Operating performance of Chinese overseas M&A

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University of Amsterdam, Amsterdam Business School

Master in International Finance

Operating Performance of Chinese Overseas

M&A

August 2015

Abstract

In 2014, China became the world's second largest economy, until then, it has been the largest emerging market in the world. In the meantime, Chinese overseas M&A markets not only increased in deal numbers but also in transaction amount every year. However, with limited M&A experiences, what is the operating performance of overseas M&A in Mainland China? In this thesis, 30 listed Chinese companies were selected as sample to do a factor analysis by using 10 financial indicators. The results were compared across the year before M&A, the year of M&A, the first year after M&A and the second year after M&A. After the empirical research, the general scores for 4 years performance and the trend of development are derived. This research aims to shed more light on the operating performance of Chinese overseas M&A.

Thesis Supervisor: Dr. Jens Martin

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1 Overview of Research

1.1 Introduction

International Background

Mergers and acquisitions (M&A) have always been deemed as an effective means of promoting reform, growth and enhancing core competitiveness among various options for the development and growth of enterprises. In recent years, with the accelerating process of economic globalization, the mutual penetration of world economy deepens, the international capital flows become increasingly active and the global economic integration has been preliminarily established. With the promotion of the current economic globalization and information technology, the international capital flows play an increasingly important role in the world economic growth and technological advances, especially the overseas M&A investment, as one of the main investment in the current transnational direct investment domain. It has been an important form of world economic liberalization and greatly changed the pattern of world economic investment.

Why China?

In 2014, China became the world's second largest economy, until then, it has been the largest emerging market in the world. In recent years, the volume of China's foreign direct investment and the amount of cross-border M&A is increases constantly, as well as the proportion of overseas M&A in China's foreign direct investment market. In August 2010, Geely Group paid $1.8 billion and bought a 100% stake in Volvo Car Company; in February 2011, Sinopec acquired 100% of Occidental Petroleum's operations in Argentina for $ 2.45 billion; in May 2012, Wanda Group agreed to acquire 100% of US based AMC group (the world's second largest cinema

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group) for $ 2.6 billion; in August 2013, China Petrochemical Corporation agreed to pay $3.1 billion for a 33% stake in Apache Corp.’s Egyptian oil and gas business. China M&A Background

China's overseas M&A can be traced back to the early 1980s, which were mainly driven by state-owned enterprises acquiring resources through foreign direct investment. For example, the overseas M&A of some large state-owned enterprises like China Petrochemical Corporation, Sinopec Group and CNOOC are driven by a strong motivation to obtain resources, and the purpose of the M&A is more out of the needs of national strategic development. The real development of China's overseas M&A began with China’s entry into WTO in 2001. Since then, the transaction price, volume and enterprises of M&A improved to different extents. After the financial crisis in 2008, European countries entered a period of economic downturn, which brought a great opportunity for Chinese enterprises. As a result, China's overseas M&A activities have reached a new climax, and the involved industries gradually expanded from primarily manufacturing and mineral resource based to financial, IT and other services. China's investment motivation of strategic assets seeking has been significantly enhanced. At present, one of the important objectives of Chinese enterprises' overseas investment is to acquire well-known brands and their local distribution network. By taking brand and intellectual property as the priority and goal, the M&A activities has gradually increased and private enterprises are also developing rapidly.

Now in China

In 2014, the number of transactions for Chinese enterprises' overseas M&A was at a record high of 272, with an increase of 36% compared to the number in 2013.

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Despite the lack of large deals, the transaction amount of enterprises' overseas M&A reached 56.9 billion dollars, with an increase of 5 billion dollars as compared to that of 2013 (Figure 1-1). Private enterprises have been the driving force in the overseas M&A, with annual volume of transactions twice that of state-owned enterprises in 2014, a year-on-year increase of 94%. Private enterprises' overseas M&A activities are focused on high-tech, telecommunications and retail industries. They are actively looking for the purchase opportunities of technology, intellectual property and brand, as well as more diversified investment opportunities.

Data Source: PWC's Review of Chinese Enterprises’ M&A in 2014 and Forward-looking Statement 2015

According to the locality, in 2014, there are 96 overseas M&A of enterprises from Mainland China in North America, 83 in Europe, 64 in Asia, 17 in Oceania, 7 in Africa and 5 in South America (Figure 1-2).

144 188 206 ₁₉₁ 200 272 333 420 435 669 519 569 0 100 200 300 400 500 600 700 800 2009 2010 2011 2012 2013 2014

Figure 1-1: The Overseas M&A of Enterprises from Mainland China

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Data Source: PWC's Review of Chinese Enterprises’ M&A in 2014 and Forward-looking Statement 2015

Aim of Research

In summary, China's overseas M&A has a history of only about 30 years. Although the volume and amount of overseas M&A has expanded gradually, what is the actual performance of China's overseas M&A? Has it attained enterprises' desired results and achieved revenue growth? What is the performance of enterprises' M&A under different features? This thesis will focus on these questions.

1.2 Thesis Structure

The rest of the thesis is organized as follows. Section 2 gives an introduction of relevant theories, reviews the literature and empirical results. Section 3 discusses the data and methodology. Section 4 describes the empirical research and gets the results. The comparative analysis of enterprises' overseas operating performance under different M&A features is in section 5. Final conclusion is in Section 6.

