The role of pre-performance and size for the success of cross-border acquisitions

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The role of pre-performance and size for the success

of cross-border acquisitions

Tim van Vilsteren

Supervisor: Dr. V. Purice Co-assessor: Dr. M.M. Kramer

08 January 2016

Master’s thesis University of Groningen Faculty of Economics and Business International Financial Management

Boddemate 73 8014 JK Zwolle

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1 Abstract

Goal – The number of international acquisitions has grown significantly in the past decades. However in the case of many of these acquisitions, the results are negative for the acquiring firm. Previous literature showed that different firm types have varying acquisition results. Which type of firms choose these value-destroying targets and what are the driving factors of these choices? In its investigation of firms that acquire internationally, this research looks at differences between well performing firms and poorly performing firms, as well as between large firms and small firms.

Major findings – To test whether previous performance and size are of importance when making cross-border acquisitions, a panel data regression analysis was conducted. The results show that both pre-acquisition financial performance and pre-acquisition operating performance have a negative relationship with cross-border acquisition performance. Some evidence indicates that large firms perform better as compared to small firms. Size, using total assets as a proxy, has a positive relationship with the financial performance, when all the years are combined. This impact becomes insignificant for the post-acquisition years only.

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2

Over the past two decades cross-border mergers and acquisitions (M&As) have increased rapidly. Cross-border M&A volumes have grown from 100 billion US dollars in the 1990s to 1.3 trillion US dollars in 2006 (Hyun & Kim, 2010). Over the past three decades, researchers have been investigating financial performance effects associated with acquisitions. Most of these studies have focused on the gains and losses experienced by the acquiring and target firms. Different factors have been studied that influence these gains and losses: target type, target size and payment method. Many of these acquisition deals resulted in losses for the acquiring firms. A large portion of such losses has been due to overpayment (Baker et al., 2012).

Roll (1986) developed the Hubris hypothesis, which explains overpayment on the part of the acquiring firm. When managers are overconfident they will over-invest and pay too much for the target firm. According to Baker et al. (2012), superior existing operating performance can act as a proxy for firms with overconfident managers. They found that the market reacts negatively to M&As when managers are overconfident.

According to Moeller et al. (2004) the size of the acquiring firm is also significant for the acquisition results. The shareholder reaction returns are higher for small acquirers, when an acquisition is announced. Shareholders react less negatively when large firms announce an acquisition. These findings hold for any financing form, regardless of whether the acquired firm is public or private.

According to Martynova and Renneboog (2006) most previous research on M&As has been conducted in the United States (US) and the United Kingdom (UK). This may have been the case, because outflows from other European countries were relatively low during the first four M&A waves. Explanations for this phenomenon include the weaker investor protection and the less developed capital market system in these countries. However, the last M&A wave in the 1990s was a huge leap forward for European firms and more studies are now examining cross-border M&A deals made by European firms (Martynova & Rennoboog, 2006).

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5 On the firm level, many of these FDI deals resulted in losses for the acquiring firm. This study focuses on acquirers from developed European countries only, to attempt to explain why these FDI results were negative and what kind of firm types experienced these outcomes. When combining the above with the findings of Baker et al. (2012) and Moeller et al. (2004) new questions arise. The main research question of this thesis is: How does financial performance differ across firms during cross-border acquisitions? The following questions will also be answered in this study: (1.) Are there differences in post-acquisition financial performance development between large and small firms when making cross-border acquisitions? (2.) Are there differences in how the financial performance of well performing firms and poorly performing firms changes after cross-border acquisitions? (3.) Which factors influence these disparities in financial performance?

This study shows that financial performance and cross-border acquisitions are not straightforward. Different types of companies should not all take the same approach concerning cross-border acquisitions. The previous literature studied the investors’ reactions to acquisition announcements. This study does not focus on announcement returns, because these are indicative only of investor reactions and not representative of real financial performance. The results show that both pre-acquisition financial performance and pre-acquisition operating performance have a negative relationship with cross-border acquisition performance. Some evidence indicates that size has a positive relationship with the financial performance. This impact becomes insignificant for the post-acquisition years only.

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6 2. Theoretical framework

To explain how firm size and previous performance impact the firm performance, the literature must be discussed. First the basic theories of M&As are explained. Afterward, FDI theory is described. Thirdly, M&As and previous performance are presented. And finally, the relationship between size and M&As is elaborated upon.

2.1 Mergers and acquisitions

This section is mainly based on Straubs’ book (2007), because of his comprehensive analysis of M&A theory. ‘Merger’ and ‘acquisition’ are often used as synonyms, but the terms are not exactly the same. If a firm buys another organization and clearly establishes itself as the new owner of that organization, the purchase is called an acquisition. The target company ceases to exist, and the acquirer incorporates the organization into its own business (Straub, 2007). Acquisitions are only true acquisitions, when more than 50 percent of the targets’ firm’s equity has been purchased, leaving the buyer with complete control over the purchased firm. When a purchaser buys less than 50 percent of a firm’s equity, this is called a minority holding.

A pure merger is an understanding between two firms, often of similar size, to move forward as one new firm. The ownership of the firm is equally divided between the two existing firms. This is also called a merger of equals. Both firms’ stocks are dissolved and exchanged for the new organization’s stocks (Straub, 2007). In practice, a merger of equals is not very common. A firm usually acquires another firm and allows the target firm to communicate this acquisition as a merger of equals. A merger announcement may have fewer negative side effects as compared to an acquisition announcement. A hostile take-over is always called an acquisition. The practical difference between mergers and acquisitions lies in how the take-over is communicated to stakeholders.

According to Straub (2007) many studies include the following when they make reference of M&As: management buy-outs and management buy-ins, minority equity purchases, divestitures, spin-offs, strategic alliances and joint ventures. When this study makes reference to mergers and acquisitions, it refers only true M&As and not to the related activities mentioned above.

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7 combined into a single team, and redundant team members of the target and acquiring firm can then be fired. Furthermore, economies of scale may be achieved. An increase in size of the acquiring firm may lead to larger saving on orders. The improved supplier power may reduce purchase costs or it may lead to increased sales discounts. Furthermore, during M&As firms acquire new technologies. In this way, firms can maintain technological leadership in their industries or keep pace with their competitors. This competitive edge may stimulate revenues. Next to this, firms can increase their possibility to raise capital. At last, the target market can be expanded, which increases the firms’ sales possibilities. Moreover, new markets give rise to possible new business opportunities (Straub, 2007).

