Government-affiliation, bilateral political relations and cross-border mergers: Evidence from China

Pacific-Basin Finance Journal 51 (2018) 220–250

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Government-affiliation, bilateral political relations and crossborder mergers: Evidence from China Wenjia Zhanga, Nathan Mauckb, a b

T

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School of International Economics, China Foreign Affairs University, No. 24, Zhanlan Road, Xicheng District, Beijing 100037, China Henry W. Bloch School of Management, University of Missouri - Kansas City, 5100 Cherry Street, Kansas City, Missouri 64110, USA

A R T IC LE I N F O

ABS TRA CT

Keywords: Government ownership Political connections Bilateral political relations Cross-border mergers

This paper examines the relation between government-affiliated ownership, bilateral political relations, and cross-border mergers. Using a sample of 219 cross-border mergers conducted by Chinese listed companies from 2000 to 2013 we document three main results. First, we find that government-affiliated bidder (i.e., those with political connections and/or government ownership) abnormal returns do not differ from non-affiliated bidders in the announcement period after controlling for deal characteristics. However, longer-term post-merger bidder abnormal returns are lower for government-affiliated bidders, consistent with the general inefficiency associated with government-affiliation. Second, our results indicate that improving bilateral political relations between China and target nations are positively associated with both short and long-term bidder performance, indicating a role for economic nationalism in firm outcomes. Third, we find that the interaction between government-affiliation and change in political relations is generally unrelated to bidder performance. However, in target nations with relatively low political risk, the interaction between government-affiliation and change in political relations is positively related to longer-term bidder performance. Thus, government-affiliation can be value enhancing in certain cases.

1. Introduction Cross-border mergers and acquisitions require that target nations permit foreign buyers. While cross-border mergers are often successfully completed, Dinc and Erel (2013) document “economic nationalism” in which target nation governments prefer firms to remain domestically owned. Bertrand et al. (2016) note that host governments may interfere in cross-border merger negotiations. After deal completion, host government intervention may negatively influence bidder outcomes. Further, the literature notes that host nation government preferences differ based on the characteristics of the acquiring nation – including the form of government and the political affinity between nations. Nationalistic responses to cross-border M&A are linked to economic consequences including deterring foreign bidders from bidding in the future and higher required bidder premiums. In the context of economic nationalism from the host country, we examine two dimensions through which cross-border M&A may be influenced from the bidder perspective. The first relates to the ownership structure of the firm itself. In particular, while any firm may find that cross-border merger plans hinge on the respective governments, such a reliance may be different for governmentaffiliated bidder firms. In particular, it may be that economic nationalism is stronger in the case of government-affiliated bidder firms. If foreign private ownership is deemed sub-optimal, then foreign government ownership may be deemed even less preferable. Black

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Corresponding author. E-mail address: [email protected] (N. Mauck).

https://doi.org/10.1016/j.pacfin.2018.07.003 Received 18 January 2018; Received in revised form 22 June 2018; Accepted 9 July 2018 0927-538X/ © 2018 Elsevier B.V. All rights reserved.

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et al. (2015) note that Chinese government involvement in SOE firms “means that the decision to merge could be considered more of a political rather than economic act.” Such perceived motives may in part be the source of host nation aversion. However, government-affiliated bidders may have more direct channels through which to influence deal approval. Regardless, the existence of government connections may influence cross-border M&A. Indeed cross-border mergers are subject to scrutiny by target firm governments for national security reasons. In the U.S., for instance, the Committee on Foreign Investment in the United States (CIFUS) has the ability to block foreign acquisitions and Congress has at times expressed concern about foreign acquisitions. One well-known example is CNOOC's (China National Offshore Oil Corporation) failed effort in 2005 to acquire U.S. oil and natural gas company Unocal. CNOOC, which was 70% state-owned at the time of negotiations, referenced “unprecedented political opposition” as the reason for withdrawing from the deal (Wan and Wong, 2009). While the literature on the impact of government-affiliation on firm performance is well developed (Dewenter and Malatesta, 2001 and Megginson and Netter, 2001), the relation between government-affiliation and cross-border merger performance has received relatively little attention. While Karolyi and Liao (2017) find that government-affiliated cross-border deals are associated with higher announcement returns for target firms, they do not examine bidder performance. Bertrand et al. (2016) exclude governmentaffiliated bidder transactions from their sample based on the likelihood that such deals are based on non-profit motives. Thus, we examine the bidder performance and determinants of cross-border deals by government-affiliated Chinese firms. It may be that bidder performance is negatively influenced by economic nationalism outcomes. First, consistent with Karolyi and Liao (2017) finding of higher target abnormal returns at announcement for government-affiliated cross-border deals, it may be that such deals require an overpayment to be completed in the face of economic nationalism. While Bertrand et al. (2016) do not examine bidder type, such a prediction is generally consistent with their suggestion that stronger economic nationalism is associated with higher deal premiums. Second, an increased probability of a forced divestiture raises the risk of the deal and adds uncertainty to postdeal operations. Finally, government-affiliated bidders may be forced to reduce their stake prematurely, thus preventing them from fully realizing future expected cash flows from the deal. Ang et al. (2017) note that the U.S. government forced a divestiture of a port asset that had been purchased by a Dubai-based sovereign wealth fund and that the British government forced a stake reduction upon a Kuwaiti government-affiliated investor. In addition to target nation related pressures, bidder nation pressures may negatively influence bidder outcomes. For instance, it is likely that the appointment of CEOs with political connections is motivated by governmental political objectives rather than increasing shareholders wealth (Fan et al., 2007). One such example is China's SAFE which announced it will help facilitate cross-border deals that “support national strategic planning” (Global Times, 2017). Thus, governmentaffiliated bidder's target selection may be influenced by strategic, rather than profit-maximization motives. However, government-affiliated firms may have an advantage in cross-border deals relative to domestic counterparts. Political connections with government officials may help firms secure licenses, financing, and technology that are otherwise unavailable for non-government-affiliated firms. One such example is the link between government-affiliation and preferential lending arrangements to facilitate a deal (Khwaja and Mian, 2005; Li et al., 2008). One anecdotal case is that of BOE (Jingdongfang), a Chinese listed hightech firm with a stake from the government (State-owned Asset Supervision and Administration Commission of People's Government of Beijing Municipal). BOE received a government subsidized loan of 90 million dollars when acquiring the TFT-LCD business form Korean firm HYDIS in 2003 with a total cost of 380 million dollars.1 Another example is Bright Food, a Chinese SOE, which has conducted a number of cross-border mergers since 2008, including a recent acquisition of the largest Israeli food company Tnuva facilitated with low interest rate club loans (Wang, 2015). In China, a post-2005 increase in outbound merger activity raised concerns from the Chinese government about the loans financing such deals (Yan, 2017). It is possible that such scrutiny will differ for government-affiliated firms. Thus, the relation between government-affiliation and bidder performance in cross-border mergers is unclear a priori. The second consideration in our analysis is bilateral political relations. Given the observation of economic nationalism, it may be that target nations are sensitive to “the climate of friendliness or hostility” (Morrow et al., 1998) with bidder nations when considering cross-border M&A. In fact, Bertrand et al. (2016) use bilateral political relations, as measured by similarity in U.N. voting records, as a proxy for the level of economic nationalism likely to be faced by the bidder. Our study uses the same measure of political relations, but in a different context. On one hand, economic nationalism may be stronger in the case of nations with relatively weak political relations as argued by Bertrand et al. (2016). In short, ownership by a firm in such a nation may be particularly undesirable. On the other hand, one potential mechanism through which nations may conduct political negotiations is cross-border M&A. Based on this, it is possible that a target nation may approve or block a cross-border deal based purely on political considerations. Thus a deal could be approved to help foster improved political relations. Consistent with this possibility, Knill et al. (2012) find that SWF cross-border investments are more likely when nations have relatively weak political relations, but that political relations improve following the deal. It is also possible that a deal may be more likely to be approved if political relations improve during the time preceding the deal. Anecdotal evidence in the case of Chinese outbound mergers suggests political relations may be an important factor. One such example is Northwest Nonferrous International Investment Company Ltd. efforts to acquire U.S. mining company First gold. The deal was blocked by CFIUS. Two reasons suggested for the blocked deal include the proximity of purchased assets to U.S. military bases as well as “Chinese domination of rare earth elements” (Lipton, 2009). In both cases, it may be that a bidder from a relatively “friendlier” nation politically would have faced a better chance of success. Of course, political relations evolve over time and may be

1

Based on its public accouchements, for example http://quotes.money.163.com/f10/ggmx_000725_9103.html. 221

