The effect of horizontal cross-border mergers and
acquisitions on firm performance.
Author:
Rob van Diepen
10538887
Supervisor:
V.N. Vladimirov
Bachelor Thesis Finance
University of Amsterdam
Faculty of Economics & Business
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Statement of Originality
This document is written by Rob van Diepen who declares to take full responsibility for the contents of this document. I declare that the text and the work presented in this document is 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.
Abstract
This thesis investigates the effect of horizontal cross-border mergers and acquisitions on the stock performance of the acquirers in the US. The sample contains 1640 mergers and acquisitions from 2006 to 2016. The results show significant positive announcement returns for horizontal cross-border mergers and acquisitions (M&A) in the event study, but no evidence is found for significant better performance than other types of M&A in the cross-section analysis.
1. Introduction
The year 2015 was the biggest year for M&A transactions ever (Farrell, 2015) with a total amount of approximately 4.304 trillion dollar. A lot of deals have been done and a lot of sellers and buyers have been involved. Each buying decision had effects for the acquirers, the targets and their shareholders. For each deal, a certain risk has been there with the investment decision. Being able to pretend the outcome of an investment decision would be easier to evaluate these decisions, but there is a lot of debate on the performance of
mergers and acquisitions, especially for acquirers. Some researchers like Fuller et al. (2002) argue that there is a positive average cumulative abnormal stock return of 1.8% for acquirers in the short term. On the other hand, there are researchers that argue negative average cumulative abnormal stock returns (CARs). Examples are Officer (2003 & 2004) with -1.2% and Dong et al. (2006) with CARs between -0.2% and -1.8%. It would be easier to know the outcome of a category of merger and/or acquisition to predict the outcome of a certain deal. Investors and shareholders should worry about this, because some kind of mergers and/or acquisitions might be value destroying, while others might be value creating.
The reason that a company acquirers another company is mainly synergies.
Especially horizontal mergers tend to happen for the operating synergies (Fee and Thomas, 2004). Firms want to outperform their competitors and the competitive advantages might be economies of scale and scope or scarce recourses that a firm can get with a merger or
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acquisition. This is interesting for shareholders, because mergers and acquisitions should benefit the wealth of shareholders, but managers might pay too much for a target due to the entrenchment or hubris according to Harford et al. (2012). When this is true, takeovers might harm shareholders. A lot of research is done on the determinants of successful horizontal mergers and their motives. There are several reasons for a horizontal merger or acquisition, like more efficiency gains, more buying power and collusion possibilities (Shahrur 2005). It is possible that these motives drive horizontal mergers with positive wealth effects. According to Eckbo (1983), the bigger possibility of collusion will cause higher monopoly profits for the merging firms. One reason is that the bigger merged company will have lower input prices according to Snyder (1996), due to more competition between suppliers for delivering to the merged firm. This is the buying power hypotheses. On the other hand, we have the collusion hypotheses. A collusive or anticompetitive takeover may raise the price of the products and therefore benefit the firm itself and its rivals according to Eckbo (1983) and Stillman (1983). This is a positive industry wealth effect. However, when scale-increasing takes place, this tends to have a negative influence on the equilibrium prices in the market and causes a negative industry wealth effect.
Due to globalization, product and capital markets become more integrated. The result is that more companies acquire companies in other countries. According to Moeller and Schlingemann (2005), there are pros and cons of this. The increase in investment possibilities causes a bigger chance of realizing synergies and efficiency gains and opens doors to improved technology and interesting government policies. This would benefit shareholders, because their firm can gain value from taking over another company. But on the contrary, there are several downsides of the more integrated markets. It may be the case that the integration increases competition in the market for corporate control and therefore smaller synergies remain for the bidder. Another problem is cultural differences, which causes hubris and agency problems. This lowers the synergy potential of the takeover and will therefore lead to smaller announcement returns of the bidder. This is also a
potential problem for horizontal cross-border M&A. However, there is no consensus in the academic debate on the performance of cross-border takeovers. Eckbo & Thorburn (2000) claim that domestic bidders significantly outperform foreign bidders, but they did not find an explanation for this. While Eun et al. (1996) claim a positive combined wealth effect for the target from the United States and the foreign acquirer.
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The effects of mergers and acquisitions are widely discussed in the literature. But we still do not know everything about the effects for shareholders of acquiring firms. The increasing globalization, which creates more competitors in one market and the increasing amount of border mergers raises questions about the performance of horizontal cross-border mergers. Do firms a good job acquiring a cross-cross-border competitor to improve their own position, or is this practice value wasting? The question that this thesis tries to answer is:
Is the performance of bidders doing a horizontal cross-border merger or acquisition better than other types of mergers and acquisitions in the US?
Following Moeller et al. (2004), an event study will be used to investigate if the cumulative abnormal returns of horizontal cross-border mergers are significantly different from zero at the announcement date. After that, a cross-section analysis will be done to research which factors influence the cumulative abnormal returns and if horizontal cross-border mergers perform better than other types of mergers.
The thesis is organized as follows. Section 2 covers the literature review. Section 3 covers the research method and data. Section 4 covers the results and finally section 5 covers the conclusions and limitations of the research.
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2. Literature
Mergers and acquisitions have become common in the last decades. The result is that many papers are written about mergers and acquisitions. In this section, the relevant literature on mergers and acquisitions will be discussed. The topics being covered are: determinants of M&A, the method of payment, public and private M&A targets, the relative size of the deal and the market-to-book ratio.
2.1. Determinants of M&A
In the literature, many reasons and explanations are given for mergers and acquisitions activity. In this section, the reasons for mergers and acquisitions are separated by
determinants of mergers and acquisitions in general, horizontal mergers and acquisitions and cross-border mergers and acquisitions.
2.1.1. Determinants of mergers and acquisitions
When a company acquirers another company, there are certain motives for doing that takeover. This can be firms-specific motives, or it can be market driven.
The firm-specific motives are among other things economies of scale and scope, efficiency gains (Fee and Thomas, 2004) and monopoly gains (Stillman, 1983). This is mainly focused on getting competitive advantage, which should benefit shareholders. Secondly, market driven merger waves exist. There are several explanations for this. Rhodes-Kropf et al. (2005) mentioned that periods with high market-to-book ratios are the periods with a lot of merger and acquisition activity. Harford (2005) argues that the more neoclassical explanation is still accurate. He finds out that merger waves occur by specific industry shocks. These shocks cause large scale reallocation of assets. The essential condition is that sufficient liquidity is available to finance these reallocations. Besides that, Harford claims that low economic transaction costs, which makes capital more liquid, causes merger waves. The other
motivation is the economic motivation or behavioural model of high market-to-book ratios that Rhodes-Kropf et al. mentioned. In this explanation, managers tend to take over
companies that are currently under-priced or they take over real assets with their own over-valued stocks. Harford made a direct comparison between the two methods and found that both methods explain mergers and acquisitions, but that the neoclassical model with industry shocks causes merger waves. Sufficient capital liquidity is needed to make the transactions possible. This macro-economic factor causes industry waves to cluster. These
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clusters are the so-called big aggregate merger waves. The consequences of merger waves for shareholder can be two-part. On the one hand, the low transaction costs make it possible to do M&A in a cheaper way and therefore might lead to higher returns for the bidder. On the other hand, the merger wave might increase the premium for targets, because more bidders are involved due to increased competition between bidders. This decreases the potential return for the bidder.
