High quality of information disclosure can reduce the information asymmetry, which is very important to the effective operation of the capital market. This article studies whether participants in bond market are concerned about the quality of information disclosure based on a sample of listed companies in China’s bond market from 2007 to 2013. We find that there is no significant relationship between information disclosure quality and credit rating, indicating the credit rating agencies are not concerned about information disclosure quality. However, we find information disclosure quality can significantly reduce the bond financing cost, which indicates that bond investors are concerned about information disclosure quality.
Although the bond market develops rapidly in recent years, China’s bond market is still far behind the stock market (Bottelier, 2003) [
High quality of information disclosure can help to solve the problem of asymmetric information and reduce the moral and speculation risk. This plays a very important role in protecting the investors’ interests and promoting the development of capital market. However, as China’s bond market development is relatively lagged, Chinese academics tend to study stock market rather than bond market. Whether improving the quality of information disclosure could reduce the bond financing cost is inconclusive. This article explores whether the China’s bond market participants are concerned about the information disclosure quality, trying to make up for the lack of related research and making some policy suggestions for the development of China’s bond market.
The most two important theories studying information disclosure are the efficient market theory and signaling theory. Both of the two theories agree that the disclosure of information plays a very important role in the capital market. About the relationship between the information disclosure quality and credit rating, there are some theories and empirical literature show that the high information disclosure quality will bring a high credit rating. Currently there’s no direct literature in china studying such relationship, related researches focus on information disclosure status of the bond market, and whether credit rating could reflect the corporate’s financial condition. There’s also no direct literature studying the relationship between the information disclosure quality and bond financing cost. Relevant research focuses on the relationship between information disclosure quality and the cost of equity financing, and relationship between corporate’s financial condition and bond financing cost.
Information disclosure plays a very important role in corporate governance. For internal governance, the information disclosure quality may reflect the level of management control; for external governance, improving the disclosure quality helps to protect investors and maintain a healthy development of the market. High quality of information disclosure can solve the information asymmetry problem to a certain extent, which is very important to the effective operation of the capital market. Bond credit rating, as a comprehensive measure of corporate credit risk, should consider a lot of factors such as the bond issuing company’s economic environment, industry characteristics, competitive situation, management capabilities and financial conditions, etc. That’s why the corporate disclosed information should be an important source for bond credit rating. Barry, Brown (1985) [
Improving the quality of information disclosure will help to reduce the capital cost. The first reason is high information disclosure quality will increase the corporate stock and bond liquidity (Diamond and Verrecchia, 1991) [
In recent years, as the China’s bond market continues to mature and develop, the bond market regulation is more stringent and the investors become more rational, the China’s credit rating system is gradually improving. Several Chinese major rating agencies started to cooperate with foreign rating authorities, such as Moody’s acquisition of China Chengxin Credit Rating Group with absolute control in 2006. Also in 2006, Xinhua Finance (controlled by U.S.) acquired Shanghai Far East. In 2007, Standard & Poor’s cooperated with Shanghai New Century, the two sides also took joint venture into consideration. By cooperating with foreign rating agency authorities, China’s credit rating agencies improved their rating techniques and reputation. Zhu (2013) [
Due to the information asymmetry, the quality of accounting information and disclosure is often seen as the signal of corporate condition and performance. Wiedman (2000) [
Based on above analysis, we believe that credit rating agencies in the bond market are concerned about the information disclosure quality. Therefore, we come up with Hypotheses 1.
Hypotheses 1: The corporate information disclosure quality has a positive impact on the credit rating, the higher information disclosure quality, the higher credit rating.
As an important measure of corporate credit risk indicators, credit ratings affect bond financing cost. Although the independence and effectiveness of China’s credit rating agencies has been questioned, but recent studies have found that the China’s bond market corporate credit rating would significantly affect the corporate’s bond financing cost. As we’ve discussed earlier, if information disclosure quality can significantly affect credit rating, then information disclosure quality should also affect the company’s bond financing cost indirectly.