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subject to the political climate during negotiations. One such example is Chinese owned HNA Group's 2017 effort to buy U.S. based SkyBridge Capital. This deal was subject to regulatory review (Yan, 2017). Such oversight is complicated by the fact that SkyBridge's founder and part owner, Anthony Scaramucci, briefly served as President Donald Trump's communication director in 2017. Recently, HNA Group and SkyBridge Capital have agreed to drop the Chinese conglomerate's plan to acquire the investment firm due to the fact that US government approval would take too long.2 Beyond the likelihood of deal completion, bidder performance associated with cross-border deals may be subject to the same issues as noted for government-affiliated buyers. In particular, Bertrand et al. (2016) note higher purchase prices in cross-border deals featuring nations with relatively weak political relations. A higher purchase price lowers the probability of a positive net present value transaction, all else equal. Bertrand et al. (2016) further note greater deal risk in the case of stronger economic nationalism (i.e., weaker political relations). Risks include increased likelihood of host government intervention to protect domestic competitors, possible forced divestiture, and increased likelihood of forced reduction in stake. Thus, we examine the role of bilateral political relations in cross-border M&A performance. Combining government-affiliation and political relations creates a possible interaction. In particular, we are interested in whether the role of government-affiliation in cross-border M&A differs based on the relation between the nations involved. Tan (2017) notes that in 2017 “Chinese cross-border transactions in strategic sectors are also becoming more politically sensitive overseas.” Chinese government involvement in the financing of such deals adds a layer of government-affiliation issues to already existing political relations related sensitivity. Further, Megginson et al. (2014) note that state ownership in China is associated with a soft-budget constraint which can lead managers to “be more susceptible to corruption and pressure to invest in politically expedient projects, rather than NPV maximizing projects.” Thus, we examine the interaction between government-affiliation and political relations in the context of cross-border merger performance. China's economic and SOE reform process officially began with the Third Plenary Session of the Eleventh Central Committee of the Communist Party of China (CPC) in December 1978 and entered into a stage characterized with constructing a socialist market economy and a modern corporate system since 1992 (Liu and Gao, 1999). By the end of 2000, there were about 1080 firms listed on China's two national stock exchanges, with a majority transformed from SOEs, therefore characterized by politically connected CEOs and/or partial state ownership. This makes the Chinese economy ideal for analyzing the role of government-affiliation and crossborder merger performance. Thus, in order to expand understanding in this area, we examine 219 cross-border mergers from Chinese listed firms. Our results indicate that government-affiliated (either via political connections or government ownership) bidders, are not related to announcement returns. However, government-affiliated bidders are associated with relatively lower longer-term abnormal returns. This is consistent with the general inefficiency of government-affiliated firms described by Shleifer and Vishny (1997, 1998) as well as potentially stronger economic nationalism facing such bidders as in Bertrand et al. (2016). Further, we find that improving political relations between China and target nations is positively related to both short and longerterm cross-border M&A bidder performance. We believe we are the first to document any relation between political relations and M& A performance. Our results are consistent with Bertrand et al. (2016) who suggest that improving political relations may reduce target nation resistance to foreign bidders which improves the outcome of cross-border M&A. Further, we find that the interaction between government-affiliation and improving political relations is generally unrelated to cross-border merger performance. However, the interaction is positive when deals take place in nations with relatively low political risk. This result is consistent with government-affiliation of the bidder serving as a protection mechanism against political risk in target nations, but only in target nations in which political risk is relatively low and with improving bilateral relations. While our discussion treats both state ownership and political connections as “government-affiliated,” our empirical analysis separates the concepts and demonstrates comparable results for both groups of government-affiliation. Thus, the main contributions of our paper include: a) demonstrating that government-affiliation is generally associated with underperformance in Chinese outbound M&A, b) such underperformance is reversed in cases of improving bilateral political relations and low political risk, and c) improving bilateral political relations improve Chinese outbound cross-border merger performance. To the best of our knowledge, these results represent either a new source of evidence on questions previously addressed in the literature (the role of government-affiliation in cross-border M&A) or address a question previously unaddressed in the literature (the role of political relations in cross-border merger performance). The rest of the paper proceeds as follows. Section 2 develops the hypotheses based on related literature. Section 3 describes methodological design and data. Section 4 reports and discuss the empirical results. Section 5 concludes. 2. Hypotheses development 2.1. Government-affiliation and merger performance Government-affiliated firms face conflicting forces of market pressure and political pressure. Shleifer and Vishny (1997, 1998) argue that government-affiliated (i.e., politically connected and/or government owned) firms are associated with relatively weaker firm governance and performance. This negative relation may be due to government bureaucrats pursuing political agendas, rent seeking, extraction, and protection (e.g., Stigler, 1971; McChesney, 1987; Spiller, 1990; Shleifer and Vishny, 1998). 2 https://www.business-standard.com/article/international/hna-skybridge-drop-acquisition-scaramucci-to-return-as-partner-118050200045_1. html

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With respect to the relation between government-affiliation and firm performance, the literature suggests that firms in countries with well-functioning legal systems should not expect to receive a substantial competitive advantage or preferential treatment from their government-affiliation. In the context of developed economies, government-affiliation is associated with value destruction (Dewenter and Malatesta, 2001 and Megginson and Netter, 2001). However, in the context of developing economies, political connections may be value enhancing due to access to government funds and advantages in government contracts (Faccio et al., 2006). Government-affiliated firms in emerging economies enjoy more government support in the form of: input factors, capital market access, licenses, and technology relative to non-affiliated firms (Li and Zhang, 2007 and Li et al. 2012). In emerging economies, government-affiliation has multiple uses and is difficult to transfer through market mechanisms. Such advantages may transfer to cross-border deals such that government-affiliated acquirers outperform non-affiliated acquirers in cross-border deals. The incentive for businesses to establish political connections in transition economies ultimately arises from the state control of key resources (Xu et al., 2013). Kay and Thompson (1986), Martin and Parker (1995), and Kole and Mulherin (1997) find that stateowned firms do not exhibit weaker performance than non-state owned firms. Further, government-affiliation may help firms obtain preferential treatment from government-owned enterprises, such as banks (Khwaja and Mian, 2005; Li et al., 2008), in competitions for government contracts or more bailout funds (Faccio et al., 2006), and access to financing (McMillan and Woodruff, 2002, Bai et al., 2006, and Cull and Xu, 2000). Thus, in a relationship based economy, like China, the cultivation of political connections may be valuable. Calomiris et al. (2010) argue that the Chinese government is more willing to seize profits from privately owned enterprises (POEs) than state owned enterprises (SOEs), and find a positive relation between state ownership and firm value in China. Indeed, both Zhou et al. (2015) and Ma et al. (2016) find that Chinese government-affiliated bidders outperform non-governmentaffiliated bidders. However, they do not examine cross-border deals in their analysis. Black et al. (2015) find that Chinese bidder performance is superior in cross-border acquisitions compared to domestic acquisitions. Further, they find that SOE bidders outperform non-SOE bidders in the longer-term. However, they do not differentiate the performance of SOE bidders in cross-border deals from that of domestic deals as their results are primarily focused on domestic deals. Using a global sample, Karolyi and Liao (2017) examine cross-border mergers and find that government-affiliated deals are associated with higher announcement returns for target firms. They do not, however, examine bidder performance. However, it may be that government-affiliation leads to non-profit maximization motives such as social and political. This in turn may be associated with government-affiliated acquirer underperformance in cross-border M&A compared to non-affiliated acquirers. Consistent with this possibility, Fan et al. (2007) examine a sample of 790 partially privatized firms in China during 1993–2001 and find that firms with politically connected CEOs underperform those without politically connected CEOs by almost 18% based on three-year post-IPO stock returns. Additionally, politically connected firms have poorer three-year post-IPO earnings and sales growth. Similarly, Sun and Tong (2003) find that state-owned Chinese firms have relatively lower profitability and valuation. This evidence is consistent with Shleifer and Vishny (1994, 1998) and Hellman et al. (2000) who find that politicians' intervention in business activities is more severe when institutional constraints are weak. H1a. Government-affiliated bidders are associated with relatively stronger announcement returns in cross-border M&A relative to non-affiliated bidders. H1b. Government-affiliated bidders are associated with relatively weaker announcement returns in cross-border M&A relative to non-affiliated bidders. H2a. Government-affiliated bidders are associated with relatively stronger longer-term abnormal returns in cross-border M&A relative to non-affiliated bidders. H2b. Government-affiliated bidders are associated with relatively weaker longer-term abnormal returns in cross-border M&A relative to non-affiliated bidders. Our analysis regarding H1 and H2 differs from previous research in that we do all of the following: a) focus on China as the bidder nation, b) examine acquirer announcement returns rather than target announcement returns, c) examine longer-term merger performance in addition to announcement returns and d) consider the interaction between government-affiliation and political relations in our analysis of merger performance. 2.2. Bilateral political relations and merger performance The role of bilateral political relations in cross-border M&A has received limited attention. The foreign direct investment (FDI) literature indicates that political relations affect FDI flows through various causal mechanisms such as influencing perception, expectation, cost structure, and investment decisions of investing firms (Nigh, 1985; Li et al., 2010) and impacting relevant government regulatory policies (Li and Vashchilko, 2010). In particular, relatively stronger bilateral political relations are positively associated with FDI. However, to the best of our knowledge, the literature is silent with respect to the relation between political relations and crossborder merger performance, regardless of firm ownership structure. In the context of M&A, Ang et al. (2017) find that governmentaffiliated firms are more constrained than non-affiliated firms in cross-border deals. In particular, government-affiliated cross-border mergers are generally limited to target nations with which the bidder nation has relatively positive bilateral political relations. 223

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However, they do not consider merger performance in their analysis. In order to generate predictions for the relation between political relations and cross-border merger performance, we rely on Dinc and Erel (2013) who study economic nationalism in cross-border mergers. They find that when target nation governments oppose a deal, it is less likely to be completed relative to when the target nation government supports the deal. In particular, they find that merger premiums are higher in the case of target nation government opposition to the deal. Importantly, their results indicate that nationalist reactions are less likely if the nations have a higher level of trust. While the paper does not examine political relations, Bertrand et al. (2016) use a bilateral political relations measure based on U.N. voting similarity to estimate the level of economic nationalism faced by bidder firms. They find higher premiums in cases of weaker political relations. Thus, we may extend the Dinc and Erel (2013) and Bertrand et al. (2016) findings to predict that improving political relations will be associated with a higher likelihood of cross-border merger activity and success. However, to the extent that cross-border deals may be politically motivated, rather than financially motivated, political relations may be negatively related to cross-border merger activity and performance. Specifically, Knill et al. (2012) find that SWFs are more likely to invest in nations in which political relations are relatively weak. Thus, the a priori relation between political relations and merger activity and success is not clear. Thus, our hypotheses are: H3. Improvement in bilateral political relations between China and cross-border merger target nations is unrelated to short-term bidder performance. H4. Improvement in bilateral political relations between China and cross-border merger target nations is unrelated to long-term bidder performance. H5. Improvement in bilateral political relations between China and cross-border merger target nations is unrelated to merger activity. To the best of our knowledge, the literature has not addressed the interaction between government-affiliation and political relations in the context of cross-border mergers. Given that Dinc and Erel (2013) find that one of the reasons for poor cross-border merger performance is opposition from the target nation government, it may be that government-affiliated bidders in the context of improving political relations may be best able to earn support of target nation governments. However, it may be that governmentaffiliated cross-border mergers are politically motivated rather than financially motivated. Specifically, one of the potential weaknesses of government-affiliated firms is that they may act in the best interest of the government, rather than shareholders. Thus, our hypothesis is: H6. Government-affiliated bidder cross-border mergers in target nations with improving bilateral political relations are unrelated to short-term bidder performance. H7. Government-affiliated bidder cross-border mergers in target nations with improving bilateral political relations are unrelated to long-term bidder performance. In summary, we examine the role of economic nationalism from the perspective of Chinese bidder firms. Specifically, we relate a measure of bilateral political relations to the determinants and performance of Chinese outbound cross-border M&A. 3. Methodological design and data 3.1. Methodological design Cross-border merger performance is commonly evaluated using event study methodology (e.g., Conn and Connell, 1990; Doukas and Travlos, 1988; Kiymaz and Mukherjee, 2000; Kuipers et al., 2009). We follow this convention in our analysis. 3.1.1. Measuring Bidder's performance An event study will be conducted to measure the stock market reaction to cross-border M&A announcements by Chinese bidders. Bidders' announcement-period returns are obtained by subtracting the normal or expected return in the absence of the event, ARit = Rit − E(Rit), from the actual return in the event period. To measure the expected return, E(Rit), we follow convention in using the market model (Kallunki et al., 2002).