Although firms strive to achieve synergies, in many cases combining two firms leads to a decrease in value. Executive managers and vendors communicate synergy opportunities to other stakeholders, which may not actually exist. Sometimes, when executive managers and vendors stand to gain individually from a successful merger or acquisition, they will create a false image of increased value as they try to combine the two firms (Straub, 2007).

M&As are not only used to realize synergies. Acquisitions with an intend to diversify are long-term strategies, and are not intended to benefit directly from synergies. According to Jensen (1986) diversification programs will lead to lower total gains and it may take multiple years to reap benefits from these types of investments. There are two modes of diversification: internal growth and external growth. Internal growth is developed by internal business development and M&As are designed to grow a firm by acquiring the capabilities of another firm. When diversifying, firms have to change their internal systems, management operations and administrative layout. External growth is the possibility to expand into different industries. A firm buys the knowledge of another firm and is consequently able to start a new type of business without having to pay start-up costs (Straub, 2007).

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8 2.2 Foreign direct investments

M&As are not conducted solely on a domestic basis. Many firms expand internationally to penetrate new markets. International expansion is also called foreign direct investment. FDIs are investments made by companies into an entity or organization located in another country (Hymer, 1976). When utilizing this definition it is important to distinguish between FDI and foreign portfolio investment. If the acquiring firm does not have a significant influence and control over the company in which it invests, it is called a portfolio investment. If it does have significant control over the new target firm, the investment is a FDI (Hymer, 1976).

According to Hymer (1976) there are two conditions necessary for FDIs to take place. First, foreign multinational enterprises need to have advantages over local firms. These enterprises are at a disadvantage as compared to local firms, because local firms enjoy certain aspects (i.e., knowledge of the legal system, culture, language, currency and consumer preferences). These general handicaps faced by multinational enterprises can be overcome if the foreign company has certain firm-specific advantages. It may have a well-known brand, less expensive financing, better management skills, firm-specific knowledge, strong research and development skills, or economies of scale. Secondly, market imperfections make FDIs feasible (Hymer, 1976).

According to Buckley and Casson (1976) the following five types of market imperfections lead to the internationalization of firms. First, there can be time differences in coordinating resources. A company cannot have the best price for its resources if it does not take its time in selling these resources. The second imperfection is price discrimination, so that supply and demand do not regulate price. Third, unfair negotiating will take place if companies have a monopoly. Fourth, a buyer cannot be perfectly informed when purchasing a product; this is defined as knowledge imperfection. The last type of market imperfection refers to government interventions meant to stimulate the domestic market.

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9 country for such a reason. Although there can be various cost advantages when expanding abroad, there are also costs incurred with deciding to outsource production to other countries. These costs are transaction costs, coordination costs, search costs and contract costs (Luo et al., 2008).

Based on the above assumptions, Dunning (2008) developed a theoretical framework to explain the internalisation theory. This precursor of the current eclectic paradigm is also called the ‘OLI’ paradigm. It states that a multinational enterprise will only make a FDI if three OLI conditions are met. That is, (1) a firm should have ownership advantages (‘O’) compared to the other firms, (2) the new market should have location advantages (‘L’) that can be exploited through the firm’s ownership and (3) the firm should have the possibility to internalise (‘I’) these advantages to expand its operations into foreign markets.

Firms wishing to expand internationally have to face multiple uncertainties and a high degree of risk. Information is needed to reduce these uncertainties and risks. Integration of this information leads to intensive management input, because firms are forced to collect and process all of the new information. When firms lack international experience and they still want to expand internationally, they can partner with local firms to lower their costs (Buckley & Casson, 1990).

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10 Figure 1: Relative FDI outflows

Source: Eatwell et al. Challenges for Europe in the World. (2014).

2.3 Pre-acquisition performance

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11 Manager ability

The first view states that firms with a better operating efficiency will have better management skills in their possession. These acquiring firms have the ability to integrate the target firm into their current operations. If the target firm is well-integrated, the combined firms should be able to benefit from each other. Synergies may improve the operations and financial performance of the acquiring firm. According to this theory investors would then react positively towards the M&A announcements (Baker et al., 2012).

Overconfidence

The manager overconfidence theory states an opposite view. Managers from well performing firms become overconfident because of previous organizational success, and they attribute these successes to their own abilities. Overconfidence may stimulate managers to act irrationally when pursuing a new deal. These managers may overestimate their own capabilities and may thus harm their firms by acquiring value-destroying targets. According to Roll’s (1986) Hubris hypothesis, managers who are overconfident will over-invest and pay too much for target firms. Acquiring firms with overconfident managers may rush into new acquisitions to obtain market share. Furthermore, these managers may make themselves and others believe that they are acting within the interests of their boards and shareholders. According to Baker et al. (2012) this could lead to poor target selection and weak post-acquisition integration. The weak integration will lead to lower financial performance. This may influence shareholders and cause them to react negatively to new acquisitions of well performing companies (Baker et al., 2012). Aktas et al. (2011) found that previous M&A deals influence the managers’ overconfidence. Their research indicated that rational managers could become irrational when previous deals had gone well. Such managers begin to acquire more aggressively and to overbid in many M&A situations. Baker et al. (2012) used media exposure as a source of manager overconfidence. They found that the media-based overconfidence has a negative effect on acquisition announcement returns. Overconfident managers destroy shareholder value, due to their poor pre- and post-M&A handling.

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12 Overvaluation

The overvaluation theory may be another important reason why shareholders react negatively towards acquisition announcements. According to this theory high-valued acquirers are not backed up by the necessary financial fundamentals and this leads to worse announcement returns for higher-valued firms compared to lower-valued firms. With their overvalued equity, these high-valued firms may acquire targets overvalued to a lesser degree. Shareholders automatically correct for this overvalued equity (Dong et al., 2002).

Free cash flow theory

Jensen’s (1986) free cash flow theory states that acquirers that have exceptional pre-acquisition operating performance will generate large free cash flows. When the cash flow increases, it is likely that cash reserves will exceed the investment opportunities. Managers still desire to expand their empires by acquiring new firms. When cash is at hand, these managers act less rationally. The resultant M&As are more likely to destroy – rather than to create – value for their firms. Thus managers over-invest the free cash flows (Richardson, 2006).

According to Harford (1999) firms with large cash flows are more likely to acquire value-destroying targets. Managers of firms with exceptional operating performance want to increase their domains by acquiring firms, even if the firms do not add value. Baker et al. (2012) tested this empire-building theory and found that operating performance (cash flow / total assets) negatively influences M&A announcement returns. They found proof for the empire-building theory and free cash flow theory. Investors reacted adversely when firms with relatively large cash flows announced new acquisitions.