Apart from the intermediary role of credit rating, the information disclosure quality will directly affect the corporate’s financing cost from the following aspects: firstly, improve the quality of information disclosure will help increase the liquidity of corporate stocks and bonds, thereby reducing the capital cost (Diamond and Verrecchia, 1991) [
Based on above analysis, we believe that investors in the bond market are concerned about the quality of information disclosure. Therefore, we come up with Hypotheses 2.
Hypotheses 2: The corporate information disclosure quality has a negative impact on corporate bond financing cost, the higher information disclosure quality, the lower bond financing cost.
Since rating agencies sequence the corporate credit ratings in order, we use sequencing logic model (Ordered Logit) in our empirical analysis. Based on previous research on credit rating (Elton et al., 2004; Perraudin and Taylor, 2004) [
When credit rating agencies are rating a corporate’s credit, they also give a rating of the bond issued by the corporate. But in the practice of credit rating in China, the rating standards of short-term financing bond are different from other types of bond, thus research often uses the corporate credit rating directly, namely the credit rating for issuing corporates. This article also directly uses the corporate credit rating, and in order to make the results more robust, we also use the bond rating1 as the robustness test by eliminating the sample of short-term financing bond.
Credit means the corporate credit rating, because the sample of this article is listed company, the rating data does not cover all areas. The corporate credit rating in this article has 6 grades, which are AAA, AA+, AA, AA−, A+ and A. AAA is the best grade. In order to do regression analysis, we assign AAA with value of 6, AA+ with value of 5, etc.2.
DisQuality means disclosure quality of corporate information. There are many ways to measure the disclosure quality: Botosan (1997) [
According to existing research results, we choose accounting information quality, basic business financial condition, macroeconomic, corporate nature and bond characteristic as control variables of our model.
AccQuality is the variable for the accounting information quality. De Angelo (1981) [
Credit rating can be largely influenced by the corporate financial condition, so we control some relevant micro-financial variables in the model. Roe is return on equity, equals net profit/average shareholders’ equity. CFO is corporate cash flow, equals operating cash flow/total assets. Lev is the asset-liability ratio, equals total liabilities/total assets. Size is the size of scale, equals to the natural logarithm of total assets. Some studies show that domestic macroeconomic development will have a significant impact on the corporate credit rating. Therefore, this article controls the macro-economic factor GDPgrow which is the real GDP growth of the year. In addition, for examining the differences between SOEs and non-SOEs, we use the State variable, a dummy variable that equals 1 when the company is state owned.
Some bond features4 such as bond duration and guarantee will also affect the credit risk; we use Duration and Guarantee variables. Guarantee is a dummy variable which equals 1 if there’s guarantee. This article includes a sample of short-term financing bonds, medium-term notes, enterprise bonds and corporate bonds. Type is the bond type dummy variables (including Type S, Type M, Type E, Type C), Type S equals 1 means it is short- term financing bonds; TypeM equals 1 means it is medium-term notes; Type E equals 1 means it is enterprise bonds; Type C equals 1 means it is corporate bonds. In order to reduce the endogenous problem between the dependent and independent variables, the independent variables in this article will all use the previous year data of the bond issuing.
With bond interest rates issued at different times can be affected by economic environment of that time, and the rates also diversify from bond duration due to liquidity difference. Therefore, drawing Zhang (2008) [
Spread means bond financing cost, calculated by the bond interest rate minus the bank lending rate of the same period. The same period means the same time and same duration. Other variables are the same with previous.