2.1.2. Horizontal mergers and acquisitions
The reasons and explanations for mergers and acquisitions discussed before were about mergers and acquisitions in general. There are several motives for doing horizontal takeovers in particular. Fee and Thomas (2004) argue that horizontal mergers lead to improved productive efficiency and increasing buying power. According to them, managers of firms which do a horizontal merger or acquisition see improved productive efficiency as the most important source of the anticipated synergy gains. The improved productive efficiency can come from increasing economies of scale and the elimination of overlapping facilities. The increasing buying power can lead to discounts from suppliers or the new company can use their combined purchasing to force the suppliers to compete on prices to sell to the new combined firm. Fee and Thomas also found a positive and significant
abnormal return at the merger announcement for the merging firms. Firms competing in a concentrated industry had significant increasing post-merger cash flows and lower cost of goods sold. This indicates that horizontal merged firms can benefit from the increase in buying power, which is positive for the shareholders of the bidder.
Shahrur (2005) also found evidence that increasing efficiency is the most important consideration for M&A. But on top of that he found evidence for the increase in buying power for the bidder if the suppliers are concentrated. In that case, there is more
competition between few suppliers to deliver to the post-merger company. In his research, he could not find evidence for the collusion hypotheses, which is the same result as Eckbo (1983) and Stillman (1983) found. This means that antitrust laws prevent firms to collude when the competition in the market is reduced. The merger or acquisition does not benefit the shareholders for this particular reason.
2.1.3. Cross-border mergers and acquisitions
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different characteristics than domestic mergers and acquisitions. Research on this is done by Rossi and Volpin (2004) and what they found is that targets are more likely to be in a country with poor investor protection and weaker accounting standards. Secondly, they found that bidders are more likely to come from countries with good investor protection. They claim that these results show that mergers and acquisition activity is important for assimilating corporate governance standards. Besides that, they also investigated that the acquisition premium in countries with higher shareholder protection is higher. When a bidder has to pay a higher premium, this indicates that bidder announcement returns should be lower. The side note is that this is driven by the deals with targets from the UK and the US. Erel et al. (2012) found the same result in the matter of investor protection. On top of that they investigated that the shorter the distance between two countries, the more likely that acquisitions between those two countries happen. It is also found that mergers are more likely to occur between countries that trade more frequently with each other. It is plausible that these countries have less cultural differences and therefore more potential synergies. This means that the potential benefits for shareholders of the bidder are higher.
Besides this, Erel et al. discussed the change in value of a firm due to firm-specific and country-specific factors and if those factors can influence the probability of a merger. In a lot of mergers and acquisition, one private firm is involved. Therefore the country-specific measures are more available. What they found is that the exchange rate for acquirers appreciates relative to that of the target in the one-to-three years before the deal. Also the acquirers’ stock return is higher in the local currency between one-and-three years before the deal. The result of this price pattern is that the market-to-book ratio in the country of the acquirer is about 9.93% higher at the time of the merger or acquisition. A higher market-to-book ratio for the acquirer is what is also found by Rhodes-Kropf et al. (2005) for
domestic mergers and acquisitions. Erel et al. showed that the difference in the exchange rate and the market-to-book ratio influence the cross-border mergers and acquisitions volume. This shows that the relative valuation is an important factor for the probability of a merger or acquisition and also the attractiveness of a merger or acquisition.
The results of cross-border M&A on cumulative abnormal returns differs across the literature. Moeller and Schlingemann (2005) found a positive but insignificant effect for cross-border mergers in their sample over the period 1985-1990, but a highly significant negative effect for the period 1991-1995. Eckbo and Thorburn (2000) found an insignificant
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cumulative abnormal return for US bidders in Canada and significantly lower CARs than the domestic bidders in Canada.
Following this knowledge of horizontal M&A and cross-border M&A, the hypothesis is formulated:
Hypothesis 1
Bidders doing a horizontal cross-border merger have a positive cumulative abnormal performance at the announcement.
The reason for this expectation is that horizontal mergers result in significant positive cumulative abnormal returns for acquirers. These kind of deals are mainly focused on synergies and competitive advantage and should therefore be value creating. Although the effect of cross-border mergers is not significant according to the literature, a horizontal cross-border merger should contain the synergies and competitive advantage involved with horizontal M&A.
On top of that, this thesis investigates if horizontal cross-border mergers are better than other types of mergers and acquisitions. This leads to the second hypothesis:
Hypothesis 2
Bidders doing a horizontal cross-border merger have a better performance than other types of mergers and acquisitions.
Following the literature, horizontal mergers are focussed on operating synergies. Combining firms leads to efficiency gains and therefore positive cumulative abnormal returns.
Horizontal cross-border mergers give firms the opportunity to expand their market and increase scaling. Combining the two kinds of mergers, should lead to higher performance than other types of M&A besides horizontal M&A and cross-border M&A. The other types of M&A contain amongst other things conglomerate M&A, which should have lower operating synergies and competitive advantage than horizontal cross-border M&A and therefore lower announcement returns.
2.2. Method of payment
The method of payment in financing a takeover is an important decision. This decision has among other things consequences for the ownership structure of the acquirer, financial
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leverage, tax and cash flows according to Faccio and Masulis (2005). A bidder has to make a decision if he makes a bid in cash or in equity. In the case of a cash bid, it is likely according to Faccio and Masulis, that the bidder has to finance this with debt, because most
companies have a limited amount of cash and liquid assets. This increase in debt might raise the financial distress costs of a company. The other option is an equity bid, but this has consequences for corporate control. When the ownership of the target is currently very concentrated, they claim that making an equity bid might create a new block holder in the ownership structure of the bidder. The bidder has to take the trade off into account of the current debt capacity and the amount of leverage or the current corporate governance structure. The results indicate that the bidder prefers paying with cash when the voting control of their dominant shareholders is threatened. This explains why many private companies are financed with cash instead of equity. Besides this, Faccio and Masulis (2005) claim that sellers’ willingness towards accepting stock payments declines when there is information asymmetry about the bidder’s equity value and future earnings. On the other hand, sellers are willing to accept a stock payment when the merger is an intraindustry merger, where they know the risks in that industry and the prospects of that industry. This would be in favour of horizontal mergers, even if it is a cross-border merger. What Faccio and Masulis found in their results is that the likelihood that the two companies are in the same industry is the highest for pure stock deals. But the drawback of equity bids with cross-border mergers is the home bias that investors have. The problems for the sellers are among other things: higher trading costs, lower liquidity, exposure to exchange risk and limited access to new firm information. The results indicate that cash-only deals are the most common with cross-border mergers and acquisitions.