Rit = αi + βi Rmt + εit , wheret = −126, …, −22. Thus, the abnormal returns are calculated from actual returns during the event period and the estimated coefficients from the estimation period:

i − βi Rmt , where t = −5, …, +5. ARit = Rit − α Average abnormal returns (AARt) is the sample mean on trading day t: N

AARt =

∑ j = 1 ARjt N

Cumulative average abnormal returns (CAAR) will be calculated during different event windows, encompassed by event days 224

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(−n, +n), where event day 0 is the acquisition announcement date. Over an interval beginning with day T1, and ending with day T2, the CAAR are:

CAART1, T2 =

1 N

T2

N

∑ j=1 ∑ ARjt t = T1

To measure the bidder long-term abnormal performance, we follow Brockman et al. (2013) and calculate the Buy-and-hold market-adjusted stock returns (BHAR) from the first month following the merger announcement to a three-year holding period. We use the dividend adjusted value-weighted monthly comprehensive market return for A-share stocks constructed by China Stock Market and Accounting Research (CSMAR) database as the benchmark. 3.1.2. Measuring government-affiliation In China, top management of SOEs are normally appointed by the government. Specifically, the Organization Department of the CPC Central Committee and their operations follow the guidelines of the State-owned Assets Supervision and Administration Commission. Thus, we use state ownership as one indicator of the firm's government-affiliation. If the government is one of the top three owners, we also classify the firm as government-affiliated under our state as the top-3 owner alternative definition. Moreover, previous studies on the political connections of China's listed firms use top management (CEOs or Chairmen of board) affiliation with the government as an indicator of the firm's political connections (Fan et al. 2007; Li et al., 2008; Wang and Qian, 2010; Calomiris et al., 2010).We use top management's political connections, defined as serving as a current or former government bureaucrat—that is, a current or former officer of the central or local governments or the military—as an alternate proxy for government-affiliation. CSMAR Governance Structure database is available to retrieve detailed information about the ownership and experience information of most top managers. In fact, we find the majority (87 out of 127) CEOs (or Chairmen of the board) with political connections currently shoulder the duties from People's Congress and People's Political Consultative, which are platforms for them to managing state affairs, discuss politics and building up political connections. 3.1.3. Measuring bilateral political relations Following Gupta and Yu (2009), Knill et al. (2012), and Bertrand et al. (2016), the proxy for political relations is based on United Nations voting records. The rationale behind this method is that nations with more (less) closely related votes in the UN General Assembly are likely to have stronger (weaker) political relations. Bertrand et al. (2016) interpret this BPR measure as a proxy for the level of economic nationalism faced by foreign bidders. The degree to which countries' votes are similar is quantified using Gartzke's “S” measure (Gartzke, 1998), where “S” is the proxy for bilateral political relations (BPR), following Knill et al. (2012) using the equation:

d ⎤ BPR = 1 − ⎡2∗ ⎣ dmax ⎦ Where BPR is bilateral political relations, d is the sum of the distance between votes for a given bilateral pair and year, and dmax is the maximum possible distance between votes for a given bilateral pair and year. The distance between votes is calculated by both two category vote data (1 = “yes” or approval for an issue; 2 = “no” or disapproval for an issue, see “S1” in Appendix A) and three category vote data (1 = “yes”, 2 = “abstain”, and 3 = “no”). For simplicity, we only present the results using BPR calculated with two category vote data (“S1”) in this paper.3 3.2. Data sources The China Stock Market and Accounting Research (CSMAR) database is used to obtain M&A deal information, stock returns, ownership structure, financial data, and information about management identity. Deals are collected based on the following criteria: 1) bidders are Chinese listed companies; 2) targets are overseas companies (companies from Hong Kong and Macau are excluded); 3) to minimize the influence of window dressing and profit transfer, relatedparty transactions are excluded; 4) bidders that conduct multiple announcements within 3 months are eliminated. There are 219 deals from 2000,the year Go Out Policy (also referred to as the Going Global Strategy) was formally initiated by the Third session of the 9th National People's Congress to promote Chinese investments abroad, to 2013 left after all these criteria are satisfied, among which 192 deals are traded around their merger announcements, including 180 successful deals and 12 unsuccessful deals. Table 1 (Panel A) reports the frequency for the entire sample. Among these 219 deals, 100 bidding companies are SOEs with the state as the biggest owner, 109 with the state as one of the top three owners, and 127 with political connections (see Panel. A of Table 1). Panel B of Table 1 shows that the United States, Virgin Islands and Japan are the countries/regions that attract the most Chinese bidders. 3

Results are qualitatively identical when BPR is calculated with three category vote data (“S2”). 225

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Table 1 Descriptive statistics for cross-border M&As from China. Panel A: frequency descriptiona Year

Number of mergers

Cash payment

SOEs (State as the biggest owner)

State as the Top 3 owner

Bidders with top management political connections

Average deal size (in $1000)

2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 Total

3 1 4 3 1 1 6 20 27 32 31 31 49 7 219

3 1 3 3 1 1 5 19 25 30 31 31 49 7 212

1 1 2 2 1 1 4 12 17 17 14 10 17 1 100

1 1 3 2 1 1 5 14 17 19 16 10 18 1 109

1 1 2 3 1 1 5 15 18 21 17 16 23 3 127

1028.0 2178.0 3088.3 5151.2 6993.0 1,500,000.0 9276.8 217,901.2 285,109.0 848,589.2 49,509.9 68,142.5 97,167.1 6547.1 226,003.3

Host country

Number of deals

Host country

Luxembourg Malaysia Mauritius Netherlands New Zealand Philippines Portugal Russian Federation

1 1 4 3 2 1 1 2

South Africa Spain Sweden Switzerland Thailand United Kingdom United States Virgin Islands, British

Singapore

13

Panel B: Number of mergers classified by host countryb Host country Number of Host country Number of deals bids Australia 11 France 14 Barbados 1 Germany 13 Bermuda 1 Ghana 1 Bolivia 2 India 2 Brazil 1 Indonesia 1 Brunei Darussalam 1 Ireland 1 Bulgaria 1 Italy 2 Canada 8 Japan 25 Cayman Islands Denmark

4 3

Kazakhstan Korea

1 8

Total

Number of deals 1 3 1 1 6 10 36 32

219

a This table provides descriptive statistics for the entire sample, of all 219cross-border bids conducted by Chinese listed firms during 2000–2013. Panel A reports the number of merger bids, number using cash payment, number of bidders with the state as the biggest owner, number of bidders with the state as the top-3owner, number of bidders with top management political connections, nominal average deal value (in thousand dollars) b In this table, the entire sample, 219 deals conducted by Chinese bidders, are classified by host countries, where the targets firms are located

4. Empirical results 4.1. Bidders' short-term merger announcement abnormal returns This section discusses the market reaction to Chinese bidders' oversee merger announcements. Table 2 reports the cumulative average abnormal returns (CAARs) for the acquirers based on ownership structure. We are unable to detect a significant difference in short-term returns between the successful deals and unsuccessful deals (column (1)–(2)). Here and throughout our analysis, we examine three alternate definitions of government-affiliation: state ownership (SOE), one of top three owners is state owned (Top 3 Owner), and political connections of firm management (Top Management PC). SOE bidders experience higher returns around the announcements than the non-SOE bidders (column (3)–(4)), with differences of 3.97% in the event window (0, 5), significant at the 10% level. Similarly, the difference between Top 3 Owner and the control group is 4.94% in the event window (0, 5), significant at the 5% level. Finally, the difference between Politically Connected bidder announcement returns and the control group is 4.19% in the event window (0, 5), significant at the 10% level. Thus, the results provide statistically weak evidence that indicates that the market reacts more favorably to cross-border deals involving bidders with Chinese government-affiliation. 4.2. Bidders' longer-term post-merger performance Table 3 reports the long-term performance of Chinese acquirers classified by types of government-affiliation. Results are presented for BHAR over the one, two, and three years following the merger announcement. We focus our discussion on the three-year postmerger results. In all cases, cross-border mergers by Chinese bidders are associated with positive abnormal returns. This is consistent with Black et al. (2015) who find that outbound Chinese mergers are associated with superior returns relative to domestic deals. These returns are economically smaller for government-affiliated bidders (all definitions), and the difference is statistically significant (1% in the case of SOE and Top 3 Owner and 10% in the case of Top Management PC). Thus, longer-term results are counter to announcement returns and suggest longer-term underperformance of government-affiliated bidders in cross-border deals. We 226

227

AR(−5) AR(−4) AR(−3) AR(−2) AR(−1) AR(0) AR(+1) AR(+2) AR(+3) AR(+4)

AR(−5) AR(−4) AR(−3) AR(−2) AR(−1) AR(0) AR(+1) AR(+2) AR(+3) AR(+4) AR(+5) Car(0,1) Car(0,2) Car(0,3) Car(0,5)

T-test

[−1.333] [0.667] [−0.153] [0.269] [−1.385] [2.527] [0.668] [−0.040] [−0.844] [−0.105]

(5) Top 3 owner

(n = 97) −0.37% 0.23% −0.05% 0.09% −0.39% 1.05%** 0.26% −0.01% −0.25% −0.03%

(n = 92) −0.18% −0.37% 0.42% −0.31% 0.06% 0.05% −0.51% −0.73%* −1.42%** −0.91%***

(6) Non-top 3 owner

−0.12% 0.28% 1.04% 0.42% 0.21% 0.04% −0.61% −0.01% −0.25% −0.81% −1.23% −0.57% −0.57% −0.82% −2.85%

[−0.878] [−0.272] [0.442] [−0.427] [−0.670] [1.549] [−0.238] [−1.200] [−1.997] [−1.609] [−0.756] [0.844] [0.153] [−0.656] [−0.963]

−0.29% −0.09% 0.12% −0.14% −0.20% 0.60% −0.08% −0.39% −0.86%** −0.44% −0.23% 0.52% 0.13% −0.73% −1.40%

(2) Unsuccessful deals

(n = 12)

t-test

(n = 177)

(1) Successful deals

Table 2 Daily AR/CAAR around merger announcements.

