【作 者】
Youtang Zhang，Ying Peng，ABM MuniburRahman
【作者单位】
（School of Management，Wuhan University of Technology，Wuhan，P.R. China，430070）
【摘 要】
Abstract: Through the learning from existing research in China, this paper analyses the mechanism of industry environment which has deep effect on enterprise financial risk and summarizes the seven dimensions of industry environmental risks. According to these seven dimensions, authors create index system and build structural equation model of industry risks. Results obtained from the simulations confirm that development of the financial risk is highly related to the industry environment. The early-warning simulation model of enterprise classifies in seven subsystems. It is also testified in this paper that the early-warning systems are capable of identifying potential financial risks, which is conducive to developing effective financial policies in accordance with the external changes.
Keywords: Industry Risk； Financial Early-Warning； Structural Equation Model； Early-Warning Simulation1 Summary of Chinese Enterprise Financial Early-warning System
1.1 Development of Chinese Enterprise Financial Early-warning System
Financial early warning system were introduced to China in 1980s, Lian Yu(1988)proposed the “Enterprise Management Research in Adversity” in the first place. Then, Yu (1993) developed a theory of enterprise’s early-warning management system that emphasizes on the equal importance among the early-warning management, risk management and traditional enterprise management， performance improvement management. After reviewing the financial reports of 81 special treatment companies in Shanghai and Shenzhen stock exchange plus other 1266 companies from 2002 to 2003, Cuixia Wang (2006) found out that the Bayesian network was contribute to establish the financial early-warning system. Jiaxiang Zhu, Qingmei Tan and Xiangyuan Jing(2008) built a two stage early-warning model by using the grey system theory to process three vital parameters: the rate of return on net assets, financial leverage and liquidity ratio. Huizhen Mou(2009) put forward the “suitable for enterprise” micro-warning model construction concept, which evaluated comprehensively in two aspects: financial indicators and non-financial factors on enterprise risk status. In 2010, Runchun Xie(2010) developed a model including the single type, the conservative type, and the early-warning risk type strategies, and then used them to forecast financial risk for corporations in China stock market. In this model, the net profit and the net profit per share are considered as the two major contributing factors to define the financial risk of the industry.
In summary, various methods had been used to investigate the financial early-warning issue, ranging from the basic single variable model to the latest neural network model. Recent activities in the field of applications of the financial early-warning models have been increased in order to detect the potential financial risk. However, the existing outcomes are mostly derived from the micro-economic factors, leading to an absence of a comprehensive insight into the external risk monitoring.
1.2 Development of Chinese Enterprise Financial Early- Warning that Based on Monitoring Industrial Risk
In 2007, Xiaoqin Tong compared the risks of different industries according to the corresponding beta values. Juan Chen (2008) introduced the life cycle of industry to the financial risk assessment systems. Zhanhua Ying (2009) analysed some influential factors, such as the external environment, the financial conditions and the operation results, then calculated the industry risk index along with the relevant levels. Zhenghua Lu (2009) investigated the link between the industry environment and the cash holdings using financial data of China"s listed companies from 2001 to 2005. The paper also argued that the more environmental changed, the higher financial leverage got, and also caused the higher degree of shareholder protection and the less cash holding.
1.3 Analysis of Existing Chinese Research on Financial Risk Early- warning Problems
(a) The existing financial risk early-warning models are unable to present the differences between countries or industries, because this function is ignored in the modelling phase. Therefore, it is highly required to compare the characteristics of different industries in the future research project.
(b)The selection of variable which influences the result of the early-warning model is not comprehensive. Most of the variables of the early-warning models related only the accounting data and financial ratios, but without other quantifiable factors, which can reflect more problems than financial ratios.
(c) Less attention has been paid to the cash flow index. The cash flow index plays a very important role in reflecting the enterprise’s financial situation, so it should be included in the early-warning indices.
(d) The authenticity of the early-warning result is influenced by the selection of the financial indicators derived from the policy of accrual basis accounting. Financial index system of accrual basis accounting is easily manipulated by managers. Thus, the existing early-warning models are difficult to find the company’s financial crises.
2 Analysis of Financial Early -warning
Mechanism of Enterprise Based on Industry Risk Monitoring
2.1 Analysis of Financial Early-warning Mechanism of Enterprise Based on Economic Cycle
The research of economic cycle concentrates on the boom-and-bust of the national wide economy country, rather than in one single industry only. In general, economic cycle that occurs repeatedly can be divided into four periods: recovery, prosperity, recession and depression. Fluctuations in the economic cycle can be shown by the indices, such as the gross national product, national income, total investment, consumption, price index, etc.