Bond-related data in this article comes from the Wind financial terminal database, the time period is from 2007 to 2013, including short-term financing bonds, medium-term notes, enterprise bonds, corporate bonds. Due to the current data is based on the listed companies; we eliminate the non-listed companies from the bond-related data6. The GDP data is collected from the China Statistical Yearbook, the bank lending and deposit rates are collected from the People’s Bank of China website. We also eliminate the sample from financial sector. We winsorized all the continuous financial data at 1% level to avoid outliers. The sample selection process is shown in the
AccQuality is characterized by three variables: Big 4, Conserv, VarRoe. We noticed only 11.30% companies in the sample choose Big 4 to do the financial report audit. Conserv has a mean with 0.0420 and median with −0.0152, which means the reports are robust in general, but the 0.2601 deviation shows huge differences among companies. VarRoe, earnings volatility, is relatively small; its standard deviation also shows big differences. We can also see big difference among samples in Roe, Size, Lev, CFO, CR, QR, and LLR. About the company type, the 48.02% of the samples were state-owned. From the bond features, 23.73% of the sample has guarantee. The average bond duration is 3.9442 years, the median is 0.4932, and standard deviation is 2.2444, which shows huge differences in bond durations.
Listed Companies in Shenzhen Stock Exchange’s (2006-2012) | 6846 |
---|---|
Match with the bond-related data (issued bond from 2007-2013) | 359 |
Eliminating the sample from financial sector | 5 |
Final sample | 354 |
Variable | Sample | Mean | Standard Deviation | Median | Minimum | Maximum |
---|---|---|---|---|---|---|
DisSz | 354 | 3.0932 | 0.5320 | 3.0000 | 2.0000 | 4.0000 |
Spread | 351 | −0.3215 | 1.0389 | −0.5000 | −2.5800 | 3.3400 |
Credit | 354 | 3.9492 | 1.0308 | 4.0000 | 1.0000 | 6.0000 |
Rating | 257 | 3.4358 | 0.8551 | 3.0000 | 1.0000 | 5.0000 |
Big4 | 354 | 0.1130 | 0.3170 | 0.0000 | 0.0000 | 1.0000 |
Conserv | 354 | 0.0420 | 0.2601 | −0.0152 | −0.4852 | 1.0425 |
VarRoe | 354 | 0.0027 | 0.0042 | 0.0009 | 0.0000 | 0.0265 |
Roe | 354 | 0.1040 | 0.0762 | 0.0926 | −0.0854 | 0.3850 |
Size | 354 | 22.6768 | 1.1434 | 22.5090 | 20.6730 | 25.3587 |
Lev | 354 | 0.5032 | 0.1556 | 0.5111 | 0.1340 | 0.8058 |
CFO | 354 | 0.0405 | 0.0752 | 0.0371 | −0.1827 | 0.2734 |
CR | 354 | 1.4915 | 1.0339 | 1.2523 | 0.2949 | 6.7566 |
QR | 354 | 1.0895 | 0.8610 | 0.8668 | 0.1984 | 5.6689 |
LLR | 354 | 17.6434 | 18.4839 | 11.6301 | 0.0000 | 73.0281 |
GDPgrow | 354 | 8.1444 | 0.9286 | 7.7000 | 7.7000 | 14.2000 |
State | 354 | 0.4802 | 0.5003 | 0.0000 | 0.0000 | 1.0000 |
Duration | 354 | 3.9442 | 2.2444 | 5.0000 | 0.4932 | 10.0000 |
Guarantee | 354 | 0.2373 | 0.4260 | 0.0000 | 0.0000 | 1.0000 |
Credit | DisSz | Spread |
---|---|---|
1 (A) | 2.0000 | 1.4400 |
2 (A+) | 3.1071 | 0.4518 |
3 (AA−) | 3.0667 | 0.3893 |
4 (AA) | 2.9940 | −0.3798 |
5 (AA+) | 3.2885 | −0.9404 |
6 (AAA) | 3.3750 | −1.4006 |
With the increase of the corporate credit rating, the spread becomes lower, indicating the higher information disclosure quality, the lower bond financial cost, which supports the Hypothesis 2. In order to verify the results from multiple angles, the article also examines the average information disclosure quality and spread in different bond credit rating grades, as shown in
As the corporate credit rating is based on the corporate’s financial condition and macroeconomic environment, and previous research shows that corporate credit rating is also affected by the quality of accounting information. We controlled accounting information quality variables (Big4, Conserv, VarRoe), corporate basic variables (Roe, Size, Lev, CFO, CR, LLR, State), macroeconomic variable (GDPgrow), bond characteristic variables (Duration, Guarantee, Type) and industry, the regression results are shown in
Model 1 uses Credit as the dependent variable, so we eliminate variables that are related to bond characteristics (Rating) such as Duration, Guarantee and Type. Model 2 uses Rating as the dependent variable, it’s sample is smaller because it doesn’t include the short-term financing bonds.
