Quality Analysis and Key Factor Research in Japan’s Economic Growth Based on Factor Analysis

Abstract

Factor analysis is suitable for the conditions of complex correlation, large sample size, many influencing factors, and many selected indicators; able to conduct research on a variety of subjects; and can better apply multivariate statistical methods to the evaluation of financial performance. Among other common methods of evaluating financial performance, factor analysis methods can reduce dimensionality and simplify data basically design weights. Factor analysis can reduce the dimensionality of data even with large sample sizes. It avoids a lot of complicated calculations, reduces the difficulty of calculation, and solves the problem of overlapping factor information, reflecting the advantages of multivariate statistics. Based on the factor analysis method, this study analyzes the quality analysis and key factors in Japan’s economic growth: (1) integrity principle residents’ financial risk is composed of multiple factors, not only unilateral factors but also the following factors: determine the influencing factors, select indicators, and identify risks and potential hazards. Financial risk itself is comprehensive, and the assessment of risk arising from a single factor is less important. (2) The requirements for the selected financial indicators are that the unit and order of magnitude should not only be accurate but also consistent, the financial information reflected by the selected financial indicators should be direct and clear, and financial data should be obtained in multiple ways, through annual reports and the Internet, to collect financial data. The analysis of financial performance based on scientific principles can show the current operating conditions, so the selection of financial indicators must be true, effective, and objective, so as to correctly reflect the real situation, and it is beneficial to combine the analysis results and carry out the following steps. (3) Financial risks may arise in the process of financing or in the process of investment. Financial risk in a broad sense is more in line with the characteristics of risk, and it arises in every link related to financial activities. The analysis is carried out in terms of financial risk in a broad sense. It can be seen that the economic quality scores were higher in 2011 and 2017. (4) Use several representative factors to replace the original variables for analysis, and explain the problems existing in the Japanese economy according to the results. The results show that the key factors of economic growth include gross national product, inflation, employment rate, and the balance of payments.