The cycle of the micro-economic organization is identical to the macro-economy. The cause of the enterprise risk can be summarized as the Fig.1.
It is certain that cycle fluctuations in the national economy will lead to some periodic financial risks in various types of economic organizations. For example, there are four stages in the real estate industry: recovery, prosperity, recession and depression. According to the Fig.2, the real estate industry fluctuates in accordance with the national economy, although the strength is higher than the later.
The fluctuation of real estate industry has also the same impact on real estate enterprises. On the different period of industry life cycle, it is different on policy, cash, supply and demand etc. Therefore, real estate enterprises face different degrees and different types of risk.
2.2 Analysis of Financial Early-warning Mechanism of Enterprise Based on Industry Risk Monitoring
The formation process of the enterprises’ financial risk indicates that the financial system of enterprise cannot respond to the changes of the industry environment, at this point, financial early-warning shows its importance. predict the influence from the environment changes, and find the source of the financial risk, and then enterprises can propose the financial risk control strategies. The real
fact of the financial early-warning system is that the spe-
cific enterprises should be limited by the nature of their
industry and characteristic. And the prediction of the enterprises should use some sensitivity analysis. System dynamics can imitate the enterprises financial early-warning system by strict logical analysis, then explain the relationship between variables. Depending on the financial early-warning models, managers are able to solve management issues in enterprises.
3 Structure of the Industries Financial Monitoring Model based on Structural Equation Model
3.1 The Dimension of Industry Risk
Industry risk can be categorized into seven dimensions: resource risk, competition risk, life cycle risk, technology change risk, credit risk, the risk of tax rate, and interest rate risk. They respectively influence on every aspect of financial activities. The interactive model of the industry risk and the financial risk is shown in the Fig.3.
3.2 Design the Industry Risk Indices
The authors classified every factor of the risk into the qualitative index and the quantitative index. The qualitative index is assessed by the expert scoring method，using geometric means to get the comprehensive score of every index. The other standard formula has been formulated by existing researchers to get the result of quantitative index. The process is shown in Fig.4.
3.3 Structural Equation Models Construction of Industry Risk
In this paper, AMOS 17.0 software package is used to analyse the industry risk, which is professional tool to establish the structural equation models. Fig.5 shows the initial structure equation model designed by the AMOS 17.0 software. The model includes two risks: the first is the industry risk that includes industry resources risk, inustry competition risk, industry lifecycle risk, industry technological innovation risk, industry credit risk, tax risk and industry interest rate risk; the second is the enterprise financial risk. The structural model includes three path relationships, which represent causality between seven exogenous latent variables and an internal one. Moreover, according to the practice of the structural equation model analysis, the model sets up 16 significant error variables of e1-e25 and a latent residual variable of e26.In the structural equation model the initial path coefficient of all error variables or residual ones are fixed to 1 and each latent variable must be fixed to an observation quota whose path coefficient is 1.
3.4 Descriptive Statistical Analysis of Industry Risk based on Structural Equation Model
Descriptive statistical analysis is briefly to check the specificity and distribution of each indicator variable in research sample data. Descriptive statistical analysis is obtained the effective data by using SPSS 17.0; data in Table 1. According to the descriptive statistical analysis data of the 220 valid samples, we figure out that the mean of the three observation data in the selected industry resources risk is between 2.99 ~ 3.09, the standard deviation of data is between 0.738 ~ 0.767, the variance of data is between 0.545 ~ 0.589. The mean of the three observation data in industry competition risk latent variable is between 2.92 ~ 3.11, the standard deviation of data is between 0.696 ~ 0.748, the variance is between 0.485 ~ 0.559. Furthermore, the mean of three observation data in industry lifecycle risk latent variable is between 2.92 ~ 3.11, the standard deviations between 0.696 ~ 0.748, the variance of them is between 0.485 ~ 0.559. The mean of the three observation data in industry technology change risk is between 2.82 ~ 3.00, the standard deviation of them is between 0.726 ~ 0.774, the variance of them is between 0.528 ~ 0.599. While the mean of three observation data in industry credit risk is between 2.25 ~ 3.00, the standard deviation of them is between 0.782 ~ 0.796, the variance of the data is between 0.527 ~ 0.595. Moreover, the mean of the three observation data in industry tax risk is between 2.81 ~ 3.00, the standard deviation of the sector is between 0.731 ~ 0.778, where the variance of them is between 0.527 ~ 0.598. The mean of three observation data in industry interest rate risk is between 2.81 ~ 3.00, the standard deviation of them is between 0.725 ~ 0.776, the variance of the data is between 0.525 ~ 0.598. For another factor, the mean of three observation data in industry financial risk is between 2.87 ~ 3.04, the standard deviation is between0.718 ~ 0.786, the variance of them is between 0.545 ~ 0.589.Overall mean of the underlying data is between 2.82 ~ 3.09, the standard deviation is between 0.515 ~ 0.617.