The attractiveness of the bid for target shareholders is not the only determinant of the method of payment. Hovakimian et al. (2001) found that firms with more tangible assets, higher earnings growth and asset diversification have a bigger debt capacity. Firms with volatile assets have a lower debt capacity. This means that firms with a lot tangible assets might prefer cash financing a merger or acquisition. Faccio and Masulis argue that high-growth bidders are attractive for sellers when these bidders do an attractive equity bid. This might be interesting for sellers to get shares in these companies instead of cash, to benefit from the future growth of the bidder.
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board of the acquirer thinks that his own stock is overvalued. This is in line with the pecking order theory. However, they also argue that both cash and equity offers for private firms receive positive reactions. This can be explained by the fact that the bidder has less
information about the target. When this is the case, it is better to do a stock offer according to Hansen (1987).
Based on the literature, it is more likely that horizontal cross-border M&A is more financed with cash than other types of M&A. The cross-border part should make existing shareholders of the target reluctant to accept shares due to information asymmetry and the home bias of investors. It is likely that shareholders of the target do not know much about the bidder. Thereby, having shares across the border creates exchange risk and higher trading costs. Therefore, the third hypothesis is:
Hypothesis 3
Horizontal cross-border takeovers contain more cash deals than equity deals.
2.3. Type of target
When a target is public, shareholders of the bidder has easily access to information about the firm of interest. When the target is private, it is more difficult to get information about this target. Fuller et al. (2002) did research to the effect of the target being public or private on bidder announcement performance. What they found is that bidder shareholders gain when they acquire a private firm, but they lose when they acquirer a public firm. This can be explained by the liquidity effect, because private firms cannot be as easily sold and bought as public firms. The less liquid an investment is, the less attractive an investment is and therefore less valuable. Bidders will include this discount in the offer. Therefore, bidders receive higher announcement returns when they acquire a private firm.
Besides the effect of the type of the target, they also investigated the effects on bidder announcement returns of combinations of the type of the target and the method of payment. Cash offers for public targets result in insignificant positive bidder returns, but equity offers for public target result in significant negative bidder returns. For private targets, both payment methods lead to significant positive returns, but the bidder
announcement returns are bigger for equity offers. The reason for this is that private firms’ ownership is very concentrated. Acquiring such a company with equity creates block holders in the new firm, who will better monitor the management. This causes the higher bidder
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returns according to Fuller et al.
The third effect investigated by Fuller et al. is the effect of the type of target and the size of the target on bidder announcement returns. What they found is that for private firms, the bigger the relative size of the target, the bigger the effects already mentioned. For private firms, the bigger the relative size of the target, the higher the bidders’
announcement returns. For public firms, the bigger the relative size of the target, the more positive the announcement returns for cash offers, but the more negative for stock offers.
2.4. Relative size
Moeller et al. (2004) found that the size of the acquirer matters for the announcement returns and therefore the performance. In their sample there was an equally weighted abnormal announcement return of 1.1%, but the acquirer firms lost 25.2 mln dollars in market capitalization on average when a merger or acquisition was announced. This means that the shareholders of bigger acquirers have lower returns when a merger or acquisition is announced than shareholders of smaller companies do. This can be explained by the fact that one quarter of the acquiring firms that acquirer public firms is small, while half of the acquiring firms that acquire private firms is small. As mentioned in section 2.3 Fuller et al. (2002) showed that abnormal returns with acquiring a private firm are higher. On top of that, smaller companies tend to pay with cash instead of equity. Where paying with equity is a sign that the management of the acquirer thinks his own stock is overvalued (Fuller et al. 2002). Moeller et al. (2004) claim that firms further in their life-cycle are more likely to be large and at the boundaries of their growing potential. On top of that, Jensen (1986) pretends that empire-building managers would rather want to make acquisitions than paying the cash to the shareholders. The surplus of cash in combination with the lack of growth opportunities, make large companies more likely to do wasteful mergers and acquisitions. Thereby, small companies are more likely to have managers with more
ownership than larger firms (Demsetz and Lehn, 1985). Therefore, these firms are less likely to do wasteful M&A and should have higher announcement returns.
2.5. Market-to-Book
Dong et al. (2006) investigated the effect of misvaluation on the announcement effects of mergers and acquisitions. They used the price to book value of equity and the price to residual income value as a proxy for misvaluation. They find out that high valuation targets
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are more likely to be paid with equity than cash. It is also found that these targets with high valuation are less likely to be hostile and these targets receive lower bid premiums.
For bidders Dong et al. found that high valuation bidders are more likely to use equity than cash. High valuation bidders tend to pay high premiums, because their own stock is high valuated. When a high bidder valuation announces a bid, this is associated with lower abnormal stock returns. On the contrary, Moeller et al. (2005) did not find a significant relation between the market-to-book ratio and the announcement returns of the bidder. However, Lang et al. (1991) and Servaes (1991) found that firms with a higher market-to-book ratio make better acquisitions. On top of that Servaes (1991) found that firms with a low market-to-book ratio have lower performance. This evidence is relatively old and therefore it is unsure if it still holds.
Rhodes-Kropf’s et al. (2005) research indicates that high value-to-book firms tend to takeover low value-to-book firms. It is likely that firms with a high m-to-b have little growth opportunities and try to buy growth with acquisitions. The companies with a high market-to-book ratio have a higher probability of paying with stock. Bidders paying with cash are less overvalued than bidders paying with stock. For the targets, they find that targets paid with cash are undervalued relative to targets paid with stocks. So M&A where targets are paid with cash should perform better.
Erel et al. (2012) did research to cross-border mergers and acquisitions and included the market valuations in their research. They found that the trend of higher valued bidders acquiring lower valued bidders also holds for cross-border mergers. On top of that,
companies from wealthier countries can benefit from their lower cost of capital when they acquire a firm in a less wealthy foreign country.
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3. Methodology and Data
This section presents the research methodology and dataset that is used to find the effect of horizontal cross-border mergers and acquisitions on firm performance. First the dataset and the collection of the data will be discussed. Secondly, the methodology for the event study followed by the methodology for the cross-section analysis. Finally the summary statistics of the methodology will be discussed.