This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

1. Introduction

With the development of the era of big data, the application of factor analysis has become wider and wider, and it can study a variety of objects and can better apply multivariate statistical methods to the evaluation of financial performance. Factor analysis is suitable for the conditions of complex correlation, large sample size, many influencing factors, and many selected indicators. Among other common methods of evaluating financial performance, factor analysis methods can reduce dimensionality and simplify data basically design weights. Factor analysis can reduce the dimensionality of data even with large sample sizes. It avoids a lot of complicated calculations, reduces the difficulty of calculation, and solves the problem of overlapping factor information, reflecting the advantages of multivariate statistics. It can objectively reflect the problem and has the characteristics of sample integrity and information integrity [1–3]. The basic idea of factor analysis is to group variables according to the size of the correlation and use software for analysis and processing, and it does not have subjective initiative. In the current era of rapid development of information technology, public factors are described according to the weight of each variable, and the statistical analysis of economic data is heavy and boring. The factor analysis method provides convenient analysis conditions not only for financial staff but also for social, which lays the foundation for comprehensive development. The method of layer-by-layer reasoning proves the stability and gives the error analysis; each group of variables represents a basic structure, namely, the common factor. For the Stokes/Darcy model, the BDF2 tree-structured modular gradient divergence stable scheme is proposed, which takes into account a single evaluation index, also proves the stability and error estimation of the scheme, and realizes the final prediction. Nodes represent all datasets, and numerical experiments show that the format is indeed not affected by large parameters, which improves computational efficiency. Create subnodes for node characteristics and based on their values, reflecting the compound influence of multiple indicators on performance evaluation. The velocity-corrected projection method is divided into two substeps at each time step, and a new subnode velocity term is generated for each subnode in the same way. In the process of decision generation, it is revised [4–6]. After the decision is fully formed, some branches are removed actively; however, this method also has shortcomings. Due to the risk error of overfitting caused by system splitting, the accuracy will be reduced, and the nonphysical boundary conditions of pressure are introduced, and there is no correlation between different decision trees, which leads to the phenomenon of numerical boundary layer. In order to overcome the artificial pressure boundary condition, a rotation velocity correction projection method is proposed, which consists of multiple decisions. Correction of projection is based on rotational velocity for most decision tree classification results. Modular gradient divergence stabilization is not affected by large parameters, and random forest is more stable. We can combine the rotation velocity correction projection method and the modular gradient divergence stabilization method, use a supervised strong classifier, study the stability and error analysis of this combined method, find a hyperplane as the decision boundary, and use the numerical calculation example to verify the effectiveness of this method. Judging from the results of time evolution, the standard for measuring the quality of economic growth is the pros and cons. In recent years, the quality of urban economic growth in Japan has been significantly improved. To measure the quality of economic growth, the evolution of the distribution at the national city level shows a unimodal state; that is, the quality of economic growth at the city level does not appear in cities with higher and lower levels and different trends. The index system of the quality of economic growth at the provincial level is clarified, and the quality of economic growth at the three regional levels in the east, the middle, and the west presents a bimodal evolution state, which means that the convergence characteristics of economic growth are heterogeneous. Specifically, there may be a trend of club convergence. From the perspective of spatial evolution, the measurement indicators can be roughly divided into two types: total factor productivity and comprehensive indicator systems [7–10]. The spatial agglomeration effect of the quality of urban economic growth in Japan is significant, and the quality of urban economic growth in the whole region presents an agglomeration pattern of “high in the center and low in the periphery.” When the total factor productivity is included in the comprehensive index system, the distribution of differences in the quality of economic growth in the central and western cities is relatively uniform, and there is no polarization phenomenon, but there is a trend of differential diffusion in the eastern region. The index system is constructed from three aspects of efficiency, stability, and economic structure. Compared with the quality level of economic growth in the eastern region, the convergence of the quality of economic growth in the central and western regions is still low. In the selection of efficiency dimension indicators, based on the empirical analysis of the convergence of the econometric model, the input-output angle and total factor productivity are combined. There is an absolute convergence trend of $\beta$ in the whole domain. After controlling the initial conditions of economic individuals and including spatial factors, it is found that the spatial convergence trend of urban economic growth quality has strengthened, which is reflected by economic fluctuation indicators, environmental changes, and changes in people’s livelihood status. The corresponding convergence rate is significantly improved, and the convergence period is shortened [11–13]. The quality of economic growth needs to include economic scale, which can reflect the quantity of economic growth; that is, cities with similar structural characteristics and locations have a higher probability of economic convergence. From the perspective of different regions, the eastern region has the highest spatial convergence speed, which should also be included in the five aspects of economic structure, economic development potential, and final economic benefits, followed by the central region. From the perspective of index composition, for the single index of total factor productivity, its measurement at home and abroad has reached a relatively mature stage. The narrowing of the difference in the quality of economic growth is mainly due to the convergence of economic growth momentum and economic growth results. At present, it can be specifically classified into parametric and nonparametric methods and further subdivided into traditional residual methods and exponential methods. The agglomeration economy has a significant impact on the quality of urban economic growth, which can be combined with ordinary least squares estimation in the macrodimension. In the microdimension, due to the existence of simultaneity error and sample selection bias, the spatial convergence rate of urban economic growth quality under the factor of industrial agglomeration has been significantly improved. It needs to be combined with the OP semiparametric estimation method [14, 15]. From the perspective of different regions, industrial agglomeration has the greatest effect on promoting the speed of spatial convergence in the western region. Potential output method includes data inclusion analysis method and stochastic frontier analysis method. The former mainly uses the Malmquist index to measure the change of efficiency and reflects the quality level of economic growth through the change difference. As for the horizontal method of comprehensive indicators of economic growth quality, since the indicator system covers a large number of indicators and a large amount of information, industrial agglomeration mainly achieves a positive effect on the convergence of economic growth quality by promoting the convergence of economic growth modes and structures. Therefore, comprehensive indicators are used for measuring. The key lies in the extraction of each indicator information.

