Canadian Social Science

● Dummy variable SJ: if the company was given “qualified” “disclaimer” or “adverse” audit opinion in the year of 2009, the variable SJ takes the value of 1. If the company’s audit opinion of 2009 was unqualified, the variable takes the value of 0.

● DR is equal to a company’s total debt divided by total assets.

● AGE is determined by subtracting the year of the company’s stock listing from 2009. It measures the total years of stock listing for a company.

Furthermore, this paper selects the ratio of debt restructuring gains to total profits as dependent variable Y. The ratio reflects the contribution of debt restructuring gains to total profits in the same year.

2. Empirical Analysis and Results

2.1 Industy Classification of Research Sample

TableⅡ shows the industry classification of the research sample. 207 listed companies that experienced debt restructurings and obtained debt restructuring gains in 2009 are classified into 12 industries. Obviously, debt restructurings involve more and more industries, among which manufacturing industry makes up the largest proportion, accounting for 67.15%. Therefore, manufacturing industry remains to be the main force of debt restructurings.

Table 2

Industry Classification of Reaerch Sample

a. The result is based on CSRC Industry Classification 1st Standard.

2.2 Descriptive Statistics Analysis

First, this paper compares types of audit opinion and status of listed stock between research sample and control sample. The results are as follows, 65 companies of the research sample are listed as ST or *ST, accounting for 31.40% of the total; 46 companies of the research sample were given unclean audit opinion in 2009, accounting for 22.22% of the total. In contrast, 24 companies of the control sample are listed as ST or *ST, accounting for 11.59% of the total; 11 companies of the control sample were given unclean audit opinion in 2009, accounting for 5.31% of the total.

This reflects that listed companies with debt restructuring gains in 2009 has larger proportions of ST or *ST companies and unclean audit opinion than those of listed companies without debt restructuring gains in 2009.

Second, this paper compares the descriptive statistics results of six independent variables between research sample and control sample. The results are expressed in Table 3. From this, it comes to the following analysis.

Table 3

Descriptive Statistics Results

b. The data is collected form RESSET database.

● Rate of return on common stockholders’ equity (ROE)

The research sample’s ROE mean is negative and significantly lower than that of the control sample (-0.5007 VS. 0.0581). The minimum value of the research sample’s ROE is also significantly lower than that of the control sample (-79.8885 VS. -1.8544). This shows that from the perspective of stockholders’ equity, listed companies with debt restructuring gains in 2009 have weak abilities to generate earnings compared with listed companies without debt restructuring gains in 2009.

● Debt ratio (DR)

The mean of research sample’s debt ratio is significantly higher than that of the control sample (1.1815 VS. 0.8309). The maximum value of the research sample’s debt ratio is as high as 55.4086. If a company’s debt ratio is higher than 100%, it means that a company’s assets can not offset its debts and the company is on the verge of bankruptcy. Therefore, from the perspectives of companies’ debt structures and long-term debt paying abilities, companies with debt restructuring gains in 2009 bear higher financial risks and lower long-term solvency.

● Operating cash flow per share (OCF)

The mean of research sample’s OCF is significantly lower than that of the control sample (0.3017 VS. 0.7068). The maximum and the minimum of the research sample’s OCF was 4.3044 and -3.3901 respectively, both lower than those of the control sample. This shows that listed companies with debt restructuring gains in 2009 have lower operating cash flow per share, which largely reflects that these companies’ capacity of cash collection from operating activities is very weak and these companies’ growth prospects are rather poor.

● The years of stock listing (AGE)

The mean of research sample’s AGE is higher than that of the control sample (11.2992 VS. 9.1546). This reflects from the perspective of overall governance structure that companies with longer years of stock listing tend to have poorer financial condition and stronger probabilities of obtaining debt restructuring gains.

2.3 Linear Regression Analysis

In order to further analyze the correlation between the ratio of debt restructuring gains to total profits and financial indicators in the research sample, this paper uses multiple linear regression analysis on the following regression model.

Y=α0+α1ROE+α2OCF+α3ST+α4DR+α5AGE+α6SJ+ε

α0 is the constant term of the regression equation. αi is the estimated coefficient of independent variables (i=1,…,6). ε is the random item of the regression equation.

● Multicollinearity diagnosis

Multicollinearity in regression models is a result of strong correlations between independent variables. The existence of multicollinearity may result in lack of statistical significance of individual independent variables while the overall model may be strongly significant. Multicollinearity may also result in wrong signs and magnitudes of regression coefficient estimators, and consequently in incorrect conclusions about relationships between independent and dependent variables. Examining the correlation coefficients between independent variables can help detect multicollinearity.

Table 4 shows the correlation coefficients of variables in the research sample, which indicates that the correlations between most independent variables are weak. The maximum value of correlation coefficients 0.690 exists in between independent variable ST and SJ. Typically, correlation coefficient greater than or equal to 0.8 is considered as strong relationship between variables. The maximum value 0.690 is less than 0.8. Therefore, multicollinearity doesn’t exist in six independent variables, which can be included in the regression model.

Table 4

Correlation Coefficients of Variables in Research Sample

● Significance test of regression model

This section first conducts the overall significant test in order to verify whether there are strong linear relationships between dependent variable and six independent variables. As illustrated in TableⅤ, since significance F (0.002445) is less than α (0.05), the overall regression model is strongly significant. Therefore, the ratio of debt restructuring gains to total profits has significant linear relationship with six independent variables ROE, OCF, ST, DR AGE and SJ. However, this doesn't mean the dependent variable is significantly correlated with every independent variable. To determine statistical significance of individual independent variables, T test on every individual coefficient αi is needed.

Table 5

Output of Linear Regression Analysis

Compared P values in TableⅤ with the given significant level, independent variable ST is significant at 1% level and independent variable DR is significant at 10% level. This suggests that among the six independent variables, ST and DR are significantly correlated with the ratio of debt restructuring gains to total profits. In addition, the correlations are positive.

2.4 Paired Samples T Test

From the above linear regression analysis, it can be seen that ROE, OCF, AGE and SJ have no significant linear correlations with the dependent variable. The lack of statistical significance of individual independent variables is most probably due to the fact that these four variables don’t relate to the ratio of debt restructuring gains to total profits, but rather they are pertinent to whether companies obtained debt restructuring gains in 2009 or not. Thus, taking the control sample into consideration can help eliminate the deficiency of linear regression analysis.

In order to further explore the major factors influencing debt restructuring gains, this paper uses paired samples T test for comparison of matched pairs between the research sample and the control sample. The results are shown in TableⅥ.

The paired samples T test compares the means of matched pairs. In this paper, matched pairs are composed of one variable from the research sample and the same variable from the control sample. It computes the difference between the matched pair for each case, and tests to see if the average difference is significantly different from zero.

This paper compares the mean of six independent variables (ROE, OCF, ST, DR, AGE, SJ) between companies with (research sample) and without (control sample) debt restructuring gains in 2009. The purpose is to identify the major influencing factors of debt restructuring gains in listed companies.

As illustrated in TableⅥ, there is significant difference between the research sample and the control sample in terms of OCF, ST, AGE and SJ. P values of these four independent variables are all significant at 1% level. It means that most listed companies with debt restructuring gains have lower operating cash flow per share and longer years of stock listing. These companies have greater likelihood to be marked as ST and to be given unclean audit opinion in the year of obtaining debt restructuring gains. This empirical result supports hypothesis 1, hypothesis 3 and hypothesis 4.

The means of the other two variables are not significantly different between the research sample and the control sample. P values of ROE and DR are not significant at 1% level. It indicates that existing data is not enough to prove that ROE and DR have significant influences on debt restructuring gains. Hypothesis 2 has not been fully validated.

Table 6

Paired Samples Test

2.5 Conlusions of Empirical Research

This paper proposes research hypotheses from four perspectives: status of listed stock, debt ratio, years of stock listing and audit opinion. It selects 207 listed companies satisfying certain conditions as research sample and chooses another 207 listed companies as control sample for comparison. Further, this paper selects six independent variables to test the proposed hypotheses. The purpose of this paper is to analyze the correlation between debt restructuring gains and listed companies’ financial indicators and to identify the influencing factors of debt restructuring gains.

The following conclusions are arrived at by using descriptive statistics analysis, linear regression analysis and paired samples T test, combined with existing theoretical study of the new debt restructurings standard.

Four variables (OCF, ST, AGE, SJ) as indicators of operating performance, regulatory policy and governance structure largely explain why certain listed companies obtain debt restructuring gains. In terms of operating performance, companies with poor operating performance usually suffer from cash flow problems and have no margin of safety in case of unanticipated expenses. These companies with negative net profits have strong probabilities of obtaining gains from debt restructurings.

In terms of regulatory policy, companies with heavy losses may face higher pressures of supervision and penalty. These penalties include being labeled as ST for two consecutive years’ losses or being suspended listing or terminated listing for more serious situations. Therefore, companies are more likely to obtain gains from debt restructurings because of regulatory policy.

In terms of overall governance structure, governance structure is the intrinsic restricting mechanism of implementing accounting policies. Companies with longer years of stock listing tend to have bigger problems in governance structure, which will affect the choices of accounting policies. The IPO phenomenon indicates that the longer years of stock listing, the poorer operating performance will be. Therefore, these companies have strong incentives to make their financial statements look better than they actually are and to bolster investors’ confidence.

Therefore, most listed companies with debt restructuring gains in 2009 have poorer profit performance and weaker cash collection capacity. The majority of these companies are labeled as ST for their bad earnings results. These companies have longer years of stock listing and are more likely to be given unclean audit opinion in the year of obtaining debt restructurings gains.

3. Suggestions and Limitation

Based on the research conclusions, this paper proposes following suggestions in order to improve debt restructurings standards and perfect accounting supervisory system.

● To improve the acquisition mechanism of fair value. The new debt restructurings standard introduces the fair value measurement attribute. Fair value measurement provides more relevant and reliable accounting information; however, it opens the door for manipulation. For instance, managers could use deviations from market values to obtain larger gains from debt restructurings. Therefore, in the course of implementing the new standard, it is important to establish independent, authoritative, third-party appraisal agencies engaged in econometric study of fair value. This measure will help determine the fair value of assets more accurately and more scientifically.

● To strengthen the disclosure of accounting information. Fair value accounting with sufficient disclosure provides more reliable, timely and comparable information. This paper suggests including more subsidiary ledgers for disclosure and adding more details to the existing disclosing information. For instance, important financial indicators such as cash flow per share generated from debt restructuring gains can be included into disclosure. This measure can provide comprehensive and elaborate information for investors’ decision making process.

● To perfect performance evaluation system for listed companies. The existing performance evaluation system over emphasizes profit indicators. Listed companies use various means of profit manipulation catering to the needs of supervision. Therefore, both profit indicators and cash flow indicators must be taken into consideration when debt restructurings standard is formulated and implemented. Cash flow indicators are of equal importance as income and profit indicators. Including cash flow indicators into performance evaluation system will help maximize the value of the company and contribute to long term development.

This paper sets out to explore correlation between debt restructuring gains and listed companies’ financial indicators and to identify the major factors influencing debt restructuring gains by using empirical research method. The limitation of this paper lies in the fact that the sample size is relatively small. It has only examined data of 2009. Whether the findings will be transferable to other years remains to be analyzed.

With the continuous development and perfection of empirical research methods, future research on debt restructurings in listed companies will be carried out and debt restructurings activities will be further standardized.

Acknowledgment

I would like to express my gratitude to all those who have given me help to complete this paper. I am deeply indebted to my dedicated supervisor, Prof. Luo Xuemei, a respectable, responsible and resourceful scholar from Shanghai International Studies University, who has offered me with valuable guidance in every stage of the writing of this paper. Her impressive kindness and vigorous academic observation enlightens me not only in this paper but also in my future study.

I’m also extremely grateful to Dr. Cang Yongtao for giving me tremendous data and technical support, which is indispensable to the completion of this paper. My sincere appreciation also goes to all the teachers and friends who always give me stimulating suggestions and encouragement.

Last but not least, I would like to give my special thanks to my parents for their support and encouragement all the way from the very beginning of this paper.

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