Impact of cultural distance on the market reaction to cross-border
M&As
In this thesis I examine the effect of cultural distance on the market reaction for the acquiror to cross-border M&As. I use a sample that contains 178 cross-border M&As with a US based acquiror from 2011 to 2015. To examine the effect of cultural distance I use an event study and a cross-sectional analysis. The results are insignificant and therefore provide no evidence
for an relation between cultural distance and the market reaction for the acquiror.
Name: Sander Kooiman
Student number: 10751254
Bachelor: Economics and Business Specialization: Economics and Finance
Statement of Originality
This document is written by Sander Kooiman who declares to take full responsibility for the contents of this document.
I declare that the text and the work presented in this document are original and that no sources other than those mentioned in the text and its references have been used in creating it.
The Faculty of Economics and Business is responsible solely for the supervision of completion of the work, not for the contents.
1. Introduction
Mergers and acquisitions (hereafter: M&As) are getting increasingly more important (Goergen and Renneboog, 2004). Since 1996 there was a steep rise in the total value of the M&A market (Goergen and Renneboog, 2004). In the year 2015 total deal value of M&As totaled around 4 trillion US dollars and the total number of deals surpassed the 40,000 (Bommaraju et al, 2018).
Alongside of this rapid increase of M&As there was another development in the world. Due to advancements in technology (internet and telecommunication) and
globalization labor and capital markets started to become more integrated, eventually this caused a rise in the popularity of cross-border mergers (shimizu et al, 2004). In the period before 1990 only 15.5% of all mergers worldwide were cross-border mergers, this
percentage rose in the next 8 years to 25.5% (Gugler et al, 2003).
Several studies show a negative market reaction on cross-border M&As, suggesting that they are therefore value-destroying (Gugler et al, 2003; Goergen and Renneboog, 2004; Conn et al, 2005). I extend these studies by examining whether cultural difference affect the market reaction for the acquiring firm. My main prediction is that the market reaction is negatively related to the distance between the acquirer and the target. This prediction is based on the idea that when the distance between two companies is large, it becomes harder to manage those companies, given the time and cultural differences between the companies.
I test my prediction by regressing cumulative abnormal returns around cross-border M&A announcements on a proxy of cultural distance, namely the index of cultural distance by Kogut and Singh (1988), and several control variables. I estimate the model on a sample of 178 cross-border mergers. In this thesis, I do not find a significant relation between the cultural distance and the market reaction for the acquiror to cross-border M&As.
My thesis build on the work of Moeller and Schlingemann (2004) and Conn et al (2005). Moeller and Schlingemann (2004) examine a sample of more than 4000 US acquisitions, containing both domestic and cross-border mergers. They find a negative market reaction to border mergers. This finding suggests that investors perceive cross-border mergers as value-destroying. Conn et al (2005) extend Moeller and Schlingemann (2004) by showing that the long-term performance of firms conducting in cross-border mergers is worse than the performance of firms with domestic mergers. Conn et al (2005)
conjecture that cultural difference might be the reason for this relative poor performance. They state that large cultural difference can worsen the process of integrating the firm in a new environment. However, they focus primarily on the effect of cultural difference on the long-run performance, while the expectation of shareholders of these mergers with large cultural differences is an uncovered subject. The purpose of this thesis is to research the impact of this cultural difference on the expectations of the shareholders. It would be
helpful for firms if they have an idea about the market reaction on cross-border mergers and the effect of cultural differences.
This thesis is organized as follow. In section 2, I provide a brief explanation about some of the basic terms and concepts involving M&As. This is followed by a summary of the existing literature about the performance of M&As. In section 3 I explain my research design and describe the data. In section 4 I present descriptive statistics for the sample and the regression results. The final part of section 4 consists of some sensitivity checks to improve the certainty of the tests. In section 5 I present the conclusion and discuss some of the limitations and further research that can be done about this topic.
2. Literature review
2.1 General information about mergers and acquisitions
The term M&A is often used to describe a corporate control action that involves a buyer (acquirer) and a seller (target) (Berk and Demarzo, 2014). Berk and DeMarzo argue that strictly speaking, mergers are two firms that merge together (usually with a new name) and an acquisition is the act of one firm acquiring the other firm
(continuing with the same name of acquirer). They mention this as two different actions, but they do show similarities and state that the names are used interchangeably. Sherman (2018) even argues that the distinction between the two is not necessary because they both create the same result.