From Model 1’s result, we can see the coefficients of Roe and Size (7.206, 2.725) are significantly positive at 1% level, indicating high profitability and big size can lead to high credit rating. The coefficient of Lev (−8.192) is significantly negative at 1% level, showing high liability-asset ratio will bring low credit rating. All these prove that the signal effect does exist in the China’s bond market, corporate’s financial condition will affect its credit rating. The coefficient of GDPgrow (−0.455) is significantly negative at 1% level, indicating the credit rating agencies tend to give higher credit rating at the time of macroeconomic recession, known as the “counter- cyclical” phenomenon, which is consistent with previous studies. The State’s coefficient is 0.935 and significantly positive, indicating state-owned corporate gets a higher credit rating. The coefficients of accounting information quality variables (Big4, Conserv and VarRoe) are not significant, indicating credit rating agencies are not concerned about the accounting information quality, this collides with previous studies. The coefficient of DisSz is not significant, indicating the information disclosure quality doesn’t has significant impact on the corporate credit rating, and this doesn’t support Hypothesis 1. There might be 2 explanations: One is DisSz may not be a proper indicator; the other is as credit rating agencies often collect information from various kinds of channels, they don’t pay much attention on the corporate’s information disclosure quality. We’ll find out in the robustness test. Model 2’s result is almost the same with Model 1, except it has more variables. However, Model 2 also doesn’t support Hypothesis 1.
Rating | DisSz | Spread |
---|---|---|
1 (A+) | 3.0000 | 2.3500 |
2 (AA−) | 3.0500 | 1.2856 |
3 (AA) | 2.9645 | −0.0657 |
4 (AA+) | 3.1429 | −0.6732 |
5 (AAA) | 3.4359 | −1.4059 |
Variable | Model 1 | Model 2 |
---|---|---|
Credit | Rating | |
DisSz | −0.0745 | 0.135 |
(−0.32) | (0.46) | |
Big4 | 0.615 | 0.605 |
(1.45) | (1.21) | |
Conserv | −0.991 | −0.232 |
(−1.53) | (−0.27) | |
VarRoe | 48.80* | −36.61 |
(1.64) | (−0.94) | |
Roe | 7.206*** | 5.411** |
(3.85) | (2.30) | |
Size | 2.725*** | 2.280*** |
(12.36) | (8.77) | |
Lev | −8.192*** | −7.271*** |
(−5.68) | (−3.96) | |
CFO | 0.555 | −3.852 |
(0.26) | (−1.39) | |
CR | 0.125 | 0.0566 |
(0.68) | (0.26) | |
LLR | 0.0118 | 0.0119 |
(1.44) | (1.16) | |
GDPgrow | −0.455*** | −0.0235 |
(−3.31) | (−0.12) | |
State | 0.935*** | 0.947** |
(3.06) | (2.34) | |
Duration | −0.0190 | |
(−0.14) | ||
Guarantee | 1.849*** | |
(4.49) | ||
Type C | −17.99 | |
(−0.02) | ||
Type M | −19.74 | |
(−0.02) | ||
Inds | Control | Control |
N | 354 | 257 |
R2 | 0.395 | 0.434 |
Note: ***, **, and * denote statistical significance at the 1, 5, and 10 percent levels, respectively.
