This thesis encompasses 2 developed countries and a developing country in total for the period 2003-2012(because Amadeus database lack listed companies’ data from 2003 to 2004, I could only use data 2005 to 2011. However, the data I collect of Chinese listed companies from Chinese database are integrated.). These 3 countries are China, Sweden and Germany. The reason I choose Germany and Sweden is: firstly, these two countries behaves very good in decades, they are representative of Europe economy. Secondly, they are developed countries, their capital market have been developed over centuries. Moreo-ver, the governments of Sweden and Germany don’t often interrupt capital market, they advocate free market economy, and comply with the principles of market selection. In this condition, economy environment would be more reasonable, although, there may be some exploitable defects during the economy running. It is also necessary to declare that, the company management system would be more perfect after centuries’ development in these two countries. They behave more skilled on the use of market instruments comparing with the management of Chinese companies. Furthermore, I also take the fact into account that the performance among these countries during the international financial crisis and Euro-pean debt crisis. According to the result, I think Germany and Nordic countries are good examples, so I choose one of the Nordic countries. At last, there are many countries that their economy (GDP) is very good, but because of the reasons including working attitude, whether or not join the Euro zone and so on. I will not use the data from these countries. For example: Great Britain.
In this thesis, I collect 1212 listed firms in Germany and Sweden in total with the period of 2003 to 2011 from Amadeus database (some data cannot be found from 2003 and 2004). For the fast development in Chinese capital market, many companies stepped into stock market every year, which made a different number of listed companies in my sample. In Chinese listed companies sample, there 899 listed firm in 2003, 2004 and 2005, 940 firms in 2006, 1043 in 2007, 1065 in 2008, 1175 in 2009, 1481 in 2010 and 1786 listed companies in 2011. All the data was from Chinese CSMAR solution database. All types of listed compa-nies would be available; no matter they are big or small, and 1212 listed companies from Sweden and Germany for 9 years.
According to the questions raised in the beginning and the goals in this thesis, the variables I choose will be representative. Therefore, I could compare the results more clearly, and these variables could be observed their interactions more directly.
Tobin’s Q value (Q)
Tobin’s Q theory is developed by Tobin (1999); it measures how the stock market eva-luates the firms. KH Chung and SW Pruitt (1994) pointed out that it is a good value to ob-serve firm’s performance. Because Tobin’s Q cannot be found in some database (especially in European database) or in the firms’ annual reports, I have to use equations to calculate them out. (See equation 3.1)
Chuang and Pruitt (1994) said in their thesis that if the Q value is between 0 and 1, then the firm is correctly valued. If the Q is bigger than 1, this indicates there might be other factors influence the value of the firm. However, these factors are hard to detect in the annual re-port.
However, there are only data from 2005 to 2011 in Amadeus, which is a pity that I have to reduce my analyzing period. In the CSMAR solution database, I could easily get listed companies’ Tobin’s Q values in 9 years.
Debts-level is a ratio of total debt to total assets, it is a significant value to describe firm’s capital structure situation in many theses. The essence of capital structure is the variable like short-term debts, long-term debts etc, which are used to financing by firms. It means I could use Debt-level or debt-equity ratio to explain a firm’s capital structure. In this thesis, I choose to use debt-level as the value to explain listed firms’ capital structure. However, this value cannot get from Amadeus database either. In order to get the Asset-liability ratio (Debt-level), I adopt the widely used equation to calculate them out (See equation 3.3). As we all know that total debt should be long-term debt plus current liability.
LEV = totaltotal assetsdebt (Equation 3.3)
Although these variables are not used to measure firm performance or capital structure, they do have relationship with them, which could make different result without considering these variables. In order to make the analysis more reasonable, I will add these variables in-to the whole model.