There are several economic motives for a firm to acquire or merge with another firm. The most cited reason is to exploit economies of scope and scale (Berk and Demarzo, 2014). Given (1996) states that economies of scope arise when the costs decrease due to an increase in variety of products. Economies of scale occur due to the higher volume of producing goods (Given, 1996). These economies of scope and scale increase profitability
and are therefore an important economic motive (Berk and DeMarzo, 2014). Berk and DeMarzo (2014) state that vertical integration is another important motive for M&As. They define vertical integration as a merger of two firms in the same production chain, but both at a different part of this chain. They argue that this can benefit a company due to the increased coordination. A firm can make their own supply that satisfies their specific needs. The third motive for a merger is the monopoly gains that can be made due to horizontal integration. Berk and DeMarzo (2014) define horizontal integration as a merger of two firms in the same industry with the purpose of reducing competition. A final motive is acquiring a firm for its personnel, this is the so called expertise motive (Berk and DeMarzo, 2014). Berk and DeMarzo (2014) argue that it may be more efficient to acquire a whole firm with a group of experts already working together, than searching for these experts on the labor market.
There are also non-economic motives for acquiring a firm. The most cited motive is that managers conduct in empire building (Hope and Thomas, 2008). Hope and Thomas (2008) state that managers acquire firms in order to increase their reputation and/or compensation. They state that this is due to a misalignment of the interests of the shareholders and the interest of the managers. Mergers with this motive are potentially harmful for the firm and might reduce shareholder value (Hope and Thomas, 2008).
At the moment of the announcement it is not clear whether the merger is driven by economic or non-economic motives. Therefore it is not clear whether the merger creates values. The question whether mergers create value and which factors influence the market reaction is therefore an empirical question.
2.2 Market reaction to M&A announcement
There is a lot of research about the market reaction to the announcement of a takeover. Most of the studies (see e.g. Agrawal and Jaffe, 2000) find a positive market reaction for targets. This finding is supported by various studies (see Bhagat et al, 2005; Martynova and Renneboog, 2008). An explanation is given by Agrawal and Jaffe (2000), they state that acquiror usually pay large premiums on top of the normal share prices. The
investors adapt this new information causing a positive market reaction.
For the acquirors, the results are mixed. Some studies find a positive market reaction (Goergen and Renneboog, 2004), while other studies find a negative (Andrade et al, 2001) or an insignificant market reaction (Roll, 1986). Goergen and Renneboog argue that the
combined market reaction for acquiror and target is significantly positive, but due to the large premiums paid to targets, the market reaction for acquirors is less positive than the market reaction for targets.
2.3 Factors influencing the market reaction to M&A announcement
There are several factors that have an influence on the market reaction to M&A announcement. Examples include payment method, type of merger (related or unrelated), relative size, public status of the target, cross-border mergers and cultural distance.
2.3.1 Payment method
Several researchers examine the effect of the payment method on the market reaction to a M&A announcement. Most of them find a more positive market reaction for the acquiror in cases where the merger is financed by cash (see Travlos, 1987; Loughran and Vijh, 1997). There are several explanations for those findings. One explanation is provided by the study of Myers and Majluf (1984). They suggest that firms use cash to finance the
merger, because using cash improves the expected performance due to the signaling effect. This signaling effect concerns the idea that when firms use their own stock to finance the merger, they somehow think their stock is overvalued. Because they are insiders,
shareholders will think they know something that is not publicly available. Therefore the shareholders will adapt their expectation to this and this lowers the share price (Myers and Majluf, 1984). Another explanation is provided by Faccio and Masulis (2005). They suggest that firms prefer cash takeovers, because financing with equity will cause dilution and therefore worsen the vote rights of old shareholders. Especially for private firms this is an important reason to choose for all cash takeovers, because their shareholders do not want to lose their voting right (Faccio and Masulis, 2005).