As shown in
We controlled corporate credit rating (Credit) and bond rating (Rating) in Model 4 and Model 5, respectively. The coefficient of DisSz is significantly negative at 10% level in both models. The results support Hypothesis 2 again. But after controlled Credit and Rating, the significance of DisSz decreased, indicating Credit and/or Rating may have played an intermediary role between the information disclosure quality and bond financing cost. As Hypothesis 1 is not supported by our regression, we can’t tell what kind of intermediary role Credit and/or Rating may have played. Further study might be needed.
In our sample selection, we eliminated all the listed companies which didn’t issue bond. But whether a listed company issue bond or not might be influenced by many factors. So there might be some listed companies wanted to issue bond but didn’t due to some reasons and this could lead estimated results bias (Heckman, 1979) [
In order to overcome this sample selection bias, we use the Heckman (1979) [
Considering the Shenzhen Stock Exchange’s data is simply divided into four levels, it does not distinguish between voluntary disclosure and mandatory disclosure, and the evaluation index system is also not announced. More importantly, the data doesn’t cover companies listed on the Shanghai Stock Exchange, so there might be some problem. In order to make the results more robust, we use the Chinese listed companies information disclosure index (Gao Minghua, Beijing Normal University, 2010, 2012) [
The sample selection and elimination process is similar: eliminated all the financial sector data and winsorized at 1% level, as shown in
As for Hypothesis 1, we use CCDI and 4 secondary indicators to replace DisSz, other variables remain the same. The regression results are shown in
From
As for Hypothesis 2, we also use CCDI and 4 secondary indicators to replace DisSz, other variables remain the same. Results are shown in
Variable | Model 3 | Model 4 | Model 5 |
---|---|---|---|
Spread | Spread | Spread | |
Credit | −0.616*** | ||
(−7.41) | |||
Rating | −0.623*** | ||
(−6.97) | |||
DisSz | −0.180** | −0.164* | −0.150* |
(−1.99) | (−1.97) | (−1.65) | |
Big4 | −0.349** | −0.333** | −0.285* |
(−2.12) | (−2.19) | (−1.82) | |
Conserv | 0.298 | 0.245 | 0.400 |
(1.18) | (1.05) | (1.58) | |
VarRoe | 24.44** | 29.23*** | 32.15*** |
(2.15) | (2.77) | (2.86) | |
Roe | −1.581** | −0.868 | −0.706 |
(−2.20) | (−1.29) | (−1.00) | |
Size | −0.351*** | 0.126 | −0.0426 |
(−5.89) | (1.48) | (−0.55) | |
Lev | 0.778 | −0.625 | −0.319 |
(1.47) | (−1.19) | (−0.56) | |
CFO | −0.217 | −0.0721 | −0.0629 |
(−0.26) | (−0.09) | (−0.08) | |
CR | −0.0811 | −0.0952 | −0.0859 |
(−1.17) | (−1.49) | (−1.29) | |
LLR | −0.00103 | 0.000980 | 0.00230 |
(−0.32) | (0.33) | (0.75) | |
GDPgrow | 0.186*** | 0.130** | 0.154** |
(2.72) | (2.04) | (2.58) | |
State | −0.461*** | −0.323*** | −0.274** |
(−3.81) | (−2.85) | (−2.21) | |
Duration | −0.0652 | −0.0644 | −0.0615 |
(−1.46) | (−1.55) | (−1.56) | |
Guarantee | −0.147 | −0.261** | 0.165 |
(−1.13) | (−2.16) | (1.37) | |
Type S | 1.181* | 1.453** | |
(1.87) | (2.48) | ||
Type C | 1.708*** | 2.499*** | 0.325** |
(3.12) | (4.83) | (2.30) | |
Type E | −0.699 | ||
(−1.37) | |||
Type M | 1.601*** | 2.057*** | |
(2.82) | (3.89) | ||
Inds | Control | Control | Control |
N | 351 | 351 | 255 |
R2 | 0.427 | 0.510 | 0.583 |
Note: ***, **, and * denote statistical significance at the 1, 5, and 10 percent levels, respectively.
