Print Reference This Reddit This

Debt Maturity Choices Between Chinese Listed Companies Finance Essay

Many companies tend to pay increasing attentions on choosing debt maturity when deciding their corporate capital structure. By choosing a suitable debt maturity, companies can minimize agency costs, signal their performance and so on. Chinese companies often issue shares to finance or refinance themselves and use less debt compared with equity. As the continual development of debt market and corporate governance systems in China, Chinese companies, however, will issue more debt and the debt maturity issue will become a key concern for them. This paper aims to explore this area.

In the past two decades, most research have been used to analyze the factors that determine the debt maturity choice of companies in the developed countries, like Myers (1977), Barclay and Smith (1995) and etc. In recent five years, debt maturity choices of Chinese companies have been gradually analyzed. For example, multi-factors regression with panel data is employed by K. Cai et al (2008) to apply western theories (agency theories, tax theories and etc. ) to companies in China during the periods of 1999 and 2004; and also the importance of ownership concentration in China is largely analyzed by Xiao, Z.P. and Liao, L. (2007).

This paper will design a multi-factor regression to include the variables commonly used in the developed countries and the variables of distinctive Chinese features. It uses panel data of companies listed in Shanghai Stock Exchange from 2006 to 2009 to further investigate the influences of different factors on the choices of debt maturity for Chinese listed firms. Besides, year-by-year and industry tests are done to better examine the determinants of the debt maturity.

The structure of this paper is made as follows:

2 Literature Review 是不是把理论和proxies合到一起

In corporate finance, many academic papers have focused on firm’s capital structure. Among them, more and more studies have worked out to discuss the issue of debt maturity, and particularly most theories were produced to explain the issue in developed countries. In this part, theories related to debt maturity will be discussed and also proxies for the theories will be provided.

2.1. The dependent variable

In our general model, the dependent variable is debt maturity, LTDTD. Stohs and Maue (1996) considered all kinds of debts when calculating the debt maturity of a company and design a weighted method to quantify the debt maturity. K.Cai et al (2008) used the ratio of long-term debt to total debt as a proxy of the company’s debt maturity. Similar to K.Cai et al, we calculate the ratio of debt with maturity more than one year to total debt as a proxy to examine the debt maturity.

2.2. The explanatory variables

2.2.1. Agency cost theory

Agency costs are due to the conflicts of interest that probably happen between different stakeholders. In corporate finance, agency costs mainly arise between shareholders and management, between large shareholders and minority shareholders, and between management and debt holders. The firm should minimize agency costs. Agency problems about debt occur in two cases where may cause cost increasing, namely underinvestment and overinvestment. As for underinvestment issue, Myers (1977) found that the managers of company, who often serve the interests of shareholders, would probably not take the positive NPV investment opportunities if they are financed by risky debts. He explained that high risky debt is highly likely to default and shareholders will indirectly bear this kind of potential loss or cost. Myers (1977) also suggested that a firm can tackle the underinvestment problem by including restrictive covenants in the debt contract and monitoring that covenants, and shortening debt maturity.

For overinvestment, Hart and Moore (1995) analyzed it and point out that the long-term debt level has a negative relation with earnings of new investment opportunities but a positive relation with earnings of companies’ existing assets.

2.2.2. Proxies for agency cost theory

Firm size. Barclay and Smith (1995) outlined that firm size would influence the debt maturity because smaller firms would not choose to issue public debt with a huge fixed cost. K.Cai et al (2008) also regarded firm size as an explanatory variable in their models. We measure the firm size by the natural logarithm of its total asset (LNTA) every year from 2006 to 2009. The firm size is supposed to have a positive relation with the debt maturity.

2.2.3. Signalling effects theory

Flannery (1986) explored signaling effects of the firm’s debt-maturity choice. Under the condition that the market cannot tell good firms from bad ones, it is better for good ones to issue short debts because long-term debt brings relatively more risks to investors and hence would be underpriced. That is to say, good firms would not sell long-term debts at a fair price when good and bad ones issue long-term debts at the same time. Oppositely, bad firms tend to issue relatively overpriced long debts. Besides, Flannery (1986) also explained that some firms raise short term debts to finance investment opportunities with longer periods to signal their confidence on the bright prospects.

Diamond (1991) stated that firms, at the two extremes, with sufficiently good and exceedingly poor ratings will tend to raise short-term debts and those with ratings between the two will prefer to issue relatively long-term debts.

2.2.4. Proxies for signaling effects theory

Firm quality. We employ the earnings per share (EPS) to measure the firm quality. K.Cai et al argued that ‘changes of a firm’s future earnings’ (K. Cai et al, 2008 p.273) can explain the insider’s private information. Empirically, the firm quality is negatively correlated with the debt maturity.

2.2.5. Liquidity theory

Myers and Rajan (1998) explained influence of the liquidity asset on debt financing. Generally, Firms with more liquidity assets tend to have more cash to be able to repay the debt and hence are easier to raise the debt. Myers and Rajan (1998) focused more on another side of the impact of liquidity. They, however, pointed out that it is easier for firms with some illiquid assets to get access to long-term debts. K.Cai et al. (2008) examined the effect of liquidity on the debt maturity among Chinese firms and observed that the more current assets, the longer debt maturity.

