The capital structure of listed companies is significantly affected by that of peer companies in the same industry. This phenomenon is called the peer effect of capital structure. This paper studies the peer effect of capital structures of listed companies in the same industry in the Chinese A-share market. Unlike previous literatures simply using the average industry capital structure as the explanatory variable, by constructing the instrumental variable (equity shock), this paper successfully and accurately identifies the peer effect. Through the empirical analysis, this paper has found the following conclusions: 1. The peer effect’s ability to explain the capital structure of the company itself is more important than the common capital structure influence factors in the previous literatures; 2. Peer firms play an important role for the company’s own capital structure. Specifically, the company’s own capital structure will respond to the capital structures of peer firms, rather than other financial characteristics of peer firms. The conclusion of this paper has certain enlightenment significance on the capital structure theory and capital decision-making behaviors of Chinese listed companies. The capital structure of listed companies in China is not independently decided by the company managers, but in the process of decision-making the capital structures of peer firms is considered as the important reference factor. This will provide a new angle for the research on capital structure. The strong correlation among capital decision-making of peer firms will be considered.
The capital structure theory is the most intensively investigated field with the most research results in the corporate finance theory, and one of the most important problems. The old enterprise capital structure theory mainly includes MM theorem, MM model considering the enterprise income tax, tax difference school, bankruptcy cost school and balance theory, etc.; while the new capital structure theory mainly includes four schools, which are agency cost theory, signaling theory, control right theory and industrial organization theory of capital structure.
The peer effect can be interpreted as the mutual learning of different individuals in the same group. Restricted by their own information environment, the company managers cannot determine the optimal capital structure for their companies. As a result, when determining the company’s own capital structure, managers focus on capital structures or other financial characteristics of peer firms. In fact, the behavior of peer firms action is a important explanatory variable for the capital decision-making of a lot of companies. Evidence shows that the company’s own capital structure is affected by that of peer firms. Through the questionnaire survey of CFOs of listed companies, Graham & Harvey (2001) [
The purpose of this paper is to discuss about whether behaviors of peer firms affect the capital structure of the company itself. In this paper, the structure of the remaining parts is as follows. Chapter 2 is the recognition model of peer effect. The traditional model, the construction of equity shock and recognition strategy are introduced. Chapter 3 is the empirical analysis. Chapter 4 is conclusion and enlightenment.
Referring to previous empirical literature on the capital structure (Rajan & Zingales, 1995 [
The subscript i, j and t in the model corresponds to the ith listed company, the jth industry and the tth year, respectively. The dependent variable
Another method for recognization of peer effect used in this paper is construction of instrumental variable. Stock return is a known determinant of capital structure (Marsh, 1982 [
This paper estimates the return shock by building the following model.
In which,
Expected return:
Equity shock:
Data in this paper comes from CSMAR database of GTA. GTA’s financial market data feeds and delivery platforms offer access to China’s largest collection of historical data covering the most recent working day, including intraday and closing exchange pricing, fundamentals including company financial statements, corporate actions, estimates, ownership, etc. CSMAR is one of the databases under GTA. It covers all A shares and B shares companies listed on the Shanghai Stock Exchange and the Shenzhen Stock Exchange in standardized financial data presentation since the data became available in 1990.
The time span of data is from 2008 to 2015. Due to the wide use of data, we put the structure of the sample data and definition of variables in
At the same time,
As stated earlier, according to the industry classification code (three-digit code) in Guidance of Industry Classification of Listed Companies issued by China Securities Regulatory Commission in 2012, we define different industries in the samples.
Category | Variable name | Variable symbol | Variable description |
---|---|---|---|
Explained variable | Book leverage (asset-liability ratio) | bkl | Total debt/Total assets |
Market value leverage | mkl | Total debt/Market price of equity | |
Explanatory variable | Company size | size | Natural logarithm of total assets |
Market-to-book ratio | mtbr_a | Market price of equity/Total assets | |
Profitability | prof | EBITDA/Total assets | |
Asset tangibility | tang | (Net value of fixed assets + Net value of stock)/Total assets | |
Control variable | Altman Z Scoring Value | zscore | 1.2 * current capital/total assets + 1.4 * retained earnings/total assets + 3.3 * EBIT/total assets + 0.6 * market price of equity/total liabilities + sales revenue/total assets |
Non-debt tax shield | shield | Depreciation of fixed assets/Total assets | |
Selling, general and administrative costs | sgax | (Sales cost + management fees)/Total assets | |
Capital investment | capin | Capital expenditure/(net value of fixed assets of the previous period+ net value of inventory of the previous period) | |
Annual rate of return of individual share | stkrt |
Mean | Median | Std | Max | Min | |
---|---|---|---|---|---|
Firm-specific | |||||
bkl | 0.638 | 0.540 | 2.842 | 142.7 | 0 |
mkl | 0.421 | 0.338 | 0.447 | 13.51 | 0 |
size | 22.12 | 22.03 | 1.539 | 29.02 | 0 |
mtbr a | 0.728 | 0.660 | 0.575 | 15.47 | 0 |
prof | 3.081 | 0.0690 | 276.5 | 25387 | −51.95 |
tang | 0.436 | 0.431 | 0.197 | 0.975 | 0 |
Peer Firm Average | |||||
pfa bkl | 0.626 | 0.552 | 1.543 | 64.86 | 0.0649 |
pfa mkl | 0.399 | 0.374 | 0.218 | 2.033 | 0.00823 |
pfa size | 22.06 | 21.96 | 0.756 | 28.92 | 18.11 |
pfa mtbr a | 0.699 | 0.672 | 0.282 | 2.862 | 0.0755 |
pfa prof | 3.083 | 0.0728 | 112.8 | 4231 | −8.576 |
pfa tang | 0.433 | 0.421 | 0.111 | 0.787 | 0.00293 |
In this paper Rolling Window Regression was completed for the monthly stock return data of each listed company according to the return shock equation. The length of rolling window is 5 years (i.e., 60 months). The step size of each rolling is one year. At the same time, we can ensure that the observation of each rolling re- gression is up to 60 and at least 24 (there is at least the data of 24 months in five years). For example, to con- struct the stock shock of Vanke A (stock code: 000002) in 2008, we used the data of monthly return of Vanke A from January 2003 to December 2007 (five years prior to 2008) for regression according to the rolling re- gression equation. And then, we calculated the corresponding equity shock with the estimate coefficient obtained from the regression, (abnormal return of Vanke A on the market from January to December 2008) and (average abnormal return of peers) with reference to the calculation formula of expected return and equity shock.
Expected return:
Equity shock:
The equity shock
In
From results in
Variable name | Average | Median | Std |
---|---|---|---|
0.175 | 0.1763 | 0.029 | |
0.0002 | −0.0249 | 0.7032 | |
0.7496 | 0.772 | 0.7051 | |
Observation per regression | 0.5009 | 0.5129 | 0.1283 |
Adjusted R Square | 56.4992 | 58 | 4.1842 |
0.0075 | 0.0042 | 0.1312 | |
Expected return | −0.0094 | 0.0024 | 0.1193 |
Equity shock | 0.0131 | 0.0015 | 0.102 |
Peer Firm Average Equity Shock | ||
---|---|---|
Contemporaneous | 1-Period-Lead | |
Independent Vars. | Independent Vars. | |
Firm-specific Factors | ||
size | 0.00849* | 0.0119 |
(0.00485) | (0.0160) | |
mtbr_a | 0.0108 | −0.00681 |
(0.00844) | (0.0222) | |
prof | −0.0440 | −0.156 |
(0.0292) | (0.110) | |
tang | 0.0246 | 0.0773 |
(0.0167) | (0.0626) | |
PFA Characteristics | YES | YES |
Firm i Equity Return Shock | YES | YES |
Industry Fixed Effects | YES | YES |
Year Fixed Effects | YES | YES |
Observations | 8432 | 8432 |
R-squared | 0.856 | 0.933 |
their economic significance is not significant. The company size is the most significant explanatory variable. Its corresponding estimated coefficient is 0.00849, which means that a unit of growth of company size will be accompanied by less than 0.01 unit of growth of Peer Firm Average Equity Shock in the same period. Therefore, we can think that the information contained by Peer Firm Average Equity Shock does not affect the financial characteristics of listed companies (in the same period or two adjacent periods).
We used Peer Firm Average Equity Shock as the instrumental variable of peer firm average capital structure, and used Two-stage Least Squares (2SLS) to estimate Equation (1).
(1) | (2) | (3) | (4) | |
---|---|---|---|---|
VARIABLES | bkl | mkl | d_bkl | d_mkl |
Peer Firm Averages | ||||
Dependent Variable | 1.901 | 0.872** | 1.013 | 0.473*** |
(3.526) | (0.389) | (1.108) | (0.168) | |
l_pfa_size | 0.104 | −0.0515 | −0.0636 | −0.0193 |
(0.165) | (0.0406) | (0.0624) | (0.0231) | |
l_pfa_mtbr_a | 0.0502 | −0.0707* | 0.0541 | 0.0382 |
(0.0617) | (0.0390) | (0.0604) | (0.0672) | |
l_pfa_prof | −0.530 | 0.158 | −0.817 | 0.0627 |
(0.948) | (0.130) | (0.961) | (0.0969) | |
l_pfa_tang | −0.579 | −0.141 | −0.158* | −0.103 |
(0.934) | (0.136) | (0.0894) | (0.0762) | |
Firm-specific Factors | ||||
l_size | 0.0303*** | 0.0641*** | 0.00291 | 0.0338*** |
(0.00398) | (0.00339) | (0.00255) | (0.00273) | |
l_mtbr_a | −0.0409 | 0.146*** | 0.0284*** | −0.264*** |
(0.0300) | (0.0207) | (0.0104) | (0.0149) | |
l_prof | −0.726*** | −0.633*** | 0.0191 | −0.165*** |
(0.165) | (0.0466) | (0.0588) | (0.0378) | |
l_tang | 0.200*** | 0.158*** | −0.0294** | 0.00561 |
(0.0338) | (0.0164) | (0.0127) | (0.0138) | |
equity_shock | −0.00371 | −0.0448*** | −0.0196*** | −0.0644*** |
(0.0149) | (0.00758) | (0.00686) | (0.00613) | |
First Stage Statistics | ||||
F statistics | 0.0498 | 61.0902*** | 0.0029 | 90.7750*** |
Robust score chi2 | 0.7472 | 9.8141*** | 1.1266 | 3.8978** |
Robust Reg. F-stat | 0.7541 | 8.5747*** | 1.1214 | 3.6095** |
Industry Fixed Effects | YES | YES | YES | YES |
Year Fixed Effects | YES | YES | YES | YES |
Observations | 8432 | 8432 | 8432 | 8432 |
Adj. R-squared | 0.450 | 0.452 | 0.400 | 0.403 |
Notes: F statistic is the weak instrumental variable used to judge the Two-stage Least Squares Regression. The null hypothesis of the test is the existence of weak instrumental variable. Because in this paper there is only an endogenous variable, we can use F statistic. In statistical software stata we can use command estatfirststage to obtain F statistic of the first stage of regression.
The estimated results reported in the first row of
Therefore, we focus on observing Column (2) and (4) in
In general, the peer effect plays an important influence role in the deciding of capital structure of listed com- panies. Its influence is bigger than other influencing factors of capital structure. At the same time, the peer effect existing in differences among the capital structures of listed companies is often brought by the capital structures of peer firms (Financial Action), rather than financial characteristics of peer firms.
This paper used 1082 Chinese companies whose A shares were listed between 2008 and 2015 (including Shanghai A and Shenzhen A) as samples. Companies listed after 2003 (including 2003), financial-related in- dustries (industry code: J66-J69), ST and PT companies, companies whose data was lost for two consecutive years, and industries conforming to the above conditions and only having one company were eliminated. The monthly data from 2003 to 2014 was used to build equity shock (including rate of return on individual share, risk-free rate of return, rate of return of comprehensive market). This paper refers to the research of Leary & Roberts (2014) [
Based on the above empirical test, the conclusion of this paper is as follows. The listed company does not make the financing decision independently. On the contrary, the capital structure of listed companies is significantly influenced by their peer firms. That is to say, there is the peer effect. At the same time, the capital structures of peer firms have significant, positive, stable and healthy effect on the company’s own capital structure. Its explanation ability of the capital structure of the company itself is stronger than any observed traditional capital structure influencing factor.
The above conclusion in this paper has certain enlightenment significance on the capital structure theory and capital decision-making behaviors of Chinese listed companies. It can be concluded that the peer effect of capital structure of listed companies may be related to the optimal capital structure theory. Although this paper has no rigorous proof in the theoretical analysis and empirical test, and this problem is still worth discussing about. Especially, the imitation behavior of capital structures of different companies is driven by the optimal capital structure. Solving these problems will help us to have better and deeper understanding of the influencing mechanism of peer effect.
Yongjia Liang, (2016) The Recognition of Capital Structure Peer Effect of Chinese Listed Companies. American Journal of Industrial and Business Management,06,709-716. doi: 10.4236/ajibm.2016.66065