96

83 64

17 7 5

Figure 1-2: Number of Transactions over the World

North America Europe Asia Oceania Africa South America

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2 Literature Review

2.1 Synergy Effects Theory

Ansoff (1965), an American strategic manager, put forward the concept of synergy strategy to company's managers for the first time. In his opinion, synergy means enterprises can develop new businesses by identifying the matching relations between their own capabilities and opportunities. The synergy strategy can act as a bridge to connect diversified businesses in the company, which means enterprises can effectively allocate production factors, business units and environmental conditions and realize a synergy effect that is similar with increasing returns through a reasonable strategic arrangement of marketing, operation, investment and management, so as to make the company fully utilize existing strengths and expand new space for development. At that time, the Synergy Effects Theory only emphasized the function of "1+1>2" from the M&A of two enterprises. Sirower (1997) analyzed the cause of these synergy effects. He thought that this kind of effect can only emerge in a competitive environment that the acquiring company is able to defend against the threat of competitors in terms of markets and products, and on the other hand, the acquiring company can also conduct an active attack in the occupied market. On the basis of this theory, Weston (1990) made a further in-depth discussion on the Synergy Effects Theory, which is reflected in following three aspects: Operating Synergies - implies the efficiency changes in company's production and operation activities caused by the synergy and the benefits due to enhanced efficiency. Operating synergies are those that allow companies to increase their operating income and achieve higher growth, by economy of scale, complementation advantages, cost saving, expansion of market share and more

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comprehensive services; Management Synergies - mainly refer to the efficiency changes in company's management activities caused by the synergy and the benefits due to the enhanced efficiency. If the management efficiency is different between synergistic companies, the lower management efficiency company can be improve by the management efficiency of the higher one; Financial Synergies - refer to the synergies that can bring in profits in the financial metric to a combined business, such as enhanced financial capacity, reasonable tax avoidance and desired effect.

2.2 Transaction Costs Theory

In 1937, Coase, a famous economist, was the first to introduce the concept of transaction costs. He thought that enterprise is an efficiency oriented organization. Williamson (1981) divided the transaction costs into ex ante and ex post transaction costs according to the different parts in the transaction. In his opinion, ex ante transaction costs refer to costs that are incurred during the process of identifying all parties' rights, responsibilities or obligations in the transaction in advance due to future uncertainties. And these kinds of costs are related with the transparency of all parties' structure of property rights. Ex post transaction costs refer to the costs after the transaction. Not until 1993 did Hennart and Park introduce the Transaction Costs Theory into the overseas M&A for the first time. In their opinion, if some company has an advantage in the management or technology, then this company can save transaction costs while acquiring foreign enterprises in an irrelevant industry. Their studies showed that cross-border M&A can effectively decrease transaction costs and minimize investment risks during the expansion of multinational companies. Transaction Costs Theory has provided a good thinking perspective for the

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understanding of cross-border M&A activities, and has been a widely used analytical tool for strategy analysis in international market.

2.3 The Eclectic Theory of International Production

Dunning (1988) was the originator of the Eclectic Theory of International Production. According to him, this theory has following three features: (1) it has absorbed all the merits of various direct investment theories in the past 20 years; (2) it is related with the form of direct investment's equity; (3) it can explain three main modes of international enterprises' marketing activities, i.e. export, technology assignment and direct investment. The Eclectic Theory of International Production has perfectly interpreted the internal mechanism of the occurrence of cross-border M&A. According to this theory, if a company wants to carry out foreign direct investment, it must satisfy following three advantages: the advantage of ownership, location and internalization, i.e. the OLI frame of foreign direct investment. When the company has satisfied all of these three advantages, then foreign direct investment is the best plan.

2.4 Monopolistic Advantage Theory

Monopolistic advantage theory is one of the earliest independent theories of foreign direct investment put forward by Hymer (1960). It mainly illustrates the monopolistic advantages of contemporary multinational companies in the overseas investment apace. According to this theory, foreign direct investment survey should focus on the "monopolistic advantages". The motivation of multinational companies' direct investment stems from market defect. Their advantages are compensatory from the view of eliminating market barriers in the host country. The Monopolistic Advantage Theory suggets that the occurrence of cross-border M&A is due to enterprises'

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desire to utilize their own existing monopolistic advantages, such as technology, abundant funds, advanced management experience and sales networks, and to maintain such monopolistic advantages and obtain more profits through investment. The monopolistic advantages of enterprises are the motivation and premise of cross-border M&A. And the incompleteness of the domestic international market has made it possible to carry out cross-border M&A.

2.5 Empirical Results

The studies of Martynova and Renneboog (2008) showed that in a cross-border M&A, the differences in regulation of the acquiring company and the target company can materially impact the acquiring company's profits. Empirical studies showed that if the acquiring company comes from a country which is paid much attention to the protection of shareholder's equity, then this kind of regulation value can promote the target company to strengthen the protection of assets and shareholder's equity, i.e. the spillover effect out of the regulation. And if the target company comes from a country with inadequate protection for the shareholder's equity, then this weak regulatory system may influence the target company and result in negative effects on acquiring company's anticipated profit.