4 Monitoring Corporate Financial Risk of Industry with Dynamic Simulation Model
4.1 Dynamic Early-warning Simulation Model for Market and Order Subsystem
The main factors of market and order forecast subsystem include market demand, customer orders, date of delivery, fulfilled order quantity, market prices and market share. There are three mainly feedback loops: the increase of market demand, the increase of customer orders, the increase of accepted orders, the increase of fulfilled order quantity, increase production capacity, providing more products, and the increase of market demand ,this process form a positive feedback loop; the increase of market demand, the increase of customer orders, the increase of accepted orders, the date of delivery become longer, the loss of customers, and the decrease of market demand ,this process form a negative feedback loop; the increase of market demand, the increase of customer orders, the increase of accepted orders, the increase of fulfilled order quantity, the increase of market share, the reduce of potential customers, the increase of market saturation, the decrease of market demand, this process form another negative feedback loop.
To simplify the model, the market demand using table function expression was established by the enterprise manager. The stream flow rate chart of market and order early-warning subsystem is shown in Fig. 6.
(1) Customer Orders = Market Demand (Time);
(2) Order Quantity = Customer Orders;
(3) Accepted Orders = INTEG (Order Quantity-Fulfilled Order Quantity, Order Initial Value);
(4) Fulfilled Order Quantity = Shipments;
(5)Expected Shipments = Accepted Orders / Targeted Delivery Date.
4.2 Dynamic Early-warning Simulation Model for Sales and Collection Subsystem
The main factors of sales and collection subsystem are sales, sales income, receivables, credit policy and cash. There are mainly two feedback loops: if credit policy is very easy, it may lead to the increase of customers, sales and product market share. Moreover, it also leads to the reduction of inventory, the increase of receivables, profits and cash. This process can form a positive feedback loop. However, when the credit policy is very easy, it may also lead the increase of customers, rising sales, and the money and the time accounts receivable take up, at the same time the losses of the bad debt and the cost of accounts receivable may become bigger, also the reduction of profits and cash, which form a negative feedback loop.
Cash is received from selling the goods and the sales can be divided into cashes received in cash sales and credit sales. According to the forecast of sales income, output VAT, and ratio of the cash sales can get the flow of cash sales by selling the goods. And the credit sales can be got through the deadline for the account receivable. The adjustment of cash sales ratio and the deadline of the account receivable can be reflected through payment poliies. Sales and collection subsystem of the flow rate is shown in Fig.7.
(1) Sale Revenue= Sale Volume * Selling Price;
(2) Substituted Money on VAT = Sale Revenue * Tax Rate of VAT;
(3)Cash Inflows from Cash Sales= Cash Sales (Revenue + Tax) * Cash Sales Proportion;
(4) Credit Sale Revenue= (Sale Revenue + Substituted Money on VAT) * (1 - Cash Sales Proportion)
(5) Receivable = INTEG (Sale Revenue on Credit- Loss on Bad Debts-Cash Inflows from Selling on Credit, Receivable Initial Value);
(6) Cash Inflows from Selling on Credit = Receivable / Period of Receivable;
(7) Loss on Bad Debts = SMOOTH (Receivable *Rate of Bad Debt, Time of Bad Debt).