[−0.402] [−0.981] [1.416] [−0.748] [0.154] [0.108] [−1.415] [−1.937] [−2.326] [−2.886]

T-test

[−0.114] [0.293] [1.476] [0.566] [0.203] [0.021] [−0.533] [−0.006] [−0.279] [−1.208] [−1.062] [−0.201] [−0.199] [−0.252] [−0.878]

T-test

−0.19% 0.60% −0.47% 0.39% −0.45% 1.00% 0.78% 0.72% 1.17%* 0.88%**

(5)–(6)

−0.16% −0.36% −0.92% −0.56% −0.41% 0.56% 0.52% −0.38% −0.61% 0.37% 0.99% 1.08% 0.70% 0.09% 1.45%

(1)–(2)

[−0.362] [1.176] [−1.085] [0.755] [−0.949] [1.592] [1.449] [1.413] [1.716] [2.092]

T-test

[−0.144] [−0.366] [−1.215] [−0.690] [−0.378] [0.289] [0.442] [−0.397] [−0.602] [0.515] [0.830] [0.376] [0.234] [0.027] [0.408]

T-test

(7) With top management political connection (n = 112) −0.24% 0.12% 0.15% 0.20% −0.13% 0.75%* 0.13% −0.18% −0.24% −0.07%

−0.32% 0.15% 0.00% 0.10% −0.39% 0.92%**, *** 0.40% −0.06% −0.25% −0.17% −0.22% 1.33%* 1.27% 1.02% 0.63%

(n = 88)

(3) SOE bidders

[−0.932] [0.399] [0.554] [0.674] [−0.494] [1.938] [0.345] [−0.553] [−0.871] [−0.266]

T-test

[−1.116] [0.396] [−0.008] [0.296] [−1.316] [2.068] [0.942] [−0.158] [−0.773] [−0.573] [−0.672] [1.777] [1.301] [0.871] [0.413]

T-test

(8) Without top management political connection (n = 77) −0.33% −0.34% 0.22% −0.55% −0.24% 0.29% −0.47% −0.63% −1.66%** −1.02%***

−0.24% −0.25% 0.34% −0.29% 0.02% 0.25% −0.57%* −0.63%* −1.32%** −0.71%** −0.36% −0.32% −0.95% −2.27%* −3.34%**

(n = 101)

(4) Non-SOE bidders

0.10% 0.46% −0.06% 0.75% 0.11% 0.46% 0.59% 0.45% 1.42%* 0.95%**

(7)–(8)

−0.09% 0.39% −0.34% 0.39% −0.41% 0.68% 0.97%* 0.57% 1.07% 0.55% 0.14% 1.65%* 2.22%* 3.29%* 3.97%*

(3)–(4)

[0.167] [0.851] [−0.142] [1.354] [0.214] [0.707] [1.102] [0.866] [1.853] [2.195]

T-test

[−0.173] [0.774] [−0.780] [0.761] [−0.884] [1.087] [1.781] [1.125] [1.651] [1.302] [0.281] [1.666] [1.681] [1.900] [1.771]

T-test

(continued on next page)

[−0.638] [−0.751] [0.621] [−1.167] [−0.533] [0.542] [−1.175] [−1.517] [−2.318] [−2.939]

T-test

[−0.565] [−0.698] [1.226] [−0.757] [0.052] [0.556] [−1.663] [−1.770] [−2.341] [−2.316] [−0.990] [−0.488] [−1.055] [−1.764] [−2.011]

T-test

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Pacific-Basin Finance Journal 51 (2018) 220–250

−0.50% −0.46% −1.20% −2.61%* −4.03%**

[−0.332] [1.925] [1.456] [0.978] [0.655]

−0.10% 1.31%* 1.30% 1.04% 0.91% [−1.294] [−0.654] [−1.233] [−1.882] [−2.250]

T-test

0.40% 1.77%* 2.49%** 3.66%** 4.94%**

(1)–(2)

[0.799] [1.806] [1.893] [2.088] [2.179]

T-test

−0.17% 0.88% 0.70% 0.45% 0.21%

(n = 88)

(3) SOE bidders

[−0.611] [1.351] [0.806] [0.443] [0.165]

T-test

−0.48% −0.18% −0.81% −2.47% −3.97%*

(n = 101)

(4) Non-SOE bidders

[−1.053] [−0.230] [−0.781] [−1.582] [−1.924]

T-test

0.31% 1.05% 1.51% 2.92% 4.19%*

(3)–(4)

[0.583] [1.052] [1.123] [1.575] [1.730]

T-test

228 ∑N j = 1 ARjt N.

1 N

N

T2

t = T1

∑j=1 ∑

ARjt

Standardized cross-sectional t-statistics are reported as well. ***, ** and * denote statistical significance at the 1%, 5%, and 10% levels, respectively, using a two-tailed test.

CAART1, T2 =

Over an interval of two or more trading days beginning with day T1, and ending with day T2, the CAAR are:

AARt is the sample mean on trading day t: AARt =

j + βj Rmt ) ARjt = Rjt − (α

The abnormal return for the stock of firm j on day t is defined as the difference between the actual return on day t and the estimated return from the estimation period:

Rjt = αj + βj Rmt + εjt

This table presents cumulative average abnormal return (CAAR) for acquirers using the market model. The sample consists of 180 (12) successful (unsuccessful)international acquisition deals announced over the 2000–2013 period for short-term analysis, with stock prices available around the announcement, as identified in the CSMAR Database. Deals are grouped by whether the announced deal is completed or not, whether Chinese state is the biggest owner of the bidder, whether the Chinese state acts as the top 3 owner of the bidder, whether the bidder has top management with political connections. CAARs are estimated using the market model with the following regression:

AR(+5) Car(0,1) Car(0,2) Car(0,3) Car(0,5)

(2) Unsuccessful deals

(n = 12)

t-test

(n = 177)

(1) Successful deals

Table 2 (continued)

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Pacific-Basin Finance Journal 51 (2018) 220–250

229

[2.346] [4.357] [6.758]

T-test

[1.594] [1.477] [2.914]

6.46%** 16.90%*** 30.98%***

(5) Top 3 owner

(n = 96) 6.58% 7.37% 16.91%***

T-test

(6) Non-top 3 owner (n = 92) 6.37%* 26.80%*** 45.38%***

11.34% 30.38%** 60.39%***

(n = 13)

(2) Unsuccessful deals

[1.725] [4.559] [6.561]

T-test

[0.873] [2.251] [4.504]

T-test

0.21% −19.43%** −28.48%***

(5)–(6)

−4.89% −13.48% −29.41%**

(1)–(2)

[0.038] [−2.533] [−3.171]

T-test

[−0.382] [−0.996] [−2.151]

T-test

(7) With top management political connection (n = 112) 6.25%* 7.77%* 18.84%***

7.51%* 8.86%* 18.03%***

(n = 87)

(3) SOE bidders

[1.663] [1.686] [3.350]

t-test

[1.672] [1.65] [2.917]

T-test

(8) Without top management political connection (n = 76) −1.59% 17.87%** 35.87%***

5.59% 23.79%*** 41.89%***

(n = 101)

(4) Non-SOE bidders

[−0.371] [2.612] [4.607]

T-test

[1.63] [4.307] [6.396]

T-test

7.85% −10.10% −17.03%*

(7)–(8)

1.92% −14.93%* −23.86%***

(3)–(4)

[1.383] [−1.232] [−1.784]

T-test

[0.342] [−1.948] [−2.664]

T-test

This table reports the long-term performance of Chinese acquirers, measured with the buy-and-hold market-adjusted stock returns (BHAR). The sample consists of 188 (13) successful (unsuccessful) international acquisition deals announced over the 2000–2013 period for long-term analysis, with monthly stock returns available as identified in the CSMAR Database. Deals are grouped by whether the announced deal is completed or not, whether Chinese state is the biggest owner of the bidder, whether the Chinese state acts as the top3owner of the bidder, whether the bidder has top management with political connections. BHAR(tY) is the holding period abnormal return overt years after the announcement, measuring the bidders' performance. T-statistics are reported as well. ***, ** and *denote statistical significance at the 1%, 5%, and 10% levels, respectively, using a two-tailed test.

BHAR(1Y) BHAR(2Y) BHAR(3Y)

BHAR(1Y) BHAR(2Y) BHAR(3Y)

(n = 188)

(1) Successful deals

Table 3 Long-term performance of Chinese bidders after mergers.

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withhold a reconciliation of this difference for the regression analysis in which we are better able to identify if observed differences are due to other relevant deal factors. 4.3. Regressions on announcement returns Panel A of Table 4 reports regressions on Chinese bidders' short-term announcement abnormal returns (CAR(0,5)) around merger announcements, with the same three government-affiliation proxies. We include new variables: bilateral political relations levels (BPR(S1)) and political relations improvement(BPR Change) as the main explanatory variables. The level of political relations captures the relative relationship between nations while the change in political relations captures dynamic evolution of political relations between nations. Additionally, we control for other factors potentially related to the market reaction including: Relative Deal Size, M/B Ratio, Leverage, and an Asset Purchase indicator. In order to avoid a possible two-way causality problem, all control variables are based on one-year lagged values. Additionally, in cases where mutlicollinearity is present, we use orthogonalized control variables. Once the control variables are included, the relation between government-affiliation and short-term market responses to crossborder merger announcements is not statistically significant (one result is marginally significant at 10% for Top 3 Owner). Thus, the univariate results indicating a stronger market reaction to government-affiliated bidders are subsumed by deal and bidder characteristics. Collectively, we interpret the results as supporting neither H1a nor H1b and we conclude that government-affiliation is not associated with the market reaction to the announcement of cross-border mergers in our sample. This result is consistent with Black et al. (2015) who find that Chinese SOE bidder announcement returns do not differ from non-SOEs in a sample of both domestic and cross-border deals. Further, we note that Relative Deal Size is not significant in Panel A of Table 4 and indeed throughout all panels in Table 4. That said, the relative deal size in our sample is generally small with only 33 deals seeing > 5%. In unreported results, we find qualitatively identical results using absolute deal size. Interestingly, changes in bilateral political relations are positively related to the short-term CARs. In the multivariate regression, an increase of 0.01 in BPR (measured by S1) is associated with 0.576% increase in bidder's stock price, significant at the 1% level. For perspective, an increase of 0.01 in BPR would be equivalent to about a 1.5% improvement in political relations between China and the U.S. for a given year. The absolute value of changes in political relations between China and the U.S. has been at least 1.5% in 10 of the 13 years in our sample of political relations changes – with some improvements and some deteriorations in relations. In short, the economic magnitude of change in BPR is significant. This indicates that the market views deals in target nations with improving political relations with China as relatively more valuable. Thus, we reject H3 and conclude that improving political relations are positively associated with the market reaction to cross-border merger announcements in our sample. The positive reaction may be due to lower target nation economic nationalism which may improve deal outcomes (Bertrand et al. 2016). We note that bilateral political relations levels are not related to announcement returns. Thus, the change in political relations is more relevant in the context of cross-border M&A than in the level of relations. Panel B of Table 4 examines interactions between government-affiliation and political relations changes. In all specifications and for all measures of government-affiliation, the interaction is not statistically significant. Thus, the market does not anticipate that government-affiliation in deals involving target nations with improving political relations is particularly value creating. This result is consistent with H6 which suggests that the interaction between government-affiliation and political relations is unrelated to crossborder merger announcement returns. In Panels C-E of Table 4, the whole sample is divided into two groups based on political risks. Political risk is measured based on the PR Index from International Country Risk Guide. Nations with a score below (above) the median of 81 are considered high (low) political risk nations. Butler and Joaquin (1998) define political risk as the possibility that the host government will unexpectedly change “the rules of the game” for businesses. Such changes could impact both the likelihood of a deal and the performance of deals given risk premium implications. Jun and Sing (1996) find a negative relation between political risk and FDI inflows. Henisz (2000) find that the degree of risk depends on the bidder firm which may form partnerships with host-country firms resulting in a comparative advantage in relations and a mitigation of risk. Erel et al. (2012) find no evidence that political risk indicators are determinants of cross-border M&A flows and Karolyi and Liao (2017) find no relation between government-affiliated cross-border deals and political risk. Given the unique focus of our study on Chinese bidder cross-border deals, it is unclear what relation, if any, to expect between political risk and bidder performance. In Panels C and D of Table 4 we report results for the same analysis as in Panels A and B except that we now focus only on target nations with low political risk. In three of the six specifications, our proxy for government-affiliation is not significant. Further, change in political relations is not significant. Finally, the interaction terms between government-affiliation and change in political relations are not significant in any specification. Thus, in target nations with relatively low political risk, neither governmentaffiliation nor change in political relations is associated with bidder announcement returns. Panels E and F report results for target nations with relatively high political risk. Consistent with the main results in Table 4, our government-affiliation proxies are not significant. However, change in political relations is positive and significant in Panel E of Table 4 and the economic significance is roughly double. In the multivariate regression, an increase of 0.01 in BPR (measured by S1) is associated with 1.183% increase in bidder's stock price, significant at the 1% level. Thus, while improving bilateral political relations are generally associated with superior short-term bidder performance, this is particularly true in target nations with relatively high political risk. Based on Dinc and Erel (2013) who find that economic nationalism in the form of target nation government opposition is more likely in cases of weaker political climates, our results suggest that improving political relations can mitigate this issue, especially in nations with high political risk. 230