Firm size (Size)
Firm size is the natural logarithm of total assets (See equation 3.4). The reason why I loga-rithm of total asset is that, firstly, it doesn’t change the monotonicity; it still could reflect the change trend of the original variables. Secondly, the slope after taking logarithm could directly reflect the growth speed of variables. Fixed cost should be considered into the firms’ capital structure adjustment, and it will produce diverse effect to big and small com-panies. In the competitive market, different firm sizes would have diverse competitiveness in various industries. Some firms in IT industry, the smaller the company is, the better the performance would be. However, for the companies like retail industry, big firm size will help them to develop both in logistics and selling. To a great extend, firm size decides the firm performance, moreover, because of the different firm size, the capital structure may not similar with each other. How the firm size affects firm performance and capital struc-ture? I will show the results in the empirical study.
Firm size = ln(total assets) (Equation 3.4)
General speaking that a healthy developing firm will have a steady asset growth ratio, the higher of asset growth ratio to a firm indicates the faster speed of expansion of business scale. This value reflects the operating situation directly or indirectly (See equation 3.5).
Sales growth ratio is a value to measure firm’s growth condition and development ability (See equation 3.6). Higher ratio is good for firm performance. Sales growth may have influ-ence to firm performance, in order to minimize the influence; I took sales growth as con-trol variable.
Sales growth ratio = sales end of the year −sales beginning of the year (Equation 3.6) sales end of last year
Liquidity index (Liquidity)
Liquidity index is a ratio of cash flow to total assets. (See equation 3.7) Cash flow is impor-tant to enterprise operation, and it will affect the firm performance. So I need to eliminate the interference during the analysis.
Liquidity index = totalcash assetflow (Equation 3.7)
After discussing the variables and the time period, I will introduce the regression model in my thesis.
Qi = β0 + β1Levit + β2Sizeit + β3Assetgrowthit + β4Salesgrowthit + β5Liquidit + εit (3.8)
Where i denotes the cross-section dimension and t indicated the time dimension, which is time-series cross sectional data here, εit is disturbance item and β i is the coefficient of each variable. The model is used to describe the relationship between capital structure and firm performance.
Although, there are many ways to achieve this purpose, I will choose ordinary least square method (OLS) statistic regression and descriptive statistics to analyze by the software of Eviews 7.0 and Stata 12.0 instead of interview analysis; this method is easy to find out the effect to firm performance through regression coefficient.
I have some predictions about my regression result based on the model aimed at data anal-ysis made in 3.3 and historical researches as follows:
Capital structure (debts-level) may have a negative effect on firm performance in China (Tobin’s Q value) in the beginning, and a positive effect on European listed companies. Firm performance is a very important index to measure the quality of a corporate. Un-der the condition of asymmetric information, a good performance company will have a high ratio of debts-level in order to show difference with bad performance corporate, moreover, bad performance corporate will not choose to have a high debts-level, for it will bring them high risk.
After a long time developing in China’s capital market, the relationship of capital struc-ture and firm performance should change to positive effects instead of negative effects.
The debts-level should have a continuing increasing in Chinese listed companies from 2003 to 2011, and the debts-level in European listed companies should keep a still level within 2005 to 2011.
Empirical study and analysis
In this section, I will analyze the regression results of the listed companies’ data from three countries. The analysis will be divided into 3 parts. Part one is the overview of descriptive statistics and regression result of Chinese and European companies. Annual results and de-scriptive statistics in details will be discussed in part 2 and part 3 separately. LEV means the debts-level, size refers to firm size, LIQUID means liquidity index and C indicates the in-tercept of β0 in the model.