2.4 Pre-acquisition performance and international expansion

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13 (2008) found that the top one percent of all exporters owned more than 45 percent of the international trade.

According to Hitt et al. (1997) international diversification has a quadratic correlation with performance. Early international expansion strategies often turn out to be positive. Firms that start to expand internationally focus on related country and product markets (Hitt et al., 1997). These firms use a cooperative M-form to coordinate the related business into their own firms, and they have cooperating units controlled by a centralized management. The international diversification of firms leads to economies of scale, economies of scope and the transfer of knowledge and experience. Firms’ financial performance will increase, because of these advantages.

Mayer and Ottaviano (2008) found that firms with more international experience performed better as compared to firms with less international experience. Firms reduce their risk by investing in multiple countries. When diversified firms increase their international portfolio, the new countries will most likely be unrelated. These countries’ economic cycles are not perfectly correlated and this may decrease the variability of the firms’ financial performance.

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14 We have seen that well performing firms have a large chance of performing poorly when acquiring abroad. The manager overconfidence and empire-building theory both expect poor acquisition results. Furthermore, well performing firms, when expanding abroad, have a larger chance of having to invest in distant countries. This leads to an increase in coordination costs and to a decrease in acquisition performance. Therefore, the following first hypothesis is: When making cross-border acquisitions, well performing firms will have a larger decrease in financial performance as compared to poorly performing firms.

2.5 Firm size

Moeller et al. (2004) studied the relationship between firm size and acquisition announcement returns. They found a significant decrease in share price for large firms, after an acquisition announcement. Large firms’ acquisition announcements showed negative synergies. This indicates that when the companies together were combined they were worth less than beforehand. However, for small firms acquisition announcements showed positive synergies. Together the firms were worth more than separately. From the viewpoint of shareholders, large firms are therefore not expected to do as well as small firms when acquisitions take place.

Researchers have addressed multiple factors that might explain this phenomenon. Demsetz and Lehn (1985) stated that managers of small firms have a large stake in the ownership of their company compared managers of large firms. Managers of small firms may be more cautious concerning their acquisitions, because the risk is partially their own. Large firms may be more prone to over-investment as indicated by the Hubris hypothesis. Managers of large firms have fewer risks, because they have a lower level of ownership. Next to this, large firms have more resources, face fewer obstacles and already managed to grow substantially.

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15 Overpayment

When large firms overpay for acquisitions in a systematic way, negative announcement reactions are expected. The Hubris hypothesis states that overconfident managers overpay significantly for acquisitions. This hypothesis may especially be applicable for large firms. There are different reasons why large organizations may have more overconfident managers. These managers may be the people responsible for the organizations’ current success. In addition, the executive managers have made large steps to reach their current positions. Many studies have found that overconfident managers make more acquisitions. These acquisitions are accompanied with lower abnormal returns (Moeller et al., 2004; Baker et al., 2012).

According to Moeller et al. (2004) the premiums that large firms pay for acquisitions are greater than those paid by small firms. These greater premiums decrease the returns for large firms. These large firms should have more success in making acquisitions, because otherwise they will be forced to incur major losses. If a firm pays too much, the wealth of the acquiring firm’s shareholders will be redistributed to the target firm’s shareholders. In such a case, the larger firm’s overpayment only plays a role for the poorly diversified shareholder. However, if the acquisition reduces the value of both the acquiring and target firms, all shareholders lose. Synergies must be present to justify the overpayment. Moeller et al. (2004) found that large firms have negative synergies with their target firm. In contrast, small firms show positive synergies.

Size and implementation

Small businesses have many advantages over their larger competitors. Large firms are often hindered by their size, because they are slowed by past experiences and achievements. Large firms are not as adaptable as their smaller counterparts. This means that changes are harder to bring about in large firms and can only be implemented slowly. A large company must involve many individuals and processes when it wants to alter its current operations and strategy. Larger firms are not as lean as their smaller counterparts when it comes to layers of management. Communication efficiency may be lower for large firms because of these multiple layers (Edmiston, 2007).

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16 its own operations. Small firms may benefit more easily in the short-term when buying a new firm, because they can achieve expected synergies with less effort than large firms (Edmiston, 2007).

2.6 Firm size and international expansion

However, smaller firms may have more difficulties when it comes to cross-border M&As. Mutinelli and Piscitello (1998) found that international experience and firm size have a strong positive relationship. They measured international experience on a two-dimensional level. The first level represented the number of years since a firm had begun to expand internationally. The second level was the number of foreign subsidiaries a parent company owned since its last entry of a foreign country. Larger firms have more foreign subsidiaries and more years of experience expanding internationally. This experience influences the effectiveness of M&As when expanding abroad.

Small firms have less experience with managing international diversity as compared to large firms (Mutinelli and Piscitello, 1998). When investing abroad, many complex issues arise that must be tackled by managers with the adequate skills. Those managers should be able to process coordination and transaction costs, and smaller firms may not have the organizational structure to process the relevant information. Managerial and organizational development should take place to implement the relevant structures to handle international diversification. These coordination and transaction costs decrease the acquisition performance. However, according to Hitt et al. (1997), most firms – with the exception of single-business firms – receive positive returns when they first begin the process of international diversification. These firms are able to implement the relevant organizational structure and to develop the needed managerial skills to handle the coordination and transaction costs. Furthermore, firms with less international experience will expand into similar countries. This makes it easier for the smaller firms, to integrate the target into their current operations.

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17 seek targets that they can control completely. Large and experienced firms also tend to choose joint-ventures when the target countries are culturally and geographically more distant and thus bring a higher levels of risk (Mutinelli and Piscitello, 1998).

As has already been mentioned, Hitt et al. (1997) found that international experience has a quadratic relationship with performance. If relatively large firms increase their international diversification, results plateau and eventually become negative. The transaction costs and coordinating costs become too high when investing in diverse countries. The complexity will become such a burden for the diversifying firm that the benefits will no longer outweigh the costs.

According to Giovannetti et al. (2011) internationalized firms face a greater risk of failure, since the competition in the international markets is strong. However, most large firms are able to survive these negative impacts, because of their large reserves. Small firms have a lower chance of survival as compared to their larger counterparts. Both firm size and the age of a firm have a strong positive relationship with the survival of new entrants.