2.3.2 Type of merger
There are three types of mergers; horizontal merger (both firms in same industry), vertical merger (both firms in the same production chain) and a conglomerate merger (firms in unrelated industries) (Berk and DeMarzo, 2014). This section will mainly focus on the difference in performance between a horizontal and the other mergers, so between related and unrelated mergers. Many studies also examine the effect of the type of the merger on
the market reaction, unfortunately there is no consensus on this effect. For example, Doukas et al (2001) find a more positive reaction for acquirors when the firms are related. They argue that this was due to the fact that the benefits of diversification in unrelated mergers did not outweigh the extra costs incurred trying to combine two different businesses. This finding is supported by the study of Akbulut and Matsusaka (2010). In contrast to these studies there is the study of Seth (1990). He analyses 104 mergers in the 1960’s and 1970’s and finds a positive market reaction for both related and unrelated mergers. He also concludes that there was no significant difference between the two types of mergers. This insignificant finding is also supported by Kaplan and Weisbach (1992).
2.3.3 Relative size
Several studies examine the effect of the relative size of the firms on the market reaction where relative size is usually defined as dividing the deal value of the merger by the equity value of the acquiror prior to the merger. Asquith et al (1983) find that the market reaction for the acquiring firm is positively related to the relative size of the target. The study of Moeller et al (2004) confirms this conclusion. This conclusion is also supported in a study of Franks et al (1991) using a benchmark return containing one factor, while with a benchmark return containing multiple factors he found no difference in returns for relative size. The reason why studies find that the market reaction for the acquirer is positively related with the relative size may be due to larger synergies and more economic benefits (Wang and Moini, 2012).
2.3.4 Private vs public target
Fullet et al (2002) examine the effect of the public status of the target on the market reaction for the acquiring firm. They find that the market reaction is more positive when the target firm is privately held. This conclusion is supported by Draper and Paudyal (2006) who finds significant positive gains surrounding announcement of the merger and therefore concludes that: “acquiring a privately held company is an attractive option for maximizing shareholder wealth” (Draper and Paudyal, 2006). A possible explanation for the positive market reaction for acquiring a privately held firm is provided by Fuller et al (2002). They state that due to the fact that the shares of private companies can not be traded on the stock market, there is a lack of liquidity. They argue that this causes a so called “Private firm
discount”, meaning that a private firm is less valuable than an identical public firm. Fuller et al (2002) state that due to this private firm discount firms could acquire private targets for a discount relative to an identical public firm. They argue that this causes a benefit for the acquiring firm due to the advantageous value splitting between the merging firms.
2.3.5 Cross-border mergers
Several studies examine the effect of cross-border mergers on the market reaction. Moeller and Schlingemann (2004) find a negative market reaction to the announcement of cross-border mergers and therefore, conclude that investors perceive cross-border mergers as value-destroying. This conclusion is supported by various other studies (Gugler et al 2003; Conn et al, 2005). According to Anwin and Savill (1997) this value destroying effect is due to the increased risk of conducting in cross-border mergers. A possible explanation for this increased risk comes from Morosini et al (1998), they conjecture that the cultural distance between the countries is the main reason for a more negative market reaction. Also Conn et al (2005) argue that the cultural distance might be a valid reason to explain the more
negative market reaction. Before I explain why this might be a valid reason, let me provide some background information on cultural distance.
2.3.6 Cultural distance
Cultural distance is the difference in norms, values and habits between two organizations, groups or individuals (Morosini et al, 1998). There are benefits of these cultural distance but also some disadvantages. Morosini et al (1998) states that firms can benefit from large cultural distance through the diversification of their norms, values and habits and therefore changing their routines. They argue that these routines play an
important role in subjects like decision-making, entrepreneurship and innovation. According to them it is difficult to implement these routines in different cultures while acquiring these routines through acquisition is fairly easy. This way they argue that it can be beneficial to firms to merge with a firm with a large cultural distance.