2. Factor Analysis

2.1. Implementation Ideas of Factor Analysis

In general, there are multiple indicators in the comprehensive analysis method in the analysis of variables, including profitability, operating ability, solvency, solvency, and development ability. Different indicators have different degrees of correlation, so there is repetitive information, and there are obvious differences in the process of analysis. In order to effectively overcome these obvious differences, factor analysis can effectively solve this problem. The analysis method divides the whole group of indicators into different individual groups according to the matrix, which is based on the principle of principal component analysis theory, and effectively reduces the repetition of information. Common factors exist in different groups, and the original indicators have different characteristics after being rotated by the factors. Different factor loading information can be obtained separately from different original indicators. The extraction of the weight coefficient of each different common factor is obtained by the variance contribution rate. Composite scores and one-way factor scores were obtained by a weighted average method. The economic evaluation index is shown in Figure 1.

[figure(s) omitted; refer to PDF]

2.2. The Connotation and Influencing Factors of the Quality of Economic Growth

For the definition of the connotation of the quality of economic growth, the improvement and optimization of the economic structure belong to the concept of structure. The modern economic growth system is endowed with a broader connotation, such as economic benefits and economic stability and natural and ecological balance, which reflect the meaning of the concept of quality. The connotation of the quality of economic growth can be said that the focus of the connotation is different depending on the research angle. In the past, scholars proposed based on the internal and external perspectives, the perspective of efficiency, and the characteristics of high-quality economic growth, etc., more based on a single perspective. The quality of economic growth should be a comprehensive index with rich connotations, and in recent years, it has a lot to do with the nature and characteristics of the quality of economic growth in the context of high quality. A more comprehensive, systematic, and staged explanation is presented. Comparing the quality and quantity of economic growth, what is the core of the quality of economic growth. The improvement of the quality of growth is due to the improvement of efficiency, the optimization of structure, the improvement of stability, the low cost of ecological environment, the better distribution of welfare, the improvement of innovation level, and some other factors in maintaining the sustainability of economic growth. The evaluation of social and environmental benefits brought about by growth is also one of the goals of the quality of economic growth. It is shown in Figure 2.

[figure(s) omitted; refer to PDF]

3. Algorithm Model

(A) PRINIT [16–20]

Select indicator $$\begin{array}{}\text{(1)}& {Ay}_t=B_1{y}_{t-1}+B_2{y}_{t-2}+\cdots +B_s{y}_{t-s}+{\mu }_t.\end{array}$$

Factor analysis method $$\begin{array}{}\text{(2)}& \sum \sigma =\begin{array}{cccc}{\sigma }_1^2& 0& \cdots & 0\\ 0& {\sigma }_2^2& \cdots & 0\\ \cdots & \cdots & \cdots & \cdots \\ 0& 0& \cdots & {\sigma }_k^2\end{array}.\end{array}$$

Dimensionality reduction $$\begin{array}{}\text{(3)}& A=\begin{array}{cccc}1& 0& \cdots & 0\\ a_{21}& 1& \cdots & 0\\ \cdots & \cdots & \cdots & \cdots \\ a_{k1}& a_{k2}& \cdots & 1\end{array}.\end{array}$$

Quality of economic growth $$\begin{array}{c}\text{(4)}& y_t={\Phi }_1{y}_{t-1}+{\Phi }_2{y}_{t-2}+\cdots +{\Phi }_s{y}_{t-s}+A^{-1}\sum {\epsilon }_t,a_{t+1}=a_t+{\mu }_{a_t}.\end{array}$$

Factor information overlap $$\begin{array}{c}\text{(5)}& y_t=X_t\beta +A^{-1}\sum {\epsilon }_t,{\beta }_{t+1}={\beta }_t+{\mu }_{\beta t}.\end{array}$$

Sample integrity $$\begin{array}{c}\text{(6)}& h_{t+1}=h_t+{\mu }_{h_t},A\begin{array}{c}\epsilon \\ {\mu }_{{\beta }_t}\\ {\mu }_{{\alpha }_t}\\ {\mu }_{h_t}\end{array}~N\begin{array}{cccc}I& 0& 0& 0\\ 0& \sum \beta & 0& 0\\ 0& 0& \sum \alpha & 0\\ 0& 0& 0& \sum h\end{array}.\end{array}$$