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18 3. Data and methodology

This section describes the method applied to answer the main research question. The data collection method, the variables, the model and the mode of analysis are all explained.

3.1 Data collection

The acquisitions and the accompanying financial data are obtained through Zephyr Bureau van Dijk and Orbis. Manufacturing, service and merchandise firms are the only industries included in the sample. All non-profit and governmental firms are excluded from the sample, because they do not have an incentive to maximize profits.

When testing for differences in the financial performance of acquiring companies, only complete take-overs are examined. The target firms in the sample all have had more than 50 percent of their shares acquired. The investing firms hold a majority of shares in these companies and are now able to influence the target completely; the targets’ financial success or failure is the acquiring firms’ responsibility. Only new acquisitions will be included, so a rise in shares from for example 20 percent to 50 percent, or from 40 percent to 80 percent will not be included in the sample.

This study focuses on acquisitions made by European public firms from developed countries. The following developed European countries are included in the sample: Austria (AT), Belgium (BE), Denmark (DK), Finland (FI), France (FR), Germany (DE), Italy (IT), Netherlands (NL), Norway (NO), Portugal (PT), Spain (ES), Sweden (SE), Switzerland (CH) and the United Kingdom (GB). Ireland is excluded from the sample, because many non-European firms are located here because of tax benefits. The target firms can be located in any country, except in the home-country of the acquirer. All acquisitions used in the sample are cross-border acquisitions. Furthermore, the Cayman Islands, Ireland and Bermuda are excluded from the target countries used, because of their unique tax benefits.

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19 Figure 2: Market classification framework

GNI = Gross National Income, mm = Million, Company size (Full market cap) = Market value companies of a country, Security size (Float market cap) = Total amount of securities in the market and Security liquidity (ATVR) = Annualized Traded Value Ratio (traded liquidity)

Source: MSCI.com. MSCI market classification framework. (2014)

The data should be available at least three years before and three years after completion of a takeover. In the sample, only acquisitions are used. Mergers of equals are not included, because of the varying accounting rules employed around the world to deal with consolidations. If mergers were added, different kinds of International Financial Reporting Standards (IFRS) and Generally Accepted Accounting Principles (GAAP) concerning consolidation accounting would have had to be controlled for as well. Furthermore, a merger of equals is not very common in practice. Rather, a firm usually acquires another firm and allows the target firm to communicate this acquisition as a merger of equals, for beneficial shareholder reactions.

Furthermore, only public acquirers are studied. Available data on public firms is significantly higher as compared to private firms, because public firms have a disclosure obligation. However, the target firms can be either private or public.

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20 Table 1: Sample size

Year All western

European M&As

All firms that acquired one firm

Firm with available financial data1 Sample without outliers2 2008 1054 59 26 26 2009 535 48 31 31 2010 757 49 32 31 2011 966 66 42 41 Total 3312 222 131 129 Notes:

1_{For a large number of firms the financial data was not available, these firms are removed from the sample.}2_Two firms were removed from the sample, because the displayed financial data was incorrect.

Table 1 presents the steps that led to the construction of the final sample. Two outliers were removed, because these firms had strongly diverging return on assets. For both these firms, the total assets in certain years was zero and thus incorrect.

3.1 Variables

This analysis’s dependent variable is financial performance. The return on assets (ROA) is used as a proxy for financial performance (Dickerson et al., 1997). ROA measures how profitable a company is related to its assets with the following formula: ROA = net income / total assets. The ROA measure can control for the target firm size when examining financial performance, because an increase in assets has to lead to a similar increase in net income to keep the ROA at a stable level. In the present study, taxes will be added to net income, because differences in tax regimes around the world could bias the results. Therefore, the formula used is:

ROA: (net income or net loss + taxes) / total assets

The first independent variable used in this research is past financial performance. To measure the impact of past financial performance on financial performance after an acquisition, ROA is used. The previous performance is compared with post-acquisition performance to show whether there are differences between well and poor performing firms. To measure previous performance, a high previous financial performance dummy (HPF) is created.

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21 empirical studies in accounting and finance that measured size used total employees as their proxy. Many empirical studies used relative size when measuring the impact of M&As. Relative size is measured by dividing the firm size proxy by the deal size. However, this study focuses on firm-specific aspects and not on deal-specific aspects. This is the main reason why relative firm size is not used.

At last a dummy variable is created, to show whether or not an acquisition changes financial performance for all the firms combined. Dickerson et al. (1997) created a dummy variable to measure the impact of the acquisition on financial performance. Furthermore, this dummy variable will be combined with the high previous financial performance dummy and size, to test whether there are differences between firm types when making cross-border acquisitions.

Control variables

Some firm-specific control variables are added. Baker et al. (2012), Moeller et al. (2004) and Dickerson et al. (1997) utilized the leverage ratio as a control variable. The leverage ratio formula that used is:

Leverage = total debt / total debt + equity

Both current and long-term debt are used to measure total debt. The higher the leverage ratio is, the greater the financial risk for that firm (Burton et al., 2015). According to Moeller et al. (2004), firms with higher leverage perform better when acquiring, due to the interest that works as a benchmark for minimum expected returns. In addition, small firms have a higher leverage ratio compared to large firms so it is important to control for it.

The next variable included in the model is revenue or sales. If the sales increase, the net income is also likely to increase. When the net income increases and the total assets stay the same, the ROA will increase. Baker et al. (2012) used sales growth as a firm-specific control variable in their model, because it is a relative and comparable factor. In this study sales will be made relative, by dividing them by fixed assets. The following formula is used:

Fixed asset turnover = sales / fixed assets

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22 loans. Liquidity means that firms are able to pay their short-term debts. Hitt et al. (1989) used the current ratio to control for liquidity influences on M&As. A current ratio below 1 means that a firm is illiquid and that it may not be able to pay its debts (Conyon et al., 2002). The following formula is used to calculate the current ratio:

Current ratio = current assets / current liabilities

The last control variable used in this research is firm age. When a firm is older, it may have more experience with cross-border acquisitions. Furthermore, a well-established firm may have a stable income. Fowler and Schmidt (1989) found that post-acquisition financial performance increased if firms were older. Younger organizations might suffer from the liability of newness. Older organizations have experienced more types of changes over their lifespan.