A larger cultural distance can also have a negative impact on the acquiring firm. An explanation for this negative effect is given by Hofstede (1980). He states that the costs and risked associated with the integration of the merging firms are increase when the cultural distance between the merging firms increase. Stahl and Voigt (2008) extend the studies of
Hofstede (1980) and Morosini et al (1998) by arguing that the benefits of diversifying the routines of a firm are not reached due to the obstacles formed by the integration difficulties. This is supported by Weber et al (1996) stating that a larger cultural distance worsens the cooperation between the firms managers. This reduce in cooperation hinders the integration and might reduce the benefits of cultural distance even more (Weber et al, 1996). Therefore I expect that the benefits of cultural distance do not outweigh the extra costs associated with the integration process.
Assuming that the costs are greater than the benefits and that investors are rational, I predict that the market reaction for the acquiror to cross-border merger is negatively related with cultural distance.
3. Research design
3.1.1 Methodology event study
To test my hypothesis, I use the event study approach. This approach is based on the idea of rational investors and the assumption that the investors react immediately on new information. If this is the case, the market reaction can be determined by subtracting the benchmark (expected) return from the actual return (Wang and Moini, 2008).
Consistent with the idea of rational and immediate reacting investors, I calculate the expected return using the market model. This is a one factor model and is an improvement relative to the simple constant mean return model (MacKinlay, 1997). MacKinlay (1997) argues that there are also more sophisticated models that includes other factors like the industry where the firm operates, the size of the firm or the book-to-market ratio. He stated that although these additions improve the model, the benefits are limited and it makes the analysis unnecessary hard. According to Mackinlay (1997) this is because these factors only have a small contribution in the returns of a firm and therefore only have a small effect on the variance.
𝑅𝑖𝑡 = 𝛼𝑖+ 𝛽𝑖𝑅𝑚𝑡+ 𝜀𝑖𝑡 𝐸(𝜀𝑖𝑡 = 0) 𝑉𝐴𝑅 (𝜀𝑖𝑡) = 𝜎𝜀2𝑖
Rit is the realized return for security i at time t, calculated by dividing the stock price of day t
+ 1 by the stock price of day t and subtracting 1. I use the adjusted stock price to take into accounts events like a stock split. Rmt is the return of the market portfolio at time t. I use the
MSCI world index as a proxy for the normal return, because the mergers in the sample contain cross-border mergers and therefore contain globalized firms. εit is the disturbance
term with a mean of zero. αit and βi are estimated using an estimation period of 120 days.
The estimation period ends 10 days prior to the announcement of the merger, to avoid a bias due to inside information. The timeline of the event study is presented in figure 1.
After estimating model (1), I calculate the abnormal return (AR) as follow:
𝐴𝑅𝑖𝑡 = 𝑅𝑖𝑡 − (𝛼𝑖 + 𝛽𝑖𝑅𝑚𝑡+ 𝜀𝑖𝑡) (2)
ARit is the abnormal return of security i at time t, Rit and Rmt are defined as above.
Next I compute the cumulative abnormal return (CAR) over the event window. I used a three day event window (-1,1). This period is also used by Moeller et al (2004) and they state that this period is sufficient to capture the sentiment of the market while it reduces the impact of confounding events.
The calculation of the CAR is as follows:
t = - 130 beginning of estimation-period t = - 10 ending of estimation-period t = -1 beginning of announcement-period t = 0 announcement of merger t = 1 ending of announcement-period
𝐶𝐴𝑅𝑖 = ∑ 𝐴𝑅𝑖𝑡
1
−1
(3)
CARi is the summation of the abnormal returns in the event window, where the ARit is
defined as above. Then I compute the cumulative average abnormal return (CAAR). The CAAR is calculated for mergers with low cultural distance and mergers with high cultural distance. Mergers with a cultural distance below the median are classified as low cultural distance mergers and mergers with a cultural distance above the median are classified as high cultural distance mergers. The CAAR’s of both groups are tested, by using a student t-test, to see if they significantly differ from each other.