3.1 Data
To be able to answer the research question, this study uses data from different databases. Data about the mergers and acquisitions is obtained from Thomson One. The data of the stock prices of the firms and the market index returns are obtained from CRSP. The financial data of the firms are from Compustat and WIFR.
The sample contains mergers and acquisitions from the period 1/1/2006 – 1/1/2016. The acquirer must be listed on the stock exchange and has the United States country code. The target can be private or public and the value of the deal has to be disclosed. The original dataset consist of 10114 mergers and acquisitions. Besides these criteria, the companies with a SIC-code between 6000-6999 are excluded; these companies are financial services companies. Including them would create a bias due to the high leverage of those firms. After that all mergers and acquisitions with less than 50% of the shares after the event and which are not actually completed are excluded. The firms with unavailable data during the sample period are excluded from the sample period as well.
The final sample contains 1640 mergers and acquisitions. Where 582 are defined as horizontal mergers and acquisitions by matching the four-digit SIC-codes of the acquirers and targets. 316 are defined as cross-border transactions and 120 mergers and acquisitions are horizontal cross-border mergers or acquisitions. In this dataset, 394 targets where listed, and 1246 where private companies.
This study uses an event study on the cumulative average abnormal returns to determine whether the cumulative average abnormal returns are different from zero. The methodology follows Moeller et al. (2004) and Brown and Warner (1985) for the event study and a cross-section will be used to research the factors that influence the announcement performance.
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3.2.1 Methodology event study
In this thesis, an event study will be used to study the stock price pattern of the acquirer when a merger or acquisition is announced. This will be investigated by determining if the cumulative average abnormal returns are different from zero. Significant positive cumulative average abnormal returns imply that mergers and acquisitions have a positive impact on firm performance. Significant negative cumulative average abnormal returns imply a negative impact of mergers and acquisitions on firm performance.
For the research question, we need to define the firm performance. Based on the literature, the performance is measured with cumulative abnormal returns (CARs). To come to these CARs the abnormal returns need to be determined first. The abnormal returns can be calculated by:
𝐴𝑅𝑖 = 𝑅𝑖 − 𝑁𝑅𝑖
Where the normal returns (NR) are calculated by the market model. The Market Model uses normal returns according to the Capital Assets Pricing Model. This means that the formula for the abnormal return is the following:
𝐴𝑅𝑖 = 𝑅𝑖− 𝐸(𝑅𝑖) = 𝑅𝑖 − (𝑅𝑓+ 𝛼𝑖 + 𝛽𝑖(𝑅𝑚− 𝑅𝑓))
Fuller et al. (2002) checked if using value weighted market index returns instead of using a beta estimation model matter, which does not make a big difference. However, the decision to assume a constant beta for all securities is an arbitrary decision and therefore the market model will be used.
An estimation window of the CAPM parameters of 270 days before the event till 30 days before the event is used. 30 days before the event is more than Brown and Warner (1985) did, but using 30 days instead of six days, avoids a bias due to rumours before the announcement. The estimation interval of 240 days is the same as they used.
It varies per study what the time-interval for the abnormal returns is. Moeller & et al. (2004) use -1,1 , Fuller et al. (2002) use -2,2 and Dong et al. (2006) use -1,1 again. In this thesis the interval -1,1 will be used. This three-day interval will be used to calculate the cumulative abnormal returns (CARs). The model for the CARs looks like the following:
𝐶𝐴𝑅𝑖 = ∑ 𝐴𝑅𝑖 1
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The CARs will be tested if they significantly differ from zero. The test that will be used is the following: 𝐶𝐴𝐴𝑅 = 1 𝑁∑ 𝐶𝐴𝑅𝑖 𝑁 𝑖=1 Standard deviation: 𝑆𝑡. 𝐷𝑣. = √ 1 𝑁 − 1∑(𝐶𝐴𝑅𝑖− 𝐶𝐴𝐴𝑅)2 𝑁 𝑖=1 The t-test becomes then:
𝐺 = √𝑁 𝐶𝐴𝐴𝑅
𝑆𝑡. 𝐷𝑒𝑣.≈ 𝑁(0,1) 𝑖𝑓 𝑁 → ∞
This test has an approximately standard normal distribution when N is sufficiently large. Besides this test, the means of the different kind of mergers will be tested against each other. The methodology follows Moeller et al. (2004) and gives the following test statistic: 𝑡 = 𝐶𝐴𝐴𝑅𝑖−𝐶𝐴𝐴𝑅𝑗 𝑆𝑡. 𝐷𝑒𝑣. (𝐶𝐴𝐴𝑅𝑖 − 𝐶𝐴𝐴𝑅𝑗)≈ 𝑁(0,1) 𝑖𝑓 𝑁 → ∞ Where: 𝑆𝑡. 𝐷𝑒𝑣. (𝐶𝐴𝐴𝑅𝑖 − 𝐶𝐴𝐴𝑅𝑗) = √𝑆𝑖 2 𝑁𝑖 + 𝑆𝑗2 𝑁𝑗
3.2.2 Methodology Cross-section analysis:
This study also contains a cross-section analysis to research if horizontal cross-border M&A have a significant influence on the CARs. A cross-section is very common to use to research the CARs. Based on the literature review, control variables are collected to research if horizontal cross-border mergers and acquisitions under- or outperform other mergers and acquisitions types. The regression model used is the following:
16 𝐶𝐴𝑅𝑠 = 𝛽0 + 𝛽1(𝐻𝑜𝑟𝑖𝑧𝑜𝑛𝑡𝑎𝑙) ∗ (𝐶𝑟𝑜𝑠𝑠 − 𝑏𝑜𝑟𝑑𝑒𝑟) + 𝛽2(𝐻𝑜𝑟𝑖𝑧𝑜𝑛𝑡𝑎𝑙) + 𝛽3(𝐶𝑟𝑜𝑠𝑠 − 𝑏𝑜𝑟𝑑𝑒𝑟) + 𝛽4(𝐷𝑒𝑏𝑡 − 𝑡𝑜 − 𝑡𝑜𝑡𝑎𝑙 𝑎𝑠𝑠𝑒𝑡𝑠) + 𝛽5(𝑏𝑜𝑜𝑘 − 𝑡𝑜 − 𝑚𝑎𝑟𝑘𝑒𝑡) + 𝛽6(𝑡𝑎𝑟𝑔𝑒𝑡 𝑙𝑖𝑠𝑡𝑒𝑑) + 𝛽7(𝑟𝑒𝑙𝑎𝑡𝑖𝑣𝑒 𝑑𝑒𝑎𝑙 𝑣𝑎𝑙𝑢𝑒) + 𝛽8(𝑐𝑟𝑖𝑠𝑖𝑠) + 𝛽9(𝑀𝑒𝑡ℎ𝑜𝑑 𝑜𝑓 𝑝𝑎𝑦𝑚𝑒𝑛𝑡) + 𝜀(𝑗)
Where CARi is the cumulative abnormal return of the acquirer i in the three-day interval of the announcement in the period 2006-2016. The independent variables are defined as follows: Horizontal*Cross-border is equal to 1 if it the acquirer and target where in the same market with the SIC-code and the target was a foreign company. Otherwise it is 0.