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Table 4 Regressions on short-term performance of Chinese bidders. Panel A: Regressions on CAR(0, 5) for all complete deals Dependent variable: Car(0,5) Intercept SOE

−0.034** [−2.059] 0.042* [1.763]

Top 3 Owner

−0.041** [−2.399]

−0.040** [−2.147]

−0.021 [−1.352]

−0.022 [−1.537]

0.052** [2.201]

Top Management PC

0.044* [1.815]

−0.034 [−1.467]

−0.019 [−0.829]

0.002 [0.087]

0.004 [0.157] 0.380** [2.194]

Orthogonalized Relative Deal Size M/B Ratio Orthogonalized Leverage Assets Purchase 177 0.012

177 0.021

177 0.013

Panel B: Regressions with interaction terms for all complete deals Dependent variable: Car(0,5) Intercept −0.021 [−1.487] BPR Change(ΔS1)*SOE −0.190 [−0.672] BPR Change(ΔS1)*Top 3 owner

145 −0.007

144 0.026

−0.021 [−1.455]

−0.021 [−1.446]

0.021 [0.206] 0.000 [0.097] 0.095 [1.628] −0.015 [−0.485] 174 0.010

−0.016 [−0.721] −0.132 [−0.431]

0.021 [0.205] 0.000 [0.057] 0.095 [1.626] −0.013 [−0.418] 174 0.001

0.050 [0.376] 0.001 [0.135] 0.095 [1.400] −0.014 [−0.380] 142 −0.019

−0.016 [−0.743]

−0.015 [−0.681]

−0.201 [−0.810]

M/B Ratio Orthogonalized Leverage Assets Purchase 144 −0.004

144 −0.005

144 −0.002

0.052 [0.391] 0.000 [0.004] 0.098 [1.439] −0.015 [−0.425] 141 −0.017

0.053 [0.400] 0.000 [0.065] 0.097 [1.427] −0.015 [−0.437] 141 −0.018

−0.141 [−0.530] 0.052 [0.390] 0.000 [−0.022] 0.097 [1.420] −0.014 [−0.407] 141 −0.017

−0.021 [−0.844]

−0.016 [−0.745]

risks (PR Index > 81) target countries −0.030** [−2.060]

−0.016 [−0.958]

−0.014 [−1.233]

−0.006 [−0.528]

−0.035 [−1.536] 0.056** [2.201]

0.046** [2.098]

Top Management PC

−0.042 [−1.818]

BPR

0.022 [0.918] 0.024 [1.373]

0.021 [1.107] −0.175 [−1.100]

BPR Change Orthogonalized Relative Deal Size M/B Ratio Orthogonalized Leverage Assets Purchase 70

−0.002 [−0.083]

0.062** [2.602] 0.011 [0.498]

70

0.576*** [3.151] 0.043 [0.340] 0.005 [1.127] 0.107 [1.624] −0.019 [−0.562] 141 0.051

−0.073 [−0.245]

Orthogonalized Relative Deal Size

Panel C: Regressions on deals into low political Dependent variable: Car(0,5) Intercept −0.023 [−1.625] SOE 0.034 [1.488] Top 3 Owner

0.021 [0.207] 0.000 [0.071] 0.095 [1.618] −0.012 [−0.405] 174 0.001

−0.133 [−0.479]

BPR Change(ΔS1)*Top Management PC

N Adjusted R-squared

−0.035 [−1.596]

0.038 [1.567]

BPR Change(ΔS1)

N

−0.035 [−1.628]

0.047* [1.981]

BPR(S1)

N Adjusted R-squared

−0.028 [−1.332] 0.037 [1.532]

70

70

70

0.575 [2.167] 0.002 [0.584] 0.051 [0.873] 0.023 [0.649] 70

0.536 [2.139] 0.003 [0.647] 0.042 [0.737] 0.024 [0.691] 70

0.406 [1.552] 0.001 [0.270] 0.034 [0.571] 0.004 [0.114] 70

0.294 [1.176] 0.002 [0.406] 0.021 [0.355] 0.000 [−0.001] 70

−0.179 [−1.096] 0.343 [1.382] 0.000 [0.023] 0.022 [0.371] 0.001 [0.015] 70

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Table 4 (continued) Panel A: Regressions on CAR(0, 5) for all complete deals Dependent variable: Car(0,5) Adjusted R-squared

0.017

0.047

−0.011

0.013

0.003

0.036

0.062

Panel D: Regressions (with interaction terms) on deals into low political risks (PR Index > 81) target countries Dependent variable: Car(0,5) Intercept −0.010 −0.010 −0.009 −0.007 [−0.871] [−0.899] [−0.755] [−0.368] BPR Change(ΔS1)*SOE 0.050 0.065 [0.179] [0.228] BPR Change(ΔS1)*Top 3 owner 0.167 [0.629] BPR Change(ΔS1)*Top Management PC −0.150 [−0.713] Orthogonalized Relative Deal Size 0.332 [1.330] M/B Ratio 0.001 [0.181] Orthogonalized Leverage 0.024 [0.406] Assets Purchase −0.003 [−0.077] N 70 70 70 70 Adjusted R-squared −0.014 −0.009 −0.007 −0.037 Panel E: Regressions on deals into high political Dependent variable: Car(0,5) Intercept −0.079** [−1.996] SOE 0.087 [1.598] Top 3 Owner

−0.018

−0.008 [−0.410]

−0.005 [−0.274]

−0.086** [−2.079]

−0.093** [−2.149]

−0.024 [−0.742]

−0.021 [−0.785]

−0.071 [−1.482] 0.068 [1.215]

0.092* [1.696]

−0.080 [−1.622]

0.150 [0.554]

0.325 [1.302] 0.001 [0.207] 0.022 [0.371] −0.003 [−0.079] 70 −0.032

−0.142 [−0.666] 0.332 [1.333] 0.001 [0.119] 0.023 [0.394] −0.001 [−0.041] 70 −0.030

−0.085 [−1.647]

−0.039 [−0.839]

0.081 [1.409] −0.024 [−0.456]

BPR BPR Change

−0.001 [−0.025] 0.741** [2.469]

Orthogonalized Relative Deal Size M/B Ratio Orthogonalized Leverage Assets Purchase 70 0.026

−0.060 [−1.570]

0.081 [1.422] 0.098* [1.776]

70 0.022

−0.018

risks (PR Index < 81) target countries

Top Management PC

N Adjusted R-squared

−0.024

70 0.030

70 −0.012

69 0.070

−0.034 [−0.174] −0.001 [−0.154] 0.239* [1.724] −0.009 [−0.139] 67 −0.006

−0.024 [−0.126] −0.001 [−0.141] 0.249* [1.798] −0.019 [−0.287] 67 0.003

Panel F: Regressions (with interaction terms) on deals into high political risks (PR Index < 81) target countries Dependent variable: Car(0,5) Intercept −0.035 −0.035 −0.035 −0.034 [−1.238] [−1.230] [−1.249] [−0.798] BPR Change(ΔS1)*SOE −0.309 −0.315 [−0.658] [−0.572] BPR Change(ΔS1)*Top 3 owner −0.298 [−0.637] BPR Change(ΔS1)*Top Management PC −0.297 [−0.645] Orthogonalized Relative Deal Size −0.027 [−0.135] M/B Ratio −0.002 [−0.340] Orthogonalized Leverage 0.257* [1.802] Assets Purchase 0.002 [0.025] N 69 69 69 66

−0.034 [−0.177] −0.001 [−0.191] 0.243* [1.760] −0.006 [−0.099] 67 0.003

−0.022 [−0.110] −0.001 [−0.100] 0.243* [1.697] 0.000 [−0.005] 67 −0.030

−0.034 [−0.801]

−0.034 [−0.803]

1.183*** [3.621] −0.031 [−0.171] 0.009 [1.407] 0.216* [1.676] −0.011 [−0.190] 66 0.154

−0.289 [−0.527]

−0.026 [−0.130] −0.002 [−0.319] 0.255* [1.791] 0.001 [0.018] 66

−0.324 [−0.589] −0.028 [−0.140] −0.003 [−0.365] 0.259* [1.811] 0.003 [0.040] 66

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Table 4 (continued) Panel A: Regressions on CAR(0, 5) for all complete deals Dependent variable: Car(0,5) Adjusted R-squared

−0.008

−0.009

−0.009

−0.025

−0.026

−0.024

This table reports the ordinary least squares (OLS) regressions on bidder cumulative abnormal return (CAR). Individual acquirer announcementperiod cumulative abnormal returns (CAR) are measured over the 6-day event window (0, 5), beginning from the announcement (day 0) and ending at the fifth days after the announcement (day 5). The sample consists of 177 successful international acquisition deals announced over the 2000–2013 period, with stock prices and financial data available in the CSMAR Database. The whole sample is also split into two groups according to the median political risk of the host countries, which is measured using the PR Index from International Country Risk Guide, with a range of (0, 100). Three alternative measurements are used to test the effect of government affiliation status: SOE = 1 if the bidder is a state owned enterprise (SOE) with government holding the largest ownership; Top 3 Owner = 1 if the Chinese government is a top 3 owner of the bidder; Top Management Political Connection = 1 if the CEO (or Chairman of board) is or formerly was a government official of the government, an industry bureau, or the military; otherwise it is set to 0. Bilateral Political Relation (BPR) is the distance between UN voting records for China and the target nation. Specifically, BPR = 1 – [2 * d / dmax], where d is the sum of the distance between votes and dmax is the maximum possible distance between votes for them in a given year. BPR Change measures the improvement of China's political relation with the host county, and equals to BPR(t) - BPR(t-1). Other variables are the orthogonalized relative deal size (transaction value divided by bidder's circulation market value), the market-to-book equity ratio, the orthogonalized leverage ratio measured as total debt over total assets, and a dummy variable equal to one if the merger is an assets purchase deal. For each coefficient, the second row reports the t-value.*,**, and *** indicate the significance levels at 10%, 2%, and 1%, respectively.