Robustness

To make the results more robust, both financial performance and size will be measured on the basis of another proxy. To test whether previous operating performance has the same influence on the post-acquisition performance as previous financial performance, ROA is replaced by cash flow to total assets. According to Malmendier and Tate (2005) CEO overconfidence has a significant response to cash flows. Baker et al. (2012) used operating performance as a proxy for CEO overinvestment. The operating performance is measured by the following formula:

Operating performance = positive or negative cash flows / total assets

The cash flow includes the earnings before depreciation, amortization and non-cash charges. Financial analysts see cash flow as a proper measure for a firm’s financial health (Baker et al., 2012). An increased cash flow means that a firm is able to pay of its debt and acquire new assets. A high previous operating performance dummy (HPO) is created to measure whether previous operating performance affects post-acquisition performance.

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23 Table 2: Variable description

Variable Description ROA = Return on assets

HPF = High previous financial performance dummy SIZE1 = Firm size, using ln(number of employees) LEV = Leverage (debt / total assets)

FAT = Fixed asset turnover (sales / fixed assets) CR = Current ratio (current assets / current liabilities) Ln(age) = Age of a firm in years

AD = Acquisition dummy (1 for all 3 years after the acquisition) HPO = High previous operating performance dummy

SIZE2₌ _{Firm size, using ln(total assets) as a proxy}

Table 3 shows the descriptive statistics for the variables included in the regression models. Some of the independent variables are skewed. To stay consistent in the regressions, both firm age and firm size are transformed by the natural logarithm (ln). The transformation of the variables to natural logarithm variables improves the dataset’s normal distribution. Furthermore, using the natural logarithm of the explaining variables reduces the impact of the residual outliers.

Table 3: Descriptive statistics

Mean Std. dev. Minimum Maximum

Financial performance (ROA)

.0248 .29641 -1.95 5.10

Size (employees) 7.1874 1.96595 1.95 12.47

Leverage .5234 .27609 .03 3.42

Fixed asset turnover 2.6681 2.69933 0 26.58

Current Ratio 2.4437 2.83726 .07 44.46

Ln(age) 3.2779 1.11325 0 5.44

Operating performance 0.0545 .21952 -1.47 3.52

Size (assets) 13.0829 2.04939 6.59 19.03

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24 Table 4: Correlations

HPF SIZE1 _LEV _FAT _CR

Ln(age) AD

AD* HPF

AD*

SIZE1 _{HPO SIZE}2

AD* HPO AD* SIZE2 HPF 1 SIZE1 _,213*** ₁ LEV -,120*** ,232*** 1 FAT ,184*** ,008 ,079** 1 CR -,036 -,334*** -,498*** -,129*** 1 Ln(age) ,204*** _,425*** _,043 _,105***_-,261*** ₁ AD ,357*** ,050 ,046 -,066** -,090*** ,101*** 1 AD*HPF ,485*** _,170*** _-,062* _,045 _-,065** _,174*** _,617*** ₁ AD*SIZE1 _,191*** _,258*** _,075** _{-,046 -,126}*** _,169*** _,724*** _,582*** ₁ HPO ,783*** ,317*** -,057 ,138*** -,141*** ,262*** ,381*** ,459*** ,235*** 1 SIZE2 _,090*** _,843*** _,074**_-,135***_-,109*** _,318*** _,055 _,103***_,228***_,142*** ₁ AD*HPO ,436*** ,218*** -,033 ,024 -,091*** ,201*** ,651*** ,856*** ,596*** ,456*** ,126*** 1 AD*SIZE2 _,153*** _,155*** _,047 _-,075**_-,093*** _,131*** _,742*** _,615***_,880***_,181***_,180*** _,525*** ₁ Notes:

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25 Table 4 shows a correlation matrix for the independent variables. There are multiple highly correlated (0.50 or higher) explanatory variables. Some of these highly correlated variables, like high previous financial performance (HPF) and high previous operating performance (HPO), and employees and total assets, are used as similar proxies, both for previous firm performance and firm size; they will not be used in the same model. Furthermore, AD*HPO and AD*SIZE2 have a correlation higher than 0.50, but are not used in the same model.

On the other hand, some highly correlated variables are used in the same regression models. The acquisition dummy (AD) is highly correlated with AD*HPF, AD*HPO, AD*SIZE1 and AD*SIZE2. In addition to that, AD*SIZE1 is highly correlated with high previous financial performance (HPF) high previous operating performance (HPO). Furthermore, high previous financial performance (HPF) is highly correlated with SIZE2 Appendix B, C and D show that, by adding the variables gradually, these highly correlated variables do not have a large impact on the standard errors and the significance of these terms. This indicates that the multicollinearity does not cause any issues. For the acquisition dummy (AD) the changes are slightly higher, however the significance does not change.

3.2 Analysis

Paired samples t-test

First, a paired samples t-test is used to show if financial performance increases or decreases after a cross-border acquisition for public firms. This t-test consists of a sample of two matching pairs of firms. The firms are tested prior to the acquisition and as well after the acquisition. The firms are effectively used as their own control, when comparing the same number of firms before and after the acquisition. The correct rejection of the null hypothesis becomes much more likely, because between-firm variation is eliminated. The hypotheses that belong to the paired samples t-test are:

H0: There is no significant difference in financial performance when the acquisition is made. H1: There is a significant difference in financial performance when the acquisition is made.

Pooled regression analysis

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26 regression involves estimating a single equation on all of the data together. The dataset for y is stacked into a single column containing all cross-sectional and time series observations. Similarly, all of the observations on each explanatory variable are also stacked into single columns in the x matrix. Then, this equation is estimated in the usual fashion using Ordinary Least Squares (Brooks, 2014). The first regression will show a pooled regression analysis of the main variables, without any interaction effect.

The dependent variable is financial performance, with ROA as a proxy. The two main factors studied in relationship with the independent variable are: (1) previous financial performance and (2) firm size. The previous financial performance from three years, two years and one year prior to the acquisition are compared with the studied seven year period. The firms are separated into two groups – one well performing group and one poor performing group. The average of the ROA is calculated for the years prior to the acquisition. The well performing firms have a ROA that is higher than the mean, and the poorly performing firms have a ROA that is lower than the mean. A high previous financial performance dummy is created, where all well performing firms take a value of 1 and all poor performing firms take a value of 0.

The second explanatory variable added in the panel data regression model, is firm size. Firm size is measured by using the natural logarithm of employees. This variable measures the influence of firm size on the financial performance for all the years combined. The following formula displays the basic model:

ROA = α0 + β1*HPF + β2*SIZE1 + β3*LEV + β4*FAT + β5*CR + β6*Ln(age) + ε_(1.)