Credit | Rating | |||
---|---|---|---|---|
IMR | 0.644* | 1.417** | ||
(1.66) | (2.52) | |||
DisSz | −0.0476 | −0.0560 | −0.0912 | −0.0225 |
(−0.84) | (−0.77) | (−1.50) | (−0.19) | |
Big4 | 0.00580 | 0.157 | 0.0580 | 0.160 |
(0.05) | (1.19) | (0.43) | (0.77) | |
Conserv | 0.101 | −0.0936 | 0.00276 | 0.0462 |
(0.61) | (−0.48) | (0.02) | (0.15) | |
VarRoe | −24.98*** | −2.524 | −20.57*** | −27.57** |
(−4.51) | (−0.24) | (−3.69) | (−2.07) | |
Roe | −0.437 | 1.770*** | 0.110 | 1.721** |
(−0.91) | (3.32) | (0.22) | (2.03) | |
Size | 0.567*** | 1.054*** | 0.543*** | 1.069*** |
(13.96) | (6.09) | (12.42) | (4.20) | |
Lev | −1.064*** | −2.837*** | −1.027*** | −3.536*** |
(−3.23) | (−4.75) | (−2.90) | (−3.97) | |
CFO | −0.822 | −0.182 | −1.086* | −2.670** |
(−1.52) | (−0.27) | (−1.87) | (−2.51) | |
CR | −0.230*** | −0.0632 | −0.176*** | −0.250** |
(−5.90) | (−0.69) | (−4.60) | (−2.25) | |
LLR | 0.00178 | 0.00207 | 0.00245 | 0.00671 |
(0.78) | (0.76) | (1.02) | (1.59) | |
GDPgrow | −0.330*** | −0.299*** | −0.240*** | −0.194 |
(−9.70) | (−2.74) | (−7.21) | (−1.57) | |
State | −0.296*** | 0.198 | −0.214** | 0.124 |
(−3.79) | (1.52) | (−2.54) | (0.71) | |
Inds | Control | Control | Control | Control |
PR | 0.0125*** | 0.0115*** | ||
(3.10) | (2.68) | |||
Top1 | −0.00442** | −0.00548** | ||
(−2.09) | (−2.39) | |||
N | 6594 | 6497 |
Note: ***, **, and * denote statistical significance at the 1, 5, and 10 percent levels, respectively.
Indicator | Definition |
---|---|
Inte_Enfor | Mandatory disclosed information, regulated by Securities Act, Accounting Standards and other regulatory authorities, including corporate governance guidelines, ownership structure, board of directors, supervisors and executives, key financial indicators, etc. |
Inte_Volun | Voluntary disclosed information, voluntarily provided by the management of listed companies, including the composition of the board of directors (excluding managers) and supervisors, stakeholders , financial and risk analysis, etc. |
Truth | Authenticity of disclosed information, including board of directors’ responsibility statement, board of supervisors’ responsibility statement, violation investigation, whether the auditor has retained opinions. |
Time | Timeliness of information disclosure, such as financial report. |
a. CCDI is calculated based on the 4 secondary indicators, weight distribution using AHP method: Truth 0.474, Inte_volun 0.274, Time 0.156 and Inte_enfor 0.096.