Next, I estimate the following model:
𝐶𝐴𝑅𝑖 = 𝛽0+ 𝛽1(𝐶𝑢𝑙𝑡𝑢𝑟𝑎𝑙 𝑑𝑖𝑠𝑡𝑎𝑛𝑐𝑒𝑖) + 𝛽2(𝑃𝑎𝑦𝑚𝑒𝑛𝑡 𝑚𝑒𝑡ℎ𝑜𝑑𝑖) +
𝛽3(𝐼𝑛𝑑𝑢𝑠𝑡𝑟𝑦𝑖) + 𝛽4(𝑅𝑒𝑙𝑎𝑡𝑖𝑣𝑒 𝑠𝑖𝑧𝑒𝑖) + 𝛽5(𝑃𝑢𝑏𝑙𝑖𝑐 𝑠𝑡𝑎𝑡𝑢𝑠 𝑡𝑎𝑟𝑔𝑒𝑡𝑖) + 𝜀𝑖
(4)
The variables for the acquiring firm i are defined as follows. CAR is defined as above. Cultural distance is calculated by using the index of Kogut and Singh (1988). Kogut and Singh (1988) based this index on the four dimensions mentioned by Hofstede (1980). The dimensions based on Hofstede’s findings are power distance, uncertainty avoidance,
individualism/collectivism and masculinity/femininity. Per dimension a score is given for the target country and this score is compared with the score for the acquiring country (US). The deviations of the four dimensions are scaled by the variance of the particular dimension.
𝐶𝑢𝑙𝑡𝑢𝑟𝑎𝑙 𝐷𝑖𝑠𝑡𝑎𝑛𝑐𝑒𝑖 = ∑ {(𝐼𝑖𝑗 − 𝐼𝑗𝑢) 2 𝑉𝑗 } 4 𝑗=1 4 (5) where:
CDi = Cultural distance for country i
Iij = Score for country i and dimension j
Iju = Score for US and dimension j
Following the literature and my hypothesis, I predict a negative coefficient on this variable, meaning that cultural distance has a negative effect on the CAR.
Payment method is a continuous variable measured by the fraction of the deal value financed by cash, 0 being an all equity takeover and 1 an all stock takeover. I predict a positive coefficient on this variable, because prior studies predict and find that the market reaction to M&A announcement is more positive in cases where the M&A is financed by cash than cases where the M&A is financed by stock. Industry is a dummy variable equal to 1 if the merging firms have the same SIC code and 0 otherwise. Although there is mixed
evidence about the effect of relatedness on performance, I predict a positive coefficient on this variable, because most of the research suggests that being in the same industry has a positive effect on performance. Relative sizeis calculated by dividing the deal value of the merger by the market value of the acquiror four weeks prior to the announcement of the merger. I use the market value four weeks prior to the announcement to exclude the effect of the announcement on the market value. This variable is included to control for the effect size has on merger performance. The coefficient on this variable is predicted to be positive according to the existing literature. The final variable is Public status target. This variable is included to find the effect of the public status of the target on merger performance. Prior research suggests that this is an important determinant of merger performance and shareholders’ expectations.
3.2 Data
I focus on M&As with US based acquirers from the period January 1, 2011 to December 31, 2015. I focus on US based acquirers, because the US is one of the leading economies in the world and therefore interesting to research. The period is chosen because I want to focus on the more recent mergers.
For M&As with US based acquirers in the period January 1, 2011 to December 31, 2015 I collect the M&A data and relevant information for the acquiring and the target firm via Thomson One. By relevant information, I mean data such as the country of the acquiring and the target firm, the deal-value, the announcement date, the standard industrial classification (SIC) and the Datastream code.
Then I drop certain observations. First, I exclude mergers where the relevant information mentioned above is missing. Second, I exclude deals in certain industries. The
industries that I exclude are public administration and finance, insurance and real estate with primary SIC codes 60-67 and 90-99, respectively. Mergers from those industries are excluded by filtering on the SIC codes. These industries are excluded because mergers in these industries are usually driven by non-economic motives. Then I exclude mergers where less than 100% of the shares are acquired or those that are not completed. This is done to exclude the effect of firms that are acquired in different portions. I also drop all M&As with a deal value less than $25 million are excluded following Healy et al (1992), who state that the larger mergers are more interesting due to the larger consequences. Last I exclude mergers with unavailable stock data or unavailable information about the firms. Next, I retrieve the adjusted stock price from Datastream. This can be done with the Datastream code provided by Thomson One. After retrieving the daily stock price for the whole period, I calculate the daily returns. Those returns are used to calculated the abnormal returns.