Panel data analysis

A pooled regression is not sufficient. The sample consists of panel data. Panel data keeps the same firms, measuring over time some quantity associated with them. The panel consists of financial data from multiple firms for seven years. The data is balanced, because the same number of time series observations for each unit (firm) is included. The panel aspects of the data are used. A panel data model allows one to control for heterogeneity for each individual. Moreover, the dynamic aspects of company performance and persistent effects are included in a panel model (Dickerson et al., 1997).

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27 and development differences. These company-specific variables are not correctly estimated single-handedly within the model, without using fixed effects (Brooks, 2014).

Firm fixed effects may have to be added to the model. The use of firm fixed effects is logical, when there are omitted variables that vary across the firms, but do not change over time (Brooks, 2014). In this case there are many factors that differ among firms. For example, the firms are from different countries, have businesses in different industries and have different levels of technological development.

Furthermore, year fixed effects may have to be added to the regression model. There are certain variables that vary over time, but have a similar impact for all firms. The world economy may be in an upturn or downturn, and this will influence firms’ financial performance. During the time covered by this study, from 2005 to 2014, the financial crisis hit the economy, and it may have influenced the firm results. Through the included year fixed effects, persistent time effects are incorporated into the panel data regression. The second regression model shows the basic panel data analysis without any interaction effect and without the acquisition dummy variable:

ROAit = α0 + β1*HPF + β2*SIZE1_it + β3*LEV_it_{+ β4*FAT}_it + β5*CR_it + β6*Ln(age)_it + γi

+ δt + εit (2.)

The third regression formula shows whether an acquisition changes financial performance for all of the firms combined. An acquisition dummy variable is created: the pre-acquisition and acquisition years take a value of 0 and the post-acquisition years take a value of 1. The acquisition year is not included in the post-event analysis, because of the consolidation of the target within the acquirer’s financial statements. According to Baker et al. (2012) this mitigates the effect of inventory write-ups under the purchase method. The inventory is usually included in the cost of sales in the merger year. Furthermore, the effect of the acquisition is not directly measurable, because the target firm may not be fully incorporated into the acquirer’s operations. The following formula displays regressions 3:

ROAit = α0 +

β1*HPF +

β2*SIZE

1_it₊

β3*LEV

_it_{+ β4*FAT}_it+ β5*CR_it + β6*Ln(age)_it₊

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28 Past financial performance and size

Multiple factors influence an acquiring firm’s financial performance. However, untangling these various effects is not possible. Many effects can be included in the model, but this is not feasible for all of the factors that affect financial performance. Some studies use this as a justification for utilizing cumulative abnormal returns. Cumulative abnormal return differences are easier to study on different points in a day, week, month or year. The financial performance indicators are mostly yearly data. However, cumulative abnormal returns are shareholder reactions towards the firms’ actions and do not represent a true change in financial performance.

The ROA is calculated for every acquiring firm for three years before the take-over, the acquisition year and for three years after the take-over. Changes in financial performance after the acquisition are compared in the post-event period (Healy, Palepu, and Ruback, 1992). The acquisition dummy is combined with the previous high performance dummy, to see if this interaction changes the relationship with financial performance. This shows whether well performing firms perform differently compared to poor performing firms, when making cross-border acquisitions.

To regress the relationship between an acquirer’s firm size and post-acquisition financial performance, two variables must be combined. The acquisition dummy variable is united with the size variable (number of employees is used as a proxy for size) to illustrate how size affects financial performance when making an acquisition. The regression will show if the impact of size on financial performance changes after a cross-border acquisition. The relationship between pre-acquisition performance and post-acquisition performance is assumed to be negative. Furthermore, the relationship between firm size and post-acquisition performance is also assumed to be negative. The fourth regression model is as follows:

ROAit = α0 + β1*HPF +

β2*SIZE

1_it + β3*LEV_it_{+ β4*FAT}_it + β5*CR_it₊

β6*Ln(age)

_it

₊

β7*AD

it + β8*HPF*AD + β9*SIZE1it*AD + γi + δt + εit (4.) 3.4 Robustness tests

Previous operating performance

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29 total assets is calculated for the years prior to the acquisition. The well performing firms have an operating performance above the mean, and the poorly performing firms have an operating performance below the mean. As was previously mentioned, a high previous operating performance dummy (HPO) is created, where all well performing firms take a value of 1 and all poor performing firms take a value of 0. The fifth model shows the relationship between high operating performance and the financial performance of all of the firms combined. All other variables remain the same. The fifth regression is as follows:

ROAit = α0 + β1*HPO + β2*SIZE1_it + β3*LEV_it_{+ β4*FAT}_it+ β5*CR_it + β6*Ln(age)_it + γi

+ δt + εit (5.)

The acquisition dummy is added in the sixth regression model, to show whether the effect of an acquisition on financial performance changes for all of the firms combined, as has been done in the third regression model. The formula for regression 6 is:

ROAit = α0 + β1*HPO + β2*SIZE1_it₊

β3*LEV

_it_{+ β4*FAT}_it+ β5*CR_it₊

β6*Ln(age)

_it₊

β7*AD + γi + δt + ε

it (6.)

The seventh regression model shows the relationship between operating performance and post-acquisition performance. The previous operating performance of three years, two years and one year before the acquisition is compared with the financial performance of one year, two years and three years after the acquisition. The acquisition dummy is combined with the previous high performance dummy, to see if this interaction changes the relationship with post-acquisition financial performance. A negative relationship between operating performance and post-acquisition performance is expected. The following formula displays regression 7:

ROAit = α0 + β1*HPO +

β2*SIZE

1_it + β3*LEV_it₊

β4*FAT

_it + β5*CR_it + β6*Ln(age)_it₊

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30 Firm size

The eighth regression model includes the same panel data regression as the second regression. However, the proxy for size, ln(employees), is replaced by ln(assets). For regression 8 the influence of total assets on financial performance is measured by regressing the relationship between total assets and financial performance:

ROAit = α0 + β1*HPF + β2*SIZE2_it+ β3*LEV_it + β4*FAT_it + β5*CR_it + β6*Ln(age)_it + γ_i

+ δt + εit (8.)

The ninth regression model is the basic model with ln(employees) replaced by ln(assets). The interaction dummy is also added. This model indicates if financial performance changes for the total number of firms after an acquisition. The following formula displays regression 9:

ROAit = α0 + β1*HPF +

β2* SIZE

2_it+ β3*LEV_it₊

β4*FAT

_it₊

β5*CR

_it₊

β6*Ln(age)

_it₊

β7*AD + γ

i + δt + εit (9.)

