Научная статья на тему 'Based on factor analysis investment value of listed companies in China'

Based on factor analysis investment value of listed companies in China Текст научной статьи по специальности «Экономика и бизнес»

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Ключевые слова
ЛИСТИНГОВЫЕ КОМПАНИИ / ИНВЕСТИЦИОННАЯ СТОИМОСТЬ / ФАКТОРЫ ВЛИЯНИЯ / ФАКТОРНЫЙ АНАЛИЗ / LISTED COMPANIES / INVESTMENT VALUE / INFLUENCING FACTORS / FACTOR ANALYSIS

Аннотация научной статьи по экономике и бизнесу, автор научной работы — Gao Qiang

The investment value of listed companies has been the focus of attention of investors, this paper from the company’s internal investigation of its influencing factors, using factor analysis theory, set up an integrated evaluation capacity factor analysis model, and conduct a comprehensive analysis and evaluation to determine a firm’s competitive position and sort in the industry, thereby confirm if companies have investment value.

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Текст научной работы на тему «Based on factor analysis investment value of listed companies in China»

ИССЛЕДОВАНИЕ ИНВЕСТИЦИОННОЙ СТОИМОСТИ ЛИСТИНГОВЫХ КОМПАНИЙ КИТАЯ МЕТОДОМ ФАКТОРНОГО АНАЛИЗА

Гао Цян, м.э.н., преподаватель кафедры Учета, статистики и аудита Тайшанского государственного института КНР провинция

Шаньдун

Инвестиционная стоимость листинговых компаний является предметом особого интереса инвесторов. Настоящая статья рассматривает факторы влияния с позиции внутренней финансовой ситуации в компаниях, используя метод факторного анализа, проводит общий анализ и дает общую оценку инвестиционной стоимости листинговых компаний, определяет конкурентную позицию одной компании в отрасли и устанавливает, есть ли у компании инвестиционная стоимость.

Ключевые слова: листинговые компании, инвестиционная стоимость, факторы влияния, факторный анализ

BASED ON FACTOR ANALYSIS INVESTMENT VALUE OF LISTED COMPANIES IN CHINA

Gao Qiang (China Shandong), Taishan University 271021

The investment value of listed companies has been the focus of attention of investors, this paper from the company’s internal investigation of its influencing factors, using factor analysis theory, set up an integrated evaluation capacity factor analysis model, and conduct a comprehensive analysis and evaluation to determine a firm’s competitive position and sort in the industry, thereby confirm if companies have investment value.

Keywords: Listed companies; investment value; influencing factors; Factor Analysis.

1. Investment value of listed companies

Investment value of listed companies in the securities market means to invest in public offerings and secondary trading market circulation, and in those market prices are undervalued or temporarily below their “intrinsic value” of the stock investment value. This paper studies the investment value of listed companies mainly through the analysis and evaluation of the profitability of listed companies, growth and market performance of listed companies, and gives the investment value judgment. Because the investment value of the stock of listed companies is closely related to their own value , so the analysis of the intrinsic value of listed companies is based primarily on analysis and evaluation of the operating conditions of listed companies and their market performance. Therefore, analyzing the value of investments we can evaluate by analyzing the financial indicators of listed companies to determine to invest.

2. Factors affecting the investment value of listed companies

Investment value of listed companies is often assessed through a variety of financial indicators to reflect the capabilities of enterprises, in order to determine whether a company has investment value. From affecting factors the value of business investment is determined mainly by in corporate profitability, business growth capabilities, enterprise anti-risk capability and enterprise management capabilities.

(1) Corporate profitability. Investors to invest in specific companies are to get the expected return, the investor gains are directly affected by the level of profitability of the business strength. So for investors, corporate profitability is its important reference for making the right choice. Corporate profitability indicators included, such as operating margin, return on assets, return on equity, net profit rate. These indicators determine the level of the company’s profitability, higher profitability of the enterprise, the risk is relatively small, and thus the profitability indicators for investors to make an important basis for investment decisions.

(2) The ability to grow. The ability to grow of Companies can be illustrated by sales growth capacity and market share, sales growth, profit growth and other indicators. Main business growth means changes of net income from main business, compared with the previous year, which is to evaluate the status and development of business growth important indicator of ability. Compared with the previous year net profit growth rate represents the net increase or decrease in corporate profits movements, is another important indicator by which investors evaluate business growth and development capacity. Total assets growth rates reflect current asset size growth. Corporate assets are used to obtain revenue resources, but also the protection of corporate debt. Asset growth is an important aspect of development, and is another important indicator of the development of enterprises. By which investors evaluate the growth rate of total assets.

(3) Ability to resist risks. About company anti-risk capability analysis, the measurable indicators include current ratio, quick ratio, asset-liability ratio and so on. These indicators illustrate the company’s liquidity and solvency, which show the corporate ability to resist risks.