4.3 Dynamic Early-warning Simulation Model for Production and Inventory Subsystem
Production and inventory subsystem mainly contains following feedback loops: the increase of customer orders lead to the increase of requirements for shipments, production, inventory goods, and the shipments, which process form a positive feedback loop; while the increase of expected stock may cause the increase of the inventory adjustment, expected production, production, inventory goods, then the inventory adjustment may reduce, which process form a negative feedback loop; moreover, the increase of expected manufacturing quantity may lead to the increase of work-in-process products, the adjustment of work-in-process products and the expected put-into-production products, and when the increase of put-into-production leads to the increase of work-in-process products, manufacturing quantity, inventory goods, shipments, and expected manufacturing quantity, this complex process can form a positive feedback loop; the increase of expected put-into-production products lead to the increase of work-in-process adjustment, put-into-production and work-in-process products, but also the reduction of work-in-process adjustment, which form a negative feedback loop; also forming a negative feedback loop includes the increase of expected raw material, the adjustment of raw material, expected put-into-production material amount, expected material purchased, but the reduction of raw material adjustment; what’s more, the increase of customer orders which cause the increase of expected shipment, the expected manufacturing quantity, the expected put-into- production, the expected material consumption, material purchased, material using, raw material, work-in-process, manufacturing quantity, inventory goods ,and the shipment to form a positive feedback loop.
Manufacturing enterprises need to be selected the order lead time, because in the production process has its cycle and the process of sales has order handling time. In order to meet the requirements of customer order, enterprises generally through past sales data to predict customer orders demand when it happening before; if enterprises want to predict the production, they may get the result from the sales and the expected inventory; then the production may meet the need of customer orders. The continuity of sales may need the enterprises must also keep certain amount of finished products as a safety stock. It is a problem that the short of stock, production become divorced from marketing, and the redundancy of the inventory, which could be avoided by keep good relationship among the number of sales, output, and inventory. The consumption of unit production can predict the using of the materials, which can get the expected amount of materials and their inventory. Because of the existence of the order lead time, the material purchasing is the smooth order of the expected material purchasing by first order.
Generally speaking, enterprise material consumption per unit product can be classified by the consumption of raw material per unit product, fuel and energy per unit product, purchased parts per unit product, etc. Using system dynamic building the enterprise financial prediction model is to control the logistics and cash flow of enterprises. Therefore, to simplicity the model, the consumption of er unit product is not elaborated and classified.
4.4 Dynamic Early-warning Simulation Model for Purchasing and Payment Subsystem
In the sector of purchasing and payment subsystem, the main factors include purchase quantity, purchase price, purchase amount, account payable, payment policy and cash. There are two mainly feedback loops: the extend of payment period, the increase of purchase quantity, the increase of purchasing amount, the increase of cash flow, the decrease of the cash stock, then requiring a longer payment period, this process form a positive feedback loop; the increase of purchase quantity, the increase of purchase amount, the increase of payable account, the decrease of cash outflows, the increase of cash in hand, then it could increase the purchase quantity and form a positive feedback loop. The increase of purchase quantity, the increase of cash flow, extend the payment period. Because the suppliers are not satisfied, thus, it form a negative feedback loop.
Payment methods include payment before taking delivery of goods, payment while purchasing goods, payment after taking delivery of goods and payment by instalment after taking delivery of goods. Cash for procurement of goods can be divided into the cash for purchased goods on a cash basis should be predicted based on the predicted amount of money used for procurement of materials, withholdings on VAT as well as the ratio of purchased goods on a cash basis. However, the cash for purchased goods on account should be predicted according to the established tenor. The payment policy should be reflected by adjusting the ratio of purchased goods on a cash basis and the tenor of accounts payable. In Fig.8, it is shown the purchasing and payment subsystem flow.
(1)Material Purchases = Material Procurement Quantity * Material Price;
(2)Input VAT = Material Purchases * Purchase VAT;
(3)Cash Purchase = (Material Purchases + Input VAT) * Cash Ratio;
(4)Purchases on Credit = (Material Purchases + Input VAT)*(1-Cash Ratio);
(5)Account Payable = INTEG(Material Purchases on Credit - Credit Payment, Account Payable Initial Value );
(6)Credit Payment = Account Payable/Account Payable Period.