The final sample consists out of 178 cross-border mergers. The sample contains 64 mergers within the same industry (matched SIC codes). Furthermore the sample contains 53 public targets and 125 private targets. The variable Relative size contains outliers, therefore I winsorize this variable by setting all data below the 10th percentile at the 10th percentile and
all data above the 90th percentile at the 90th percentile. The descriptive statistics are
summarized in table 1.
As can be seen in table 1, CARi has a positive mean of 0.01 (eq. 1%). The mean of
Payment methodi is 0.816, meaning that on average 81.6% of the total deal value is financed
by cash and the remaining 18.4% is financed by stock. The dummy variables Industryi and
Public status targeti have a mean of 0.481 and 0.460, respectively. This means that 48.1% of
the mergers are related mergers, the remaining unrelated mergers and 46.0% are mergers with a publicly listed target, the remaining targets are privately held. The variable Relative sizei has a mean of 0.174, meaning that the average target has a market value of 17.4% of
the total deal value. Last, the variable Cultural distancei has a mean of 0.864. However, for
this variable the median is of greater interest for testing the CAARs due to the reduced impact of potential outliers. The median value of cultural distance is 0.120. Mergers with a cultural distance below the median are labeled as low cultural distance and mergers with a cultural distance above the media are labeled as high cultural distance.
Table 1 Descriptive statistics for all variables
Variables Observations Mean St. Dev. Min. Max.
CARi 178 0.010 0.069 -0.264 0.289
Payment methodi 178 0.816 0.334 0 1
Industryi 178 0.358 0.481 0 1
Relative sizei 178 0.174 0.307 0.000* 3.099
Public status targeti 178 0.302 0.460 0 1
Cultural distancei 178 0.894 1.055 0.023 3.665
This table presents the number of observations, mean, standard deviation, minimum value and maximum value of the variables.
*value is zero due to rounding.
4. Results
I examine the correlations among the independent variables to detect the possibility of a multicollinearity problem. Multicollinearity means a strong correlation between two or more independent variables. A rule of thumb for harmful multicollinearity is a correlation higher than 0.8 (Farrar and Glauber, 1967). Table 2 presents the correlations of the independent variables. As can be seen in the table, I do not find any correlations higher than 0.8 and thus find no sign of multicollinearity.
Table 2
Correlation matrix of independent variables
Cultural Distancei Public Status Targeti Payment methodi Industryi Relative sizei Cultural distancei 1
Public status Targeti -0.195 1
Payment methodi 0.003 -0.387 1
Industryi -0.008 0.024 -0.142 1
Relative sizei -0.166 0.391 -0.570 0.010 1
This table presents the correlations between the independent variables of the regression. Correlation = ρ
In table 3, I present the regression results for all variables for acquiring firm i. This table contains three set of results. I find an insignificant coefficient on cultural distance in all three regressions. Thus, there is no evidence for the hypothesis that the market reaction to cross-border merger is negatively associated with cultural distance, mentioned in section 2.3.5. However, these results need to be treated with care due to the low explanatory power of the regression and the insignificance of the effect of cultural distance on the CAR.
When I perform a student t-test on the difference between CAARs for mergers with low cultural distance and that for mergers with high cultural distance, I do not find a significant difference. Thus, it appears that there is no sign of a relation between market reaction and cultural differences. A possible explanation is that this is due to the benefits of diversification of the firm culture through merging with firms with a higher cultural distance (Morosin et al, 1988) outweighing the negative effects of cultural distance mentioned by Hofstede (1980) and Stahl and Voigt (2005). However it could also be that the assumptions for estimating the abnormal returns are not met. Assumptions like the efficient market hypothesis are difficult to be met and therefore can weaken the validity of the model. Another explanation for the low explanatory power of the regression can be wrongly chosen control variables and therefore failing to isolate the effect of the cultural distance.
The dummy variable on Public status targeti has a positive insignificant coefficient,
this is in contrast to the existing literature about the effect of the public status of the target. The coefficient on Payment methodi is negative and insignificant. The negative value
on this coefficient implies that financing a merger by cash result in a worsening of shareholders’ expectations. This finding is in contrast to the existing literature which suggested a positive effect when financing with cash due to the signaling effect involved with stock financed mergers (Myers and Majluf, 1984).