Leland and Toft (1996) examined the relationship between leverage ratio and debt maturity. He concluded that leverage ratio is bigger for firms with debt of longer maturity and this result is empirically consistent with what Barclay and Smith (1995) got. Dennis et al. (2000), however, found that they have an inverse relationship.(这段leverate是不是单拿出来)

2.2.6. Proxies for liquidity theory

We measure the liquidity as the ratio of current assets to current liabilities (CACL) and expect a commonly positive relation between the two variables. Following K.Cai et al. (2008), we measure the leverage by the ratio of the total debt to total asset (TDTA). After looking at the historical literature as mentioned above, we find that there are no clear statements about how leverage influences the debt maturity of companies.

2.2.7. Matching theory

Financing costs may be reduced through matching maturity of firm’s asset with that of firm’s liability. Myers (1977), and Hart and Moore (1994) suggested firms would try to match the maturity of their assets with that of their liabilities. Myers (1977), specifically, explained that such matching principle as ‘an attempt to schedule debt repayments to correspond to the decline in future value of assets currently in place’ (Myers, 1977 p.171).

2.2.8. Proxies for matching theory

We measure asset maturity as the ratio of fixed asset to the total asset (FATA) and predict that the asset maturity is positively associated with debt maturity.

2.2.9. Mortgage assets

Whited (1992) argued that mortgage assets would have important influences on the debt maturity. He further pointed out that mortgage assets have positive relations with the firm’s long-term debts. Myers (1984) pointed out that what kinds of assets the companies hold would determine their borrowing duration.

2.2.10 Proxies for the mortgage assets

We measure mortgage assets by the ratio of the sum of inventories and fixed assets to total assets (IFATA).

2.2.11. Interest rate

Titman (1992) found that bad firms would transfer from short-term borrowing to long-term borrowing when interest rate risk increases. Brick and Ravid (1991) investigated the debt issuance in different cases of interest rate structures. They argued that the long-term debt is optimal when the term structure of interest rate is increasing while it is better to use short-term debt when the term structure of interest rate is decreasing.

2.2.12. Proxy of term structure of interest rate

Following K.Cai et al. (2008), we measure the term structure of interest rate using difference between the yearly rate on long-term (5 years) and short-term (6 months) government bonds (LTST). Lending rates are used to replace the government bond rates due to data shortages. A direct link between the lending rate difference and debt maturity is predicted as the expectation of the term structure of interest rate would probably influence the debt maturity.

2.2.13. Other distinctive factors for Chinese Firms

In order to better examine the factors to determine the debt maturity in China, four independent variables of Chinese distinctive characters are put into the model, including percentage of shares owned by the biggest shareholder (PERBS), nature of the biggest shareholder (NATBS), refinance (REF) and economic stimulus package (ESP). Xiao, Z. P. and Liao, L. (2007) examined how bigger shareholders exert influence on the debt maturity choices of Chinese companies. They found that the percentage of shares held by the biggest shareholder is significantly and negatively correlated with the debt maturity. Additionally, they concluded that those companies whose biggest shareholders are state-owned companies tend to issue more long-term debts.

In China, listed companies prefer to refinance by equity instead of debt mainly because the equity market is more developed than the debt market and hence refinance is considered to have possibly indirect effect on debt maturity. Besides, the debt has witnessed a strong increase after economic stimulus measures took place and accordingly may impact the debt maturity. We regard the NATBS, REF, ESP as three dummy variables. For NATBS, it takes value of 1 if the nature is state-owned otherwise 0; for REF, it is 1 if the book equity changes over 5% percentage year on year otherwise 0; for ESP, it takes 1 only in 2009 for the expansion measure works just in 2009 otherwise 0.

3. The data and sample description

3.1 The data

The data are found from Thomas Reuters and RESSET databases as well as statistics published by People’s Bank of China from 2005 to 2009. These data come from 858 companies listed on Shanghai Stock Exchange. We calculate the kinds of ratios from 2006 to 2009 except that the data of book equity starting from 2005 are used to work out the percentage changes of book equity during the periods of 2006 to 2009. Specifically, the data of percentage of the biggest shareholder and nature of the shareholder are collected from RESSET database. Long-term and short-term government bond rates are replaced by lending rates respectively published by People’s Bank of China. The rest of data hence are sought from Thomas Reuters database. We ignore the companies mainly operated in the banking industry. Two methods are used to deal with firms that have incomplete data. For data in the field of percentage of the biggest shareholder and nature of the shareholder, only about 230 firms have complete data, thus we take averages of these data and fill those averages for other firms without data. For incomplete data that are in the other areas, like earnings per share, ratio of total debt to total assets and etc., these kind of firms are ignored. We are finally left with 768 firms.

3.2. Sample description

The table 1 shows the descriptive statistics of all the variables in the general regression equation.