The research of Halilt and Kiymaz (2011) showed that the acquiring enterprise will achieve positive financial returns during the period of public notice of the cross-border M&A. And meanwhile, its wealth will increase due to the difference of foreign target company's industry classification and location. Country risk factors (including economic, political and financial risks) also play a very important role in the increase of acquiring company's wealth in the cross-border M&A. In addition, the M&A of the companies from a developed country can obtain a higher wealth effect,

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and the financial returns are significantly related with the GNP growth rate. Brealey (2009) inspected the beta coefficient of the global stock market after the cross-border M&A and found out that the beta coefficient is relatively increased in the acquiring company's country and decreased in the target company's country. Mason and Harrison (2006) thought that although many studies have showed that M&A will adversely affect the long-term economic development of surrounding areas, as for the process of "venture returns", the overall impact of the M&A to the regional economic development can be more positive.

Through the theoretical analysis and establishment of modeling, the research from Froot and Stein (1992) found out that if the home country’s currency is relatively strong, the buyer company in the acquisition process will have an advantage. This will increase the wealth effect of the buyer company. In 1993, Kang and Stulz conducted a research to analyze Japanese firms acquiring US firms and concluded that there is a positive correlation between the strength of the Japanese Yen with respect to the US dollar and the excess return after M&A. Furthermore, empirical results from Harris and Titman (1991) showed that in overseas M&A, returns and currencies are system-related. If the US dollar is stronger, firms buying US companies will gain more return through cross-border M&A.

Collins and Shackelford (1995) argued that the host country’s tax rate has an impact on the operating performance of the acquiring company. In 1986, the US congress passed the Tax Reform Act, after which the change of tax rate caused the US overseas M&A market to be more active than before. By analyzing acquiring companies from Japan and the UK, they pointed out that although the benefit of the tax rate has an impact on the performance of a buyer, this impact is not significant.

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In the meantime, others also did research on the relationship between tax rate and buyer companies’ asset value (Cebenoyan and Travlos 1992). Manzon and Travlos (1994) conducted research for US acquiring companies’ asset value and stated that if the host country’s tax rate holds a relative high level, the asset value will also be higher, and vice versa. Normally, the advantages and disadvantages related to tax rate will reflect on the excess rate of return on the publication date of M&A.

Fan (2009) selected 2,765 Chinese listed companies as sample from Shanghai and Shenzhen stock exchange and claimed that the more imperfect legal system in districts, the stronger the intervention is by the local government. In reality, emerging economies still in the imperfect market system and on the stage of development. Weak supervision and social trust make transactions difficult to implement.

Culture differences also play an important role in overseas M&A. Through comparing overseas M&A in Chinese and Japanese companies, Yinbinke (2009) found that international framework and domestic institutions significantly affect to the firm’s performance. Yan (2009) studied 129 overseas M&A cases of non-financial listed companies from 2000 to 2007. He reviewed the financial indicators of cash flow and paper profit through multiple regression analysis and found out that although the performance of companies did not significantly improved after the M&A, the host country's environment system significantly influenced the performance of acquiring companies. The stricter the regulation is, the larger cultural differences exist, and the harder is the scope for improvement of acquiring companies' performance.

Nowadays, the main payment method of cross-border M&A in China is asset (stock) and cash. Both buyers and target companies tend to find the payment method which

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has advantage to them. If the management team from target company consider the stock price are undervalued, they will choose cash as their payment method, and the operating performance will increase after M&A (Myers 1984). Some scholars argued that, if the buyer company uses cash as their payment method, this indicated it has better mobility or the expected rate of turn for target company is positive. Thus the performance of acquiring company will have positive return in short-term (Dong 2006). Zhu (2011) analyzed the short-term wealth effect of acquiring companies in the cross-border M&A of 70 countries from 1978 to 2008. Then it showed that if the acquiring companies have an abnormal rate of return before the M&A, there will also be an abnormal rate of return after the M&A, and vice versa. The long-term stock performance of acquiring companies was affected by investors' emotional fluctuation and the choice of cash payment in the cross-border M&A, but the business performance has no influence to it.

Companies which have experiences in cross-border M&A will reduce their search costs, increase negotiation advantage to enhance the efficiency, this will have positive affect on their M&A performance. Fatemi (1984) pointed that although the company’s first overseas M&A will benefit its shareholders, this only happens in some specific host countries. From the perspectives of theory and practice, Yang (2009) did a research in the relationship between overseas M&A and business performance of Chinese companies, and found that although the institutional distance affects the results of M&A, the overseas experiences can help Chinese companies improve their M&A success rate.

Fang (2002) used factor analysis to compare 3 years of the Chinese firms operating performance before and after overseas M&A in 2000. By selecting 80 listed

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companies as sample and choosing 9 financial indicators as variables, the results showed that the operating performance in the year after M&A is getting better than the year of M&A, the overall performance is getting better. Zhang (2013), Liu (2014), Sun (2012) also did empirical research for Chinese companies cross-border M&A by using factor analysis. Their research showed different results, Zhang and Liu found that the performance is getting worse after M&A, Sun pointed out that the performance after M&A don’t have much change.