Principal [21–23] $$\begin{array}{}\text{(7)}& R_t=100\times \text{In}\frac{Y_t}{Y_{t-1}}.\end{array}$$

Information integrity $$\begin{array}{}\text{(8)}& Y_1=A_1\times L_1^{1-\alpha }\times \sum _{j=1}^{N_1}{X_{1j}}^{\alpha }.\end{array}$$

Common factor $$\begin{array}{}\text{(9)}& \frac{\partial Y_1}{\partial X_{1j}}=\alpha \times A_1\times L_1^{1-\alpha }\times X_{1j}^{1-\alpha }.\end{array}$$

Convergence $$\begin{array}{}\text{(10)}& X_{1j}=A_1^{1/1-\alpha }\times {\alpha }^{2/1-\alpha }\times L_1.\end{array}$$

Tree structure $$\begin{array}{}\text{(11)}& Y_1=A_1^{1/1-\alpha }\times {\alpha }^{2/1-\alpha }\times N_1\times L_1.\end{array}$$

Similar structural features $$\begin{array}{}\text{(12)}& {\pi }_{1j}=\frac{1-\alpha }{\alpha }\times A_1^{1-\alpha }\times {\alpha }^{2/1-\alpha }\times L_1.\end{array}$$

Gradient divergence stable scheme $$\begin{array}{c}\text{(13)}& {\gamma }_1=\frac{1}{\theta }\times \frac{L_1}{\eta }\times \frac{1-\alpha }{\alpha }\times A_1^{1-\alpha }\times {\alpha }^{2/1-\alpha }-\rho ,Y_2=A_2\times L_2^{1-\alpha }\times \sum _{j=2}^{N_1}{X_{2j}}^{\alpha }.\end{array}$$

Node features $$\begin{array}{}\text{(14)}& x_{ij}^{\prime }=\frac{x_{ij}-\min x_{ij}}{\max x_{ij}-\min x_{ij}}.\end{array}$$

System split $$\begin{array}{c}\text{(15)}& S_{ij}=\frac{x_{ij}}{{\sum }_{i=1}^n{x}_{ij}},e_j=-k\sum _{i=1}^n{S}_{ij}\text{In}{S}_{ij}.\end{array}$$

AHP [24–27] $$\begin{array}{}\text{(16)}& k=\frac{1}{\text{In}n},\hspace{1em}k\ge 0.\end{array}$$

Nonphysical boundaries $$\begin{array}{}\text{(17)}& w_j=\frac{d_j}{{\sum }_{j=1}^m{d}_j}.\end{array}$$

Efficiency dimension $$\begin{array}{}\text{(18)}& {QEG}_i=\sum _{j=1}^m{w}_j\times s_{ij}.\end{array}$$

Bimodal evolution $$\begin{array}{}\text{(19)}& d\text{In}{QEG}_{it}=\text{In}{QEG}_{it}-\text{In}{QEG}_{it-1}=\alpha +\beta \text{In}{QEG}_{it-1}+{\epsilon }_{it}.\end{array}$$

Productivity and composite indicators $$\begin{array}{}\text{(20)}& W_{ij}^{\prime n}=\frac{W_{ij}^n}{{\sum }_j{W}_{ij}^n},\hspace{1em}i\ne j.\end{array}$$

Economic structure $$\begin{array}{}\text{(21)}& W_{ij}^e=W_{ij}^d\mathrm{diag}\frac{{\overline{Y}}_1}{\overline{Y}},\frac{{\overline{Y}}_2}{\overline{Y}},\cdots ,\frac{{\overline{Y}}_n}{\overline{Y}}.\end{array}$$