(4) Ability to manage. Enterprise asset management capabilities can be used to reflect the level of corporate governance, specifically the total asset turnover, accounts receivable turnover, inventory turnover, the net asset turnover and other indicators reflect the enterprise’s asset management capabilities. The higher the index, the higher asset utilization, the more value created from the source to improve the profitability of enterprises. The profitability of enterprises and their management capabilities are interrelated, so it is also very important for the contribution of enterprise value.

3. Factor analysis model of Investment value of listed companies

Factor analysis is a practical multivariate statistical methods, the basic idea is to study sample correlation matrix of the original variables about internal structure relationship, which are grouped based on the correlation between the variables so that the same groups have a strong correlation between the variables, and different groups have weak correlation between variables, and to identify common factors affecting a phenomenon, which have less exhausted numerous comprehensive factor instead of the original data and the original data as much as possible to reflect the information provided.

(1) Selection of samples. Taking into account the differences of listed companies and to facilitate comparison, this paper selects the real estate industry, 20 listed companies as samples (table 1), and according to 20 2010-2012 annual financial report, classifies and analyses its financial data, and collates sample data for factor analysis.

(2) Choice of indicators. Accounting to incomplete data from the financial statements of listed real estate companies in the consolidated statements, this paper mainly used the parent data from listed companies to explore and analys; view of the desirability of data, respectively from profitability, management ability, anti-risk ability, the ability to grow in four areas corresponding to 12 selected indicators and given the corresponding code: where profitability indicators, including earnings per share X1, X2 ROE, X3 return on total assets, X4 surplus cash coverage ratio; management capacity indicators include total asset turnover X5, X6 mobile asset turnover; ability to resist risks indicators include asset-liability ratio X7, X8 current ratio, X9 quick ratio; indicators, including the ability to grow main business revenue growth X10, X11 net profit growth , X12 total asset growth.

(3) Factor analysis model. First, the adaptive testing of factor analysis.The raw data using SPSS calculated the correlation matrix, we can see most of the variables passed the null hypothesis which between the corresponding variable is 0 t-test, indicate that there is a strong correlation between indicators of relationship can be the factor analysis (Table 2).

KMO is the Kaiser-Meyer-Olkin measures of sampling adequacy, KMO value when larger, it means that the more common factors among the

Table 1. 20 listed real estate companies list

NO. Ticker Company total share capital (million shares)

1 000002 Vanke A 109.96

2 600185 Gree Real Estate 5.78

3 002146 Emori Development 18.72

4 600048 Poly Real Estate 1.55

5 000656 Jinke Share 11.59

6 000736 In Real Estate 2.97

7 600266 Beijing Urban Construction 8.89

8 600067 Ken Chase 11.77

9 600665 Tande 8.64

10 600383 Golden Group 44.72

11 000631 Shun Fat Sunny 10.46

12 000024 China Merchants Property 17.17

13 600376 First open shares 14.95

14 600684 Peart River Industrial 3.16

15 600716 Phoenix shares 7.41

16 000006 Shenzhenye A 12.86

17 000537 GuangyuDevelopment 5.13

18 600340 China Happiness 8.82

19 000402 Street 30.27

20 600748 SIDC 10.83

Table 2. KMO and Bartlett’s Test

Kaiser-Meyer-Olkin Measure of Sampling Adequacy. .416

Bartlett’s Test of Sphericity Approx. Chi-Square 327.074

df 66

Sig. .000

Table 3. Total variance decomposition table

Total Variance Explained

Component Initial Eigenvalues Extraction Sums of Squared Loadings Rotation Sums of Squared Loadings

Total % of Variance Cumulative % Total % of Variance Cumulative % Total % of Variance Cumulative %

1 3.412 28.432 28.432 3.412 28432 28.432 3.230 26.914 26.914

2 2.319 19.323 47.755 2.319 19.323 47.755 2.303 19.189 46.104

3 2.110 17.580 65.335 2.110 17.580 65.335 2.012 16.765 62.869

4 1.637 13.642 78.977 1.637 13.642 78.977 1.795 14.957 77.825

5 1.213 10.108 89.084 1.213 10.108 89.084 1.351 11.259 89.084

6 0 707 5.889 94.974

7 0.372 3.104 98.077

8 0.122 1.021 99.098

9 0090 0.754 99.852

10 0015 0.122 99.974

11 0.003 0.026 100.000

12 0.000 0.000 100.000

Source: Based on Appendix by SPSS output.

variables, the more suitable for factor analysis, according to experts Kaiser (1974) view, if the value is close to 0.5 KMO , it means suitable for factor analysis. The results can be explained by the 0.416, so factor analysis is more suitable.

