Frontiers in Business, Economics and Management ISSN: 2766-824X | Vol. 4, No. 2, 2022 106 Research on Sciplay Financial Performance Evaluation Muxi Yuan School of Economics and Management, Beijing Jiaotong University, Beijing 100091, China Abstract: This paper analyzes the financial indicators of listed companies by factor analysis. This paper collects the financial data of sciplay Company from 2018 to 2022, selects fourteen indicators that reflect the solvency, operating ability, profitability and development ability, and makes empirical analysis and comprehensive evaluation on the financial performance in recent years. The results show that there are risks in the financial operation of sciplay Company, a US stock company, and the development of various financial capabilities is relatively unbalanced. Keywords: Factor analysis, Performance evaluation, Financial indicators, Sciplay company. 1. Introduction The video game industry is intensely competitive and subject to rapid changes in consumer preferences and frequent new product introductions. The competitive landscape consists of video game and related specialty stores, mass merchants and regional chains; computer product and consumer electronics stores; toy retail chains and direct sales by software publishers. online environments have also become more competitive with the likes of Sony (PlayStation Network), Microsoft (XBox Live), Nintendo (Nintendo Switch Online), as well as other online retailers and game rental companies gaining shares of digital software sales. The Coronavirus has accelerated spending online which has benefited leading players that already have a sophisticated online business model in place as well as businesses that are considered as essential such as supermarkets which have continued to sell gaming software. Factor analysis was used to construct a model for Sciplay financial performance It is not only of positive significance to the long-term development of the company, but also a reference for other game industry companies to make evaluation, find the changing trend of the overall performance level, analyse the existing problems and give reasonable suggestions. 2. Literature Review Financial performance evaluation is the use of financial indicators of the performance of the company in a comparative analysis of a kind of evaluation method, first of all, according to the target company's industry categories and characteristics of the industry, select a series of financial index to scientific evaluation of enterprise performance, and score the evaluation results, find the problems and short board, formulate development strategies for managers, investors and enterprises. This paper chooses the factor analysis method to analysis the financial performance of Sciplay. Factor analysis is used to analyze financial indicators. Factor analysis principle is an analysis method that extracts a few principal components that can represent all variables from a number of interrelated variables, and evaluates the whole by scoring the principal components in each variable and comprehensive scores [1-8]. 3. US Stock Company’s Financial Evaluation System Construct In this paper, the construction of the basic framework of the evaluation index system is based on the table of major regulatory indicators of enterprises and related literature research. Combined with quantitative indicators and the characteristics of corporate finance, the evaluation index of corporate finance is selected from multiple levels and angles. Finally, the basic framework of the evaluation index system is as follows: solvency, operating ability, profitability and development ability. Explanatory variables include current ratio, quick ratio, asset-liability ratio (%), equity multiplier, current asset turnover rate (times), fixed asset turnover rate (times), total asset turnover rate (times), gross profit margin of sales (%), net profit margin of sales (%), return on equity (average) (%), net profit margin of total assets (%), and operating profit/current liabilities. The specific index system is shown in Table 1. 