4. Simulation Experiment

4.1. Factor Loading Matrix

In the covariance vector matrix, the current assets, fixed assets, construction in progress, intangible assets, and noncurrent assets in a sample correlation vector are average values. The random sample vector of the random sample correlation vector assumes that the functions of monetary funds, accounts receivable, and prepayments are the main contents of the assets, and the covariance matrix of all sample variables is obtained by standardizing and normalizing all the data. The variance matrix is consistent with the same sample matrix. Then, the common load factors of different indicators (short-term loans, bills payable, accounts payable, advance receipts, and current liabilities) are rotated, and the way of rotation is to rotate 90 degrees. As shown in Table 1 and Figure 3, the common load factors show different quantification and different degrees of clarity in different indicators and can also effectively determine the number of load factors and load information of the common load factors in different indicators, so that the public index factors can be accurately and quickly determined. Integrity principal residents’ financial risk is composed of many factors, not only unilateral factors but also the following factors: determining influencing factors, selecting indicators, and discovering risks and potential dangers. Financial risk itself is comprehensive, and the assessment of risk arising from a single factor is less important.

Table 1

Assets of Japanese residents in 2015-2019.

Project	2015	2016	2017	2018	2019
Money funds	19459.55	24764.71	27713.31	65229.87	40074.44
Accounts receivable	11066.07	12690.31	15232.42	16498.84	23299.92
Prepayments	131.31	274.73	970.29	3163.23	1066.79
Stock	10161.31	9167	7759.61	23838.99	31568.25
Current assets	59595.99	61087.24	67905.37	117715.61	106753.22
Fixed assets	4793.66	17821.31	15713.69	14382.47	67216.93
Construction in progress	8751.71	278.02	9883.26	38036.33	5508.01
Intangible assets	3418.56	4137.19	3804.44	9097.72	17160.55
Noncurrent assets	21289.07	28157.3	38080.16	68038.81	118595.65
Short-term loan	—	—	—	34200	—
Bills payable	—	1366	2292.21	1890	756.39
Accounts payable	6112.63	6588.1	9735.7	22081.19	30221.22
Advance payment	4039.04	4021.6	5664.64	22533.93	10669.06
Current liabilities	12741.61	16854.48	22875.08	87601.59	59003.97
Noncurrent liabilities	150	185	1357.67	1306.26	38314.54
Debt	12891.61	17039.48	24232.76	88907.85	97318.51
Owners’ equity	67993.45	72205.05	81752.77	96846.57	128030.36
Assets	80885.05	89244.54	105985.53	185754.42	225348.8

[figure(s) omitted; refer to PDF]

4.2. Indicator Selection

The most important thing in the accurate and comprehensive evaluation of financial performance is to establish an evaluation index system suitable for development. As shown in Table 2 and Figure 4, feasibility is the basis for data selection and data analysis. The requirements for the selected financial indicators are that the unit and order of magnitude should not only be accurate but also consistent, the financial information reflected by the selected financial indicators should be direct and clear, and financial data should be obtained in multiple ways, through annual reports and the Internet; the financial data is collected. The analysis of financial performance based on scientific principles can show the current operating conditions, so the selection of financial indicators must be true, effective, and objective, so as to correctly reflect the real situation, and it is beneficial to combine the analysis results and carry out the following steps. One-step planning layout. Systematic principle financial indicators are both independent and closely related to each other and affect each other, which is an organic whole with complex logical relationship. Therefore, the selection of financial indicators should be systematic and complete, not only to explain the financial situation individually but also to analyze the financial situation from a global perspective as a whole. The principle of comparability needs to carry out horizontal and vertical analysis and will compare and analyze the financial data of different years in the chemical industry, so the selected financial indicators should be comparable, which is conducive to financial performance.

Table 2

Composition matrix.