Figure 1 : Average debt maturity of Chinese listed companies from 2006 to 2009

Table 1 : Descriptive statistics

Variable

Obs

Mean

Std. Dev.

Min

Max

LTDTA

3072

0.130

0.175

0.000

0.929

LNTA

3072

21.597

1.211

15.418

27.501

EPS

3072

0.238

0.656

-21.857

6.278

CACL

3072

1.403

1.594

0.000

55.742

TDTA

3072

0.602

1.273

0.002

55.409

FATA

3072

0.493

0.223

0.001

1.000

IFATA

3072

0.672

0.163

0.024

1.000

LTST

3072

0.012

0.001

0.010

0.014

PERBS

3072

0.355

0.061

0.083

0.804

NATBS

3072

0.622

0.197

0.000

2.000

REF

3072

0.533

0.500

0.000

2.000

ESP

3072

0.250

0.433

0.000

1.000

The mean value of LTDTD (ratio of long-term debt to total debt) is 0.13. In figure 1, specific picture is shown about yearly average debt maturity and it has an increasing trend but the value is around 0.13 as a whole. In 2009, the debt maturity witnessed a slight jump up to about 0.16. Generally, this implies Chinese listed firms tend to issue short-term (less than 1 year) debt far more than long-term debt. The debt maturity decreases a lot compared with an average of 0.23 from 1999 to 2004 indicated by Cai,Fairchild and Guney (2008). This figure is still lower compared with that in develop economics, which often ranges from 0.6 to 0.8. The reasons behind may include that Chinese listed companies tend to issue equity or stock shares to get financing and the debt market is not mature yet. Besides, many listed companies rest their financing on bank loans and the banks place very strict requirements on long-term lending to companies.

The mean of EPS is 0.238 and this looks attractive in terms of firm quality. The CACL has an average of 1.4 and demonstrates that many Chinese listed firms are able to meet the fixed payment of interest rate or other liability by their liquidate assets. The IFATA has a mean of 0.672 and it shows companies hold enough assets as mortgage assets and are easier to access to the long-term debt.

The last four variables can show us some special features about Chinese firms, namely PERBS (percentage of the biggest shareholder), NATBS (nature of the biggest shareholder), REF (refinance of Chinese listed firms during periods of 2006 to 2009) and ESP (economic stimulus plan). The mean value of PERBS is 0.35, demonstrating that the biggest shareholder in the Chinese firms often have a big saying. For NATBS, the average is 0.62 and it shows the biggest shareholder in 62% firms relates to state-owned holder. These two values express distinct characters of Chinese companies, characters that the biggest shareholder covers a very big portion of shares and many Chinese firms controlled more or less by the nation. Besides, mean value of REF tells us that over half companies did refinancing by issuing shares during those periods. The ESP has an average of 0.25 because the economic stimulus plan has taken effect since 2009.

3.3 Variable correlation 这个表是否可以横向占满一页 ,重做 注意是大写字母

From correlation table 2, relation signs between LTDTD (the ratio of long-term debt to total debt) and most explanatory variables are indicated as we predicted. For common explanatory variables, the exception lies in earnings per share, which has a positive relationship with LTDTD. The reason is probably that companies of good qualities in China tend to issue long-term debt because banks can often give these companies preferable policies about interest payment on long-term debt and hence it makes sense for the companies to borrow for a longer period in terms of cost savings.

Looking at four explanatory variables of Chinese distinctive features, they all have positive relationship with the LTDTD. For PERBS, if the biggest shareholder has more shares in the company, it has bigger motivation to monitor the company’s operation and hence reduce the agency cost. Therefore, it is common not to use too much short debts to tackle the agency issue, consistent with the statement by Myers (1977).

The nature of the biggest shareholder (NATBS) is positively correlated with the LTDTD for the state-owned companies have closer relations with the government and are more probable to be controlled by the government and so probably are easier to get more long-term debt from banks in China.

The variable of REF has a positive relation with LTDTD. It may be that many Chinese listed companies choose to refinance themselves by issuing shares again, and then total debt decreases in their capital structure and accordingly the ratio of long-term debt to long-term debt increases.

Medium or long-term debt has increased from 2008 to 2009 since the expansion policy worked after the end of 2008 and it is reasonable to see a positive sign between LTDTD and ESP.

4. Methodology

We explore the debt maturity of Chinese listed companies using panel data and incorporate eleven explanatory variables in our general model. A variety of regression methods and tests are employed to further study the influence power of different factors.

4.1. The regression model

Following previous approaches by many researchers, panel data is used to run regressions and deal with the problem. The dependent variable in the general model is the ratio of long-term debt (more than one year) to total debt. Eleven independent variables are used in the general model, which are highlighted in the literature review. Of these independent variables, three dummy variables are employed, namely nature of the biggest shareholder (NATBS), refinance (REF) and economic stimulus package (ESP).For nature of the biggest shareholder, it equals to 1 if state-owned company is the biggest shareholder and 0 otherwise. For refinance, the percentage changes of book equity from 2006 to 2009 are calculated to examine whether the company refinanced by issuing shares during this period. This dummy equals to 1 if the percentage change of the book equity exceeds 5% and 0 otherwise. Finally, the dummy variable of the economic stimulus package is 1 in 2009 and 0 otherwise because the Chinese government launched 4 trillion Yuan investment plan at the end of 2008. The general model hence is as follows:

where dummy NATBS, dummy REF and dummy ESP are three dummy variables.

