 3 4 1 2 3 4 1 2 3 4

X1 0.164 0.314 0.076 0.892 0.021 0.266 0.228 0.87

8 0.146 0.743 -0.021

0.528

X4 0.501 -0.045

-0.181

0.773 0.509 0.057 -

0.058 0.75

5 0.242 0.214 0.292 0.864

X7 0.103 0.130 0.969

-0.052 0.256 0.105 0.94

2 0.123 0.308 0.172 0.854 0.270

X9 0.232 0.783 0.548 0.031 0.399 0.76

4 0.452 0.111 0.205 0.762 0.567 0.021

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X10 0.819 0.278 0.310 0.182 0.64

9 0.502 0.490 0.109 0.673 0.359 0.538 0.175

X14 0.965 0.090 0.105 -

0.041 0.87

2 0.258 0.319 -0.043

0.973 0.023 0.205 -

0.029

X15 0.845 0.130 -

0.176 0.446 0.897 0.237 -

0.003 0.332 0.948 0.094 -

0.008 0.232

X16 0.906 0.187 0.039 0.373 0.86

1 0.334 0.267 0.260 0.917 0.214 0.269 0.193

X17 0.880 0.172 0.174 0.374 0.84

2 0.299 0.372 0.237 0.888 0.240 0.320 0.190

X28 0.188 0.942 0.006 0.227 0.311 0.88

6 -

0.017 0.285 0.127 0.923 0.151 0.107

Cumulative variance

contribution rate (%)

57.804

69.798

76.104

94.838

65.668

77.684

86.932

94.422

61.491

79.702

87.021

93.998

Variance Contributio

n (%)

57.804

11.995 6.306 18.73

3 65.66

8 12.01

7 9.248 7.490 61.491

18.211 7.319 6.977

Note: The factors of 1, 2, 3, 4 of 2012 are adjusted, but they do not affect the results.

According to the rotation matrix, we can see that the profitability F1 includes five

indexes as X10, X14, X15, X16, X17, cash flow F2 contains X9 and X28, short-term

solvency F3 is mainly X7, and operational capacity indicators are X1 and X4.

4.3 The final modelThe scores of the four factors in each metal packaging industry can be calculated

from the rotation component matrix coefficients of Table 3 and the original index data

of the normalized values. Factor score expressions of 2012 are as following:

F1=0.164X1+0.501X4+0.103X7+0.232X9+0.819X10+0.965X14+0.845X15+

0.906X16+0.880X17+0.188X28；F2=0.314X1-0.045X4+0.130X7+0.783X9+0.278X10+0.090X14+0.130X15+

0.187X16+0.172X17+0.942X28；F3=0.076X1-0.181X4+0.969X7+0.548X9+0.310X10+0.105X14-0.176X15+

0.039X16+0.174X17+0.006X28

F4=0.892X1+0.773X4-0.052X7+0.031X9+0.182X10-0.41X14+0.446X15+

0.373X16+0.374X17+0.227X28；Combining the variance contribution rate to establish the function is as

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following:

W i(4 )=∑m=1

M ∅m Fm

94.838 %=57.804 %F1+11.995%F2+6.306%F3+18.733%F4

94.838 %

Wi is the objective function, and 4 in the parentheses behind the objective

function represents t = 4, indicating that there are four factors. i denotes one of 18

companies, and ∅_m denotes the variance contribution rate of each factor. The author

takes the original financial data into the above function of the 18 companies in 2012,

2014, 2015, and gets score as shown in the below (see Table 4).

Table 4 18 enterprises W value score

Ite

mCompany 2012 2014 2015 Item Company 2012 2014 2015

1 O.R.G. 2.534924 2.35439 3.163382 10 Global Printing 2.11459 1.800859 2.503728

2 MYS Group 2.215183 2.005725 2.253319 11Shandong

Huapeng1.187759 0.995193 1.257907

3Sheng Xing

Group1.816588 1.434173 2.152687 12 HXPP 1.762775 1.812986 2.186392

4 Baosteel Metal 1.199071 1.122851 1.489602 13 Novel 2.781578 2.204495 2.996095

5 Lipeng 1.404762 0.511853 0.638824 14Shandong

Pharm. Glass 1.116394 1.207466 1.509513

6Zijiang

Enterprise1.388281 1.130795 1.509601 15 Beauty Star 2.061798 1.217611 2.099535

7Zhuhai

Zhongfu1.656505 1.474872 2.117051 16

Shanghai Luxi

n1.486405 0.879493 1.290497

8Hycan

Holdings2.298393 1.850028 2.436885 17

Prince New

Materials 2.43838 2.347805 2.959429

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Haishun New

Pharm.