	1	2	3	4	5	6
Return on total assets	0.85	0.046	0.012	-0.176	-0.323	0.189
Eps	0.768	-0.086	0.44	-0.051	-0.254	-0.03
Sales cash ratio	0.726	0.083	-0.164	0.377	0.337	0.229
Cash flow ratio	0.705	0.275	-0.542	0.013	0.134	-0.12
Gross profit margin	0.691	-0.473	-0.157	-0.006	-0.169	0.163
Undistributed earnings per share	0.614	-0.093	0.589	-0.014	0.057	-0.322
Operating cash flow per share	0.547	0.2	0.342	0.33	0.508	0.103
Accounts receivable turnover	-0.069	0.783	0.237	0.205	-0.037	-0.29
Total asset turnover	0.009	0.738	0.271	-0.429	0.08	0.137
Roa	0.132	0.711	-0.068	0.436	-0.398	0.009
Inventory turnover	-0.092	0.697	0.147	-0.455	0.088	0.017
Net assets per share	0.4	-0.225	0.633	0.11	0.24	-0.297
Current ratio	0.526	0.072	-0.609	-0.228	-0.021	-0.255
Cash ratio	0.494	0.208	-0.608	-0.128	0.047	-0.285
Total asset growth rate	-0.014	0.173	0.039	0.616	-0.591	0.124
Cash-to-income ratio	-0.098	0.2	-0.211	0.29	0.539	0.397
Net profit growth rate	0.409	0.087	0.239	-0.407	-0.176	0.545

[figure(s) omitted; refer to PDF]

4.3. Economic Quality Analysis

The way of debt-based operation has uncertain risks to the financial rights and interests of stakeholders. If there are too many debts, the operation situation has not been well improved, and the debt cannot be repaid within the specified time limit, which will lead to financial difficulties. If there is no improvement, there will be the risk of bankruptcy. Being affected by the internal environment and improper management methods will also generate risks, including external factors such as macroeconomic policies and changes in the market environment. Due to these influencing factors, the risk of loss and income is uncertain, which has brought an adverse impact on the financial situation and led to the generation of financial risk. In 2017, the score was higher: 0.25746,$\text{F}4=2.71944$,$\text{F}5=0.49$. In general, the concept of financial risk is still very holistic. The various business links that it produces may occur in the process of financing or in the process of investment. Compared with the narrow concept, the financial risk in the broad sense is more in line with the characteristics of risk, and it arises from each link related to financial activities. It is based on the analysis of financial risk in a broad sense. As shown in Table 3 and Figure 5, it can be seen that the economic quality scores were higher in 2011 and 2017.

Table 3

Comprehensive score.

	F1	F2	F3	F4	F5
2000	0.41648	0.55995	1.49627	0.26501	0.34
2001	0.41606	0.65002	1.50912	0.65535	0.43
2002	0.52557	1.49627	1.00702	0.43894	0.36
2003	0.05688	0.77781	1.09591	0.16	0.44
2004	0.00568	0.93516	0.85469	0.48423	0.39
2005	0.20467	1.06447	0.49352	0.14716	0.35
2006	0.07067	1.04538	0.37502	0.01034	0.32
2007	0.17083	1.00702	1.70869	0.04021	0.1
2008	0.53396	0.81243	1.77782	0.91184	0.29
2009	0.66739	0.7232	0.95664	1.1617	0.49
2010	0.52557	0.78161	1.31122	2.60109	0.53
2011	3.64246	0.76692	0.53378	0.25094	1.55
2012	0.03782	1.28212	0.37827	0.83716	0.36
2013	0.44595	0.61294	0.7972	0.46002	0.12
2014	0.90593	0.44733	0.04924	0.43268	0.21
2015	0.90204	0.30946	0.40893	0.31798	0.32
2016	0.69659	1.7071	0.42569	0.25094	0.2
2017	1.06	1.42669	0.25746	2.71944	0.49
2018	0.85075	1.27471	0.19437	0.15926	0.71
2019	0.3077	1.29736	0.22135	0.6245	0.28

[figure(s) omitted; refer to PDF]