Listed companies in CCDI data in 2009 and 2010 | 3662 |
---|---|
Match with listed companies issued bond in 2010 and 2012 | 278 |
Model 6 | Model 7 | Model 8 | Model 9 | |
---|---|---|---|---|
Credit | Credit | Rating | Rating | |
CCDI | −0.0148 | −0.00600 | ||
(−1.28) | (−0.49) | |||
Inte_Enfor | −0.00159 | −0.00160 | ||
(−0.29) | (−0.28) | |||
Inte_Volun | −0.0110** | −0.00472 | ||
(−2.01) | (−0.86) | |||
Truth | 0.00526 | 0.00888 | ||
(0.44) | (0.66) | |||
Time | −0.000551 | −0.00127 | ||
(−0.15) | (−0.34) | |||
Big4 | 0.147 | 0.138 | 0.0867 | 0.0858 |
(1.33) | (1.24) | (0.77) | (0.75) | |
Conserv | −0.0159 | 0.0450 | −0.0722 | −0.0297 |
(−0.08) | (0.21) | (−0.34) | (−0.14) | |
VarRoe | 0.426 | 0.473 | 6.895** | 6.788** |
(0.14) | (0.15) | (2.29) | (2.23) | |
Roe | 1.467*** | 1.330** | 0.250 | 0.235 |
(2.89) | (2.58) | (0.48) | (0.44) | |
Size | 0.526*** | 0.540*** | 0.406*** | 0.410*** |
(13.64) | (13.40) | (10.12) | (9.76) | |
Lev | −1.231*** | −1.281*** | −0.778** | −0.784** |
(−3.23) | (−3.31) | (−2.01) | (−2.00) | |
CFO | −0.760 | −0.752 | −1.004 | −0.980 |
(−1.06) | (−1.05) | (−1.38) | (−1.34) | |
CR | 0.0806 | 0.0765 | 0.0551 | 0.0505 |
(1.50) | (1.41) | (1.04) | (0.94) | |
LLR | 0.0032 | 0.0029 | 0.0004 | 0.0003 |
(1.26) | (1.14) | (0.14) | (0.11) | |
GDPgrow | −0.149*** | −0.145** | 0.00373 | 0.0280 |
(−2.73) | (−2.29) | (0.07) | (0.41) | |
State | 0.254*** | 0.234*** | 0.155* | 0.144 |
(2.94) | (2.64) | (1.75) | (1.58) | |
Duration | −0.0307 | −0.0308 | ||
(−1.38) | (−1.38) | |||
Guarantee | 0.426*** | 0.422*** | ||
(4.57) | (4.49) | |||
Type C | 0.287*** | 0.288*** | ||
(2.95) | (2.93) | |||
Inds | Control | Control | Control | Control |
N | 273 | 273 | 253 | 253 |
R2 | 0.746 | 0.749 | 0.695 | 0.696 |
Note: ***, **, and * denote statistical significance at the 1, 5, and 10 percent levels, respectively.
Model 10 | Model 11 | Model 12 | Model 13 | Model 14 | Model 15 | |
---|---|---|---|---|---|---|
Spread | Spread | Spread | Spread | Spread | Spread | |
CCDI | 0.000176 | −0.00345 | −0.00630 | |||
(0.01) | (−0.27) | (−0.45) | ||||
Inte_Enfor | −0.00348 | −0.00333 | −0.00551 | |||
(−0.52) | (−0.55) | (−0.84) | ||||
Inte_Volun | 0.00914 | 0.00426 | 0.00644 | |||
(1.39) | (0.71) | (1.02) | ||||
Truth | −0.00372 | −0.00159 | −0.00168 | |||
(−0.26) | (−0.12) | (−0.11) | ||||
Time | −0.00526 | −0.00396 | −0.00614 | |||
(−1.18) | (−0.97) | (−1.43) | ||||
Credit | −0.539*** | −0.529*** | ||||
(−7.27) | (−7.06) | |||||
Rating | −0.496*** | −0.493*** | ||||
(−6.32) | (−6.29) | |||||
Big4 | −0.227* | −0.209 | −0.172 | −0.162 | −0.196 | −0.184 |
(−1.69) | (−1.55) | (−1.41) | (−1.32) | (−1.49) | (−1.40) | |
Conserv | 0.675*** | 0.634** | 0.625*** | 0.611** | 0.636*** | 0.614** |
(2.65) | (2.43) | (2.71) | (2.57) | (2.62) | (2.47) | |
VarRoe | 3.508 | 3.137 | 4.001 | 3.744 | 6.778* | 6.366* |
(0.94) | (0.84) | (1.19) | (1.11) | (1.92) | (1.80) | |
Roe | −1.918*** | −1.736*** | −1.242** | −1.148** | −1.766*** | −1.577** |
(−3.07) | (−2.75) | (−2.17) | (−1.98) | (−2.93) | (−2.59) | |
Size | −0.305*** | −0.331*** | −0.00395 | −0.0258 | −0.115** | −0.141** |
(−6.47) | (−6.72) | (−0.07) | (−0.41) | (−2.05) | (−2.42) | |