4.4. Regressions on Bidders' long-term performance Panel A of Table 5 reports regressions on Chinese bidders' three-year post-merger BHAR with the same three government-affiliation proxies as above in addition to bilateral political relations improvement (BPR Change) as the main explanatory variable. In all six specifications, the proxy for government-affiliation is negative and significant at a minimum of the 5% level. For example, the multivariate regressions show that the government-affiliation identity by means of Top3Owneris associated with about a 30.6% lower bidder return. Thus, consistent with H2b, government-affiliated bidders are associated with relatively weaker longer-term postmerger performance. We compare these results to Black et al. (2015) who document superior performance for Chinese bidders in foreign deals. While our results in Table 4 indicate that this superior announcement returns holds for government-affiliated bidders, we note that government-affiliated bidders underperform non-affiliated bidders in cross-border deals in the long-run. Such underperformance may be related to inefficiencies and/or non-profit maximization motives associated with government-affiliation in general (Dewenter and Malatesta, 2001, and Megginson and Netter, 2001) and in China in particular (Fan et al., 2007). Change in bilateral political relations is positive, significant at the 5% in the univariate regression and significant at the 10% level when control variables are added in Panel A of Table 5. Specifically, in the multivariate regressions with controls, an increase of 0.01 in BPR (measured by S1) is associated with a 1.252% higher bidder return, significant at the 10% level. Thus, we reject H4 which predicts no relation between improving political relations and longer-term bidder performance. As in Table 4, the positive reaction may be due to a lower probability of target nation government opposition in the case of improving political relations and weakening economic nationalism. Table 5 adds to our understanding that this result is not just reflected in the market's perception at the time of the announcement, but is related to longer-term performance as well. Thus, lower levels of economic nationalism may have potentially long-term implications for cross-border deals. In Panel B of Table 5 we examine the interaction between our government-affiliation proxies and changes in political relations. In five of the six specifications, the coefficient is positive and significant at the 10% level. This is weak evidence to reject H7 which predicts no relation between the interaction of government-affiliation and political relations and longer-term bidder performance. Thus, in general, government-affiliation does not add value in cases of improving political relations that is over and above what a non-affiliated bidder receives. In Panels C–F of Table 5, we bifurcate our sample based on the political risk of the target nation. Panel C of Table 5 presents results for target nations with relatively low political risk. None of the included government-affiliation proxies are statistically significant and the change in political relations is only marginally significant in one specification. Thus, the results for the full sample do not hold in the sample of low political risk nations. In Panel D of Table 5, the interaction between government-affiliation and change in political relations is positive and significant in four of six specifications (at a minimum of the 10% level). This result holds for each of the government-affiliation proxies except political connections. Thus, while government-affiliation is generally associated with weaker long-term bidder performance, in the case of improving political relations and target nations with low political risk, it is positively associated with long-term bidder performance. To the best of our knowledge, we are the first to document evidence regarding the role of the interaction between government-affiliation and changing political relations in M&A performance. Panel E of Table 5 presents results for target nations with relatively high political risk. The government-affiliation proxies are

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Table 5 Regressions on long-term performance of Chinese bidders. Panel A: Regressions on bidders' 3 Year BHAR for all complete deals Dependent variable: BHAR(3Y) Intercept SOE

0.419*** [6.787] −0.237** [−2.592]

0.454*** [7.075]

0.486*** [6.890]

0.298*** [5.074]

0.308*** [6.000]

−0.283*** [−3.138]

Top 3 Owner

0.492*** [5.937]

0.306*** [3.810]

−0.305*** [−3.475] 0.072 [0.806]

BPR Change

0.022 [0.258] 1.615** [2.548]

Orthogonalized Relative Deal Size M/B Ratio Orthogonalized Leverage Assets Purchase 186 0.030

186 0.046

186 0.048

153 −0.002

152 0.035

0.076 [0.202] −0.008 [−0.589] −0.549** [−2.565] 0.025 [0.220] 180 0.056

Panel B: Regressions on bidders' 3 Year BHAR with Interaction Terms for all complete deals Dependent variable: BHAR(3Y) Intercept 0.326*** 0.323*** 0.318*** [6.284] [6.234] [6.143] BPR Change*SOE 1.908* [1.823] BPR Change*Top 3 Owner 1.838* [1.792] BPR Change*Top Management PC 1.527* [1.661] Orthogonalized Relative Deal Size M/B Ratio Orthogonalized Leverage Assets Purchase N Adjusted R-squared

152 0.015

152 0.014

152 0.012

Panel C: Regressions on bidders' 3-Year BHAR for deals into low political risks (PR Index > Dependent variable: BHAR(3Y) Intercept 0.321*** 0.325*** 0.356*** 0.302*** 0.263*** [3.968] [3.873] [3.762] [4.580] [4.085] SOE −0.066 [−0.516] Top 3 Owner −0.068 [−0.538] Top Management PC −0.109 [−0.861] BPR −0.037 [−0.368] BPR Change 1.478* [1.671] Orthogonalized Relative Deal Size M/B Ratio Orthogonalized Leverage Assets Purchase 73

73

0.274*** [3.629]

−0.306*** [−3.543]

BPR

N

0.469*** [5.983]

−0.296*** [−3.226]

Top Management PC

N Adjusted R-squared

0.433*** [5.630] −0.260*** [−2.977]

73

73

73

0.075 [0.200] −0.008 [−0.638] −0.550** [−2.597] 0.040 [0.363] 180 0.075

0.078 [0.209] −0.007 [−0.555] −0.555*** [−2.617] 0.030 [0.267] 180 0.073

0.289*** [3.894] 1.859* [1.794]

0.286*** [3.834]

0.433 [0.946] −0.008 [−0.586] −0.656*** [−2.823] 0.066 [0.532] 148 0.034

1.252* [1.966] 0.403 [0.890] 0.000 [0.032] −0.650*** [−2.824] 0.056 [0.464] 147 0.060

0.289*** [3.857]

1.801* [1.777]

0.456 [1.005] 0.000 [−0.002] −0.691*** [−2.990] 0.054 [0.450] 147 0.056

1.400 [1.542] 0.450 [0.989] −0.001 [−0.103] −0.668*** [−2.888] 0.050 [0.414] 147 0.050

0.444 [0.978] 0.000 [−0.018] −0.691*** [−2.990] 0.058 [0.481] 147 0.056

81) target countries 0.437*** [3.269] −0.121 [−0.826]

0.438*** [3.218]

0.466*** [3.360]

0.409*** [3.423]

0.332*** [2.950]

−0.111 [−0.799] −0.145 [−1.079] −0.081 [−0.752]

−0.379 [−0.244] −0.027 [−1.107] −0.452 [−1.326] −0.154 [−0.733] 73

−0.216 [−0.146] −0.026 [−1.097] −0.426 [−1.267] −0.146 [−0.702] 73

−0.341 [−0.232] −0.026 [−1.090] −0.458 [−1.356] −0.142 [−0.703] 73

0.289 [0.205] −0.027 [−1.108] −0.384 [−1.151] −0.109 [−0.550] 73

1.365 [1.506] 0.093 [0.067] −0.018 [−0.754] −0.377 [−1.145] −0.121 [−0.616] 73

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Table 5 (continued) Panel A: Regressions on bidders' 3 Year BHAR for all complete deals Dependent variable: BHAR(3Y) Adjusted R-squared

−0.010

−0.010

−0.004

−0.012

0.024

−0.018

−0.018

−0.010

−0.019

0.006

Panel D: Regressions (with interaction terms) on bidders' 3-Year BHAR for deals into low political risks (PR Index > 81) target countries Dependent variable: BHAR(3Y) Intercept 0.296*** 0.288*** 0.279*** 0.355*** 0.346*** 0.352*** [4.836] [4.691] [4.420] [3.294] [3.183] [3.188] BPR Change*SOE 3.084** 3.046** [2.056] [2.000] BPR Change*Top 3 Owner 2.822* 2.804* [1.975] [1.932] BPR Change*Top Management PC 1.613 1.522 [1.384] [1.288] Orthogonalized Relative Deal Size 0.333 0.129 0.180 [0.243] [0.094] [0.129] M/B Ratio −0.018 −0.018 −0.020 [−0.758] [−0.772] [−0.852] Orthogonalized Leverage −0.403 −0.436 −0.382 [−1.237] [−1.333] [−1.154] Assets Purchase −0.080 −0.086 −0.107 [−0.414] [−0.447] [−0.546] N 73 73 73 73 73 73 Adjusted R-squared 0.043 0.039 0.013 0.030 0.026 −0.003 Panel E: Regressions on bidders' 3-Year BHAR for deals into high political risks Dependent variable: BHAR(3Y) Intercept 0.500*** 0.520*** 0.621*** 0.266*** [4.357] [4.354] [5.016] [2.660] SOE −0.289* [−1.790] Top 3 Owner −0.304* [−1.884] Top Management PC −0.446*** [−2.787] BPR 0.236 [1.511] BPR Change

(PR Index < 81) target countries 0.364*** [4.444]

75 0.084

75 0.017

0.276** [2.233]

0.292*** [2.693]

0.121 [0.807] 1.643* [1.787]

Assets Purchase 75 0.033

0.586*** [4.679]

−0.476*** [−3.364]

Orthogonalized Leverage

75 0.029

0.518*** [4.188]

−0.395*** [−2.704]

Orthogonalized Relative Deal Size M/B Ratio

N Adjusted R-squared

0.483*** [3.958] −0.343** [−2.371]

74 0.029

0.462 [0.909] −0.001 [−0.031] −1.046*** [−2.943] 0.190 [1.129] 70 0.134

0.413 [0.823] −0.001 [−0.057] −1.094*** [−3.108] 0.237 [1.402] 70 0.155

0.470 [0.960] 0.001 [0.088] −1.077*** [−3.149] 0.186 [1.155] 70 0.199

0.377 [0.712] −0.004 [−0.239] −0.969** [−2.570] 0.185 [1.019] 70 0.067

1.354 [1.454] 0.374 [0.711] 0.009 [0.466] −1.085*** [−2.943] 0.142 [0.822] 69 0.089

Panel F: Regressions (with interaction terms) on bidders' 3-Year BHAR for deals into high political risks (PR Index < 81) target countries Dependent variable: BHAR(3Y) Intercept 0.361*** 0.361*** 0.364*** 0.302*** 0.302*** 0.297*** [4.321] [4.318] [4.358] [2.758] [2.750] [2.726] BPR Change*SOE 1.320 1.378 [0.890] [0.928] BPR Change*Top 3 Owner 1.319 1.338 [0.892] [0.905] BPR Change*Top Management PC 1.519 1.843 [1.057] [1.252] Orthogonalized Relative Deal Size 0.432 0.429 0.447 [0.812] [0.807] [0.845] M/B Ratio 0.006 0.005 0.009 [0.287] [0.274] [0.475] Orthogonalized Leverage −1.089*** −1.084*** −1.114*** [−2.906] [−2.895] [−2.982] Assets Purchase 0.132 0.134 0.122 [0.753] [0.760] [0.699] N 74 74 74 69 69 69

