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The capital structure of listed companies in the industry variance analysis

Author: XieLei YuanYiZhangHuanFeng From: www.yourpaper.net Posted: 2009-09-11 06:45:57 Read:
[Paper Keywords] the capital structure of the industry characteristics of panel data
[Abstract] Based on 10 industries listed companies in China for five consecutive years, the panel data by industry to establish dynamic adjustment model. The study found: the capital structure of listed companies in China there is a significant difference among industries; capital structure affect the magnitude and direction of the influence factors for different industries, there is a great difference; various industries affected by macroeconomic factors, capital structure adjustment costs fitness and capital structure of the same.
This paper uses panel data to build a sub-industry capital structure of listed companies in China dynamic adjustment model through multiple industries listed companies for five consecutive years of data analysis to try a more comprehensive analysis of the capital structure of listed companies in China industry differences, and produce these The reason for the difference will be explained.
Model variables and dynamic adjustment model
Selection of model variables. Selected profitability is closely related with the operating characteristics of the industry, company size, growth, assets can be secured, non-debt tax shield and earnings volatility indicators as factors affecting the company's capital structure into the dynamic adjustment model, indicators code and calculations methods are shown in Table 1:

A measure of the capital structure, academia commonly used in three ways: total liabilities / total assets, total liabilities / shareholders' equity, long-term liabilities / total assets. In this paper, total liabilities / total assets (DEBT) to measure the company's capital structure. There are more difficult to calculate the enterprise value of the assets due to the market value of this article DEBT indicators book value.
Dynamic adjustment model. The above analysis shows that the optimal capital structure is the result of the combined effects of many factors, the optimal capital structure can be expressed as:
DEBT * it = 0 1PR Oit 2SIZEit 3GR OWit 4TANGit 5NDTSit 6INVAit it (1)
However, there are adjustment costs as companies adjust the capital structure, not the capital structure adjusted to the optimal value, but to follow a moving target, and the use of the following adjustments to the model:
DEBTit-DEBTit-1 = (-DEBTit-1) (2)
: DEBT * it optimal capital structure of the company period t; DEBTit DEBTit-1, respectively, for the year t and year t-1 of actual capital structure; coefficient is used to measure the cost of recapitalization, is smaller , shows that enterprises bear the adjustment costs higher. Adjust-DEBTit a function of the formula (2) into (1) and adding time fixed effects variables Tt and individual specific effect control variables t to control the factors related to the company of individuals but not the model contains other capital structure impact the eventual establishment of the capital structure of the dynamic adjustment model are as follows: DEBTit = 0 1PROit 2SIZEit 3GROWit 4TANGit 5NDTSit 6INVAit (1-) DEBTit-1 Tt t it (3)
Model Tt is the time fixed effect variables, can be understood as the impact of changes in macroeconomic factors on capital structure adjustment; residual error term it not observed over time and cross-section of individuals at the same time change. Model XIAO Zuo-ping (2004) and Wang Hao, Zhao Jun (2004) used the model of the principle is basically the same. Roughly the same characteristics generally have the same industries and enterprises, the model does not include the the enterprise characteristics effect variable.
Second, the determination of the sample and data sources
Listed companies Industry Classification Guidelines, this paper Industry Classification Standard for SFC released in April 2001, 13 industry categories, various industry categories further sub-industry categories. The manufacturing industry under the Sectors were also analyzed in order to examine the differences in capital structure of similar industry. According to the 2001 the SFC official announcement classification results, first select all non-financial listed A-share listed companies in Shenzhen and Shanghai December 31, 1998; in order to avoid the influence of outliers, the sample removed in 1999 to 2003 years been ST and PT companies and in the debt ratio is greater than 1 in any year; same time in order to ensure the reliability of the model estimates, taking into account the minimum requirements of the sample in the statistical sample of less than 30 industry analysis; finally complete data of 588 companies to obtain complete data from 1999 to 2003 for five consecutive years, a total of 2,940 units of observation. Samples belonging to six industry categories, including 338 manufacturing companies, belonging to five industry categories. Sample distribution of the various industries are shown in Table 2:

Third, the empirical analysis
Industry statistical description of the differences in capital structure and hypothesis testing. After finishing the asset-liability ratio of the various industries statistical description, as shown in Table 3. From Table 3, it can be found that the average debt ratio among the industry there is a more obvious difference. Sub-industry perspective, the code for the J of the real estate industry's highest debt ratio, the 5-year average of 0.534, followed by wholesale and retail trade, the class integrated industry, IT industry, manufacturing and electricity, gas and water production and supply industries. Operating characteristics significantly liabilities of the real estate industry, wholesale and retail trade industry, there are a lot of short-term liabilities, less electricity, gas and water production and supply industry investment demand, and thus relatively low debt ratio. View from various industries in the manufacturing industry categories, the highest debt ratio of machinery and equipment instrumentation industry, the food industry's lowest debt ratio. In addition to the manufacturing sector in 2003, the standard deviation is greater than 0.2, and other industry standard deviation is less than 0.2, the higher the degree of concentration of the industry debt ratio. Meanwhile, the debt ratio in the industry in five years there have been varying degrees of growth.

Various factors that affect the choice of capital structure in the inter-industry analysis. The model contains unobservable firm characteristics effect t lags of the explanatory variables, the time dimension of this study only five years, so direct LSDV method model estimate is biased. Arellano & Bond (1991) studies have shown that use of GMM technique can be unbiased estimate, Kiviet (1995) gives the LSDV estimated corrective method, the study also confirmed that the small sample size and the time dimension is smaller corrective method LSDV estimated variance The variance is much smaller than the GMM estimation. This article carve industry model estimation, the sample size of some of the industry LSDV estimated corrective methods Kiviet (1995) This article, corrective LSDV estimates. Eviews 5.0 software under various Sectors estimate model (3), respectively, of each of the industry categories and manufacturing, and the after finishing corrective estimated results are shown in Table 4, Table 5.

From the statistical test of the model, the coefficient of determination is greater than 0.9, in addition to electricity, gas and water production and supply industry, other industry the DW test values ??are greater than 2, the model estimates show that these industries have achieved a high goodness of fit may be due to the use of panel data model to reduce multicollinearity between the regressors increase the degrees of freedom of the validity of the parameter estimates, at the same time the same industry affect the capital structure homogeneity the same industry, product market and operating environment. Time dummies there are obvious differences in the performance of different industries. Industry categories in the wholesale and retail trade and the consolidated class industry time dummies are not significant, these two industries are perfectly competitive industry, a business presence of diversity, and their capital structure choice is generally affected by macroeconomic factors than small. The food industry in the manufacturing industry time dummies did not pass the test of significance.
From various influencing factors, the profitability of the various industry categories and manufacturing Sectors (PRO) coefficients are negative, and in addition to the production of electricity, gas and water supply industry coefficient is not significant, other industry the coefficient at the 1% level significantly, the results support the pecking order hypothesis of Myers and Majluf (1984), the signaling theory goes against Ross (1977), indicating that these industries are seldom taken into account in the choice of financing structure signal passed. The coefficient of the electricity, gas and water production and supply industry not pass inspection, its investment demand is less strong lending policies consistent. The profitability of the various sectors of the direction of the capital structure is the same, but the size of the force, but there is a big difference.
Growth (GROW), the IT industry has a high growth potential, but the correlation coefficient of growth but did not pass the test of significance may be due to the IT industry, the demand for funds, enterprises in obtaining funds to consider more the ability to obtain full funding, and access to capital the randomness; correlation coefficient of the growth of the food industry is not significant; growth of other industries correlation coefficients were significant for the growth of the pharmaceutical industry has the largest significant positive correlation coefficient with the industry growth, capital demand is consistent.
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