3 Data and Methodology

3.1 Sample selection and data source

This thesis attempts to inspect the performance changes during the window periods in the year before the M&A (Y-1), the year of the M&A (Y), the year after the M&A (Y+1) and two years after the M&A (Y+2), and has selected overseas M&A events from Jan.1, 2008 to Dec. 31, 2012. Because the data of listed companies are transparent, with high reliability and easy to obtain, therefore, this thesis focuses on mainland china’s listed companies as the primary data for carrying out factor analysis. The selection of study samples and the collection of relevant data in this thesis are all from China Stock Market & Accounting Research (CSMAR) database, and events verified through reviewing relevant financial website and public notices about M&A disclosed by the Exchange. The selection satisfies the following principles:

(1) If there are several overseas M&A events at the window period in the same company, it should be subject to the latest M&A event; if the same event has several notices or announcements, it should be subject to the date of the first notice.

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(3) Due to the differences in the financial accounting, samples are excluding the overseas M&A events of financial enterprises such as bank and securities companies. (4) It is excluding unfinished or failed overseas M&A as well as the overseas M&A with related transactions.

After meeting the above conditions, 30 sample companies were selected.

3.2 Variable selection

Because the single indicator is difficult to evaluate the overall performance of enterprises' M&A, this thesis has established a comprehensive evaluation indicator system for a better measurement of the performance before and after overseas M&A. It has selected 10 indicators from profitability, operating capacity, growth capacity and solvency to reflect the operation status of the listed companies. See Table 3-1.

Table 3-1: The Indicators of Enterprises' M&A Operation Performance

Financial Indicators Variable Names Variable Description

Profitability Operating Margin Operating Profit/Operating Income Net Return on Equity Net Income/Average Stockholders' Equity Earnings Per Share Net Income/Total Common Shares

Operating Capacity Inventory Turnover Rate Operating Cost/Average Occupancy Amount of Inventory

Turnover of Total Assets Operating Income/Average Total Assets

Accounts Receivable Turnover Operating Income/Average Occupancy Amount of Accounts Receivable

Growth Capacity Growth Rate of Operating Income (Operating Income in the Current Period - Operating Income in the Previous Period)/Operating Income in the Previous Period Solvency Asset/Liability Ratio Total Liabilities/Total Assets

Current Ratio Current Assets/Current Liabilities

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3.3 Methodology

Select 10 indicators among 30 sample companies by the statistical software called R Programming Language to carry out the factor analysis in accordance with Y-1, Y, Y+1, and Y+2, and then get the general scores of M&A performance of different years. In order to avoid repetition due to the same analysis steps at window periods in all years, this thesis will take the year before M&A (Y-1) sample data as the example to introduce the empirical research.

4 Empirical Research

4.1 Feasibility test of the factor analysis

The correlation between selected indicators should be tested before the factor analysis, this is feasibility test and this process can be done by means of Kaiser-Meyer-Olkin (KMO) test and Bartlett test of sphericity. KMO value represents the appropriateness of the original data for factor analysis. When the KMO is more than 0.7, the effect of factor analysis is quite good; when the KMO is less than 0.5, then it is not suitable for the factor analysis. Bartlett’s test is used to test if variables are independent from each other. The sample data adopt two methods to carry out a correlation test for the variables, and the Table 4-1 is the test results in the year before M&A.

Table 4-1: The Result of KMO test and Bartlett’s test

KMO test 0.59

Bartlett’s test

Chi-square 292.3526

df 45

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Table 4-1 shows that the KMO value of the factor test is only 0.59, and this deficiency may be caused by the limited number of sample companies. But according to the inspection standard, it is usually considered suitable for a factor analysis when the KMO value is over 0.5. Besides, the P-value from Bartlett’s test is almost zero and less than 1% of the significance level. The rejection of the null hypothesis shows that these ten indicators are not independent and the sample data is suitable for the factor analysis.

4.2 Common factor extraction

One of the important characteristics of factor analysis is extract common factors from all the variables. It’s using several common factors to reflect the sample information. By using principle component analysis (PCA) method we can transfer all the variables to several main factors, this is extracting the common factor. PCA is a widely used method for factor extraction.

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As shown in the Figure 4-1, The Y axis represents the eigenvalues corresponding to the components in the X axis. The curve has a sharp decreasing trend for the first three eigenvalues. After that, it becomes flat. Thus, we should choose 3 factors. In addition, we can see that these three eigenvalues of actual data are larger than the simulated data, which also suggests that there are three factors we should pick.

Table 4-2: Factor Explained

Factor 1 Factor 2 Factor 3

SS loadings 3.39 1.93 1.47

Proportion Variance 0.34 0.19 0.15

Cumulative Variance 0.34 0.53 0.68

Proportion Explained 0.50 0.28 0.22

Cumulative Proportion 0.50 0.78 1.00

Furthermore, table 4-2 explains the first 3 factors cumulative variance is 68% and the first line is their eigenvalues. Because the other 7 factors cumulative variance rate which reflects to the model is so small, the R programming language omitted them automatically.

4.3 Calculate the factor score and general score

From the previous analysis we can get rotated component matrix for the year before M&A (Table 4-3).