The last regression displays the interaction effect of ln(assets) with the previously mentioned acquisition dummy variable. This interaction effect shows whether the relationship between size and performance changes, when an cross-border acquisition is made. For regression 10 a negative relationship between size and post-acquisition performance is expected:

ROAit =

α0 +

β1*HPF +

β2*SIZE

2it +

β3*LEV

it+

β4*FAT

it +

β5*CR

it +

β6*Ln(age)

it +

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31 4. Results

This section displays the results of both the paired samples t-test and the regression models. Furthermore, this chapter shows which factors are important when making cross-border acquisitions.

4.1 Paired samples t-test Table 5: Paired samples t-test

Before After Number of observations 387 387 Average ROA .0393 .0078 Standard deviation .34632 .27163 Test T-statistic 1.995 Probability .047

The paired samples t-test shows the change in financial performance after a cross-border acquisition for public firms. This t-test consists of a sample of two matching pairs of firms. The firms are tested prior to the acquisition and again after the acquisition. H0: There is no difference in financial performance when the acquisition is made. H1: There is a significant difference in financial performance when the acquisition is made. As can be seen above, the sample mean before the acquisition is larger as compared to the sample mean after the acquisition. The test is significant with a 0.05 significance level, and the null hypothesis is therefore rejected. This means that there is a significant difference in financial performance, when European public firms make cross-border acquisitions. The ROA average decreases, meaning the average financial performance of the developed European firms dropped after making cross-border acquisitions.

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32 4.2 Basic model

Table 6: Regressions

Variables Regression 1 Regression 2 Regression 3 Regression 4

HPF 0.1273*** 0.0040 0.0058 0.0227 (0.0188) (0.0269) (0.0312) (0.0329) SIZE1it 0.0303*** 0.0048 0.0049 0.0087 (0.0053) (0.0228) (0.0229) (0.0232) LEVit _-0.3205*** -0.2327*** -0.2324*** _-0.2320*** (0.0379) (0.0452) (0.0453) (0.0454) FATit 0.0095*** 0.0026 0.0026 0.0020 (0.0034) (0.0041) (0.0041) (0.0041) CRit -0.0035 0.0095** 0.0095** 0.0093** (0.0038) (0.0038) (0.0038) (0.0038) Ln(age)it 0.0161* 0.0548 0.0547 0.0555 (0.0090) (0.0438) (0.0438) (0.0460) AD - - -0.0040 -0.0490 (0.0341) (0.0639) HPF*AD - - - -0.0561* (0.0306) SIZE1it*AD - - - 0.0093 (0.0078) Constant -0.1561*** -0.0994 -0.0993 -0.1322 (0.0488) (0.2016) (0.2017) (0.2028) Observations 903 903 903 903 R-squared 0.2091 0.6071 0.6071 0.6092 Adj. R-squared 0.2038 0.5331 0.5325 0.5337 F-statistic 39.4752*** 8.2026*** 8.1351*** 8.0723*** Notes:

Standard errors in parentheses. * = Significant at 0.10 level. ** = Significant at 0.05 level. *** = Significant at 0.01 level.

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33 Table 7: Likelihood ratio test for fixed effects

Effects Test Statistic

Degrees of freedom P-value Cross-section F 5.8837 (128,759) 0.0000 Cross-section Chi-square 622.4040 128 0.0000 Period F 1.5938 (9,759) 0.1128 Period Chi-square 16.9067 9 0.0502 Cross-Section/Period F 5.6135 (137,759) 0.0000 Cross-Section/Period Chi-square 631.8666 137 0.0000

Testing for individual effects is necessary before carrying out the panel data regression (Brooks, 2014). The likelihood ratio test chooses the appropriate estimation techniques for the panel data model. The characteristics of the possible specification are performed to test whether cross-section fixed effects or period fixed effects have to be added. Cross-cross-section fixed effects and period fixed effects are tested both separately as well as together (Bell & Jones, 2015). The following hypotheses are tested: H0: Fixed effects are not correct; H1: Fixed effects are preferred (Brooks, 2014). The outcome for the cross-section fixed effects shows a p-value lower than 0.05. This is indicative that a cross-section fixed effects model is appropriate for this panel data. The outcome for the period fixed effects shows a p-value larger than 0.05, which indicates that a period fixed effects model is not sufficient for this panel data. However, the combination of the cross-section fixed effects and the period fixed effects is significant and is a sufficient estimator for the panel data. The Hausman test will demonstrate whether the data estimation improves by using period random effects.

Table 8: Hausman test for random effects

Effects Chi-Sq. Statistic

Degrees of

freedom P-value

Cross-section random effects 35.9199 6 0.0000

Period random effects 19.1868 6 0.0093

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34 model is incorrect. As can be seen in table 8, the p-value is less than 1% and the test is significant. The period and cross-section fixed effects specifications are preferred instead.

Panel data

The second model, displayed in Table 6, is significantly improved compared to the pooled regression. Previous financial performance does not significantly impact financial performance (ROA) for all the seven years together. Furthermore, the size (using employees as a proxy) does not significantly influence financial performance. This indicates that size does not matter for firms’ financial performance, when acquisitions are not taken into account. In addition, age is also insignificant.

The third regression shows the basic model; however, the acquisition dummy is added. The dummy shows that cross-border acquisitions do not significantly decrease the financial performance for all the firms together. However, a significant decrease is found in the paired samples t-test. This decrease in post-acquisition financial performance may only be relevant for a certain type of firm.

Both leverage and the current ratio are significant for all panel data regressions. Leverage has a negative impact on financial performance. The higher a firm’s debts, the higher its interest payments. These interest payments will reduce the firm’s net income and lower its financial performance. Furthermore, the current ratio measures a company’s ability to pay off its short-term debts in the next twelve months. The current ratio has a positive relationship with financial performance, because an inability to pay short-term debts means increasing costs to acquire short-term funds. Both of these results are in line with previous literature.