4.2. Regression methods

Generally, four methods are used to deal with the regression on panel data. They are pooled method, fixed effects method, random effects method and mixed effects method. Greene (2008) argues that the pooled method can be used if there is no unobserved variable; otherwise, fixed effects, random effects or mixed effects method should be considered. The crucial point to choose the method is whether the unobserved variables are correlated with the observed variables. The fixed effect method is chosen when the observed and unobserved variables are correlated; the random effect method is employed when the two kinds of variables are not correlated. Both fixed effects and random effects are examined in the mixed effects method. There are probably some variables not in our general model. For example, Xiao, Z. P. and Liao, L. (2007) put total shares owned by the second, the third, the fourth, and the fifth largest shareholders into their model and also observe this variable is significantly and positively correlated with the debt maturity. Adopting wrong or unreasonable methods will produce biased results. Therefore, we use all the methods mentioned above except the pooled method to regress the model.

4.3. Tests

We followed testing approaches used by K. Cai et al. (2008) to examine specific situations categorized by year and industry.

Year-by-year tests. We run time-series regression from 2006 to 2009 respectively to seek potential different effects or factors that determine the debt maturity of Chinese listed companies. The reason behind is that financial crisis may influence some factors (some explanatory variables in the general model) and then accordingly have indirect on debt maturity or even direct effects.

Industry tests. Industries are dropped if the number of companies is less than10 and also the banking industry is not considered due to its special feature in China. Then we are left with 11 industries. The breakdown of the industries is as follows:

Table 3: Industries breakdown

Industry

Observations from 2006 to 2009

Agriculture, Forestry, Fishing and Hunting

92

Conglomerates

160

Construction

68

Electricity, Gas and Water Supply

152

Information Technology

200

Manufacturing

1736

Mining

64

Real Estate

112

Social Services

84

Transportation and Storage

156

Wholesale and Retail Trade

228

The biggest industry is manufacturing industry which includes more than 50% companies. It can be divided into 10 sub-industries, namely Electrical Equipment, Food and Beverages, Machinery Equipment and Meters, Medicine and Biological Products, Metals and Non-metallic Mineral Products, Paper and Printing, Petroleum, Chemical Product and Plast, Textile, Apparel and Leather, Wood Products and Others. The wholesale and retail trade follows suit as the second largest industry in terms of listed firms.

5. Results

5.1. General model results

Table 4: Results of debt maturity choice of Chinese listed companies using the general regression model with different methods

Independent variables

Fixed-effects

Random-effects

Mixed-effects

LNTA

0.076

0.059

0.050

(0.006)

(0.003)

(0.002)

EPS

-0.005

-0.004

-0.002

(0.004)

(0.004)

(0.005)

CACL

0.016

0.017

0.023

(0.002)

(0.001)

(0.002)

TDTA

0.004

0.003

0.006

(0.002)

(0.002)

(0.002)

FATA

0.131

0.151

0.161

(0.033)

(0.022)

(0.017)

IFATA

0.029

0.102

0.230

(0.033)

(0.026)

(0.023)

LTST

-3.762

-3.077

-3.374

(1.433)

(1.367)

(2.071)

PERBS

0.061

0.068

0.036

(0.067)

(0.052)

(0.044)

NATBS

-0.011

-0.022

-0.032

(0.021)

(0.016)

(0.014)

REF

-0.006

-0.003

0.007

(0.004)

(0.004)

(0.006)

ESP

0.016

0.021

0.022

(0.005)

(0.004)

(0.007)

_cons

-1.583

-1.300

-1.177

(0.127)

(0.071)

(0.057)

R-sq

0.234

0.272

Number of obs

3067

3067

3067

Estimated standard errors are put in the parentheses. The significance level is 5%

Table 4 presents the regression outcome of the general model using fixed effects, random effects and mixed effects techniques. We discuss the implications of the every explanatory variable as follows.

In all three specifications, the LNTA variable has significantly positive coefficients. This implies that firm size has influences on the debt maturity choice of companies. Specifically, the larger the company size, the longer the debt maturity. Our finding about the firm size in China is consistent with the situations in the developed countries, like what Stohs and Mauer (1996) reported. Besides, K. Cai et al. (2008) also observed the same result when analyzing the Chinese companies.

As for the EPS variable, the coefficients in three methods are negative but insignificant. The negative signs of coefficients provide some weak support to the signaling effect theory argued by Flannery (1986) as firms of good quality tend to choose short-term debt. The insignificance of the coefficients on the variable is also found in the Indian firms by Raju, Majumdar (2010), and firms in most western countries.