Packaging

2.629575 3.593783 4.247465 18 Xintonglian 3.876985 2.1087 2.917521

According to the 18 metal packaging industry financial analysis and evaluation

of model scores, the author selects the sorting for the first two data on average of the

9th and 10th arithmetic by the principle of the minimum classification error in order,

and the median of 2012, 2014 and 2015 is 1.94, 1.64, 2.17 individually. And this

index is set to a three-year financial risk threshold. In this way, the company is

considered to have no financial risk if it is greater than the threshold value. If the

index is below this threshold, the company is considered to have financial risk. There

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are 8 companies are below this threshold which are Sheng Xing Group Co., Ltd.,

Baosteel Metal Co., Ltd., Lipeng Co., Ltd., Zijiang Enterprise, Zhuhai Zhongfu

Enterprise Co., Ltd., Shandong Huapeng Glass Co., Ltd., Shandong Pharmaceutical

Glass Co., Ltd., and Shanghai Luxin Packing Material Science and Technology Co.,

Ltd. Meantime, HXPP was below the threshold in 2012. But it climbed up above the

threshold in 2014 and 2015 which shows the company has improved its finance for

these two years. Analysis of the company’s financial indicators found that its cash

flow ratio increased from 6% in 2012 to 30% in 2014 and 15% in 2015, operating

profit accounted for debt ratio also increased by 4 points, and asset returns in 2014

and 2015 were also higher than in 2012. In addition, the W value is relatively high

Prince New Materials Co., Ltd. that its cash flow ratio, operating profit ratio is higher

debt ratio, and other indicators are better than other companies via 10 financial

indicators

4.4 Model validation

Only 18 companies are listed companies in the metal packaging industry, and

they are not ST enterprises disclosed by the SFC. Thus, in order to verify the

effectiveness of the financial risk assessment, the author selects the manufacturing

industry and the highly correlative basic metal industry as a verification target. There

are 108 companies in basic metals industry that there were eight enterprises

continuously disclosed as ST or * ST in 2014 and 20145. According to the above

evaluation system and model, calculate the ST value of the eight companies; and

verify the results shown in the table below.

Table 5 ST and non-ST enterprises W value comparison in basic technique industry

CompanyOperating income (100 million yuan)

2014 2015

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*ST Shenhuo 118.71 1.023346 0.8913934*ST Vanadium Titanium 91.35 0.989397 1.719169*ST Jien 20.08 0.259491 -0.473514ST Hua Ze 17.97 3.687755 6.8913138

*ST Jinrui 12.81 0.680783 0.1941885*ST Lu Feng 6.71 0.364632 1.4357544*ST East Tantalum 6.23 0.239591 -0.407854*ST Alkene carbon 5.97 0.124776 0.2926182

As shown in the table, the W value was less than 1.64 and 2.17 of the ST and * ST

companies were 7 in 2014 and 2015. Except ST Hua Ze, the rest of the ST value of the

enterprise W was low, so the accuracy rate was 87.5% which was acceptable.

4.5 Case Study – Taking Company A for Example

4.5.1 Financial risk profile for the Company A

Based on the model developed in the previous chapter based on exploratory

factor analysis, the author combined it with the company's original financial data to

calculate the W-value score of China Aluminum Can Shareholding Co., Ltd. The

results were as follows:

Table 6 China Aluminum Can Shareholding Ltd., Co. W value score

Code X1 X4 X7 X9 X10 X14 X15 X16 X17 X28 W value2014 6.765 1.051 0.374 0.804 0.843 0.129 0.242 0.135 0.199 0.316 2.910 2015 7.406 10.621 1.066 0.669 1.818 0.140 0.182 0.134 0.148 0.155 4.360