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Table 5 (continued) Panel A: Regressions on bidders' 3 Year BHAR for all complete deals Dependent variable: BHAR(3Y) Adjusted R-squared

−0.003

−0.003

0.002

0.071

0.071

0.082

This table reports the ordinary least squares (OLS) regressions on bidder's long-term performance. BHAR(3Y) measures the long-term performance of Chinese bidders in complete deals. The whole sample is also split into two groups according to the median political risk of the host countries, which is measured using the PR Index from International Country Risk Guide, with a range of (0, 100). Three alternative measurements are used to test the effect of government affiliation status: SOE = 1 if the bidder is a state owned enterprise (SOE) with government holding the largest ownership; Top 3 Owner = 1 if the Chinese government is one of the top 3 owners of the bidder; Top Management Political Connection = 1 if the CEO (or Chairman of board) is or formerly was a government official of the government, an industry bureau, or the military; otherwise it is set to 0. Bilateral Political Relation (BPR) is the distance between UN voting records for China and the target nation. Specifically, BPR = 1 – [2 * d / dmax], where d is the sum of the distance between votes and dmax is the maximum possible distance between votes for them in a given year. BPR Change measures the improvement of China's political relation with the host county, and equals to BPR(t) - BPR(t-1). Other variables are: orthogonalized relative deal size (transaction value divided by bidder's circulation market value), the market-to-book equity ratio, the orthogonalized leverage ratio measured as total debt over total assets, and a dummy variable equal to one if the merger is an assets purchase deal. For each coefficient, the second row reports the t-value.*,**, and *** indicate the significance levels at 10%, 2%, and 1%, respectively.

negative and significant in all six specifications, with the marginal significance in two of those cases. Compared to Panel A of Table 5, the link between bilateral political relation and longer-term bidder performance is weaker in nations with high political risk. Similarly, in Panel F of Table 5 none of the interaction terms between government-affiliation and change in political relations are significant. 4.5. Determinants of cross-border mergers In Table 6 we address H5 which predicts that changes in bilateral political relations are unrelated to government-affiliated firm cross-border merger propensity. Table 6 reports the results of a logit regression in which each cross-border merger is an observation. The dependent variable takes the value of 1 if the deal is government-affiliated and zero otherwise. Our variable of interest is change in political relations. This analysis allows us to determine if government-affiliated firms differ in their selection criteria relative to non-affiliated firms. In addition to political relations, we include polity difference and legal protection as well as economic factors (stock market returns, GDP per capita, GDP growth rate, and important trade partner relations). Geographic distance is included based on prior research which indicates a positive relation between geographic proximity and merger activity (e.g., Anderson, 1979; Stulz and Williamson, 2003; Portes and Rey, 2005). Panels A, B, and C of Table 6 differ based on the government-affiliation proxy used as the dependent variable. In all Panels, the results indicate that government-affiliated cross-border merger likelihood does not differ on any of the considered dimensions, including change in political relations. Thus, government-affiliated Chinese bidders appear to have the same determinants as nonaffiliated bidders. Taken together with the bidder performance results, our analysis indicates that any differences in bidder performance are not due to differences in the selection of target nations. 4.6. Robustness 4.6.1. Measuring long-term performance We also conducted robustness checks with regard to the bidder's long-term performance. Inferences from long-horizon tests “require extreme caution” (Kothari and Warner, 1997) and “the analysis of long-run abnormal returns is treacherous” (Lyon et al. 1999). So, principal component analysis on accounting variables is also conducted to measure the long-term performance of bidders. It is mathematically defined as “an orthogonal linear transformation that transforms the data to a new coordinate system such that the greatest variance by some projection of the data comes to lie on the first coordinate (called the first principal component), the second greatest variance on the second coordinate, and so on” (Jolliffe, 2002, p.28). By principal component analysis, a number of possibly correlated variables are transformed into a smaller number of uncorrelated variables. Firstly, nine fundamental financial ratios have been extracted from three aspects of the bidders, including liquidity ratios (liquid ratio, quick ratio and interest coverage ratio), profitability ratios (return on assets, return on equity and earnings per share) and development capability ratios (EPS growth rate, net income growth rate and gross profit growth rate). Secondly, since different industries may experience different levels of performance and growth prospects, we construct the new (abnormal) variable (e.g. abnormal liquid ratio) using each firm-specific ratio minus the industrial average in the same year to eliminate the industrial influence.

236

237

−0.428 [−0.419] 0.321 [0.650]

−0.112 [−0.111] 0.104 [0.211]

Panel B: Regressions on Top3owner Dependent variable: Top3owner All deals Intercept −0.024 0.018 [−0.137] [0.121] BPR 0.072 [0.278] BPR Change −2.020 [−1.050] Return Diff

Distance Trade Partner

GDP Growth Diff

−0.009 [−1.296] −0.002 [−0.208] −0.056 [−0.954] 0.110 [0.933] −0.344

−111.259

−121.271

−122.490

Log Likelihood

−0.004 [−0.648] −0.001 [−0.113] −0.064 [−1.082] 0.137 [1.157] −0.447 [−1.121] −0.199 [−0.226] −0.086 [−1.167] 169

177

−2.813 [−1.448]

178

GDP Diff

0.117 [0.197]

0.275 [0.123] −0.009 [−1.279] −0.002 [−0.216] −0.053 [−0.902] 0.084 [0.930] −0.381

0.082 [0.138]

−110.984

−1.280 [−0.570] −0.003 [−0.468] −0.003 [−0.235] −0.052 [−0.896] 0.091 [0.999] −0.520 [−1.430] −0.378 [−0.438] −0.096 [−1.328] 168 −62.257

90

Host countries with high political 0.436 0.236 [1.545] [1.101] −0.580 [−1.331] 1.097 [0.442]

−62.919

91

0.672 [0.272]

0.097 [0.454]

−48.169

3.067 [0.868] −0.013 [−1.102] 0.067** [2.160] 0.004 [0.031] 0.118 [0.944] −1.374** [−2.003] −0.812 [−0.598] −1.110*** [−2.818] 87

8.306*** [2.655]

risk (PR Index < 81) 8.953*** 8.093*** [3.144] [2.737] −1.475 [−1.418] 3.915 [1.127] −0.018 −0.021* [−1.481] [−1.728] 0.042 0.069** [1.198] [2.210] 0.051 0.038 [0.472] [0.346] −0.002 0.168 [−0.010] [1.286] −1.564* −1.015

−48.389

−0.010 [−0.877] 0.047 [1.316] 0.013 [0.117] 0.013 [0.069] −1.724** [−2.161] −1.774 [−1.165] −1.002*** [−2.798] 88

8.649*** [2.915] −0.965 [−0.943]

0.142 [0.531] −0.190 [−0.462]

−0.116 [−0.765]

−0.220 [−1.262] 0.258 [0.980]

N

Polity

Anti-self Dealing

Trade Partner

Distance

GDP Growth Diff

GDP Diff

Return Diff

BPR Change

BPR

Intercept

Host countries with high political risk (PR Index < 81)

All deals

Dependent Variable: SOE

Panel A: Regressions on SOE

Table 6 Logit regressions on government affiliation proxies.

−51.942

80

−7.113** [−2.071]

−0.234 [−0.979]

Host countries with low political −0.307 −0.103 [−1.268] [−0.435] 0.521 [1.390] −5.399 [−1.619]

−53.009

80

−0.478* [−1.930] 0.586 [1.528]

−46.331

−8.144* [−1.908] −0.027 [−1.603] 0.051* [1.975] −0.070 [−0.747] −0.353 [−1.441] 1.450* [1.866] 1.935 [1.270] −0.030 [−0.277] 80

−1.483 [−1.118]

(continued on next page)

risk (PR Index > 81) −1.624 −0.724 [−0.786] [−0.552] 0.600 [0.727] −5.047 [−1.220] −0.038** −0.037** [−2.177] [−2.072] 0.034 0.040 [1.351] [1.620] −0.075 −0.078 [−0.788] [−0.824] −0.268 −0.480* [−0.710] [−1.947] 1.039 1.280*

−47.464

−0.030* [−1.801] 0.039 [1.526] −0.069 [−0.727] 0.025 [0.066] 1.038 [1.482] 1.742 [1.177] −0.022 [−0.200] 80

−3.074 [−1.468] 1.054 [1.269]

Host countries with low political risk (PR Index > 81)

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178 −123.342

177 −122.125

238

Trade Partner

Distance

GDP Growth Diff

GDP Diff

Panel C: Regressions on political connection Dependent variable: Political Connection All deals Intercept 0.316* 0.357** [1.817] [2.335] BPR 0.073 [0.279] BPR Change −0.901 [−0.465] Return Diff

N Log Likelihood

Polity

Anti-self Dealing

All deals

Dependent Variable: SOE

Panel A: Regressions on SOE

Table 6 (continued)

−0.007 [−0.937] −0.014 [−1.142] −0.053 [−0.853] 0.194 [1.495] −0.582 [−1.391]

0.639 [0.608] 0.116 [0.224]

[−0.864] −0.053 [−0.060] −0.080 [−1.094] 169 −111.985

1.645 [0.716] −0.009 [−1.109] −0.014 [−1.132] −0.053 [−0.850] 0.154 [1.622] −0.662* [−1.750]

0.864 [1.357]

[−1.048] −0.075 [−0.087] −0.080 [−1.122] 168 −111.628 90 −61.729

Host countries with high political 0.540* 0.460** [1.931] [2.103] −0.285 [−0.664] 0.636 [0.252]

91 −61.694

[−1.457] −0.440 [−0.323] −1.089*** [−2.924] 87 −48.019

risk (PR Index < 81) 8.977*** 6.964** [2.892] [2.270] −2.307** [−2.108] 2.769 [0.813] −0.018 −0.020 [−1.442] [−1.646] 0.007 0.046 [0.185] [1.332] −0.016 −0.001 [−0.146] [−0.011] −0.163 0.141 [−0.858] [1.006] −2.899*** −1.854** [−3.022] [−2.275]

[−1.958] −1.791 [−1.185] −0.963*** [−2.883] 88 −48.017

Host countries with high political risk (PR Index < 81)

80 −53.691

Host countries with low political 0.192 0.282 [0.816] [1.199] 0.320 [0.873] −1.559 [−0.479]