Horizontal equals 1 if the companies are in the same market determined with the SIC-code and 0 otherwise. Cross-border equals 1 if the target is a foreign company and 0 otherwise. Debt-to-total assets is the total debt of the acquirer divided by the total assets of the acquirer. The method of payment is determined by the percentage of the payment made in cash. Book-to-market is calculated by the book value of the acquirer divided by the market value of the acquirer. Relative deal value is determined by the value of the deal divided by the market capitalization of the acquirer. Target listed equals 1 if the target is a public company, otherwise it is 0. Crisis equals 1 if the merger or acquisition announcement was in the period 2007-2008, otherwise it equals 0.
3.2.3 Summary statistics
To find all of the data needed to investigate in horizontal cross-border mergers have a positive performance, different databases are combined. The descriptive statistics can be found in table 1. Due to missing data, the sample contains 1640 observations.
For the variable Relative Deal Size, winsorizing is used with the 1st and 99th percentiles.
Table 1
Obs Mean Std. Dev. Min. Max.
CARs 1640 0.0010439 0.0478978 -0.2976779 0.5873924 HM*cross-border 1640 0.0731707 0.260496 0 1 HM 1640 0.354878 0.4786223 0 1 Cross-border 1640 0.1926829 0.3945265 0 1 Debt-to-total assets 1640 0.4648926 0.2274897 0 2.924848 Book-to-market 1640 0.5602134 0.4680175 0 7.807 Target Public 1640 0.2402439 0.4273618 0 1
Relative deal size 1640 1.146185 5.095961 0.0000181 41.18078
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18
4. Results
Section 4 presents the results of this study. First the results of the event study are presented. After that, the results of the cross-section analysis will be explained.
4.1 Results Event study
The results of the event study can be found in table 2. Different event studies are done for the different M&A types. The Cumulative Average Abnormal Returns (CAARs) are tested on significance. What can be derived from the event study is that for the total sample of M&A, there is not a significant positive CAAR over the three-day interval. This is in line with the current academic debate about the performance of acquirers in M&A as explained in the introduction. The results also show that there are more negative CAARs than positive for the whole sample. This means that the negative CAARs are smaller than the positive CAARs. Different results are found for the M&A classes of interest. A highly significant positive CAAR is found for horizontal cross-border M&A. This is in line with hypotheses 1. The result is that horizontal cross-border M&A is on average value creating. There are also more positive CAARs than negative CAARs. The relative performance of horizontal cross-border M&A is tested in de cross-section analysis.
The results for horizontal and cross-border M&A separately are positive and slightly significant. This means that doing a horizontal or cross-border M&A is on average value creating for US-companies.
Table 2 Acquirer Total Sample Number of observations 1640 CAAR (-1,1) 0.10439% % positive 48.84% T-value 0.88 HM*cross-border Number of observations 120 CAAR (-1,1) 1.0757% % positive 63.33% T-value 2.61*** HM Number of observations 582 CAAR (-1,1) 0.36306%
19 % positive 51.55% T-value 1.91* Cross-border Number of observations 316 CAAR (-1,1) 0.43912% % positive 53.80% T-value 1.88*
*** Significant at the 1% level. ** Significant at the 5% level. * Significant at the 10% level.
Table 3 presents the results of the test of the means. The conclusion of the event study is that horizontal cross-border M&A have a significant higher mean than the other M&A in the sample and therefore perform better. The difference between the mean of horizontal cross-border M&A and horizontal M&A is slightly significant. The difference between the mean of horizontal cross-border M&A and cross-border M&A is not statistically significant.
Table 3 Difference Whole sample 0.97131%** (0.0042873708) HM 0.71264%* (0.0045385929) Cross-border 0.63658% (0.0047384675)
*** Significant at the 1% level. ** Significant at the 5% level. * Significant at the 10% level.
Tables 4 and 5 show the results for the tests on the use of cash bids with the different types of M&A. In table 4, the results show that horizontal M&A and cross-border M&A have the highest percentage of cash payments and that every type of M&A has an average use of cash of more than 50 percent. The preference for cash in horizontal cross-border M&A is in line with hypothesis 3.
Table 4 Number of
observations
Average cash used Std. Dev.
Whole 1640 0.6670439 0.4108747
HM*cross-border 120 0.6507683 0.419843
HM 582 0.6791259 0.4097225
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Table 5 shows the results of comparing the average use of cash between the different types of M&A. No significant difference is found between the different M&A classes. This indicates that there is a preference for takeovers in cash in general. This can be explained by the pecking order theory, where paying with equity is a signal to the market that the stock of the bidder is currently overvalued. To avoid a bad sign to the market, cash could preferably be used to pay the target shareholders.
Table 5 Difference with
whole sample HM*cross-border -0.0162756 (0.039646) HM 0.012082 (0.019783) Cross-border 0.0067656 (0.025212)
*** Significant at the 1% level. ** Significant at the 5% level. * Significant at the 10% level.
4.2 Results Cross-section analysis
In this section the results of the cross-section regression are presented. Table 6 presents the results of the regression of the acquirers CARs on the factors that may have an influence on the CARs. Because of the low explanatory power of the regression, the results must we treated carefully, although low explanatory powers are not uncommon in empirical studies on announcement performance. The event study analysis suggests that there are differences in the performance between horizontal cross-border M&A, horizontal M&A, cross-border M&A and other types of M&A. The cross-section checks witch factors influence the performance of these M&A.
Each independent variable has been suggested by academic theory as an explanatory variable for the announcement returns of the bidder. The variable of interest is the dummy variable HM*cross-border which is 1 if the deal is a horizontal cross-border M&A. The same is done for horizontal M&A and for cross-border M&A. The effect of horizontal cross-border M&A is positive, but insignificant in all regressions. The result is that there is no significant evidence for hypothesis 2. This means that the cross-section does not indicate that there is a significant outperformance of horizontal cross-border M&A compared with other types. This insignificant effect is found in every regression. The effect of horizontal M&A is also positive,
21
but insignificant in all regressions. The positive performance is in line with Fee and Thomas (2004), but this study does not found significant evidence for the claim of Fee and Thomas. The result for cross-border deals is also positive and insignificant. Eckbo and Thorburn (2000) also found insignificant positive returns, whereas Moeller and Schlingemann (2005) found negative and significant returns for a part of their sample. In this thesis, a more actual period is used. This might partly explain the differences.