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Table 4-3: Rotated Component Matrix

Component

1 2 3

Operating Margin 0.2150 -0.3417 0.6913

Net Return on Equity -0.1350 0.1876 0.9179

Earnings Per Share -0.1261 0.1493 0.7746

Receivable Turnover 0.0475 0.5120 0.0317

Inventory Turnover Rate 0.9047 -0.0471 -0.0063

Turnover of Total Assets -0.2341 0.9693 0.0267

Growth Rate of Operating Income -0.0703 0.1544 0.0120

Asset/Liability Ratio -0.6942 0.1535 0.0553

Current Ratio 0.9879 -0.1384 -0.0379

Quick Ratio 0.9869 -0.1480 -0.0295

Table 4-4: Component Score Coefficient Matrix

Component

1 2 3

Operating Margin X1 0.0056 -0.0089 0.1644

Net Return on Equity X2 0.0223 -0.0104 0.7292

Earnings Per Share X3 0.0053 -0.0027 0.1770

Receivable Turnover X4 0.0013 0.0041 0.0023

Inventory Turnover Rate X5 0.0142 0.0056 0.0111

Turnover of Total Assets X6 0.1484 1.0590 -0.1011

Growth Rate of Operating Income X7 0.0001 0.0008 0.0005

Asset/Liability Ratio X8 -0.0034 -0.0004 0.0049

Current Ratio X9 0.5154 0.1297 -0.0455

Quick Ratio X10 0.5167 0.1146 0.0986

From Table 4-4 we can find that factor 1 has great impact on current ratio and quick ratio, thus factor 1 indicates solvency; factor 2 influences turnover of total assets a lot, so factor 2 means operating capacity; the load value of operating margin, net

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return on equity and earning per share is relative high, from this we can get factors 3 implies profitability.

From the above analysis we can get each factor score through Table 4-4: f = 0.0056X + 0.0223X + 0.0053 + 0.0013 + 0.0142 + 0.1484 + 0.0001 − 0.0034 + 0.5154 + 0.5167 f = −0.0089X − 0.0104X − 0.0027 + 0.0041 + 0.0056 + 1.0590 + 0.0008 − 0.0004 + 0.1297 + 0.1146 f = 0.1644X + 0.7292X + 0.1770 + 0.0023 + 0.0111 − 0.1011 + 0.0005 + 0.0049 − 0.0455 + 0.0986

Put the results into 30 sample companies' variables from X1 to X10 can get each

sample company's three factor scores of the year before M&A (Table 4-5).

Table 4-5:The Factor Score before the Year of M&A (Y-1)

Sample Numbers Stock Code Factor 1 Factor 2 Factor 3

1 000060 -0.4110 -0.3486 -0.4456 2 000157 -0.2697 0.1906 1.6431 3 000338 -0.3241 0.1771 0.9483 4 000528 -0.3027 0.1974 0.5199 5 000536 -0.3962 -1.1017 0.1418 6 000768 -0.1501 -0.0713 -0.8939 7 000898 -0.5468 -0.4491 -0.7585 8 000932 -0.5581 -0.0635 -0.7574 9 000990 -0.4363 -0.4500 -1.1836 10 002024 0.0239 2.4177 0.3728 11 002050 -0.1682 0.6594 0.5576 12 002057 -0.2527 -0.4899 -0.8966 13 002081 -0.0659 0.9420 0.6757

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19 14 002031 0.1565 -0.4936 -0.7738 15 002311 0.1883 2.1954 -0.6375 16 002405 3.4350 -0.4675 0.3420 17 002583 0.2580 -0.8983 -0.4194 18 600028 -0.3653 1.0584 -0.0302 19 600031 -0.2274 0.0855 2.2001 20 600188 0.0456 -0.3283 1.0707 21 600196 -0.4200 -1.2874 2.6601 22 600352 -0.3613 -0.8917 0.1447 23 600389 -0.6858 -0.6407 -1.2197 24 600432 -0.4337 -1.1933 -0.3941 25 600500 -0.2176 1.0425 -0.4538 26 600536 -0.3565 -0.4096 -0.4743 27 600690 0.0868 2.3694 -0.4652 28 601519 3.7003 -0.4318 -0.4851 29 601727 -0.4879 -0.7836 -0.3644 30 601777 -0.4575 -0.5358 -0.6236

According to the following formula we can get sample companies' general scores before the year of M&A (see Table 4-6).

F

=

0.34

0.68 +

0.19

0.68 +

0.15

0.68

Table 4-6: The General Score before the Year of M&A (Y-1) Sample Numbers Stock Code Y-1 Sample Numbers Stock Code Y-1

1 000060 -0.4069 16 002405 1.7099 2 000157 0.3663 17 002583 -0.1863 3 000338 0.1420 18 600028 0.0424 4 000528 0.0375 19 600031 0.5199 5 000536 -0.4015 20 600188 0.2496 6 000768 -0.3406 21 600196 0.2493 7 000898 -0.5844 22 600352 -0.3369 8 000932 -0.5047 23 600389 -0.8250 9 000990 -0.6481 24 600432 -0.5902 10 002024 0.6494 25 600500 -0.0056 11 002050 0.2172 26 600536 -0.4011 12 002057 -0.4849 27 600690 0.4361

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13 002081 0.3637 28 601519 1.6194

14 002031 -0.2468 29 601727 -0.5186

15 002311 0.4003 30 601777 -0.5212

The same steps and method can be used to get 30 sample companies' general scores of the other three years, see Table 4-7.