The CACL variable has predicted positive signs on its coefficients and also they are significant in all three techniques. This result gives big support to the liquidity theory as Morris (1992) pointed out ???? and also demonstrates that the liquidity asset is a key determinant for debt maturity choice in China.

The coefficients on the TDTA variable representing leverage are positive and significant under the fixed effects and mixed effects methods. The results support the analysis by Leland and Toft (1996), and Barclay and Smith (1995), which stated that the leverage has a positive relation with the debt maturity.

We have strong evidence from the regression results that most companies in China match their asset maturity with debt maturity as indicated by the positive and significant coefficients on the FATA variable. The maturity-matching theory is also often supported by the research done in the developed countries (e.g., like Stohs and Mauer, 1996)

The IFATA variable has positive signs for its coefficients and the coefficient estimates are significant under random effects and mixed effects methods. This is in line with the situations in China. Most Chinese banks tend to check the mortgage assets when dealing with the application of the debts. The more mortgage assets, the longer debt the banks can lend with ease.

Results on interest rate give us a picture as we predicted. The coefficients for LTST are of negative signs and significant except by using the mixed effects specification. In China or even in any other countries, companies are sensible to issue debt at a preferable cost. More long-term debt would be sold when the difference between the long-term and short-term interest rate is relatively low.

Four variables with special Chinese features produce different results. The coefficients on the PERBS variable are insignificant positive, which demonstrate the share percentage share of the biggest shareholder is irrelevant in determining the debt maturity. K. Cai et al. (2008) also did some analysis for the ownership concentration for Chinese firms and the results are in line with ours on the share percentage of the biggest shareholder. Additionally, the variable for the nature of the biggest shareholder has negative coefficients and the coefficient is only significant under the mixed effects method. This is inconsistent with what we predicted and also not agree with result got by Xiao, Z. P. and Liao, L. (2007), which argued that the companies would issue more long-term debt if the biggest shareholder is stated-owned company. The explanation for our results maybe the banks tend to pay increasing attention on the company itself, like its asset, size and etc, instead of the shareholder.

The REF variable produces mixing results with negative signs for coefficients under the fixed effects and random effects methods. But the coefficients are insignificant under all three methods, telling us that the whether the companies refinance by issuing shares does not influence the debt maturity choice. Finally, the ESP variable has significantly positive coefficients as we predicted. The Chinese companies actually issue more long-term debt since the economic stimulus measures took effect in 2009.

5.2. Year-by-year test results

Table 5:Yearly analysis of debt maturity determinants of Chinese listed firms

2006

2007

2008

2009

LNTA

0.044

0.044

0.052

0.061

(0.005)

(0.005)

(0.004)

(0.005)

EPS

0.006

0.000

0.008

-0.023

(0.118)

(0.011)

(0.007)

(0.014)

CACL

0.020

0.017

0.037

0.046

(0.002)

(0.004)

(0.006)

(0.006)

TDTA

0.025

0.040

0.020

0.005

(0.010)

(0.012)

(0.007)

(0.003)

FATA

0.208

0.148

0.190

0.168

(0.034)

(0.034)

(0.031)

(0.037)

IFATA

0.170

0.174

0.269

0.317

(0.045)

(0.046)

(0.043)

(0.051)

PERBS

-0.090

0.028

0.005

0.094

(0.110)

(0.090)

(0.080)

(0.085)

NATBS

-0.010

-0.018

-0.040

-0.042

(0.033)

(0.028)

(0.025)

(0.027)

REF

0.014

0.007

0.010

0.004

(0.011)

(0.012)

(0.011)

(0.013)

_cons

-1.047

-1.071

-1.330

-1.536

(0.111)

(0.111)

(0.100)

(0.111)

Adj R-squared

0.311

0.221

0.305

0.312

F-statistic

39.370

25.080

38.430

39.590

Estimated standard errors are put in the parentheses. The significance level is 5%. Independent

variables of LTST and ESP are dropped in the regression equations because they have same values

during each one year period.

The table 几 presents yearly regression results. The coefficient estimates of four explanatory variables, LNTA, CACL, FATA and IFATA are all significant at 5% level and positive from 2006 to 2009. Moreover, the signs of the coefficient estimates are consistent with what we predict in the section of literature review. This may certify that firm size, liquidity, asset maturity and mortgage assets are important factors for Chinese listed companies when deciding their debt structures.

Regarding the EPS variable, the signs of the coefficient estimates are positive except that in 2009. These coefficient estimates are all insignificant at 5% level. This result does not provide many supports to the signaling effects theory. For TDTA variable, the table presents that coefficient estimates are all positive and significant at 5% level except that in 2009. Our result above nearly agrees with the argument made by Leland and Toft (1996) as well as Barclay and Smith (1995).

Variables of Chinese distinctive features produce different results. For PERBS variable, the coefficient estimates are insignificant at 5% level during the whole period. The signs of the estimates, however, are mixed and positive in 2007, 2008 and 2009. The coefficients of NATBS variable are negative and insignificant. Both significance and signs of PERBS and NATBS are almost totally different from the result got by Xiao, Z. P. and Liao, L. (2007). The reason?