Debt-to-value proxies the amount of leverage of the acquirer. Faccio and Masulis (2005) argue that firms with a current high debt position are restricted in issuing further debt. The debt capacity forces them to do an equity offer. Where the market can interpret this as a signal the shares of the bidder are overvalued and therefore have a negative announcement return. This is also found in this study. The coefficients are negative and highly significant for debt-to-value. Debt has a negative influence on the announcement returns of the acquirer and this is proving the signalling theory.
Book-to-market has a negative influence on the performance of bidders and is only significant at the 10% level in regression 5 and 6. This is still in line with Servaes (1991) and Lang et al. (1991). They found that a high market-to-book ratio has a positive influence. The book-to-market ratio is the reversal. In this study, a high book-to-market ratio has a negative influence. This means that a high market-to-book ratio has a positive influence and that firms with a high market-to-book ratio are better acquirers.
The relative deal size has not a significant influence on the CARs. Positive influence was predicted by Moeller et al. (2004). They find that relative bigger acquirers have weaker performance. The bigger the relative deal size, the smaller the acquirer is relative to the target. The smaller acquirers tend to have a better performance according to Moeller et al. They eliminated deal sizes that were less than 1% of the market value of the acquirer, where this study did not eliminate M&A’s from the data because of the deal size.
A crisis dummy is included for robustness. The crisis dummy has a negative influence on the CARs, but this influence is insignificant. The reason for this is that the market model is also influenced by the crisis and therefore the estimation of the normal returns.
The influence on public targets is slightly negative but not significant. Fuller et al. (2002) find that bidding on a public target leads to negative bidder returns. The reason for this is that private bidders receive a discounted bid, because the investment of the existing shareholders is illiquid. Bidders make a lower bid for that reason and get the fruits of that
22
effort in their CARs. This study finds no evidence for this hypothesis.
The cash coefficient is negative, but highly insignificant. This can be explained by the fact that private target bids are more positive regardless of their payment method as the results from Fuller et al. (2002) show. The sample in this study contains 1246 private targets, where cash should not have a significant influence. For the minority of public targets, this study does not find evidence that cash payments have a significant influence on their announcement return, whereas Fuller et al. indicate that cash should have a positive influence.
Table 6: dependent variable: Cumulative Abnormal Returns
Regressor 1 2 3 4 5 6 HM*cross-border 0.0079064 0.0085229 0.008212 0.0080696 0.0080707 0.0081308 (0.0056696) (0.0057702) (0.0057327) (0.0057478) (0.0057458) (0.0057518) HM 0.0023569 0.0020709 0.0023264 0.0023784 0.0023382 0.0023194 (0.0027542) (0.002834) (0.0028156) (0.0027968) (0.0027983) (0.0027997) Cross-border 0.0010711 0.0012879 0.0012005 0.0012713 0.0012553 0.0010916 (0.0032702) (0.0032788) (0.0032799) (0.0032808) (0.0032791) (0.0033125) Debt-to-value -0.0142777*** -0.014072*** -0.0137675*** -0.0148942*** (0.0050194) (0.0049697) (0.005017) (0.0053181) Book-to-market -0.0035073 -0.0037861 -0.0038779* -0.0048238* (0.0022143) (0.0023346) (0.0023345) (0.0026247) Target Public -0.0018085 -0.0017739 (0.0026159) (0.0026098)
Relative deal size 0.0000201 0.00000758 0.00000826
(0.0001767) (0.0001783) (0.000178) Crisis (2007-2008) -0.0030392 (0.0026836) Cash used -0.0022618 -0.0021151 -0.0020407 (0.0031324) (0.0031417) (0.0031226) Constant -0.0005774 0.0014418 0.0100587*** 0.0115425** 0.0117856** 0.0141045** (0.017372) (0.0020145) (0.0038785) (0.0049059) (0.0049135) (0.0058498)
Industry fixed effects no yes yes yes yes yes
Adjusted R2 0.0037 0.0110 0.0164 0.0167 0.0170 0.0179
Number of Observations 1640 1640 1640 1640 1640 1640
*** Significant at the 1% level. ** Significant at the 5% level. * Significant at the 10% level.
4.3 Robustness checks
The reported results contain robust standard errors to avoid problems with
heteroscedasticity and serial correlation (Eicker, 1967 as cited in Stock and Watson, 2015) which are common in economic research. On top of that, the model is checked for
23
robustness with the different regressions. The results show that regression 6 has the highest explanatory power. The other regressions show a difference in the significance of book-to-market and the constant. Whereas debt-to-value remains highly significant across all regressions.
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5. Conclusion
This thesis investigated the effects of horizontal cross-border M&A on the performance of bidders using a sample of 1640 M&A in the period 2006 to 2016. Both event studies and cross-sections are used. What is new in this thesis is the focus on horizontal cross-border M&A instead of horizontal or cross-border M&A as a whole. The abnormal return with the announcement of horizontal cross-border mergers is found to be 1.08% in the event study, which satisfies the first hypothesis that horizontal cross-border M&A have a significant positive performance. The results of the cross-section show a positive cumulative average abnormal return for horizontal cross-border M&A on the three-day interval (-1,1) between 0.79% and 0.85%, but this effect is not significant in any regression. Both the event study and the cross-section do not show results that satisfy the second hypothesis, that horizontal cross-border M&A outperform other types of M&A. However, the event study shows that horizontal cross-border M&A outperform other M&A when we do not distinguish between other types of M&A.
The results show that firms doing a horizontal cross-border merger does not waste money on this type of M&A. Managers get paid to benefit the wealth of the shareholders of the company that they manage. There is no evidence that horizontal cross-border M&A is the value maximizing way to benefit these shareholders.
The shortcoming in this study is the way the performance is measured. In M&A, targets may be small relative to the bidder. Even good acquisitions can have little impact on the stock price of the bidder when they are announced. Thereby, private companies do not have a market-to book ratio that can be measured. Eckbo and Thorburn (2000) showed that this also might have an effect. The last shortcoming in the way the results are measured is that only the surprise component can be measured. The results are based on the three-days around the announcement date. We cannot measure if rumours started earlier and
shareholders were expecting the M&A to happen.
More research is needed to investigate the effect of horizontal cross-border M&A further. The effect might be different in other countries than the US, for instance in Europe. Secondly, other models that measure the performance differently for instance with
operating income might give a different result. Finally, the effects may be different when the results are over a bigger timeframe, for instance five days instead of three days.