4.4. Key Factors of Economic Growth

The key factors for economic growth include gross national product, inflation, employment rate, and the balance of payments. These factors were analyzed and compared using factor analysis. In the univariate evaluation, the evaluation process lacks comprehensiveness and integrity, so the use of this model is limited. Use multivariate statistical methods to construct linear functions of several variables. Assess financial risk based on the results. It makes up for the shortcomings of using only one variable in the univariate model method and makes the evaluation results more comprehensive. However, this method has higher requirements on data, and the data should be normally distributed, but this condition cannot be satisfied in all cases, and the model is complex. Refer to expert opinion and qualitative and quantitative analysis of the actual situation. The principle is simple and easy to practice. However, the expert judgment is used in the analysis process, which is easily affected by human subjectivity, which may make the analysis results lack certain objectivity and scientific. Factor analysis uses a small number of factors to represent complex variable relationships. According to the matrix formed by the selected variables and the internal relationship between them, $\text{HHI}=0.0038$, $\text{Gini}=0.156$, $\text{THEIL}=0.0378$, and $\text{CV}=0.2798$ were used to replace the original variables for analysis and used to explain the problems of the Japanese economy. The selected data are objective, and the meaning of the factors extracted from the original data cannot be accurately determined. The factor analysis method can solve such problems to a large extent because of its advantages of simplifying analysis indicators and simple operation and can quickly obtain financial risk evaluation results while ensuring a high accuracy rate. It is shown in Table 4 and Figure 6.

Table 4

Key factors of economic growth.

Year	HHI	Gini	THEIL	CV
2005	0.0038	0.156	0.0378	0.2798
2006	0.0037	0.127	0.0253	0.2293
2007	0.0037	0.1209	0.023	0.2176
2008	0.0036	0.1051	0.0187	0.2005
2009	0.0036	0.1131	0.0207	0.2072
2010	0.0036	0.0955	0.0154	0.1804
2011	0.0036	0.0926	0.0152	0.1816
2012	0.0036	0.0874	0.0131	0.1665
2013	0.0036	0.0894	0.0136	0.1698
2014	0.0035	0.089	0.0138	0.1724
2015	0.0035	0.083	0.0121	0.1583
2016	0.0035	0.0791	0.0111	0.1505
2017	0.0035	0.0612	0.0069	0.1183
2018	0.0035	0.0729	0.0097	0.1425

[figure(s) omitted; refer to PDF]

5. Conclusion

Factor analysis is suitable for the conditions of complex correlation, large sample size, many influencing factors, and many selected indicators; able to conduct research on a variety of subjects; and can better apply multivariate statistical methods to the evaluation of financial performance. Among other common methods of evaluating financial performance, factor analysis methods can reduce dimensionality and simplify data basically design weights. Factor analysis can reduce the dimensionality of data even with large sample sizes. It avoids a lot of complicated calculations, reduces the difficulty of calculation, and solves the problem of overlapping factor information, reflecting the advantages of multivariate statistics. Based on the factor analysis method, this study analyzes the quality analysis and key factors in Japan’s economic growth: (1) integrity principle residents’ financial risk is composed of multiple factors, not only unilateral factors but also the following factors: determine the influencing factors, select indicators, and identify risks and potential hazards. Financial risk itself is comprehensive, and the assessment of risk arising from a single factor is less important. (2) The requirements for the selected financial indicators are that the unit and order of magnitude should not only be accurate but also consistent, the financial information reflected by the selected financial indicators should be direct and clear, and financial data should be obtained in multiple ways, through annual reports and the Internet, to collect financial data. The analysis of financial performance based on scientific principles can show the current operating conditions, so the selection of financial indicators must be true, effective, and objective, so as to correctly reflect the real situation, and it is beneficial to combine the analysis results and carry out the following steps. One-step planning layout. (3) Financial risks may arise in the process of financing or in the process of investment. Financial risk in a broad sense is more in line with the characteristics of risk, and it arises in every link related to financial activities. The analysis is carried out in terms of financial risk in a broad sense. It can be seen that the economic quality scores were higher in 2011 and 2017. (4) Use several representative factors to replace the original variables for analysis, and explain the problems existing in the Japanese economy according to the results. The key factors for economic growth include gross national product, inflation, employment rate, and the balance of payments.

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