4.5 Dynamic Early-warning Simulation Model for Investment and Financing Subsystem
The main factors of investment and financing subsystem contain short-term investment, long-term investment, investment income, investment in fixed assets and intangible assets, short-term loan, long-term debt, interest, capital and divided distribution. There are following feedback loops: the increase of sales may lead to the increase of sales revenue, increased profits, cash, investment in fixed assets, production capacity and output, this process form a positive feedback loop; the increase of new investment in fixed assets may lead to the decrease of cash, profits, newly added fixed assets, but the increase of depreciation, which form a negative feedback loop. From cash perspective, if cash increase may cause the increase of investment and its income , this process form a positive feedback loop; but the increase of cash may also lead to the increase of investment, at the same time the money may become less, which form a negative feedback loop; when lack of cash lend from bank, this may lead to the increase of short-term and long-term loan, cash, investment, manufacturability, output, sales volume, and profit, which form a positive feedback loop; and if the increase of loan, may cause the increase of interest, at the same time the decrease of cash and profit, which form a negative feedback loop; when the increase of the capital investment, cash increase, then make the increase of manufacturability, output, sales volume, profit, and cash, this process may form a positive feedback loop; but the increase of the investment capital, may also lead the decrease of divided distribution, which cause the decrease of cash, then from a negative feedback loop.
The forecast of long term investment is decided by the enterprise strategy which is designed by management, and expressed through the function in the model. Newly investment of manufacturability is adjusted by the prediction of expected manufacturability and existing manufacturability, when the existing manufacturability lower than expected manufacturability, it should increase the investment of manufacturability, which forms the production bility, then the manufacturability could by the expected. If the process finish, the fixed assets are formed. And the value of fixed assets gradually reduce, which could evaluate by using time. The flow diagram of the investment early-warning subsystem is shown in Fig.9.
(1)Manufacture of Expectation = max (0, Expected Order Amount + Inventory Productions Adjustment);
(2)Manufacturability Adjustment = (Manufacture of Expectation-Manufacturability) / Time of Manufacturability Adjustment;
(3)Investment Amount = Manufacturability Adjustment + Alternative Investment;
(4)Retirement = Manufacturability / Manufacturability
Period;
(5)Manufacturability=INTEG(Investment Amount -Retirement, Initial Manufacturability);
(6)Newly Added Fixed Asset Investment = Investment Amount*Investment Cost;
(7)Fixed Assets= INTEG (+Newly Added Fixed Asset Investment-Depreciation, Initial Value of Fixed Assets);
(8)Depreciation=Fixed Assets/Depreciated Year;
(9)Accumulated Depreciation = INTEG(+Depreciation, Initial Accumulated Depreciation)
Financing policy is reflected through the adjustment of the long-term liabilities ratio and the investment of the capital ratio. If assuming the long-term debt ratio is 0.6, capital ratio is 0.2, the new long-term assets are applied 60% by long-term debt, 20% by the invested capital, and other parts by the funds of the own enterprise and short-term borrowing. The decision of the long-term loan, its period and the investment capital amount, could be determined by the financing policy and newly added long-term assets. The interest of short-term can be got from the interest rate, the time of loan and the total loan principle. Financing subsystem flow rate is shown in Fig.10.
(1)Borrow Short-Term Borrowing=IF THEN ELSE (Cash Difference< 0, -Cash Difference, 0);
(2) Short-term Borrowing=INTEG(+ Borrow Short-term Loan- Pay Short-term Loan, Initial Short-term Loan);
(3) Short-term Loan Interest= Short-term Borrowing *Short-term Borrowing Interest Rate;
(4)Pay Short-term Loan = Short-term Borrowing/Short-term Borrowing Limitation;
(5) Borrow Long-term Loan = MAX (0, (Newly Increased Fixed Assets Investment + New Long-term Investment-Retained Profits) * Long-term Debt Ratio);
(6) Long-term Liabilities = INTEG (+Borrowing Long-term Debt-Pay Long-term Debt, Initial Value of Long-term Liabilities);
(7) Long-term Debt Interest = Long-term Liabilities * Long-term Debt Rate;
(8) Repayment of Long-term Debt = Long-term Liabilities/Long-term Debt Limitation;
(9) Invested Capital = (New Fixed Assets Investment + Long-term Investment - Retained Earnings) * (1- Long-term Debt Ratio);
(10) Equity = INTEG (+ Investment Equity-Capital Reduction, Initial Value).4.6 Dynamic Early-warning Simulation Model for Profit Subsystem
The forecast of the profit can get through income subtracting expenses. Income includes sales revenue, other operating income, investment income, subsidies income and non-operating income. Expenses includes cost of sales, sales tax and additional, other operation expenses, selling expenses, management fees, financial expenses, non-operating expenses, and income tax. To simplify the modelling, other operating income, other operation expenses, subsidies income, non-operating income, and non-operating expenses are not considered. The profit warning subsystem of the flow rate is shown in Fig.11.