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Appendix A: Figures and Tables Event Study
Whole sample HM*Cross-border HM 99% .138306 .5873924 Kurtosis 23.49591 95% .0707093 .3892855 Skewness 1.369595 90% .0477761 .3118824 Variance .0022942 75% .0192198 .1996681 Largest Std. Dev. .0478978 50% -.0003232 Mean .0010439 25% -.0193617 -.2341968 Sum of Wgt. 1640 10% -.0447213 -.2515527 Obs 1640 5% -.0605567 -.2661815 1% -.1331885 -.2976779 Percentiles Smallest CARs
. sum CARs, detail
99% .1280864 .1779062 Kurtosis 5.144847 95% .0956949 .1280864 Skewness .3335636 90% .058434 .1225672 Variance .0020379 75% .0335849 .1122203 Largest Std. Dev. .0451433 50% .0067072 Mean .010757 25% -.0123078 -.0842107 Sum of Wgt. 120 10% -.0383293 -.0871455 Obs 120 5% -.0580278 -.0893008 1% -.0893008 -.1426106 Percentiles Smallest CARs(?)
. sum CARs, detail
99% .1402221 .1996681 Kurtosis 9.543644 95% .07994 .195677 Skewness -.2514603 90% .0566814 .1779062 Variance .0021046 75% .0223599 .1651231 Largest Std. Dev. .0458761 50% .0007963 Mean .0036306 25% -.0182425 -.1210576 Sum of Wgt. 582 10% -.0422221 -.1426106 Obs 582 5% -.058182 -.2661815 1% -.1127891 -.2976779 Percentiles Smallest CARs(?)
28 Cross-border 99% .1225672 .1779062 Kurtosis 5.878048 95% .0759022 .1719262 Skewness .355799 90% .0532027 .1280864 Variance .0017287 75% .0237726 .1225672 Largest Std. Dev. .0415771 50% .0017112 Mean .0043912 25% -.0149652 -.0893008 Sum of Wgt. 316 10% -.0418939 -.1345996 Obs 316 5% -.0574293 -.1426106 1% -.0893008 -.1549391 Percentiles Smallest CARs
29
Appendix B: Figures and Tables Cross-Section
99% .138306 .5873924 Kurtosis 23.49591 95% .0707093 .3892855 Skewness 1.369595 90% .0477761 .3118824 Variance .0022942 75% .0192198 .1996681 Largest Std. Dev. .0478978 50% -.0003232 Mean .0010439 25% -.0193617 -.2341968 Sum of Wgt. 1640 10% -.0447213 -.2515527 Obs 1640 5% -.0605567 -.2661815 1% -.1331885 -.2976779 Percentiles Smallest CARs
. sum CARs, detail
99% 41.18078 670.4057 Kurtosis 740.1859 95% 4.362327 256.7497 Skewness 24.30406 90% 1.347964 176.1329 Variance 412.3869 75% .1898238 168.683 Largest Std. Dev. 20.30731 50% .0238628 Mean 2.130232 25% .0029321 3.11e-06 Sum of Wgt. 1640 10% .0003816 2.02e-06 Obs 1640 5% .0001127 2.01e-06 1% .0000181 1.30e-06 Percentiles Smallest relative deal size
. summarize relativedealsize, detail
99% 41.18078 41.18078 Kurtosis 48.49346 95% 4.362327 41.18078 Skewness 6.579277 90% 1.347964 41.18078 Variance 25.96882 75% .1898238 41.18078 Largest Std. Dev. 5.095961 50% .0238628 Mean 1.146185 25% .0029321 .0000181 Sum of Wgt. 1640 10% .0003816 .0000181 Obs 1640 5% .0001127 .0000181 1% .0000181 .0000181 Percentiles Smallest relativedealsize, Winsorized fraction .01
. summarize winRDS, detail
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Correlation
Regression 1
regress CARs HMCrossborder HM Crossborder, robust
Regression 2
regress CARs HMCrossborder HM Crossborder K L M N O P Q R, robust
cashused -0.0175 -0.0111 0.0218 0.0080 0.0581 -0.1229 0.0862 0.0177 0.0489 1.0000 Crisesdummy -0.0120 -0.0128 -0.0076 -0.0392 -0.1449 -0.2858 0.0188 -0.0457 1.0000 winRDS -0.0042 -0.0005 -0.0120 0.0682 -0.0461 0.1835 -0.1035 1.0000 TargetPublic -0.0199 -0.0045 0.0035 -0.0178 0.1010 -0.0890 1.0000 Bookmarket -0.0311 0.0019 -0.0122 0.0295 -0.0789 1.0000 Debttovalue -0.0621 -0.0185 0.0337 -0.0394 1.0000 Crossborder 0.0342 0.5751 0.0254 1.0000 HM 0.0401 0.3788 1.0000 HMCrossbor~r 0.0570 1.0000 CARs 1.0000 CARs HMCros~r HM Crossb~r Debtto~e Bookma~t Target~c winRDS Crises~y cashused (obs=1640)
. correlate CARs HMCrossborder HM Crossborder Debttovalue Bookmarket TargetPublic winRDS Crisesdummy cashused
_cons -.0005774 .0017372 -0.33 0.740 -.0039848 .00283 Crossborder .0010711 .0032702 0.33 0.743 -.0053431 .0074853 HM .0023569 .0027542 0.86 0.392 -.0030453 .0077591 HMCrossborder .0079064 .0056696 1.39 0.163 -.0032141 .0190268 CARs Coef. Std. Err. t P>|t| [95% Conf. Interval] Robust Root MSE = .04785 R-squared = 0.0037 Prob > F = 0.0864 F( 3, 1636) = 2.20 Linear regression Number of obs = 1640
_cons .0014418 .0020145 0.72 0.474 -.0025095 .005393 R -.0235816 .0235765 -1.00 0.317 -.0698251 .0226619 Q -.0053706 .0028807 -1.86 0.062 -.0110209 .0002798 P -.0113333 .0075974 -1.49 0.136 -.0262349 .0035684 O -.0026241 .0047844 -0.55 0.583 -.0120083 .0067601 N -.0050198 .0037412 -1.34 0.180 -.0123579 .0023183 M -.0049017 .0063096 -0.78 0.437 -.0172775 .0074741 L .0060877 .0047628 1.28 0.201 -.0032541 .0154295 K .0058912 .0106634 0.55 0.581 -.0150242 .0268066 Crossborder .0012879 .0032788 0.39 0.695 -.0051433 .007719 HM .0020709 .002834 0.73 0.465 -.0034877 .0076294 HMCrossborder .0085229 .0057702 1.48 0.140 -.0027948 .0198407 CARs Coef. Std. Err. t P>|t| [95% Conf. Interval] Robust Root MSE = .04779 R-squared = 0.0110 Prob > F = 0.1017 F( 11, 1628) = 1.57 Linear regression Number of obs = 1640