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35 4.3 Robustness tests

Table 9: Operating performance as a proxy for previous performance

Variables Regression 5 Regression 6 Regression 7

HPO 0.0019 0.0032 0.0183 (0.0252) (0.0302) (0.0315) SIZE1it 0.0046 0.0048 0.0064 (0.0228) (0.0229) (0.0231) LEVit -0.2328*** -0.2326*** -0.2335*** (0.0452) (0.0453) (0.0454) FATit 0.0026 0.0026 0.0022 (0.0041) (0.0041) (0.0041) CRit _0.0095** 0.0095** 0.0099** (0.0038) (0.0038) (0.0038) Ln(age)it 0.0549 0.0549 0.0514 (0.0438) (0.0438) (0.0462) AD - -0.0028 -0.0519 (0.0352) (0.0640) HPO*AD - - -0.0666** (0.0331) SIZE1it*AD - - 0.0115 (0.0080) Constant -0.0980 -0.0981 -0.1031 (0.2018) (0.2019) (0.2028) Observations 903 903 903 R-squared 0.6071 0.6071 0.6096 Adj. R-squared 0.5331 0.5325 0.5342 F-statistic 8.2023*** 8.1347*** 8.0839*** Notes:

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36 Table 10: Total assets as a proxy for size

Variables Regression 8 Regression 9 Regression 10

HPF 0.0104 0.0148 0.0361 (0.0268) (0.0311) (0.0328) SIZE2_it _0.0532_*** 0.0535*** 0.0583*** (0.0204) (0.0204) (0.0206) LEVit -0.2034*** -0.2025*** -0.1988*** (0.0464) (0.0466) (0.0465) FATit 0.0038 0.0039 0.0034 (0.0041) (0.0041) (0.0041) CRit 0.0083** 0.0083** 0.0081** (0.0038) (0.0038) (0.0038) Ln(age)it 0.0292 0.0288 0.0237 (0.0440) (0.0441) (0.0456) ADit _- _-0.0095 _-0.1069 (0.0340) (0.0979) HPF*AD - - -0.0624** (0.0297) SIZE2_it_*AD _- _- 0.0093 (0.0070) Constant -0.6954** -0.6976** -0.7499*** (0.2796) (0.2799) (0.2802) Observations 903 903 903 R-squared 0.6106 0.6107 0.6135 Adj. R-squared 0.5372 0.5367 0.5389 F-statistic 8.3232*** 8.2558*** 8.2198*** Notes:

In the seventh regression, the proxy for firm size is changed to the natural logarithm of total assets instead of the natural logarithm of total employees. The relationship with total assets and financial performance (ROA) is significant. This means that when the size of a firm is large, financial performance will be high. When the size of a firm is small, the firm has a relatively poor financial performance.

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37 5. Discussion

Some evidence was found that when making cross-border acquisitions, the financial performance of the studied European public firms decreases. Many studies have found similar negative results regarding the foreign expansion of firms by means of acquisitions. According to Woodcock et al. (1994), different entry modes result in different performance levels. Their results showed that new ventures outperform joint ventures and that both of these entry modes outperform acquisitions. However, when adding multiple other factors explaining financial performance no negative impact of acquisitions on financial performance was found. According to Giovannetti et al. (2011), only a small number of firms are able to cope with international competitiveness when expanding abroad through acquisitions. The question remains, which firm aspects are important when expanding abroad? Giovanetti et al. (2011) stated that, for example, firms paying higher wages, employing more highly skilled workforce and investing more in research and development are better able to cope with the competitive international environment. The next section discusses which additional firm-specific aspects influence cross-border investments. These firm-specifics are drivers for the decrease in financial performance.

5.1 Previous acquirer performance

Baker et al. (2012) showed that shareholders react negatively towards M&A announcements made by firms that have performed well in the past. Such an observation leads to several questions about why shareholders react negatively to the announcements of well performing firms. Do these acquisitions truly have a negative effect on the future financial performance of the acquiring firms?

The results show that previous acquisition performance has a negative relationship with post-acquisition performance, when organizations acquire firms outside their home-country. This means that well performing firms, have poorer results after cross-border acquisitions as compared to firms that performed poorly prior to the acquisitions. This result is in line with the findings of Baker et al. (2014).

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38 integration of target firms may result in neutral or negative synergies. Furthermore, the poor target selection and the weak implementation of these targets can lead to a decrease in financial performance. This may explain the negative relationship between pre- and post-acquisition performance when acquiring firms.

Free cash flow theory

Furthermore, a negative relationship was found between high previous operating performance and post-acquisition financial performance. Baker et al. (2012) found a similar significant negative relationship between operating performance and M&A announcement returns. Jensen’s (1986) free cash flow theory states that acquirers that have exceptional pre-acquisition performance, will generate large free cash flows. The theory predicts that managers with intent on expansion and with poor investment possibilities use their extra cash flow to make poor investments. These managers over-invest and want to expand their current businesses, even if doing so may destroy value. The negative influence of pre-acquisition operating performance on post-acquisition financial performance supports the free cash flow and empire-building theory.

International diversification

Firms that are involved in international activities are different from domestically oriented firms (Giovannetti et al., (2011). Productivity and performance levels increase when firms become more internationally diversified, indicating that the degree of international expansion and performance have a positive relationship (Mayer and Ottaviano, 2008).

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39 According to Hitt et al. (1997) international diversification has a quadratic correlation with performance. Early international expansion strategies often bring positive results. Poorly performing firms have less international experience. When these poorly performing firms start to expand internationally, they focus on related country- and product-markets. This causes fewer issues for these poorly performing firms and may prevent a decrease in financial performance. Further research is needed to clarify if the difference in post-acquisition performance between well and poorly performing firms can be explained by international diversification.

5.2 The role of size for acquisitions

No direct relationship between size and post-acquisition performance was found when making cross-border acquisitions. This does not support the results found by Moeller et al. (2004). However, some evidence was found that larger firms perform better as compared to smaller firms. In many studies size positively impacts financial performance due to their stable structure and lower variability of their income (Fowler and Schmidt, 1989). The influence of size is insignificant when firms make an international acquisition. Smaller firms may have less experience in cross-border acquisitions, but when small firms choose for cross-border acquisitions they have advantages compared to larger firms. Larger firms may be hindered by their size. They have more hierarchical layers and thus are not as easily adaptable as their smaller counterparts. The changes that have to made because of the acquisitions are implemented slowly (Edmiston, 2007). This may be the reason for the insignificant relationship between size and financial performance in the first three years. The larger firms need more time to integrate the new targets into their operations. However, in the long-term the international experience of the larger firms may lead to synergies.

Cross-border acquisitions and size

According to Mutinelli and Piscitello (1998) international experience and size have a strong positive relationship. They found that larger firms have more foreign subsidiaries and more years of international experience as compared to smaller firms. This experience influences how effectively new acquisitions can be integrated.

The role of pre-performance and size for the success of cross-border acquisitions