In addition to the second regression the final regression includes also the variables industry and relative size. The coefficient on both variables are highly insignificant and although inclusion of these variables increases the explanatory power of the regression, measured by R2. The coefficient on Industry
i, which is a dummy variable of value 1 if the
merging firms are within the same industry and of value 0 if the merging firms are not within the same industry, is positive. This finding is in line with prior research, but due to the high insignificance this does not serve as evidence for a positive effect. The coefficient on the variable Relative sizei is highly insignificant and is therefore in line with the indecisive
literature. An explanation for this insignificant coefficient can be the importance of the size of the bidding firm instead of the relative size of the target. Goergen and Renneboog (2004) argued that smaller bidding firms have larger announcement returns than large bidding firms. It is difficult to distinguish the difference between small bidding firms and large relative target size and this can therefore disturb regression on this variable.
Table 1
Effect of “Cultural distancei” on market reaction measured by CARi
Dependent variable = CARi (1) (2) (3)
Cultural distancei 0.005 (0.005) 0.006 (0.005) 0.006 (0.005)
Public status targeti 0.015
(0.012) 0.017 (0.013) Payment methodi -0.015 (0.017) -0.021 (0.020) Industryi 0.009 (0.011) Relative sizei -0.018 (0.021) Constant 0.005 (0.007) 0.012 (0.017) 0.017 (0.022) R2 0.006 0.026 0.034 Adjusted R2 0.000* 0.009 0.006
This table presents the regressions that test the effect of “Cultural distancei on the market
reaction measured by CARi. The sample and control variables are defined in section 3.
*zero due to rounding.
**significant at 0.05 level for a two-tailed test.
As a sensitivity check, the event window is lengthened from a three day period (-1,1) to a five day period (-2,2), the regression results are included in appendix 1. This lowers the explanatory power measured by R2 slightly from 0.034 to 0.033. The variable of interest
Cultural distancei remains positive, but decreases from 0.06 in the first model to 0.03 in the
second model. However, the model with a five day period increases the p-value of the variable from 0.274 to 0.554, making the variable even more insignificant.
Changing the cultural distance from a continuous variable to a dummy variable serves as the second sensitivity check. This dummy equals 1 if the target has a high cultural distance and equals 0 if the target has a low cultural distance. High and low cultural distance follows the definition mentioned in section 3.2, i.e. cultural distance below median is low cultural distance and cultural distance above median is high cultural distance. This change increases the effect of cultural distance from 0.006 for the continuous variable to 0.007. However, the dummy variable is less significant than the continuous variable and also lowers the explanatory power of the regression.
As a final sensitivity check, I regress CARi against Cultural distancei and the control
variables, using robust standard errors to account for heteroskedasticity. This change does not affect the explanatory power of the model (R2 remains at 0.034) and the coefficient
(Cultural distancei remains at 0.006). Also, the p-value of the coefficient on Cultural distancei
slightly decreases from 0.247 to 0.223, but remains insignificant.
5. Conclusion
In this thesis I examine the impact of cultural distance on the market reaction for the acquiror to cross-border M&As. This study is an extension to the studies of Moeller and Schlingemann (2004) and Conn et al (2005). Moeller and Schlingemann (2004) find a negative market reaction for the acquiror to cross-border mergers and Conn et al (2005) extend this by conjecturing that cultural differences might be an explanation for this. However, they focus primarily on the effect of cultural distance to the long term
performance, while I focus on the market reaction. From the prior research I hypothesize that cultural distance is negatively related with the market reaction for the acquiror.
To examine this hypothesis, I analyze a sample of 178 cross-border mergers with a US based acquiror from the period January 1, 2011 to December 31, 2015. First I calculate the CAR per firm i. After obtaining these results, I regress CARi against Cultural distancei and
several control variables. Thereafter, I test mergers with a low cultural distance against mergers with a high cultural distance using a student t-test.
The regression does not provide any evidence for the hypothesis mentioned above. The coefficient on the variable Cultural distancei is slightly positive, but highly insignificant.