31
Regression 3
regress CARs HMCrossborder HM Crossborder Debttovalue Bookmarket K L M N O P Q R, robust
Regression 4
regress CARs HMCrossborder HM Crossborder Debttovalue Bookmarket winRDS cashused K L M N O P Q R, robust
Regression 5
regress CARs HMCrossborder HM Crossborder Debttovalue Bookmarket TargetPublic winRDS cashused K L M N _cons .0100587 .0038785 2.59 0.010 .0024514 .017666 R -.024944 .0244285 -1.02 0.307 -.0728587 .0229707 Q -.0053852 .0028882 -1.86 0.062 -.0110501 .0002797 P -.0118145 .0075623 -1.56 0.118 -.0266473 .0030183 O -.0024242 .0049522 -0.49 0.625 -.0121375 .0072891 N -.0052989 .003752 -1.41 0.158 -.0126583 .0020604 M -.0065408 .0064319 -1.02 0.309 -.0191565 .0060749 L .0060354 .0047369 1.27 0.203 -.0032557 .0153265 K .0030045 .0123006 0.24 0.807 -.0211222 .0271313 Bookmarket -.0035073 .0022143 -1.58 0.113 -.0078504 .0008359 Debttovalue -.0142777 .0050194 -2.84 0.005 -.0241229 -.0044326 Crossborder .0012005 .0032799 0.37 0.714 -.0052328 .0076338 HM .0023264 .0028156 0.83 0.409 -.0031962 .007849 HMCrossborder .008212 .0057327 1.43 0.152 -.0030322 .0194562 CARs Coef. Std. Err. t P>|t| [95% Conf. Interval] Robust Root MSE = .04769 R-squared = 0.0164 Prob > F = 0.0097 F( 13, 1626) = 2.15 Linear regression Number of obs = 1640
_cons .0115425 .0049059 2.35 0.019 .00192 .0211651 R -.0245888 .0243939 -1.01 0.314 -.0724356 .0232579 Q -.0052876 .0029283 -1.81 0.071 -.0110313 .0004561 P -.0118807 .007568 -1.57 0.117 -.0267247 .0029632 O -.0022936 .0049562 -0.46 0.644 -.0120149 .0074277 N -.0052896 .0037612 -1.41 0.160 -.0126669 .0020878 M -.006976 .0065088 -1.07 0.284 -.0197424 .0057905 L .0061579 .0047555 1.29 0.196 -.0031696 .0154854 K .0037743 .0122484 0.31 0.758 -.02025 .0277985 cashused -.0022618 .0031324 -0.72 0.470 -.0084057 .0038821 winRDS .0000201 .0001767 0.11 0.909 -.0003264 .0003666 Bookmarket -.0037861 .0023346 -1.62 0.105 -.0083653 .0007931 Debttovalue -.014072 .0049697 -2.83 0.005 -.0238197 -.0043243 Crossborder .0012713 .0032808 0.39 0.698 -.0051637 .0077064 HM .0023784 .0027968 0.85 0.395 -.0031073 .007864 HMCrossborder .0080696 .0057478 1.40 0.161 -.0032042 .0193434 CARs Coef. Std. Err. t P>|t| [95% Conf. Interval] Robust Root MSE = .04771 R-squared = 0.0167 Prob > F = 0.0191 F( 15, 1624) = 1.90 Linear regression Number of obs = 1640
32
O P Q R, robust
Regression 6
regress CARs HMCrossborder HM Crossborder Debttovalue Bookmarket TargetPublic winRDS Crisesdummy cashused K L M N O P Q R, robust _cons .0117856 .0049135 2.40 0.017 .0021481 .0214231 R -.0243704 .0240587 -1.01 0.311 -.0715597 .0228189 Q -.005302 .0029269 -1.81 0.070 -.0110429 .0004388 P -.0115571 .0074594 -1.55 0.121 -.0261882 .003074 O -.0022639 .0049443 -0.46 0.647 -.0119618 .0074339 N -.0052324 .0037575 -1.39 0.164 -.0126024 .0021377 M -.0068921 .006452 -1.07 0.286 -.0195472 .005763 L .0063735 .0047305 1.35 0.178 -.002905 .0156521 K .0033244 .012243 0.27 0.786 -.0206893 .0273381 cashused -.0021151 .0031417 -0.67 0.501 -.0082773 .0040471 winRDS 7.58e-06 .0001783 0.04 0.966 -.0003422 .0003574 TargetPublic -.0018085 .0026159 -0.69 0.489 -.0069393 .0033223 Bookmarket -.0038779 .0023345 -1.66 0.097 -.0084568 .0007009 Debttovalue -.0137675 .005017 -2.74 0.006 -.023608 -.003927 Crossborder .0012553 .0032791 0.38 0.702 -.0051764 .0076869 HM .0023382 .0027983 0.84 0.404 -.0031504 .0078269 HMCrossborder .0080707 .0057458 1.40 0.160 -.0031993 .0193408 CARs Coef. Std. Err. t P>|t| [95% Conf. Interval] Robust Root MSE = .04772 R-squared = 0.0170 Prob > F = 0.0193 F( 16, 1623) = 1.87 Linear regression Number of obs = 1640
_cons .0141045 .0058498 2.41 0.016 .0026306 .0255784 R -.0237772 .0244102 -0.97 0.330 -.071656 .0241016 Q -.0052653 .002932 -1.80 0.073 -.0110161 .0004856 P -.0114274 .0074167 -1.54 0.124 -.0259746 .0031199 O -.0023532 .0049681 -0.47 0.636 -.0120978 .0073914 N -.0053239 .0037528 -1.42 0.156 -.0126848 .0020369 M -.0072985 .0064179 -1.14 0.256 -.0198867 .0052897 L .0062332 .004727 1.32 0.187 -.0030385 .0155049 K .0046619 .0124136 0.38 0.707 -.0196865 .0290104 cashused -.0020407 .0031226 -0.65 0.514 -.0081654 .004084 Crisesdummy -.0030392 .0026836 -1.13 0.258 -.0083029 .0022244 winRDS 8.26e-06 .000178 0.05 0.963 -.0003409 .0003574 TargetPublic -.0017739 .0026098 -0.68 0.497 -.0068927 .003345 Bookmarket -.0048238 .0026247 -1.84 0.066 -.0099719 .0003243 Debttovalue -.0148942 .0053181 -2.80 0.005 -.0253252 -.0044631 Crossborder .0010916 .0033125 0.33 0.742 -.0054056 .0075887 HM .0023194 .0027997 0.83 0.408 -.003172 .0078109 HMCrossborder .0081308 .0057518 1.41 0.158 -.0031509 .0194125 CARs Coef. Std. Err. t P>|t| [95% Conf. Interval] Robust Root MSE = .04772 R-squared = 0.0179 Prob > F = 0.0285 F( 17, 1622) = 1.76 Linear regression Number of obs = 1640