4. Empirical Analyses 4.1. Factor Analysis Adequacy Test In order to ensure the feasibility of factor analysis on the original data, firstly, KMO test and Bartlett spherical test are carried out on whether the original variables can meet the basic conditions for factor analysis. KMO test, Bartlett spherical test standard: KMO value is selected between 0 and 1. If KMO value is closer to 1.0, the results show that there are many common factors among the selected original variables, that is, the better the effect of factor analysis of these variables is, the best effect is greater than 0.9, more than 0.7 is acceptable, and less than 0.5 is not suitable for factor analysis. Bartlett spherical test, approximate chi-square, significance P < 0.001 indicates that the variables are highly correlated, which is enough to provide a reasonable basis for factor analysis. The test results of KMO and Bartlett are as follows. It can be seen from the results that KMO value is 0.581>0.5, and the significance p=0.000, which indicates that these variables are suitable for factor analysis, and the variables are highly correlated, which can provide a reasonable basis for factor analysis. 107 Table 1. Index system construction Criteria layer Specific indicators attribute liquidity ratio + quick ratio + debt paying ability Asset liability ratio (%) - Equity multiplier + Turnover rate of current assets (times) + operating capacity Turnover rate of fixed assets (times) + Total assets turnover rate (times) + Gross profit margin of sales (%) + profitability Net profit rate of sales (%) + Return on net assets (average) (%) + Net interest rate of total assets (%) + Operating profit/current liabilities (%) + Developing ability Year-on-year growth rate of operating profit (%) + Year-on-year growth rate of operating income (%) + Table 2. KMO and Bartlett test Kaiser-Meyer-Olkin measurement of sampling adequacy. Bart's sphericity test .581 Approximate chi- square 272.233 freedom 55 significance .000 After the preliminary test, the variance of the common factors extracted from the explanatory variables is determined, and the principal component analysis is the selected method. If the selected common factor can represent most of the information in the data, it indicates that the common factor is representative. As shown in the common factor variance in the following table, the information extracted from each original variable in this analysis is given. For example, the common factor variance of flow rate is 0.983, which indicates that several common factors can explain 98.3% of the variance of flow rate. In addition, most of the variance of variables are above 90%. Therefore, the overall effect of the data extracted by factor analysis is ideal. Table 3. Common factor variance initial draw Z flow ratio 1.000 .983 Z quick ratio 1.000 .983 Z asset-liability ratio 1.000 .997 Z equity multiplier 1.000 .996 Z turnover rate of current assets 1.000 .961 Z turnover rate of fixed assets 1.000 .967 Z total assets turnover rate 1.000 .995 Z gross profit margin of sales 1.000 .951 Z net profit rate of sales 1.000 .969 Average return on equity z 1.000 .943 Z net interest rate of total assets 1.000 .972 Z Operating profit current liabilities 1.000 .893 Z Year-on-year growth rate of operating profit 1.000 .826 Z Year-on-year growth rate of operating income 1.000 .795 Extraction method: Principal component analysis. 4.2. Data Standardization Due to the difference of measurement, the data will be standardized. Names and data of standardized indicators are as follows. 4.3. Extract Common Factors As shown in Table 4, the characteristic root and variance contribution rate table gives the explanation of the total variance of the original variable by each principal component. According to the results, the total variance of common factor 1 interpretation is 54.901%, which means that common factor 1 can cover 54.901% of all the information contained in the original data. The total variance of common factor 2 interpretation is 23.088%, which means that common factor 2 can cover 23.088% of all the information contained in the original data. The total variance of common factor 3 108 interpretation is 9.307%, which means that common factor 3 can cover 9.307% of all the information contained in the original data. The total variance of common factor 4 interpretation is 7.191%, which means that common factor 4 can cover 7.191% of all the information contained in the original data. It can also be seen from the gravel diagram results in the following figure that component 1, component 2, component 3 and component 4 occupy a very significant factor position, and the values of their eigenvalues are all greater than 1. Therefore, it is reasonable to extract four common factors. Table 4. Explanation of Total Variance component part Initial eigenvalue Extract the sum of load squares. Sum of squares of rotating loads amount to Variance percentage Cumulative% amount to Variance percentage Cumulative% amount to Variance percentage Cumulative% one 7.686 54.901 54.901 7.686 54.901 54.901 4.768 34.058 34.058 2 3.232 23.088 77.990 3.232 23.088 77.990 4.568 32.626 66.684 three 1.303 9.307 87.297 1.303 9.307 87.297 2.563 