Table 4-7: The General Score for the other three years (Y, Y+1, Y+2)

Sample Numbers Stock Code Y Y+1 Y+2

1 000060 -0.2201 -0.2738 0.1163 2 000157 0.7323 0.1965 0.0802 3 000338 0.5527 0.7646 0.3407 4 000528 0.1989 -0.0148 -0.4272 5 000536 -0.3861 -0.2067 -0.2598 6 000768 -0.4196 -0.4210 -0.5647 7 000898 -0.6469 -0.5975 -0.8170 8 000932 -0.6838 -1.3108 -0.6710 9 000990 -0.4505 -0.4683 -0.2082 10 002024 0.3930 0.4288 0.5482 11 002050 0.2901 0.2259 0.2753 12 002057 0.7195 -0.2442 -0.0477 13 002081 0.7448 0.4488 0.4896 14 002031 -0.0373 -0.0080 -0.2838 15 002311 0.3493 0.5990 0.5447 16 002405 0.8336 1.7872 0.7680 17 002583 -0.2163 -0.3236 -0.4190 18 600028 -0.2740 -0.0921 0.3748 19 600031 0.5341 -0.0122 -0.4201 20 600188 -0.2085 0.6282 -0.0290 21 600196 -0.4402 -0.3623 -0.1500 22 600352 -0.1709 -0.3496 -0.3637 23 600389 -0.5656 -0.0953 -0.2116 24 600432 -0.6839 -0.8192 -1.0003 25 600500 0.0144 -0.1163 0.1640 26 600536 -0.6218 -0.0789 -0.0967 27 600690 0.2063 0.8932 1.0307

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28 601519 2.3247 0.6234 2.0161

29 601727 -0.0985 -0.3011 -0.2536

30 601777 -0.3308 -0.4998 -0.5250

4.4 Results

According to the above comprehensive performance scores of 30 sample companies among the four years before and after the M&A, the overall performance scores is summarized in the Table 4-8.

Table 4-8: The Overall Performance Scores of Different Years before and after M&A

Year Y-1 Y Y+1 Y+2

Result 2.914335e-16 -2.38698e-15 -8.812395e-16 -3.469447e-16

The overall performance trend is analyzed in the Figure 4-2.

Y-1 Y Y+1 Y+2

Overall Performance

Trend 2.91E-16 -2.39E-15 -8.81E-16 -3.47E-16 -3.00E-15 -2.50E-15 -2.00E-15 -1.50E-15 -1.00E-15 -5.00E-16 0.00E+00 5.00E-16

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In general, the overall performance level presents an upright V-shape. Besides, the sample companies had the worst performance in the year of M&A, and then since the first year of M&A, their performance level gradually increased. It shows that Chinese enterprises' overseas M&A can really positively impact the corporate performance.

The worse performance in the year of M&A may be due to the tremendous challenges that overseas M&A are facing in the technology, management and cultural differences, as well as payment in cash as the main way of takeover of the most enterprises. All of these factors can give much pressure on enterprise' finance and management in the year of M&A. The gradually improved performance in the first year after the M&A shows that overseas M&A resulted in expected synergy effect for the companies, and has expanded the business scale and improved the performance level to some extent.

Table 4-9: Test on the difference of the sample mean score before and after M&A

T Value df P-Value

95% Confidence Interval of the Difference

Lower Upper

Y-Y-1 -5.5414e-16 57.836 1 -7.955243e-17 9.703609e-18

Y+1-Y-1 -2.5186e-16 57.975 1 -0.3105053 0.3105053

Y+1-Y 3.1464e-16 57.684 1 -0.3193387 0.3193387

Y+2-Y-1 -1.3586e-16 58.000 1 -0.3134424 0.3134424

Y+2-Y 4.224e-16 57.825 1 -0.3221818 0.3221818

Y+2-Y+1 1.1481e-16 57.979 1 -0.3102378 0.3102378

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5 The Comparison Analysis of Enterprises' Overseas Operating

Performance under Different M&A Features

5.1 The Comparison of Operating Performance Based on the

Different Industries

-0.04 -0.02 0 0.02 0.04 0.06

Y-1 Y Y+1 Y+2

Figure 5-1: Non Manufacturing

Non Manufacturing -0.15 -0.1 -0.05 0 0.05 0.1 0.15

Y-1 Y Y+1 Y+2

Figure 5-2: Manufacturing

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Table 5-1: Non Manufacturing General Scores Description

Number Minimum Maximum Mean Value Standard Deviation

Y-1 19 -0.8250 1.7099 0.0163 0.7125

Y 19 -0.6839 2.3247 0.0104 0.7508

Y+1 19 -1.3108 1.7872 -0.0324 0.6748

Y+2 19 -1.0003 2.0161 0.0530 0.6736

Table 5-2: Manufacturing General Scores Description

Y-1 11 -0.5212 0.5199 -0.0282 0.3916

Y 11 -0.4196 0.7323 0.1129 0.3998

Y+1 11 -0.4998 0.8932 0.0560 0.4470

Y+2 11 -0.5647 1.0307 -0.0916 0.4831

As shown in the tendency chart Figure 5-1 and Figure 5-2, the condition of the overseas M&A performance of China's non-manufacturing industry is slightly better than the manufacturing industry. The performance of manufacturing industry after the M&A is in a downward trend, while the non-manufacturing industry presents a better overall level in the second year than the former three years in spite of the unsatisfactory performance in the first year after the M&A. It shows that after the break-in period, the corporate value can be successfully enhanced. The possible reason of this condition is that most of China's manufacturing sector belongs to the restructuring after the M&A, including the integration of management, system, corporate culture and technology development reasons.