Second, Determine the number of common factors.

Using SPSS software to the initial value of 12 indicators do descriptive statistical analysis, based on eigenvalues greater than one standard and varimax rotation method to extract factors can be extracted five factors (named F1, F2, F3, F4 and F5), as shown in Table 3. These five factors eigenvalues were 3.230, 2.303, 2.012, 1.795, 1.351, the cumulative contribution rate of 89.084%, representing the majority of information, so extracted five factors can well explain the problem analysis.

The main factor determining the number of extracted after using principal component analysis obtained factor loading matrix. Then calculated using SPSS initial factor loading matrix, can be seen from Table 3, a typical representative of each variable common factor is not very prominent, several common factors for each indicator on the front load values are almost hard to be a reasonable explanation for its practical significance, so to further rotation. Variance maximization approach to factor rotation, we get rotated factor loading matrix as shown in Table 4.

Table 4 Extraction result after rotation factor Rotated Component Matrix(a)

Component

1 2 3 4 5

X1 .035 -.116 -.020 .938 .173

X2 .954 .058 .085 -.007 .163

X3 .963 .186 .009 -.095 -.079

X4 .035 .060 .149 -.068 .827

X5 -.031 -.065 .969 -.014 088

X6 -.028 -.023 .971 -.067 -.018

X7 -.475 -.518 -.164 .150 .539

X8 .030 .989 -.058 -.022 .001

X9 .030 .989 -.058 -.022 .001

X10 .000 .050 -.062 .919 -.238

X11 .545 -.116 -.216 -.012 .492

X12 .929 -.057 -.120 .167 -.081

Extraction Method: Principal Component Analysis.

Rotation Method: Varimax with Kaiser Normalization.

Rotation converged in 5 iterations.

Source: Based on Appendix by SPSS output.

Visible through factor analysis above indicators were separated into five categories: The first category factors, including the total return on assets, return on net assets, total asset growth, net profit growth; second factors, including the quick ratio, liquidity ratio; third category factors, including mobile asset turnover, total asset turnover; fourth category factors, including earnings per share, the main business income growth; fifth factors, including surplus cash coverage ratio, asset-liability ratio.

(4) Establish factor comprehensive evaluation model

The economic significance of the main factors were identified and named after, we must solve the factor scores. The factors by observing were a linear combination of variables, then a weighted average of the observed variables is the factor scores, and the variable factors of importance to have to be expressed by the size of the weight. Through factor analysis of SPSS Regression functionality provided, obtained by orthogonal rotation of the coefficient matrix of factors (Table 4).

Table 4. Factor score coefficient matrix

Component

1 2 3 4 5

X1 .006 .020 .043 .534 .150

X2 .298 -.013 .058 .002 .106

X3 .299 .012 .018 -.053 -.069

X4 -.012 .120 .068 .000 .640

X5 .007 -.003 .486 .049 .055

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X6 .009 .001 .486 .018 -.024

X7 -.138 -.144 -.093 .066 .374

X8 -.053 .459 -.014 .044 .114

X9 -.053 .459 -.014 .044 .114

X10 -.005 .051 .025 .517 -.147

X11 .164 -.036 -.106 -.015 .351

X12 .299 -.086 -.036 .075 -.088

Component Score Coefficient Matrix Extraction Method: Principal Component Analysis.

Rotation Method: Varimax with Kaiser Normalization.

Source: Based on Appendix by SPSS output.

The various main factor score for each variable is equal to the standard value of the product and factor score coefficients accumulate. The formula is:

F1 = 0.006X1 + 0.298X2 + 0.299X3 + ... + 0.299X2

F2 = 0.020X1 - 0.013X2 + 0.012X3 + ... - 0.086X2 F3 =-0.043X1 + 0.058X2 + 0.018X3 + ... - 0.306X2

F4 =-0.534X1 + 0.002X2 - 0.053X3 + ... + 0.075X2

F5 = -0.150X1 + 0.106X2 - 0.069X3 + ... - 0.088X2

Consolidated factor score is calculated for each one common factor variance contribution rate multiplied by the appropriate factor score corresponding to the sample. The formula such as:

f=Ê (v / vt f

i=1

F. V. V

Where, F is the comprehensive quality score, 1 is for each factor score, 1 is rotated variance contribution rate, T is for the cumulative Table 5 Composite factor scores and sort table