18.305 84.989 four 1.007 7.191 94.488 1.007 7.191 94.488 1.330 9.499 94.488 five .529 3.776 98.264 six .147 1.047 99.310 seven .055 .392 99.703 eight .027 .191 99.894 nine .009 .065 99.959 10 .004 .026 99.984 11 .002 .014 99.998 12 .000 .002 100.000 13 5.932E-7 4.237E-6 100.000 14 1.110E-16 7.930E-16 100.000 Figure 1. Gravel diagram 4.4. Naming of Common Factors According to the factor load matrix after orthogonal rotation in the following table, the larger the correlation coefficient value, the greater the representativeness of the factor to the original variable. Taking 0.5 as the importance standard, it can be seen that the relationship between the first factor and the original variables is larger among the four factors, indicating that it has the largest amount of explanation for the original variables. Finally, the retained correlation coefficient values are shown in Table 5. The first factor is heavily loaded on four variables: Z current ratio, Z quick ratio, Z asset-liability ratio and Z equity multiplier. These variables represent solvency, so the first factor is named as solvency factor; Similarly, the second factor is named as the operational capability factor; The third factor, profitability, is named as the population change factor; The fourth factor, profitability, is named development ability factor; 109 Table 5. Composition Matrix A after Rotation component part one 2 three four Z flow ratio .873 Z quick ratio .873 Z asset-liability ratio .907 Z equity multiplier .907 Z turnover rate of current assets .824 Z turnover rate of fixed assets .935 Z total assets turnover rate .963 Z gross profit margin of sales .820 Z net profit rate of sales .907 Average return on equity z .852 Z net interest rate of total assets .730 Z Operating profit current liabilities .628 Z Year-on-year growth rate of operating profit -.686 .526 Z Year-on-year growth rate of operating income .873 4.5. Calculate the Common Factor Score According to the scores of each factor calculated by spss25.0: FAC1, FAC2, FAC3, FAC4, combined with the above four common factors obtained by spss25.0, the total variance value is explained as the weight of each factor, and then the weighted average sum is used to calculate the comprehensive score of enterprise financial status evaluation. The formula is as follows: Comprehensive f=(0.54901*fac1+0.23088*fac2+0.09307*fac3+0.07191*fac 4)/0.94488 Table 6. factor score FAC1 FAC2 FAC3 FAC4 F zong 2018 Annual Report -1.87499 1.95602 -.11568 -1.02485 -.70 First Quarterly Report of 2019 -2.50974 -1.06275 -.79454 1.53859 -1.68 2019 interim report -.18969 -.28233 2.50836 .30638 .09 2019 Third Quarterly Report .01361 .37667 1.75087 .12764 .28 2019 Annual Report .47788 1.44577 .32897 .23329 .68 Quarterly report in 2020 .43347 -1.13706 .30881 -1.18748 -.09 Mid-year report 2020 .78891 -.23252 -.19105 .80404 .44 Quarterly report in 2020 .87800 .37888 -.44389 1.12756 .64 2020 annual report .81829 .87571 -.67104 1.23838 .72 Quarterly report of 2021 .31888 -1.19491 -.22696 .69808 -.08 2021 mid-year report .45411 -.45703 -.64780 -.64970 .04 Third Quarterly Report of 2021 .52828 .14997 -.91134 -.76613 .20 2021 Annual Report .12380 .45285 -.89933 -1.03729 .02 Quarterly report of 2022 -.26084 -1.26928 .00462 -1.40851 -.57 5. Comprehensive Analysis Generally speaking, when the value of factor score is less than 0, it means that the financial capability of the enterprise is not satisfactory; On the contrary, when the value is greater than 0, it means that the enterprise has better financial ability. As can be seen from Table 6, the factor scores are greater than 0 and less than 0, which indicates that there are risks in the financial operation of sciplay Company. According to the ranking of sciplay Company's financial comprehensive scores in each quarter from 2018 to 2022, as shown in Figure 4 below, it can be seen that sciplay Company's financial factor comprehensive scores ranked first in 2020, and the comprehensive scores in each quarter in 2020 were in the top position, with satisfactory financial ability; In the first quarter of 2018 and 2019, the ranking was lower, and the comprehensive score was too low, indicating that the comprehensive performance of financial ability was worrying; In 2021 and the first quarter of 2022, the ranking was low, and the financial performance was average. Based on the analysis of four common factor scores, in each quarter of 2018-2022, the scores of the four common factors are all negative, which indicates that the development of various financial capabilities of sciplay is relatively unbalanced. 110 Figure 2. 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