The estimates on final variable (REF variable) are positive and insignificant. This implies that whether Chinese listed companies refinance themselves by issuing shares or not does not change their choices on debt maturity.

5.3. Industry test results

Table 6: Analysis of debt maturity determinants of Chinese listed firms across industries

LNTA

EPS

CACL

TDTA

FATA

IFATA

LTST

PERBS

NATBS

REF

ESP

_cons

obs

1.Agriculture

Fixed

0.087

0.001

0.056

-0.065

0.290

0.055

4.041

0.193

0.075

-0.019

-0.016

-2.093

92

0.036

0.027

0.012

0.031

0.196

0.196

6.195

0.230

0.124

0.022

0.020

0.799

Random

0.060

0.008

0.051

-0.034

0.378

-0.062

5.200

0.236

0.112

-0.025

-0.012

-1.553

92

0.022

0.026

0.012

0.028

0.125

0.134

6.026

0.217

0.104

0.021

0.020

0.482

Mixed

0.049

0.039

0.038

0.031

0.426

-0.057

5.018

0.141

0.042

-0.048

-0.008

-1.290

92

0.018

0.038

0.017

0.033

0.083

0.104

10.745

0.277

0.112

0.032

0.035

0.417

2.Conglomerates

Fixed

0.001

0.045

0.283

1.151

0.809

-0.114

-1.446

-0.255

0.020

0.002

0.020

-1.081

160

0.031

0.033

0.028

0.130

0.121

0.107

4.947

0.330

0.066

0.015

0.018

0.624

Random

0.029

0.063

0.290

1.029

0.835

-0.010

-5.180

-0.133

0.018

-0.005

0.005

-1.720

160

0.014

0.031

0.026

0.099

0.092

0.089

4.379

0.196

0.044

0.014

0.015

0.280

Mixed

0.028

0.099

0.332

1.025

0.856

0.149

-6.182

-0.058

0.014

-0.032

-0.003

-1.881

160

0.010

0.037

0.033

0.094

0.086

0.085

6.333

0.141

0.034

0.020

0.021

0.220

3.Construction

Fixed

-0.037

0.019

0.372

0.628

0.965

-0.231

0.869

0.042

0.133

0.001

0.015

-0.244

68

0.022

0.027

0.039

0.117

0.115

0.109

4.384

0.192

0.080

0.014

0.015

0.418

Random

-0.006

0.021

0.355

0.675

0.883

-0.211

-1.444

-0.058

0.135

0.005

0.003

-0.842

68

0.017

0.028

0.038

0.117

0.105

0.098

4.349

0.178

0.079

0.014

0.015

0.338

Mixed

-0.010

0.046

0.334

1.091

0.818

-0.079

-3.950

-0.042

0.078

0.011

-0.006

-1.023

68

0.018

0.057

0.054

0.176

0.130

0.091

9.261

0.236

0.125

0.027

0.031

0.384

4.Electricity

Fiexed

0.113

0.010

0.110

0.161

0.394

0.147

-7.385

-0.121

0.034

-0.014

0.029

-2.657

152

0.036

0.025

0.020

0.093

0.245

0.249

5.149

0.366

0.074

0.017

0.018

0.781

Random

0.082

0.010

0.118

0.242

0.541

0.054

-5.127

-0.338

0.055

-0.019

0.033

-2.050

152

0.016

0.024

0.018

0.074

0.186

0.210

4.840

0.262

0.064

0.016

0.016

0.358

Mixed

0.069

0.010

0.162

0.357

0.650

0.240

-1.833

-0.446

0.037

-0.057

0.036

-2.055

152

0.010

0.047

0.021

0.074

0.178

0.255

10.360

0.208

0.075

0.031

0.034

0.257

5.Information technology

Fixed

0.099

-0.008

0.007

0.370

0.190

-0.140

-4.541

-0.077

-0.069

0.023

-0.007

-2.124

200

0.019

0.028

0.003

0.089

0.114

0.118

4.957

0.225

0.083

0.015

0.015

0.379

Random

0.029

-0.033

0.007

0.201

0.114

0.014

2.120

-0.043

0.032

0.029

0.016

-0.758

200

0.010

0.026

0.003

0.067

0.079

0.094

4.858

0.212

0.071

0.016

0.015

0.215

Mixed

0.005

-0.038

0.008

0.119

0.136

0.112

3.761

-0.088

0.095

0.048

0.022

-0.319

200

0.008

0.026

0.003

0.057

0.065

0.080

6.267

0.215

0.066

0.018

0.020

0.173

6.Manufacturing

Fixed

0.073

-0.005

0.062

0.008

0.182

0.079

-3.680

-0.038

0.040

-0.001

0.007

-1.660

1736

0.008

0.004

0.005

0.004

0.046

0.047

1.790

0.088

0.026

0.006

0.006

0.166

Random

0.058

-0.003

0.049

0.005

0.182

0.156

-4.364

0.044

-0.020

0.003