(1) Cost of Sales =Sales * Unit Manufacturing Cost;
(2) Sales Tax Amount = Sales Revenue * Rate of Sales Tax;
(3) Selling Expenses = Sales Revenue * Rate of Selling Expense;
(4) Management Fees = Sales Revenue * Rate of Management Fees;
(5) Profit = Sales Revenue-Cost of Sales- Sales Taxes Amount - Selling Expenses - Management Fees + Short-term Borrowing Interest - Long-term Liabilities Interest + Short-term Investment Income+ Long-term Investment Income;
(6)Income Tax=IF THEN ELSE(Profit >0, Profit * Income Tax Rate, 0)
(7) Net Profit = Profit * (1 - Income Tax Rate);
(8)Dividend Payout Ratio = IF THEN ELSE (Proportion of Cash Difference in Retained Earnings >0, Function of Dividend Payout Ratio (Proportion of Cash Difference in Retained Earnings, 0));
(9)Dividend =Retained Earnings*Dividend Payout Ratio;
(10)Retained Earnings = INTEG (+Net Profit-Dividend, Initial Value of Retained Earnings);
(11) Payable Taxes = INTEG (Sales VAT- Income VAT + Sales Taxes Amount + Income Tax-Pay Taxes, Initial Value of Taxes Payable);
(12) Pay Taxes = Payable Tax / Tax Payment Period.
4.7 Dynamic Early-warning Simulation Model for Cash Flow Subsystem
The cash flow is divided into cash flow of operating activities, investment activities, and financing activities. Cash inflow of operating activities mainly includes cash received from sales and provides labour; cash outflow of operating activities mainly includes cash pays from purchasing goods and labours. Cash flow of investment activities are mainly including cash received from recoup investment, investment income, net cash from disposal of fixed assets, intangible assets and other long-term assets; cash outflow of investment activities mainly includes cash pays from purchasing and constructing of fixed assets, intangible assets and other long-term assets.
Cash flow of financing activities includes cash received from investment, obtaining loans; cash outflow of financing activities includes paying back the debt, dividends, profits or interest. The cash flow warning subsystem of the flow rate is shown in Fig.12.
(1)Cash Inflow from Operating = Cash of Credit Sale Recovery + Cash of Cash Sale Recovery;
(2)Cash Outflow from Operating = Credit Cash + Cash in Cash + Management Fees + Tax Cash + Wage Payment Cash + Fuel and Power Costs + Operating Expenses;
(3)Net Cash Flow from Operating Activities =Cash Inflow from Operating Activities - Cash Outflow from Operating Activities;
(4)Cash Inflow from Investing Activities = Short-term Investments Recovery + Long-term Investments Recovery + Short-term Investment Income+ Long-term Investment Income;
(5)Cash Outflow from Investing Activities =Short-term Investments+ Newly Added Long-term Investment+ Newly Added Fixed Asset Investment;
(6)Net Cash Flow from Investing Activities =Cash InFlow from Investment Activities- Cash Outflow from Inestment Activities;
(7)Cash Inflow from Financing Activities =Borrowing Long-term Liabilities+ Equity+ Borrowing Short-term Borrowings;
(8)Cash Outflow from Financing Activities =Long-term Liabilities Interest +Repayment of Long-term Debt +Repayment of Short-term Loans +Short-term Loans Interest+Dividend;
(9)Net Flow from Financing Activities =Cash Inflows from Financing Activities -Cash Outflow from Financing Activities;
(10)Net Cash Flow =Net Cash Flow from Operating Activities +Net Cash Flows from Investing Activities +Net Cash Flow from Financing Activities;
(11)Cash = INTEG (+ Net Cash Flow, Initial Cash).
5 Conclusion
This study attempts to help enterprises’ managers to gain a frame model for the industries, identify business potential financial risks and formulate effective financial policies. This paper is trying to reveal the enterprise financial risk of industry environmental factors from the analysis of 220 Chinese companies’ financial datas which construct a structural equation model.
On the other hand, the full playing application advantages of data processing and simulation process realize the dynamic application of the model by introducing the system dynamics simulation model to the early-warning system of enterprise finance and modifying the relevant variables and parameters constantly.
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