Research on management evaluation of enterprise sales cash flow percentage method based on the application of quadratic linear regression equations

In this case, the discount rate of the income method evaluation process selects the weighted average cost of capital (WACC). The calculation formula is as follows: (1) WACC=Ke×E(D+E)+Kd×D(D+E)×(1−T) WACC = {K_e} \times {E \over {(D + E)}} + {K_d} \times {D \over {(D + E)}} \times (1 - T) The calculation formula of profitable capital cost is as follows: (2) Ke=Rf+βL×MRP+RC {K_e} = {R_f} + {\beta _L} \times MRP + {R_C} In the formula, R_f represents the risk-free rate of return; β_L represents the enterprise risk coefficient; MRP represents the market risk premium and R_C represents the enterprise specific risk adjustment coefficient. The method for determining the risk-free rate of return R_f is as follows: Treasury bonds are generally considered risk free. After calculating the compound interest factor, the average value from 1999 to the evaluation base date is 3.91%, and the risk-free rate of return for this assessment is 3.91%. The calculation formula of enterprise risk coefficient β_L is as follows: (3) βL=[1+(1−T)×D/E]×βU {\beta _L} = \left[ {1 + (1 - T) \times D/E} \right] \times {\beta _U} In the formula, T represents the income tax rate, which is 15% of the income tax rate implemented by the enterprise. D/E represents the current actual situation of the company and the capital structure of comparable listed companies, and the management's future financing strategy is determined at 15.60%; β_U represents the risk coefficient of the company without financial leverage.

By querying find information, select the Shanghai and Shenzhen A-share computer, communications and other electronic equipment manufacturing listed companies without financial leverage corporate risk coefficient β_U and use the above calculation formula to calculate the financial leverage corporate risk coefficient β_L. By querying find information, β_U is 0.9124. Then, the formula = 0.9124 * (1 + (1 − 15.60%) * 13%) = 1.03.

Currently, there is a popular method to measure the risk capital premium in stock markets outside the United States. This method was proposed by A swath Damodaran, a well-known finance professor and evaluation expert at New York University's Stern School of Business. Based on the risk premium and country risk premium in the stock market, the country risk premium for this project is obtained in the Chinese market. After inquiry, the risk premium of mature stock market is 6.18%, and the country risk premium of China is 0.93%. Therefore, the current equity risk premium MPR of the Chinese market is approximately 7.11%. Figure 1 shows the basic model of stock risk premium [3]. The debt capital cost is 4.90% of the bank loan interest rate over 5 years on the assessment base date. Determination of the WACC is given as follows: (4) WACC=Ke×E(D+E)+Kd×D(D+E)×(1−T)=12.01% WACC = {K_e} \times {E \over {(D + E)}} + {K_d} \times {D \over {(D + E)}} \times (1 - T) = 12.01{\rm{\% }}

Fig. 1

The sales percentage method assumes that the ratio of assets, liabilities and sales revenue is stable, and so, the assets and liabilities can be predicted based on the estimated sales revenue and percentage, and then, the future cash flow of the company can be predicted. The advantage of this method is simple, practical and understandable, but there are some limitations due to the use of assumptions. The sales percentage method can be used for financial forecasting and can also be used to evaluate the bad debt balance of accounts receivable. This article uses the sales percentage method to predict the company's future cash flow. The thesis uses the sales percentage method to predict the short-term capital needs of the enterprise, and the following assumptions must be met: the proportion of annual income, costs, assets, liabilities and other items is consistent with sales revenue. Only when there is a feasible connection between these projects, the future relevant data can be predicted by the owner's current and existing income, expenses, assets, liabilities and equity [4].

Cash flow refers to the cash inflows or corporate outflows of cash receipts and payments and cash settlement systems over a period of time. Cash flow management occupies an increasingly important position in corporate management and is an important tool to ensure the company's continued operation. The cash flow status within a certain period of time can provide a theoretical basis for corporate decision-making, and at the same time, corporate cash flow is a decisive factor in the company's value.

The cash put into use will continue to flow and will eventually be converted into cash over time. Therefore, the discounted cash flow is an indicator of the company's value. The basis for estimating the value of cash flows is to calculate the present value of future income through the discount rate. The overall evaluation model can be expressed as a formula: (5) V=∑t=1FCFF(1+r)t V = \sum\limits_{t = 1} {{FCFF} \over {{{(1 + r)}^t}}} Among them, V represents the value of the enterprise, n represents the life of the asset, FCFF represents the cash flow of the enterprise in t period and r represents the discount rate.