The student t-test for testing between the two groups is also highly insignificant and therefore does not provide evidence for the hypothesis.
Certain limitations of this thesis are the control variables included and the sample. The control variables are used to control for the different characteristics of the merger. However, the distance between the two firms is only measured by the cultural distance. Inclusion of variables like geographic distance, economic distance and administrative distance might improve the explanatory power of the merger and make the coefficient on Cultural distancei more reliable. The sample size is another limitation of this thesis. I use a
fairly small sample in comparison with prior research on this subject and this may affect the results I find.
Suggestion for future research are extending this research by dealing with the limitations mentioned above. Furthermore, this study is done for US based acquirors. It would be interesting to examine whether the results will be different for other countries.
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7. Appendix
Appendix 1: Regression event window (-2,2)
Appendix 2: Dummy variable Cultural distance
Regression Statistics Multiple R 0.181921555 R Square 0.033095452 Adjusted R Square 0.004987762 Standard Error 0.075354666 Oberservations 178 ANOVA df SS MS F Significance F Regression 5 0.033429776 0.006685955 1.177451862 0.322209866 Residual 172 0.976672022 0.005678326 Total 177 1.010101798
Coefficients Standard Error t Stat P-value Lower 95% Upper 95% Laagste 95.0% Hoogste 95.0%
Intercept 0.033328688 0.024501584 1.36026667 0.175525635 -0.015033817 0.081691193 -0.015033817 0.081691193
Cultural distance 0.003298822 0.005558809 0.593440505 0.553665963 -0.007673445 0.01427109 -0.007673445 0.01427109 Public status target 0.011033925 0.013959353 0.790432395 0.430363754 -0.016519775 0.038587625 -0.016519775 0.038587625 Payment method -0.032014027 0.021739282 -1.472634943 0.14267703 -0.074924156 0.010896103 -0.074924156 0.010896103 Industry 0.012800153 0.011942737 1.07179393 0.285314159 -0.010773045 0.036373351 -0.010773045 0.036373351 Relative size -0.02015125 0.023383712 -0.861764387 0.390017017 -0.066307242 0.026004741 -0.066307242 0.026004741 Regression Statistics Multiple R 0.173770655 R Square 0.030196241 Adjusted R Square 0.002004271 Standard Error 0.068651743 Oberservations 178 ANOVA df SS MS F Significance F Regression 5 0.025240654 0.005048131 1.071093682 0.37814382 Residual 172 0.810646627 0.004713062 Total 177 0.835887281
Coefficients Standard Error t Stat P-value Lower 95% Upper 95%
Intercept 0.020538796 0.022204119 0.924999339 0.356262203 -0.023288853 0.064366445 Cultural distance dummy 0.007383995 0.010681558 0.691284425 0.490318785 -0.013699822 0.028467812 Public status target 0.017116432 0.012906408 1.326196416 0.186533003 -0.008358909 0.042591774 Payment method -0.023370385 0.019666384 -1.188341696 0.236337038 -0.06218892 0.015448151 Industry 0.008366956 0.010879276 0.769072873 0.44290484 -0.013107126 0.029841039 Relative size -0.020961683 0.021061541 -0.995258756 0.321008522 -0.062534051 0.020610685
Appendix 3: Robust OVERALL FIT Multiple R 0.185089454 R Square 0.034258106 Adjusted R Square 0.006184214 Standard Error 0.068507824 Observations 178 ANOVA Alpha 0.05 df SS MS F p-value sig Regression 5 0.028635915 0.005727183 1.220283441 0.301609783 no Residual 172 0.807251366 0.004693322 Total 177 0.835887281
coeff std err t stat p-value lower upper
Intercept 0.01693747 0.039128636 0.432866345 0.665654165 -0.060296674 0.094171614
Cultural distance 0.005543706 0.004535781 1.222216338 0.223298098 -0.003409256 0.014496668
Public status target 0.01738314 0.013254345 1.311505071 0.191435153 -0.008778977 0.043545257
Payment method -0.021381077 0.03429308 -0.623480818 0.533794523 -0.089070547 0.046308392
Industry 0.008522715 0.010969246 0.776964498 0.438246957 -0.013128956 0.030174386