0.011

-1.361

1736

0.004

0.004

0.004

0.004

0.031

0.036

1.714

0.066

0.020

0.005

0.006

0.093

Mixed

0.049

0.003

0.036

0.008

0.159

0.274

-5.679

0.060

-0.065

0.018

0.013

-1.177

1736

0.003

0.005

0.003

0.004

0.023

0.030

2.488

0.055

0.017

0.007

0.008

0.073

7.Mining

Fixed

-0.017

0.006

0.221

1.399

0.960

-0.026

-7.268

-0.120

-0.468

0.005

0.037

-0.592

64

0.039

0.013

0.048

0.162

0.277

0.272

26.115

2.224

1.050

0.039

0.051

0.786

Random

0.058

0.021

0.021

0.174

0.004

0.132

0.449

-1.736

-0.272

-0.009

0.072

-0.589

64

0.016

0.019

0.044

0.103

0.141

0.160

13.988

2.195

0.302

0.052

0.041

0.775

Mixed

0.043

0.040

-0.032

0.036

-0.149

0.041

-7.170

-0.551

-0.469

0.044

0.071

-0.211

64

0.011

0.021

0.033

0.075

0.098

0.142

15.335

2.526

0.235

0.053

0.047

0.861

8.Real estate

Fixed

0.143

0.001

0.077

0.006

-0.115

0.289

-1.743

0.783

-0.383

-0.048

0.037

-3.192

112

0.044

0.085

0.017

0.005

0.203

0.166

13.347

0.780

0.221

0.037

0.047

0.969

Random

0.051

0.020

0.066

0.002

0.047

0.182

13.167

-0.167

-0.043

-0.051

0.091

-1.245

112

0.020

0.066

0.013

0.004

0.099

0.115

11.390

0.471

0.146

0.035

0.039

0.424

Mixed

0.040

0.003

0.066

0.004

0.071

0.236

15.050

-0.296

0.045

-0.042

0.091

-1.099

112

0.016

0.062

0.012

0.004

0.078

0.105

13.205

0.408

0.133

0.039

0.045

0.352

9.Social services

Fixed

0.102

-0.046

0.137

0.263

0.463

-0.172

-20.351

-0.491

-0.173

-0.022

-0.049

-1.928

84

0.042

0.027

0.021

0.172

0.176

0.141

7.543

0.320

0.122

0.022

0.024

0.829

Random

0.069

-0.035

0.142

0.503

0.468

-0.165

-17.211

-0.223

-0.105

-0.011

-0.040

-1.538

84

0.028

0.022

0.019

0.146

0.120

0.122

7.324

0.278

0.106

0.022

0.023

0.528

Mixed

0.034

-0.055

0.137

0.948

0.736

-0.353

-11.425

0.040

-0.004

0.029

-0.017

-1.240

84

0.022

0.026

0.026

0.168

0.122

0.153

13.241

0.340

0.123

0.036

0.042

0.440

10.Transportation and Storage

Fixed

0.053

0.013

0.010

0.480

0.045

0.122

1.129

0.285

-0.117

0.000

0.072

-1.376

156

0.038

0.033

0.003

0.129

0.213

0.207

7.890

0.536

0.168

0.026

0.027

0.820

Random

0.051

0.012

0.012

0.371

0.257

0.128

3.696

0.010

-0.075

0.005

0.078

-1.376

156

0.018

0.033

0.003

0.097

0.119

0.160

7.603

0.313

0.111

0.026

0.025

0.370

Mixed

0.034

0.024

0.019

0.341

0.313

0.259

6.130

-0.110

0.001

0.043

0.083

-1.172

156

0.013

0.043

0.003

0.085

0.105

0.162

11.419

0.280

0.102

0.035

0.037

0.294

11.Wholesale and retail trade

Fixed

0.074

0.027

0.070

0.047

0.083

0.131

-7.792

-0.109

-0.192

-0.022

-0.004

-1.490

228

0.016

0.024

0.015

0.019

0.083

0.084

3.514

0.170

0.058

0.010

0.012

0.336

Random

0.051

0.000

0.064

0.047

0.092

0.178

-5.318

-0.021

-0.119

-0.020

0.006

-1.123

228

0.011

0.021

0.014

0.016

0.062

0.069

3.365

0.136

0.043

0.010

0.012

0.235

Mixed

0.046

-0.061

0.067

0.044

0.120

0.220

-4.385

-0.009

-0.073

-0.002

0.008

-1.110

228

0.009

0.024

0.016

0.019

0.053

0.067

5.996

0.120

0.034

0.017

0.020

0.200

Estimated standard errors are put in the parentheses. The significance level is 5%.

In this part, we analyze the factors influencing debt maturity across 11 industries in China. The regression results are shown in table6. As did before, we still employ three techniques, fixed effects, random effects and mixed effects methods. We only discuss the most impressive findings below. 用词恰当吗?