Comprehensive compare of Chinese and European regression result 2003-2011
Table 4 and table 5 show the descriptive statistics of Chinese and European (Germany and Sweden) listed companies during 9 and 7 years separately, it is clearly that the mean value of Tobin’s Q is 1.51 in China and 2.14 in Europe two countries. This suggests the firm per-formance in Europe is better than Chinese listed companies overall. The Q value is 20 times between maximum and minimum value, however it is much larger in European companies (max value 150.9885, min value 0.), which explains the firm performance in two regions has a big gap especially in Europe. As to the capital structure of debts-level, Euro-pean companies is higher than Chinese companies in general, they have a mean value of 53.32%, whereas 45.86% was reflected in Chinese companies. Raghuram G. Rajan and Luigi Zingales (1995) shows that debts-level in America, Japan, Germany, France, Italy, Great Britain and Canada are 58%, 69%, 73%, 71%, 70%, 54% and 56% respectively, and they are all greater than 50% level. The research from Booth et al. (2001) shows the debts-level in developing countries of Brazil, Mexico, Malaysia, and Zimbabwe are 30%, 35%, 43% and 42% respectively, they are all less than 50% level. This situation is similar like Chinese debts-level. Debts-level indicates the unbalanced developing of Chinese capital market, where has a quickly development of stock market, and bond market keep a slow speed of developing. Zuoping Xiao (2005) also mentioned small number of Chinese investing or-ganizations; Chinese creditors’ interests cannot be protected with a high cost of legal sys-tem and overvalued of stock price in general which make more companies tend to equity finance are the factors caused by a low level of debts -level. In terms to firm size and li-quidity index, similar situation could be found in two regions, where Chinese companies have a little higher value in firm size. European companies have a much higher mean value of asset growth and sales growth, and they are 1.14 and 1.8 which are 5 and 3 times respec-tively to Chinese companies, it indicates European companies have a healthier and faster speed of growth contrasting to Chinese companies.
During 2003 to 2011, there were a continually increasing number of suitable observing stocks because of the fast development of Chinese capital market. In this regression, there are 10053 observations, and number of sample is 110054. As to the European regression, there are 3349 observations, and the number of sample is 13429.
I have discussed about that Tobin’s Q value indicates firm performance and debts-level (Asset-liability ratio) is used to describe firm’s capital structure. From a whole period view in 9 years I get the Chinese companies’ regression result is showed in table 6; it basically re-flects the expectation of most previous Chinese researches. As the coefficient is -1.2813, which means debt-level has negative effect on Tobin’s Q. 1% increasing on leverage will lead to a 1.28% decreasing of Q value. As the probability is equal to 0 which is smaller than critical value of 10%, in the mean time, t-statistic is -22.079, and this explains a significantly negative correlation between capital structure and firm performance. The outcome also confirms the conclusions in empirical studies from Guihua Huang & Frank Song (2006), Chao Chen & Yulei Rao (2003), Dongzhi Yu (2003) and most regression results of Chinese listed companies. However, the negative relationship is different from my prediction of model, although China’s capital market has developed for decades, the relationship be-tween capital structure and firm performance still doesn’t fit the assumption.
Besides the debts-level, firm size and asset growth also significant negatively affects with Q value, although this relationship is not too strong. It is still different from my imagination. Only sales growth and liquidity index shows positive relationship with Q value, if liquidity index booms 1%, the Q value will increase 1.5524%. However the correlation is not signif-icant on sales growth because of the probability is bigger than 0.1.
This result tells us that most Chinese listed companies tend not to finance through issuing debts, because it is no good for firm performance if a company issues debts to finance in Chinese domestic investing environment. Moreover, most Chinese listed companies’ per-formance depends on the cash flow, the higher proportion of cash flow, the better benefits they will have.
1.2 Background and Chinese institutional environment
1.4 Purpose and delimitations
1.5 Research approach and literature source
1.6 Disposition of thesis
2 Theory of capital structure and corporate performance
2.1 Capital structure
2.2 Theory of corporate performance .
2.3 Review of empirical studies of the relationship between capital structure and firm performance
3 Research methodology
3.1 Sample set
3.2 Data description
3.3 Regression models
3.4 Research predictions
4 Empirical study and analysis
4.1 Comprehensive compare of Chinese and European regression result 2003-2011
4.2 Analysis of Chinese regression result
4.3 Analysis of European regression result
5 Conclusion and suggestions for further researches
5.2 Suggestions for further studies
List of references
GET THE COMPLETE PROJECT
The comparison of impact from capital structure to corporate performance be-tween Chinese and European listed firms