Lev | 0.840* | 0.955** | 0.248 | 0.335 | 0.477 | 0.582 |
(1.81) | (2.04) | (0.58) | (0.77) | (1.05) | (1.28) | |
CFO | 1.796** | 1.829** | 1.163 | 1.203 | 1.252 | 1.303 |
(2.03) | (2.07) | (1.45) | (1.49) | (1.48) | (1.54) | |
CR | −0.0544 | −0.0538 | −0.00881 | −0.0104 | −0.0311 | −0.0345 |
(−0.84) | (−0.83) | (−0.15) | (−0.18) | (−0.51) | (−0.56) | |
LLR | −0.00500 | −0.00473 | −0.00392 | −0.00382 | −0.00447 | −0.00426 |
(−1.60) | (−1.49) | (−1.38) | (−1.32) | (−1.49) | (−1.40) | |
GDPgrow | 0.0226 | 0.0601 | −0.0301 | 0.000329 | 0.0163 | 0.0671 |
(0.33) | (0.78) | (−0.49) | (0.00) | (0.25) | (0.85) | |
State | −0.382*** | −0.355*** | −0.234** | −0.220** | −0.302*** | −0.278*** |
(−3.58) | (−3.25) | (−2.37) | (−2.18) | (−2.94) | (−2.65) | |
Duration | 0.0249 | 0.0273 | 0.00845 | 0.0103 | 0.0149 | 0.0172 |
(0.92) | (1.00) | (0.34) | (0.42) | (0.58) | (0.67) | |
Guarantee | −0.111 | −0.122 | −0.181* | −0.188* | 0.0985 | 0.0828 |
(−0.97) | (−1.06) | (−1.74) | (−1.79) | (0.87) | (0.73) | |
Type C | 0.0574 | 0.0380 | 0.449** | 0.425** | 0.319*** | |
(0.27) | (0.18) | (2.23) | (2.09) | (2.77) | ||
Type M | −0.133 | −0.137 | 0.0463 | 0.0361 | −0.333*** | |
(−0.67) | (−0.69) | (0.25) | (0.20) | (−2.90) | ||
Inds | Control | Control | Control | Control | Control | Control |
N | 273 | 273 | 273 | 273 | 253 | 253 |
R2 | 0.604 | 0.610 | 0.676 | 0.679 | 0.669 | 0.675 |
Note: ***, **, and * denote statistical significance at the 1, 5, and 10 percent levels, respectively.
All the coefficients of CCDI and 4 secondary indicators in
The regression results of CCDI partly supported the hypothesis of this paper, but there are some differences. The reason for this result might be that although investors are concerned about the quality of corporate information disclosure, usually they cannot assess the disclosure quality themselves, so they often use the authorities’ evaluation result. Clearly, Shenzhen Stock Exchange data has much more influence than CCDI.
As a signal, information disclosure plays an important role in the capital market, although many studies supported the signal theory from the theoretical and empirical level, but researchers in China found only participants of the stock market are concerned about information disclosure quality. This article assumes signal theory also applied in China and participants of the bond market should be concerned about information disclosure quality. But our empirical results only support Hypothesis 2: investors and corporates who have issued bonds are concerned about information disclosure quality and credit rating agencies are not. The reason for this result may be that credit rating agencies have a lot of channels to access information and the disclosed information doesn’t gain enough attention. The main contribution of this article is to make up for the lack of research of the information disclosure quality in China’s bond market.
This paper is sponsored by the National Natural Science Fund (713022023), the Fundamental Research Funds for the Central Universities, Beijing Normal University (2012WYB35), and Beijing Higher Education Young Elite Program (YETP0298).