In agriculture industry, the coefficients on LNTA and CACL variables are positive and significant, which are consistent with the agency theory and liquidity theory. The EPS variable has a positive but insignificant coefficient and this does not support signaling effect theory. The results for IFATA and LTST variables are confusing and different with what the theories predict. As for Chinese distinctive factors, the stated-controlled firms in the agriculture industry can get more long-term financing by looking at the positive signs of coefficients on NATBS. It makes sense that Chinese officials make much supports to one of the most important industry in China.

In conglomerates industry, cofficients of LNTA, CACL and FATA are positive and significant under nearly all the specifications. This demonstrates that the firm size, liquidity and asset maturity are key determinants of debt maturity in conglomerate companies. LTST variable has a negative but insignificant coefficient estimates, and this weakly implies that companies within the industry will issue short debt when the rate difference between long term and short term debts is big. Both PERBS and NATBS see their insignificant coefficients. The coefficients on PERBS are negative, which shows reverse relation between shares owned by the biggest shareholders and debt maturity, consistent with the findings obtained by Xiao, Z. P. and Liao, L. (2007). Xiao, Z.P. and Liao, L.(2007) explain that the companies, whose biggest shareholder has a larger percentage shares, tend to issue short debt to solve agency problem between shareholders and creditors. The positive coefficient on NATBS reflects the stated-owned companies are financed by more long-term debt.

In the construction industry, contrary to most other industries, the LNTA and the IFATA variables have negative coefficients. Besides, the coefficients on the EPS and NATBS variables are positive. These three coefficient estimates are different from the full sample estimates, showing some special features of the construction industry. In the electricity industry, the results of nearly all the common variables are consistent with those of full sample data. The variables related to shareholders, however, have different results from the general regression. The results for the PERBS and NATBS variables are in line with analysis made by Xiao, Z.P. and Liao, L.(2007). The negative coefficients on PERBS explain that companies within the industry issue short term debts to solve conflicts between shareholders and creditors; the positive coefficients on NATBS imply that the closer ties between the companies and the government, the more long term debt issued.

In the information technology industry, the results for LNTA, EPS, CACL, TDTA, FATA and IFATA almost agree with the general results of the whole sample. The LTST and NATBS coefficients are mixed under three specifications.

In the manufacturing industry, the results of all the variables are basically in line with the results from the entire sample. The slightly apparent difference lies in the coefficients on the ESP variables. Compared with those in the full sample, in the manufacturing industry, the coefficient estimates on the ESP variable are insignificant, leading to the argument that the economic stimulus policy has little effect on the debt maturity of companies.

The mining industry and real estate industry share commons for the results. The coefficients on the REF variable lead to some differences: the estimates are all negative for the companies in real estate industry. Contrary to the general results, both industries have positive signs for the coefficients on EPS variable and negative signs for the PERBS variable. The positive estimates on the EPS variable do not support the signaling effects explained by Flannery (1986).

In the social services industry, the coefficients on the IFATA and ESP variables are negative, which are different from the positive signs of the general result. This reflects that the more mortgage assets and the economic expansion measures make companies within the industry to sell short-term debt. Additionally, the negative signs on coefficients of the EPS variable provide some support to the signaling effect theory.

In the transportation and storage industry, the positive coefficients on the EPS and LTST variables are inconsistent with the general results and also against the signaling theory. But the insignificance of the two estimates show the signaling effect and interest rate are irrelevant with. Among distinctive Chinese factors, only the economic stimulus policy is a key determinant of the debt maturity since its variable’s coefficients are significant.

Finally, for wholesale and retail trade companies, PERBS has negative but insignificant coefficients. Besides, the ESP also has insignificant coefficients and this demonstrates that the four trillion stimulus measures have little influence on the firms’ debt maturity.

6. Conclusion

This paper examines the determinant of debt maturity among Chinese listed companies. We choose the companies listed in Shanghai Stock Exchange and drop the companies that belong to banking industry. We run the multi-factor panel data regression and also do the tests. The research finds that firm size, liquidity, leverage, asset maturity and mortgage assets, difference between long-term debt and short-term debt have important influences on the debt maturity choices in Chinese firms as do on firms in the developed countries. I then investigate and develop four factors of distinctive Chinese features, on ownership concentration, refinance and recent economic expansion measures, and suggest that only the economic stimulus plan began in 2009 produce a positive effect on the debt maturity and other factors do not work. Compared with previous research on Chinese firms, I find that the ownership issue plays a decreasingly crucial role on debt maturity choice. Besides, the tests, year-by-year and industry test, are done to further explore the situations among the Chinese listed firms and general evidence is discovered that the Chinese listed companies see apparent difference across industries but small difference on year from 2006 to 2009.

However, there are some limitations on the paper. For example, the data is not completer for some variables and according methods are employed to solve it. In addition, more sensible explanations should be found for the decreasingly important impacts of the ownership concentration on Chinese listed companies.

Need help with your literature review?

Our qualified researchers are here to help. Click on the button below to find out more:

Literature Review Service

Related Content

In addition to the example literature review above we also have a range of free study materials to help you with your own dissertation: