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Study on the Peer Effect of Unrelated M&As Decisions

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ABSTRACT

This paper takes 7464 pieces of M&As events for A-share listed companies in China from 2010 to 2015 as samples and investigates the industry peer effect of unrelated M&As decisions. The research shows that there exists the peer effect when companies make unrelated M&As decisions in the same industry. This paper also makes further examinations and finds that the M&As experience and scale of companies enhance the peer effect. Moreover, companies with more industry competitions have more obvious industry peer effect. This paper enriches the M&As decisions theory and has guiding significance in practice.

1. Introduction

On April 19th, 2016, But’one Information Corporation announced the acquisition of Nanjing Xinchuanhui Electronic Technology Co., Ltd. and entered the field of Internet of Things and Electronic Information System for the achievement of diversified development. In order to achieve the diversified layout, Guangdong Highsun Group Co., Ltd. announced the acquisition of Chenzhou Xintianhan Culture Management Co. Ltd. and entered into the entertainment industry on October 11st of the same year. Nowadays, M&As have become a trend as many companies make M&As in recent years and many M&As are strategic. Companies merger and acquire companies from other industries so as to enter into new growing markets. Some studies show that unrelated M&As can significantly affect the value of company so that the selection between related M&As and unrelated M&As in the face of M&As type decisions becomes an important link for M&As decisions.

The existing literature on company M&As type decision mainly studies the cause and economic consequences from the perspective of corporate entities. The research of unrelated M&As motivations is an immense number of books. Jensen and Murphy (1990) consider that the agents of companies choose diversification decision in order to increase their compensation [1] . Wernerfelt (1988) believes that enterprises in growth process can balance the effective utilization of existing resources and the exploitation of new resources through the way of diversification and M&As [2] . There are also some literatures which focus on the decisions of M&As from the perspective of industry. Bettis and Hall (1982) suggest that industry opportunities can make the company choose related diversification, and the company without opportunities may choose unrelated diversification [3] . Li (2009) finds that industry opportunities can promote the implementation of unrelated mergers and acquisitions, that is, companies enter into a strong profitability of the industry by means of mergers and acquisitions [4] . There has been a wealth of literatures to study the types of M&As decisions from individual to industry, but a little about the interaction between firms. This paper will discuss the influencing factors of M&As decisions from the perspective of interaction.

The peer effect is a kind of theories commonly used in the study of sociology and pedagogy. The peer effect defined by Winston and Zimmerman (2003) referred that individual behaviors and results were not only affected by their own characteristics but also influenced by those who had the same status, namely, people in the same group [5] . In recent years, some literatures have introduced the peer effect theory into the field of financial research. Shue (2013) found in the study that the financial policies among companies led by MBA graduates of Harvard Business School had the peer effect and companies led by graduates in the same group had significant interaction in financial policies of salary, M&As financing and cash holding [6] . Wan (2016) found that there was the industry peer effect as Chinese listed companies made M&As decision [7] , but there were rare literatures that analyzed M&As type decisions from the perspective of the peer effect.

This paper takes the M&As events of China’s listed companies from 2010 to 2015 as the sample and investigates the peer effect of unrelated M&As decision among companies in the same industry. The study results show that there is the peer effect as listed companies in the same industry make unrelated M&As decision and the M&As experience of company and industry competition can enhance the impacts of the peer effect.

Compared with the existing research, this paper points out the innovation and significance are as follow: Firstly, the previous literatures studied the unrelated M&As decisions more from the individual characteristics of companies than interaction between companies, however, this paper examines the types of M&As decisions for the companies from interaction between companies, expand the existing corporate merger theory. Secondly, most existing research on peer effect focused on the sociology and pedagogy, although the peer effect research has a small amount of literature examines the effects of recent company specific financial decisions, but there is still no direct examination of unrelated M&As decisions, and this paper extends the research scope with group effect in a certain extent; Finally, this paper also has practical significance and makes some complementary explanations for the reasons behind the existing unrelated M&As in China so that investors can reasonably evaluate enterprise mergers and acquisitions activities.

The structure of the paper is organized as followed: Section 1 is an introduction of this paper. In Section 2, I discuss literature reviews and theoretical analysis. In Section 3, I discuss the sample selection, variables and models. Empirical results and analysis have been developed in Section 4. Conclusions have been discussed in Section 5.

2. Literature Review and Theoretical Analysis

According to the strategic fitness of both parties in M&As, M&As can be divided into related M&As and unrelated M&As. In the case of established M&As strategy, what type of M&As should be selected becomes the another major decision of company.

It can be seen from the overseas researches that there are only few literatures that directly study the factors of company M&As type decision. Most literatures have studied the economic consequences of M&As in different types and disprove the rationality of certain M&As behavior. Such literatures that directly studied M&As type decisions as that Aggarwal and Baxamusa (2013) studied the selection of company M&As types based on the asymmetric information theory. They found that companies with lower cash holdings, higher capital costs, greater financing restraint level and more serious asymmetric information are more inclined to carry out unrelated M&As rather than select related M&As [8] . The existing literatures do not reach a consistent conclusion on the economic consequences of M&As in different types. Most studies disprove the rationality of M&As behavior from economic consequences. Morck, Shleifer and Vishny (1990) found that the performance of related M&As is superior to the unrelated M&As’, and that unrelated M&As harm the value of company and there is so-called “Diversified Discount” [9] . However, there are also different opinions in some researches. Aggarwal and Baxamusa (2013) believed that the internal capital market advantage brought by unrelated M&As can promote the value of company.

The motivation theories that companies select unrelated M&As mainly include agency hypothesis, overconfidence hypothesis, resource-based hypothesis and market power theory. Jensen and Murphy (1990) argued that the management carried out unrelated M&As is for the pursuit of their own professional reputations and their own payoffs [1] . Morck, Shleifer and Vishny (1990) thought that the management implemented unrelated M&As to reduce their own occupational risks. Amihud and Lev (1981) found in the study that company owners can spread risks by diversifying their stock portfolios but company managers can only reduce the operational risks by diversifying the company [10] . Resource-based hypotheses believe that abundant resources tend to exist in diversified companies. Penrose (1959) argued that an effective organizational form of implementing economic activities in diversified companies is the scope economy [11] . Based on the market power theory, Villalonga (2000) proposed three motivations for the diversification of companies. The first was the diversification of companies was to achieve the predatory pricing in other industries so as to realize cross- subsidies. The second was companies ally with other companies achieve multidimensional competition in other markets. The third is that companies take advantage of diversification to ally with large companies to conduct mutual purchase so as to push out small competitors in the market [12] .

In China, there are a lot of researches on the motivation of M&As decision. Hong (2006) believed that the assumption of free cash flow and overconfidence theory would affect the unrelated M&As decision [13] . According to the prospect theory, Li and Zhou (2007) argued that the profitability of companies would affect the unrelated M&As decisions and the property of companies might affect the unrelated M&As decisions [14] . Fang (2008) argued that intervention of local governments would affect the unrelated M&As decision of enterprises [15] . Li, Zhao and Liu (2009) found in the study that industry opportunities and political connection would influence companies making unrelated M&As decisions [16] . Wang and Wei (2013) argued that the industry restructuring pressure and the resource endowment of companies were related to unrelated M&As decisions. However, all above studies do not analyze the unrelated M&As from the interaction among groups of companies. This paper tries to introduce the peer effect theory of social psychology to explain the motivation of unrelated M&As.

The peer effect theory refers to the theory that individual behaviors are not only affected by his own factors but also influenced by behaviors of people in the same group, which results in the change of his own behaviors, namely, who lies down with dogs must rise up with flea. Extensive studies of the peer effect existed in studies of pedagogy and sociology in the early years. Sacerdote (2001) found in the study that the performance of American college students was influenced by behaviors of other students in the same dormitory [17] . Studies from Lazear (2001) showed that a good student could have positive impacts on other students and students would gain more benefits by staying with good students [18] . There are also similar studies in China. The study from Men (2013) implied that there was the peer effect in the labor market of Chinese college graduates and the skills and knowledge of college students in the labor market were influenced by other students in their class. Recently, studies that take advantage of the peer effect appear in the financial field. Shue (2013) found in the study that the financial policies among companies led by MBA graduates of Harvard Business School had the peer effect. Leary (2014) found that the peer effect existed in the financing decisions of companies.

There are rare researches on unrelated M&As decisions of companies based on the peer effect. Organizational learning theory and institutional theory believe that organizations are more likely to study as facing the uncertain environment. Information asymmetry is a kind of manifestations for environmental uncertainty and M&As activities usually have very high information asymmetry and unrelated M&As have higher information asymmetry than that of related M&As. Aggarwal and Baxamusa (2013) found in the study that unrelated M&As had higher information asymmetry than that of related M&As, while Ellison and Fudenberg (1993) showed that the peer effect was based on information asymmetry and limited rationality [19] . In conclusion, unrelated M&As need more learnings from people in the same group for their higher information uncertainty, based on which Hypothesis 1 is proposed:

H1: Companies in the same industry have the peer effect when making unrelated M&As decisions.

In order to further explore the influence mode of the industry peer effect in the unrelated M&As decisions, this paper further tests peer effect with the theory of learning and information asymmetry.

In the field of social learning and research, studies conducted by Galef (1996) showed that direct involvement in the process of making decisions could leave profound impacts and memories, which could be easily recalled later and by which subsequent similar decisions would be affected [20] . Based on this, this paper assumes that companies that have the experience of M&As or experience of unrelated M&As are more likely to be affected by the industry peer effect of unrelated M&As decision. Besides, Park (2003) found that the enterprise’s scale before M&As was significantly and positively related to M&As of company [21] . Moreover, companies with larger enterprise scale own stronger abilities to access information so that this paper assumes that they have more obvious industry peer effect of unrelated M&As decisions. Select the enterprise scale as a regulatory factor to make regression. Based on this, Hypothesis 2 is proposed:

H2: In the same industry, companies obtaining more information have enhancement effects on the peer effect of unrelated M&As decisions among companies, namely, companies with the experience of uncorrelated M&As will enhance the peer effect and companies with larger scale will enhance the peer effect.

Studies made by Aggarwal and Baxamusa (2013) showed that companies with unrelated M&As have higher information asymmetry than companies with related M&As or without M&As. while Ellison and Fudenberg (1993) found in the study that the emergence basis of the peer effect was information asymmetry and limited rationality as companies made decisions. Therefore, in industries with high industry competition, in order to cope with the fierce competition in the industry, companies would tend to imitate M&As decision of group members in the industry. This paper defines that one subtracts Herfindahl index of industry revenue as the industry competition (Competition). and the larger this value, the fiercer competition for industry of companies. Based on this, Hypothesis 3 is proposed:

H3: Companies with higher industry competition have the more obvious peer effect of unrelated M&As decisions.

3. Study Design

3.1. Sample Selection

This paper selects M&As events of Chinese A-share listed companies from 2010 to 2015 as research samples. The data of M&As events comes from Wind Information database and all other variables are from CSMAR database. Excluding financial industry samples and M&As events with transaction value of less than one million yuan, M&As classified by M&As targets of “Diversification Strategy” and “Business Transformation” in Wind Information database are defined as unrelated M&As and others are related M&As. And this paper removes samples with missing data. There are 1547 unrelated M&As events and 5917 related M&As events, totally 7464 samples. All continuous variables are processed by 1% winsorization. The annual distribution of samples is as follow (Table 1).

3.2. Variables and Models

This paper makes regression with the binary logit model and the dependent variable is the dummy variable D i v ( i , p , t ) , which indicates the probability that the sample company i in industry p make unrelated M&As in year t. The explanatory variable U n r e l a t e ( i , p , t 1 ) indicates the M&As frequency of the same industry and refers to the mathematical expectation of probability for unrelated M&As made by companies other than company i in industry P in year t-1. If the unrelated M&As decision made by the sample company is significantly influenced by the irrelevant unrelated M&As decision of other companies in the group, namely, the regression coefficient β1 for U n r e l a t e ( i , p , t 1 ) is significantly positive, and then it indicates there is the industry peer effect of unrelated M&As decisions. This paper refers to the literature from Aggarwal and Baxamusa (2013) and a series of factors that affect the types of M&As decisions includes: whether the company is acquired by a related company(Related), whether the chairman and the CEO are dual (Dual), the nature of company’s property (Property), shareholdings of first majority shareholder (First), independent director ratio (Lndep), board size (ESH), shareholdings of the management(Board), overconfidence degree of executives (Overcon), cash stock ratio (Cash), intangible assets

Table 1. Annual samples distribution.

The above data comes from the Wind Information database.

ratio (Intang), Tobin’s Q (Tobinq), operating cash flow ratio (OCF), asset size(Size), total asset and net profit ratio (ROA), asset-liability ratio (Lev), growth rate of operation revenue (Growth), discretionary accruals of company (Accrual), financing restraint level (FC) and diversification level before M&As (Bedrivers). All above control variables lag by one year. This paper also controls the annual fixed effects. Table 2 elaborates the definition of all variables. Model 1 is as follows:

Table 2. Definition of variables.

D i v ( i , p , t ) = β 0 + β 1 U n r e l a t e ( i , p , t 1 ) + β 2 R e l a t e d ( i , p , t ) + β 3 D u a l ( i , p , t ) + β 4 P r o p e r t y ( i , p , t ) + β 5 F i r s t ( i , p , t ) + β 6 L n d e p ( i , p , t ) + β 7 B o a r d ( i , p , t ) + β 8 E S H ( i , p , t ) + β 9 O v e r c o n ( i , p , t ) + β 10 C a s h ( i , p , t ) + β 11 I n t a n g ( i , p , t )

+ β 12 T o b i n g q ( i , p , t ) + β 13 O C F ( i , p , t ) + β 14 S i z e ( i , p , t ) + β 15 R O A ( i , p , t ) + β 16 L e v ( i , p , t ) + β 17 G r o w t h ( i , p , t ) + β 18 A c c r u a l ( i , p , t ) + β 19 F C ( i , p , t ) + β 20 B e d r i v e r s ( i , p , t ) + φ ν t + ε i , t (1)

In order to test Hypothesis 2, this paper defines M&As experience as that if unrelated M&As has been made in first three years before M&As, take value of 1; if not, 0. Asset size is the total assets of the company. Construct model 2 and model 3:

D i v ( i , p , t ) = β 0 + β 1 U r e l a t e ( i , p , t ) S i z e ( i , p , t ) + β 2 E x p e r i e n c e ( i , p , t ) + β 3 Re l a t e d ( i , p , t ) + β 4 D u a l ( i , p , t ) + β 5 Pr o p e r t y ( i , p , t ) + β 6 F i r s t ( i , p , t ) + β 7 L n d e p ( i , p , t ) + β 8 B o a r d ( i , p , t ) + β 9 E S H ( i , p , t ) + β 10 O v e r c o n ( i , p , t ) + β 11 C a s h ( i , p , t )

+ β 12 I n tan g ( i , p , t ) + β 13 T o b i n g q ( i , p , t ) + β 14 O C F ( i , p , t ) + β 15 S i z e ( i , p , t ) + β 16 R O A ( i , p , t ) + β 17 L e v ( i , p , t ) + β 18 G r o w t h ( i , p , t ) + β 19 A c c r u a l ( i , p , t ) + β 20 F C ( i , p , t ) + β 21 B e d r i v e r s ( i , p , t ) + φ ν t + ε i , t (2)

D i v ( i , p , t ) = β 0 + β 1 U r e l a t e ( i , p , t ) S i z e i , p , t ) + β 2 S i z e ( i , p , t ) + β 3 R e l a t e d ( i , p , t ) + β 4 D u a l ( i , p , t ) + β 5 P r o p e r t y ( i , p , t ) + β 6 F i r s t ( i , p , t ) + β 7 L n d e p ( i , p , t ) + β 8 B o a r d ( i , p , t ) + β 9 E S H ( i , p , t ) + β 10 O v e r c o n ( i , p , t ) + β 11 C a s h ( i , p , t )

+ β 12 I n t a n g ( i , p , t ) + β 13 T o b i n g q ( i , p , t ) + β 14 O C F ( i , p , t ) + β 15 S i z e ( i , p , t ) + β 16 R O A ( i , p , t ) + β 17 L e v ( i , p , t ) + β 18 G r o w t h ( i , p , t ) + β 19 A c c r u a l ( i , p , t ) + β 20 F C ( i , p , t ) + β 21 B e d r i v e r s ( i , p , t ) + φ ν t + ε i , t (3)

In order to test Hypothesis 3, this paper defines that one subtracts Herfindahl index of industry revenue as the industry competition (Competition), and the larger this value, the fiercer competition for industry of companies. Structure model 4:

D i v ( i , p , t ) = β 0 + β 1 U r e l a t e ( i , p , t ) C o m p e t i t i o n ( i , p , t ) + β 2 C o m p e t i t i o n ( i , p , t ) + β 3 Re l a t e d ( i , p , t ) + β 4 D u a l ( i , p , t ) + β 5 Pr o p e r t y ( i , p , t ) + β 6 F i r s t ( i , p , t ) + β 7 L n d e p ( i , p , t ) + β 8 B o a r d ( i , p , t ) + β 9 E S H ( i , p , t ) + β 10 O v e r c o n ( i , p , t )

+ β 11 C a s h ( i , p , t ) + β 12 I n tan g ( i , p , t ) + β 13 T o b i n g q ( i , p , t ) + β 14 O C F ( i , p , t ) + β 15 S i z e ( i , p , t ) + β 16 R O A ( i , p , t ) + β 17 L e v ( i , p , t ) + β 18 G r o w t h ( i , p , t ) + β 19 A c c r u a l ( i , p , t ) + β 20 F C ( i , p , t ) + β 21 B e d r i v e r s ( i , p , t ) + φ ν t + ε i , t (4)

4. Empirical Results and Analysis

4.1. Descriptive Statistics

It can be seen from the descriptive statistics in Table 3 that the probability of unrelated M&As in all M&As events is 21%, which indicates that 21 unrelated M&As is selected out of 100 M&As.

Table 3. Descriptive statistics.

The above data is calculated by the author.

4.2. Regression Results

4.2.1. Regression Results of Hypothesis 1

Table 4 shows the binary logit model regression results for Hypothesis 1. Column (1) in Table 4 shows the regression results without the explanatory variable Unrelate and column (2) shows the regression results with the explanatory variable Unrelate. In the regression results in column (2), coefficient β1 of Unrelate is 1.140 and is significant at the level of 1%, which shows that sample company i is significantly influenced by the unrelated M&As decisions of other companies in the group to implement unrelated M&As and indicates that there is the peer effect among the unrelated M&As decisions of companies in the same industry. The coefficient for the nature of property (Property) in the regression results is significantly negative, which indicates that there is high probability for non-state-owned enterprises to select unrelated M&As. The coefficient of operating cash flow (OCF) is significantly negative, which indicates that companies with cash flow constraints have a lower probability of selecting unrelated M&As. The coefficient for discretionary accruals of company (Accrual) is significantly negative, which indicates that companies with higher discretionary accruals are less likely to select unrelated M&As. The coefficient of company diversification level before M&As is significantly positive, which indicates that companies with high diversification before M&As are more likely to select unrelated M&As.

Table 4. Regression results for Hypothesis 1.

The above data is calculated by the author.

4.2.2. Regression Results of Hypothesis 2

The column (1) of Table 5 is the regression result of M&As experience as a moderating variable. This paper finds that the coefficient of Unrelate *Experience is significantly positive, which indicates that the M&As experience can enhance the peer effect of unrelated M&As decisions in the same industry. If this paper defines the M&As experience as that M&As events occurs within first three years before M&As, their regression results are similar. In addition, column (2) of Table 5 is the regression result of company’s asset size as a moderating variable. We find that the asset size can enhance the peer effect of unrelated M&As decisions in the same industry. The above results show that the companies with stronger learning ability have the more obvious industry peer effect of unrelated M&As decisions.

4.2.3. Regression Results of Hypothesis 3

Table 6 is the regression result of Competition as a moderating variable. This paper finds that the coefficient of cross-multiplying term Unrelate *Competition

Table 5. Regression results of Hypothesis 2.

The above data is calculated by the author.

is significantly positive at the level of 1%, which indicates that the more fierce competition in the industry of sample company i, the stronger industry peer effect of unrelated M&As decisions is.

5. Conclusions and Policy Suggestion

With M&As events of China’s A-share listed companies from 2010 to 2015 as samples, this paper studies the industry peer effect of unrelated M&As decision. Study results show that there is industry peer effect in M&As type decisions of listed companies. This paper also makes further tests for the research results and finds that the M&As experience and enterprise scale of companies enhance the

Table 6. Regression results of Hypothesis 3.

The above data is calculated by the author.

industry peer effect of unrelated M&As decision, and industry competition can promote the industry peer effect of unrelated M&As decisions.

This paper has the significance in three aspects. Firstly, the previous literatures studied the unrelated M&As decisions more from the individual characteristics of companies than interaction between companies; however, this paper examines the types of M&As decisions for the companies from interaction between companies, and expands the existing corporate merger theory. Secondly, it theoretically enriches the existed M&As decisions type theory and cross-checks the peer effect factors in M&As decisions types with application of organizational behavior, organizational sociology and finance, which explains the emergence reasons of unrelated M&As and provides reference for future financial researches. Finally, this paper provides practical countermeasures and suggestions for enterprises to optimize M&As decisions and applies the peer effect theory to the evaluation of M&As behaviors so as to help investors make more reasonable assessment of company’s M&As activities. For future researches, the geographical peer effect of unrelated M&As and economic consequences caused by unrelated M&As peer effect are the directions for discussion.

Conflicts of Interest

The authors declare no conflicts of interest.

Cite this paper

Yu, L. (2017) Study on the Peer Effect of Unrelated M&As Decisions. Modern Economy, 8, 1441-1454. doi: 10.4236/me.2017.812096.

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