From Table 6, the financial risk of China Aluminum Can Shareholding Ltd., Co.

is very small. The W-score for 2014 was 2.91, which is greater than the critical value

of 1.64 and W-score of 4.36 in 2015, which was also greater than the 2015 threshold

of 2.17. Compared with the average of the indicators of 18 listed companies can be

found that all the indicators of China Aluminum Can Shareholding Ltd., Co. are

greater than the average value. The cash flow ratio, return on net assets, total assets

net profit rate and return on invested capital of China Aluminum Can Shareholding

Ltd., Co. is more than twice of the average of the 18 companies. In the above analysis,

we also found that the higher the cash flow ratio of the financial risks of enterprises

are smaller which illustrates this indicator is very important to measure financial risk.

In the enterprise's operation, each department and link has the factor leads to

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financial risk, and the above project has also proved these risks existence. Hence,

enterprise risk control should cover all aspects of business management such as how

to identify potential financial risks by analyzing operational data, How to prevent the

emergence of risk, and how to implement effective measures to reduce the loss to the

minimum after risk occurs which is the most important research issue of financial risk

control system. Through the exploration and study of the actual case, people can very

intuitive and systematic analyze financial risk control problem.

4.5.2 Financial risk control strategy

A. Standardize the enterprise management structure

Enterprise management is mainly based on property rights and the board of

supervisors of professional managers. Only clear property rights can be clarified

rights and responsibilities by the size of property, and companies can have a stronger

thrust and broader space for development. Professional managers refer to those who

are responsible for the operation and management of enterprises by the owners of

property rights in the name of the shareholders' meeting and a board form supervisors

of the operator system. Once the professional managers make inappropriate business

decisions, the board of directors can correct them on time through the supervision

system.

B. Establish the financial risk warning system

Financial risk early warning system's main role is to prevent the deviation of

corporate financial operations from the established goals, and the potential financial

risks predict and warn before the risks occur. The good or bad effect of risk

prevention and control is mainly determined by the sensitivity of the risk early

warning system. The high sensitivity represents it can more effectively identify the

various financial risks and early warning the management of the enterprise can come

up with risk mitigation plan to avoid financial risks. .

C. Perfect enterprise financial operation mechanism

First of all, it is important to establish a reasonable incentive and restraint

mechanisms. Incentive mechanism can be divided into two levels as spiritual and

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material aspects. Spiritual encouragement is mainly on the spiritual level to praise

such as congress recognition, award certificates, advanced selection, business experts

and so on. The main forms of material incentives are to enhance wages, cash

incentives, stocks transfer, annual bonus, additional insurance and so on. In the

establishment of incentive mechanism, it is also need a relevant penalties mechanism

which can effectively spur staff proactive. Common penalties system has a fine,

compensation for losses, seizure of bonuses, administrative sanctions, and criticism

and so on.

Next, it is necessary establish a viable investment decision-making mechanism.

One, before investing, the investment feasibility analysis is done, the quantitative

analysis method is used to establish the decision model, the data and the research

result are guiding direction of investment behavior, and the investment decision error

can be minimized. The other, the investment opportunities, investment risks, the

company's financial status, the current investment situation and so on are fully

considered as factors, taking into account the financial matching factors and fully

consider their own capital flow.

Finally, it is crucial to enhance the enterprise's financial risk management

system. The establishment of the financial risk management system is an important

measure to guarantee the financial security of the enterprise, which can reduce the

occurrence of financial risk events hugely. If the enterprise structure is confusing, the

division of responsibilities of various departments is unclear, and the lack of

protection from financial risk management system, and then the consequences are

unimaginable.

Chapter Five Conclusion

In this study takes the sample data from 18 listed metal packaging enterprises in

China for the period of 2012-2015. The financial risk evaluation system of the metal

packaging industry is constructed by selecting 29 indicators by the indicators of the

manufacturing industry. On the basis of this, taking Company A as an example, the

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conclusion is that the financial risk of Company A is less than the industry average. It

is suggested that Company A should standardize the enterprise management structure

to establish the financial risk early warning system and perfect the financial operation

mechanism as the next step of the company's financial risk control recommendations

and countermeasures.

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Reference

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