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5.2 The Comparison of Operating Performance Based on the

Different Holding Subjects

0 0.05 0.1 0.15 0.2 0.25

Y-1 Y Y+1 Y+2

Figure 5-3: Non State-Owned

Non State-Owned -0.16 -0.14 -0.12 -0.1 -0.08 -0.06 -0.04 -0.02 0

Y-1 Y Y+1 Y+2

Figure 5-4: State-Owned

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Table 5-3: Non State-Owned General Scores Description

Y-1 14 -0.6481 1.6194 0.1510 0.5980

Y 14 -0.4505 2.3247 0.2007 0.7241

Y+1 14 -0.4998 0.8932 0.0706 0.4626

Y+2 14 -0.5250 2.0161 0.1625 0.7149

Table 5-4: State-Owned General Scores Description

Y-1 16 -0.8250 1.7099 -0.1321 0.6020

Y 16 -0.6839 0.8336 -0.0857 0.5409

Y+1 16 -1.3108 1.7872 -0.0618 0.6994

Y+2 16 -1.0003 0.7680 -0.1422 0.4698

As seen from the Figure 5-3 and Figure 5-4, the changes of the overseas M&A performance of the non state-owned enterprises and state-owned enterprises present an opposite trend. The overall M&A effect of the non state-owned enterprises decreases in the first year after the M&A but bounces back in the next year; while the performance of state-owned enterprises starts improving in the year of the M&A, but decreases in the second year after the M&A. The reason might be that China's large state-owned enterprises have more advantages in the national fiscal support than non state-owned enterprises, such as additional subsidies in government taxes and funds, helping them to benefit more during early stages of the M&A. However, the non state-owned enterprises maybe have more advantages in management efficiency (any major decisions made by non-state-owned enterprises only need to be approved by the board of directors, while state-owned enterprises'

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major decisions must be submitted to the government authorities at the higher level for approval) and culture integration at the later stage, when the daily operations are gradually merged by the acquiring company, they will fully take advantage of the synergy effect and achieve performance improvement.

5.3 The Comparison of Operating Performance Based on the

Different Continents Distribution of the Target Company

0 0.05 0.1 0.15 0.2 0.25 0.3 0.35

Y-1 Y Y+1 Y+2

Figure 5-5: Asia Asia -0.2 -0.1 0 0.1 0.2 0.3

Y-1 Y Y+1 Y+2

Figure 5-6: Europe

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Table 5-5: Asia General Scores Description

Y-1 9 -0.8250 1.6194 0.1018 0.7310 Y 9 -0.6218 2.3247 0.1808 0.8913 Y+1 9 -0.3496 0.6234 0.1145 0.3637 Y+2 9 -0.3637 2.0161 0.2907 0.7295 -0.25 -0.2 -0.15 -0.1 -0.05 0

Y-1 Y Y+1 Y+2

Figure 5-7: North/South America

North/South America -0.3 -0.25 -0.2 -0.15 -0.1 -0.05 0

Y-1 Y Y+1 Y+2

Figure 5-8: Oceania

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Table 5-6: Europe General Scores Description

Y-1 8 -0.3406 1.7099 0.2502 0.6612

Y 8 -0.4196 0.8336 0.2723 0.4618

Y+1 8 -0.4210 1.7872 0.2461 0.7186

Y+2 8 -0.5647 0.7680 -0.1157 0.4689

Table 5-7: North/South America General Scores Description

Y-1 6 -0.6481 0.3637 -0.1721 0.4425

Y 6 -0.4505 0.7448 -0.1415 0.4529

Y+1 6 -0.4998 0.4488 -0.2125 0.3550

Y+2 6 -0.5250 0.4896 -0.0454 0.3934

Table 5-7: Oceania General Scores Description

Y-1 7 -0.5902 0.4361 -0.2693 0.4262

Y 7 -0.6839 0.7195 -0.2168 0.5287

Y+1 7 -1.3108 0.8932 -0.2463 0.7798

Y+2 7 -1.0003 1.0307 -0.2026 0.6964

As seen from the Figure 5-5 to Figure 5-8, the operating performances in the countries and regions excluding Europe all slightly decreased in the first year of the M&A but are further recovered in the second year. The operating performance in European countries stabilized in the first three years but falls in a straight line in the fourth year. The possible reason is European countries' strong competition advantages and more differences in culture than other continents, resulting in a bad

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integration between the target companies and acquiring companies causing adverse effect on the performance of the parent company after the M&A.