F1 F2 F3 F4 F5 Composite SCOre F Score ranking Market ranking

Shun Fat Sunny -739.3923 6406.269 -195.313 615.204 1591.11 1424.17 1 1

Gree Real Estate -9.426119 82.98067 -1.60889 8.42700 20.9722 18.7892 2 13

Guangyu Development -0.603125 29.13671 -1.85288 3.20513 10.2419 7.57781 3 2

InRealEstate -1.231916 10.95896 -0.25699 1.36268 2.75179 2.51662 4 12

Phoenix shares -1.229953 10.63698 -0.24885 1.06132 3.26585 2.46377 5 20

First open shares 3.0702146 0.590827 -1.99001 0.31101 8.41490 1.79607 6 3

China Happiness -0.076014 2.170943 0.246298 11.7557 -5.3646 1.78676 7 11

Jinke Share -0.146481 1.928757 0.650653 0.93757 6.63479 1 48961 8 15

Pearl River Industrial -0.043254 3.195898 0.377205 1.58141 2.22630 1.29321 9 16

Golden Group -0.265842 2.491242 0.196958 0.72388 3.78914 1.09380 10 4

China Merchants Property -0.250985 1.987668 0.115669 1.26308 2.20541 0.86489 11 10

Shenzhenye A -0.244496 1.531729 0.496164 0.16838 3.11773 0.77175 12 8

Poly Real Estate -0.136773 1.487439 0.100383 1.16292 1.75976 0.71563 13 5

Emori Development 0.3016697 0 894898 -0.02813 0.76651 0.81987 0.51092 14 7

SIDC -0.340770 1.307614 0.148566 0.47132 1.74718 0.50662 15 6

Beijing Urban Construction -0.312831 1.778653 0.085196 0.08836 1 44442 0.50204 16 9

Vanke A -0.057160 1 454484 -0.20978 1.08093 -0.3038 0.39963 17 18

Ken Chase 0.211635 -0.25738 0.445364 0.31119 1.30670 0.309711 18 19

Street -0.164875 -0.07711 -0.40328 0.00380 -3.5267 -0.58741 19 14

Tande -0.367758 -1.1079 -1.03838 -0.2103 -11.551 -2.04046 20 17

variance contribution rate, n is the number of factors. In turn, the total variance is explained, we get composite score:

F = (6 .914%xF1 + 9 .189%xF2 + 6 .765%xF3 + 4 .957%xF4 +1 .259%xF5)/9 .084%

samples can be obtained by calculating the company’s overall score and sort. And to compare its market ranking, results are shown in Table 5:

Table 9 shows the results, the results of factor analysis of listed companies on the market value of the investment rankings and investment value of listed companies ranked exists a little difference, indicating that some in the stock market value of the stock is undervalued and some is overvalued. That certain investment value of listed companies and some are not suitable investments. Among the studied 20 listed real estate companies are undervalued stocks: Gree real estate, the real estate, Phoenix shares, Jinke shares, Pearl River Industrial, stocks of these companies in accordance with the results of this factor analysis with investment value in 2013, investors may be appropriate as a reference. In the above stocks overvalued stocks are: Shenzhen Industry A, Poly Real Estate, Emori Development, the real development, Beijing Urban Construction, these enterprises temporarily due share price high, does not have investment value.

4.Conclusion and recommendations In this paper, a lot of literature review and synthesis, based on the investment value of listed companies built evaluation index system, and to expand empirical research for the real estate listed companies example. In this paper, factor analysis method is used to evaluate overall value of listed companies. Through analysis, we draw the preliminary following conclusions: (1) the real estate industry in recent years is as the value of investment in the industry. (2) through the main analysis of this paper, in 2013 a number of listed companies with higher integrated factor are to be the choice object by investors. (3) Market irrational factors, and other factors may cause some companies do not have the investment value, such as Poly Real Estate, Beijing urban construction.

These results in the use of factor analysis investment value of listed companies also found the conclusions of the limitations of the model. This makes the following recommendations in order to enable researchers to further and more objectively reflect the investment value of listed companies, in quantitative research, investors can select about ten years time series indicators to analyze the volatility of the value of corporate stock, and thus verify factor model conclusions. While investors may be appropriate to consider corporate management structure, corporate culture, goodwill and other non-quantitative factors into factor analysis model, the model to reflect the analysis of business investment as the value of a variety of factors.

References

[1]Qian Fenglin. Investment value of listed companies in China’s stock market An Empirical Study [J]. Chinese Circulation Economy, 2004,

(3).

[2] Han Zhao state, Xie Ming Jie. Listed Companies Investment Value Evaluation Model and Empirical Analysis [J]. Central University of Finance, 2004, (11).

[3]Yin Zimin. Factor analysis in enterprise growth evaluation [J]. Mathematical Statistics and Management, 2000.

[4] Chen Zhiling, WANG Heng investment value of listed companies Comprehensive Assessment System [J]. Decision and Research, 2006,

(2).

[5] Chen Wei. Based on factor analysis of listed companies in the steel industry investment value analysis Business Economics 2009, (02): 78-82

УДК 336.77:332.834

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