Among them, V represents the value of the enterprise on the basis of the assessment, FCFF represents the future free cash flow of the company, WACC represents the WACC and g represents the steady growth rate of the company's cash flow. The growth rate in this model needs to be consistent with the macroeconomic growth rate. The discount rate used is WACC. Generally speaking, the WACC of a company is lower than the cost of equity; so, the result of this model is more sensitive to the growth rate. (6) V=FCFFWACC−g V = {{FCFF} \over {WACC - g}}

The enterprise value is the sum of the cash flow limit before stabilisation and the present value of the cash flow in the stable state, expressed as follows: (7) V=∑t=1FCFF(1+r)t+FCFFn+1(WACC−g)(1+WACC)n V = \sum\limits_{t = 1} {{FCFF} \over {{{(1 + r)}^t}}} + {{FCF{F_{n + 1}}} \over {(WACC - g)(1 + WACC{)^n}}}

Regression analysis is the use of a large amount of observation data to analyse the specific form of correlation between independent variables and dependent variables, determine the causal relationship and use mathematical models to express their specific relationship. The statistical method of studying the relationship between one or more random variables (y₁, y₂, ..., y_i) and other variables (x₁, x₂, ..., x_k) is also known as multiple regression analysis. Usually, y_i is the dependent variable and x_k is the independent variable. The main contents of regression analysis are as follows:

(1) Starting from a set of data, determine the quantitative relationship between certain variables, that is, establish a mathematical model and estimate the unknown parameters; (2) test the credibility of these relationships and (3) when many independent variables affect the relationship of dependent variables, it is important to define the influence of independent variables (or smaller); the influence of independent variables is not significant, and independent analyse strings are replaced. In the model, excluding those unimportant variables is usually used to predict or control a specific process using relational hypothesis expressions.

Figure 2 shows the relationship between operating costs and sales revenue. It can be seen from the figure that the result of the quadratic regression of operating cost and sales revenue is the best, the quadratic function R is 1 and the goodness of fit is the highest and 100% significant. Therefore, the quadratic function relationship of operating cost y₁ and sales revenue x is obtained using the following formula: (8) y=1.955×10−5x2−1.67x+6.8897.943 y = 1.955 \times {10^{ - 5}}{x^2} - 1.67x + 6.8897.943

Fig. 2

Figure 3 shows the results of regression analysis of business tax and surcharges and sales revenue. It can be seen from the figure that the results of the quadratic regression of business taxes and surcharges and sales revenue are the best, and the quadratic function R has a square value of 1, and the goodness of fit is the highest and 100% significant [7]. Therefore, the quadratic of operating cost y₂ and sales revenue x functional relation is as follows: (9) y=2.042×10−7x2−0.02x+667.13 y = 2.042 \times {10^{ - 7}}{x^2} - 0.02x + 667.13

Fig. 3

Figure 4 shows the results of regression analysis of sales expenses and sales revenue. It can be seen from the figure that the results of the quadratic regression of sales expenses and sales revenue are the best, and the quadratic function R has a square value of 1, and the goodness of fit is the highest and 100% significant. This results in the quadratic function of operating cost y₃ and sales revenue x relationship: (10) y=3.404×10−6x2+0.461x−10266.728 y = 3.404 \times {10^{ - 6}}{x^2} + 0.461x - 10266.728

Fig. 4

Although the goodness of fit of linear regression is lower than the goodness of fit of the quadratic function because the curve has begun to show a downward trend, if the increase in sales revenue in the later period is still predicted by the quadratic curve, the sales expenses will begin to decline, which does not conform to the actual situation. Therefore, the regression prediction of sales expenses and sales revenue is calculated according to the revised linear regression formula [8].

Although each financial index has the highest degree of fit with the sales revenue quadratic regression, after actual calculations, when this equation is used for cash flow forecasting, each year's profit is negative [9]. The calculation results are shown in Table 1:

The calculation results show that the annual profit of each forecast is negative, indicating that the regression model does not meet the reality. Therefore, the revised one-variable linear regression model is used for data analysis. The linear model is theoretically more realistic. With the increase in sales revenue, it is reasonable that all revenues such as operating costs and management expenses will increase accordingly. Therefore, the revised linear regression equation is used to re-predict the relevant financial indicators [10]. Based on the revised regression equation, the financial index calculation table is shown in Table 2:

As shown in Table 2, the indicator values and annual profits are basically in line with reality, indicating that the forecast is effective and credible [11].