6 Conclusions

This thesis focused on 30 Chinese listed companies with overseas M&A events from 2008 to 2012 as the sample, selecting four years from the year before the M&A to the second year after the M&A as the time period, and adopting the method of factor analysis to analyze sample data and calculate the total scores in different years to analyze the changes of acquiring companies' performance during the window period. And in summary, this thesis has come to a conclusion as follows: The performances of enterprises with overseas M&A in the year of the M&A and two years after the M&A are both lower than the performances in the year before the M&A, but the overall M&A performance trend presents an upright V-shape. It shows that the business performance of these companies have been increasing every year through overseas M&A after the depression period of the M&A. It is also basically consistent with the mainstream development of the global overseas M&A. The gradually increased performance after the first year of the M&A means that most enterprises can well absorb and integrate the target company's advantages in finance, strategy, human resources and corporate culture, as they made relevant risk prevention before the M&A, so as to help enterprises produce sufficient synergy effect. Although China's history of the overseas M&A is not long, comparing with the initial stages of development, enterprises have learned a lot of lessons in recent years. China's enterprises are now more experienced and overseas M&A are focused on long-term strategic considerations.

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This thesis has also compared and analyzed the influence of the distribution of M&A enterprises, the holding subjects and the distribution of target companies to the business performance of overseas M&A.

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8 Appendix

Overview of Sample Companies

Number Stock Code Buyer Company Year Target Company Ownership Industry Country Continent

1 000060 Shenzhen Zhongjin Lingnan

Non-ferrous metal Co., Ltd. 2009 Perilya Ltd. state-owned non-manufacturing Australia Oceania

2 000157 Zoomlion Heavy Industry Science

and Technology Co., Ltd. 2008

CIFA Compagnia Italiana

Forme Acciaio state-owned manufacturing Italy Europe

3 000338 Weichai Power Co., Ltd. 2009 KION Group state-owned manufacturing Germany Europe

4 000528 LiuGong Machinery Co., Ltd. 2010 Huta Stalowa Wola S.A. state-owned manufacturing Poland Europe

5 000536 CPT Technology Co., Ltd. 2012 FocalTech Systems Co.,

Ltd. non state-owned manufacturing Taiwan Asia

6 000768 AVIC Aircraft Co., Ltd. 2009 FACC state-owned manufacturing Austria Europe

7 000898 Ansteel Co., Ltd. 2009 Gindalbie Metals Ltd. state-owned non-manufacturing Australia Oceania

8 000932 Valin Steel Group 2009 Fortescue Metals Group

Ltd. state-owned non-manufacturing Australia Oceania

9 000990 Chengzhi Shareholding Co., Ltd. 2011 Bioenergy non state-owned non-manufacturing America North America

10 002024 Suning Commerce Group Co.,

Ltd. 2009 Laox Co., Ltd. non state-owned non-manufacturing Japan Asia

11 002050 Sanhua Holding Group 2009 Helio Focus Ltd. non state-owned manufacturing Israel Asia

12 002057 Sinosteel Anhui Tianyuan

Technology Co.,Ltd. 2008 Midwest Co., Ltd. state-owned non-manufacturing Australia Oceania

13 002081 Goldmantis 2012 HBA Internationnal non state-owned non-manufacturing America North America

14 002301 Greatoo Inc. 2011 Reiner Jung OPS GmbH

& Co. non state-owned manufacturing Germany Europe

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Resources Ltd.

16 002405 NavInfo Co., Ltd. 2011 Mapscape state-owned non-manufacturing Netherlands Europe

17 002583 Hytera Communications Co., Ltd. 2012

Rohde & Schwarz Professional Mobile Radio GmbH

non state-owned non-manufacturing Germany Europe

18 600028 Sinopec Group 2008 Tanganyika Oil Co., Ltd. state-owned non-manufacturing Canada North America

19 600031 Sany Heavy Industry Co., Ltd. 2012 Putzmeister Holding

Gmbh non state-owned manufacturing Germany Europe

20 600188 Yanzhou Coal Mining Co., Ltd. 2009 Felix Resources Ltd. state-owned non-manufacturing Australia Oceania

21 600196 Fosun Pharmaceutical Co., Ltd. 2010 Chindex International non state-owned non-manufacturing British Virgin

Islands North America

22 600352 Zhejiang Longsheng Group Co.,

Ltd. 2010 DyStar Group non state-owned non-manufacturing Singapore Asia

23 600389

Jiangshan Agrochemical & Chemical Limited Liability Co., Ltd.

2012 Ladda Group Hoding

Co., Ltd. state-owned non-manufacturing Thailand Asia

24 600432 Jilin Jien Nickel Industry Co.,

Ltd. 2009 Metallica Minerals Ltd. state-owned non-manufacturing Australia Oceania

25 600500 Sinochem International

Corporation 2008 GMG Global Ltd. state-owned non-manufacturing Singapore Asia

26 600536 China National Software and

Service Co., Ltd. 2009 Japan Powerise Co., Ltd. state-owned non-manufacturing Japan Asia

27 600690 Haier Group 2009 Fisher & Paykel

Appliances Ltd. non state-owned manufacturing New Zealand Oceania

28 601519 Dazhihui 2012 T&C Financial Research non state-owned non-manufacturing Japan Asia

29 601727 Shanghai Electric Group Co., Ltd. 2010 Goss International state-owned manufacturing America North America

Operating performance of Chinese overseas M&A

University of Amsterdam, Amsterdam Business School

Master in International Finance