80 −54.058

[1.703] 2.322 [1.586] 0.041 [0.385] 80 −46.837

(continued on next page)

risk (PR Index > 81) −0.498 −0.200 [−0.242] [−0.156] 0.165 [0.205] −0.515 [−0.135] −0.009 −0.009 [−0.554] [−0.543] 0.025 0.026 [1.037] [1.066] −0.109 −0.107 [−1.125] [−1.117] −0.263 −0.319 [−0.705] [−1.303] 0.488 0.495 [0.744] [0.734]

[1.478] 2.278 [1.552] 0.049 [0.446] 80 −47.340

Host countries with low political risk (PR Index > 81)

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178 −120.801

177 −119.850

−0.234 [−0.253] −0.101 [−1.264] 169 −106.520

−0.191 [−0.209] −0.099 [−1.254] 168 −105.909 91 −60.852

90 −60.111

−2.386 [−1.491] −0.883** [−2.311] 87 −45.651 80 −54.442

80 −54.710

2.929* [1.934] 0.047 [0.396] 80 −50.623

2.931*** [1.936] 0.047 [0.397] 80 −50.635

Host countries with low political risk (PR Index > 81)

The dependent variable, Government Affiliation takes three alternative measurements: SOE = 1 if the bidder is a state owned enterprise (SOE) with government holding the largest ownership; Top 3 Owner = 1 if the Chinese government is one of the top 3 owners of the bidder; Top Management Political Connection = 1 if the CEO(or Chairman of board) is or formerly was a government official of the government, an industry bureau, or the military; otherwise it is set to 0.The whole sample is also split into two groups according to the median political risk of the host countries, which is measured using the PR Index from International Country Risk Guide, with a range of (0, 100). Return Diff is the difference in real local stock market returns between the target nation and China for a given year. GDP Diff is the GDP per capita difference (in thousands) between the target nation and China. GDP Growth Diff is the GDP growth rate difference between the target nation and China. Distance is the great circle distance between the capitals of the target nation and China. Trade Partner is a dummy that takes the value of one if the potential target is identified as a major trade partner with China. Anti-Self Dealing is the domicile in the Anti-Self Dealing Index, a survey-based measure of legal protection of minority shareholders against expropriation by corporate insiders, of the target country. Polity is the democracy level of the target country, as defined by the Polity IV database. Detailed variable definitions and sources are in Appendix C.Z-values are reported in the brackets under each coefficient. ***,**,*denote statistical significance at the 1%, 5%, and 10% level, respectively.

−4.474** [−2.395] −0.775** [−2.112] 88 −44.021

Host countries with high political risk (PR Index < 81)

Government Affiliation = αi,0 + β1 BilateralPoliticalRelation (BPRti Change ) + βi,2 Xi, t + εi, t .

We specify the following Logit model:

N Log Likelihood

Polity

Anti-self Dealing

All deals

Dependent Variable: SOE

Panel A: Regressions on SOE

Table 6 (continued)

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Pacific-Basin Finance Journal 51 (2018) 220–250

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W. Zhang, N. Mauck

Then, the Kaiser-Meyer-Olkin and Bartlett tests are conducted. The Kaiser-Meyer-Olkin statistic (0.547) is > 0.5 and the Bartlett statistic (1658.77) shows the null hypothesis is rejected at the 1% level. Thus, the factor model is appropriate for our analysis. Finally, principal component analysis is conducted on the new variables to determine the number of components and derive the performance score function (see Appendix B). Appendix D reports the accounting performance of Chinese bidders using principal component analysis, classified by types of government-affiliation. Results are presented for the annual score F(t) from one year before to three years following the merger announcement. Consistent with Table 3, the performance scores tend to be lower for government-affiliated bidders (all definitions), with the difference of F(2) statistically significant at 10% for all three measures. 4.6.2. Mergers and host country characteristics The impact of bilateral international relations on the timing of international merger decisions is also investigated, with intent to observe how shifts in bilateral political relations impact aggregate trends in cross-border mergers. Appendix E conducts a Tobit regression analysis on merger scale, both annual number (Panel A) and annual amount (in billions) (Panel B) of cross-border mergers, in each host country. We find that for the entire sample, bilateral political relations are positively associated with both annual number of mergers and the annual amount of mergers into the host country. Previously, we suggested that one possible reason for the positive relation between improving political relations and cross-border bidder performance is the lower likelihood of target nation opposition. This result is consistent with that interpretation in that it shows in fact that improving bilateral political relations improve merger flow between nations. 4.6.3. Granger causality analysis for individual host countries To this point, we have investigated how political relations influence merger investment. It is also possible that mergers, as an important means of FDI, may influence political relations. Polachek et al. (2007) find empirical evidence that foreign direct investment reduces conflict. The trade literature report a positive impact associated with market openness (Rajan and Zingales, 2003; Bekaert et al., 2005), whereas prior studies theoretically suggest trade may Granger-cause both improvement and deterioration in political relations (Reuveny, 2000). In addition, for large countries, like the U.S., the influence of interstate political relations could be different from small countries. Appendix F conducts granger causality analysis between political relations and merger investment on individual countries. For the whole sample, the granger causality relations exist in both directions, with F-statistics significant at 1% and 5% respectively. However, the relations become weaker for individual countries, probably due to largely reduced sample size (from 448 in total to 14 for each host country). 5. Conclusion This study investigates two dimensions through which the bidders may be influenced by economic nationalism from the host country when they conduct cross-border M&As. We specifically analyze 219 outbound cross-border mergers conducted by Chinese listed companies from 2000 to 2013. Seven hypotheses have been put forward concerning the role of firm government-affiliation and political relations in the determinants and performance of Chinese M&As. Our empirical results support neither H1a nor H1b and we conclude that governmentaffiliation is generally not associated with the market reaction to the announcement of cross-border mergers in our sample. Consistent with H2b, our analysis shows government-affiliated bidders are associated with relatively weaker longer-term post-merger performance. Such underperformance may be related to inefficiencies associated with government-affiliation in general. H3 and H4 are both rejected due to the fact that improving political relations are positively associated with the market reaction to cross-border merger announcements and longer-term bidder performance. Thus, a decline in target nation economic nationalism improves cross-border deal outcomes. Further, government-affiliated Chinese bidders appear to have the same determinants as nonaffiliated bidders (fail to reject H5). The interaction between government-affiliation and political relations is unrelated to cross-border merger announcement returns (consistent with H6). Some evidence has been provided to reject H7 which predicts no relation between the interaction of government-affiliation and political relations and longer-term bidder performance. In particular, in target nations with low political risk we observe a positive interaction between government-affiliation and improving bilateral political relations when examining longerterm M&A bidder performance. Thus, in general, government-affiliation does not add value in cases of improving political relations that is over and above what a non-affiliated bidder receives. However, in the specific case of target nations with low political risk, government-affiliation is value enhancing in deals with improving political relations. Collectively, our results suggest a complex interface between firm ownership structure, political institutions and cross-border M& A performance. In particular, the value implications of firm government-affiliation differ based on the political context of a particular deal. Further, national level political relations are found to be related to firm level M&A performance.

240

241

Denmark

Sweden

Russian Federation

Bulgaria

Italy

Australia

Germany

Portugal

Spain

Switzerland

France

Luxembourg

Netherlands

Ireland

United Kingdom

Bolivia

Brazil

Barbados

Canada

0.609 0.418 0.617 0.433 0.565 0.388 0.636 0.424 0.600 0.409 0.617 0.433 0.957 0.758 0.652 0.448 0.574

0.545 0.358 0.556 0.373 0.500 0.328 0.591 0.388 0.524 0.338 0.535 0.348 0.958 0.758 0.591 0.403 0.478

−0.745 −0.507 0.488 0.313 0.925 0.803 0.962 0.791 0.964 0.791 0.302 0.194 0.644 0.448 0.500 0.328 0.500 0.333 0.385 0.231

−0.600 −0.388 0.600 0.403 0.930 0.803 0.962 0.761 0.927 0.761 0.378 0.254 0.702 0.493 0.565 0.388 0.565 0.388 0.512 0.328

United States

S1 S2 S1 S2 S1 S2 S1 S2 S1 S2 S1 S2 S1 S2 S1 S2 S1 S2 S1 S2 S1 S2 S1 S2 S1 S2 S1 S2 S1 S2 S1 S2 S1 S2 S1 S2 S1 S2 S1

2001

2000

Host country −0.745 −0.556 0.542 0.361 0.965 0.821 0.967 0.833 0.968 0.833 0.333 0.222 0.673 0.472 0.500 0.338 0.560 0.389 0.467 0.292 0.625 0.417 0.569 0.403 0.542 0.382 0.542 0.371 0.640 0.444 0.538 0.389 0.551 0.380 0.923 0.743 0.640 0.458 0.560

2002

Appendix A Bilateral political relations with host countries: 2000–2013.

−0.770 −0.613 0.451 0.307 0.966 0.851 0.968 0.827 0.938 0.811 0.216 0.147 0.630 0.467 0.472 0.333 0.423 0.293 0.373 0.253 0.520 0.347 0.520 0.347 0.509 0.365 0.400 0.267 0.577 0.400 0.491 0.360 0.472 0.333 0.929 0.707 0.529 0.373 0.472

2003 −0.828 −0.662 0.360 0.261 1.000 0.882 0.966 0.817 0.903 0.800 0.231 0.169 0.509 0.394 0.481 0.366 0.462 0.348 0.319 0.214 0.490 0.357 0.472 0.352 0.444 0.338 0.423 0.314 0.472 0.352 0.444 0.338 0.472 0.352 0.962 0.757 0.509 0.394 0.444

2004 −0.836 −0.708 0.444 0.329 1.000 0.890 0.906 0.808 0.873 0.824 0.273 0.205 0.630 0.466 0.527 0.397 0.556 0.411 0.373 0.260 0.577 0.417 0.564 0.425 0.571 0.438 0.585 0.425 0.585 0.425 0.556 0.417 0.571 0.438 0.963 0.750 0.593 0.438 0.527

2005 −0.833 −0.682 0.292 0.224 1.000 0.877 0.946 0.835 0.972 0.877 0.333 0.271 0.600 0.471 0.538 0.424 0.531 0.417 0.377 0.282 0.594 0.459 0.531 0.412 0.538 0.424 0.538 0.424 0.600 0.471 0.576 0.459 0.538 0.424 0.971 0.800 0.600 0.471 0.538

2006 −0.841 −0.697 0.123 0.132 1.000 0.907 0.909 0.813 0.961 0.881 0.214 0.184 0.492 0.408 0.433 0.382 0.433 0.382 0.273 0.211 0.509 0.408 0.443 0.382 0.433 0.368 0.433 0.368 0.500 0.421 0.433 0.368 0.467 0.408 0.932 0.747 0.492 0.395 0.433

2007 −0.797 −0.648 0.137 0.114 1.000 0.857 0.932 0.775 0.966 0.826 0.280 0.200 0.569 0.408 0.490 0.371 0.520 0.386 0.375 0.254 0.569 0.414 0.538 0.394 0.529 0.380 0.529 0.380 0.569 0.408 0.529 0.380 0.520 0.386 0.963 0.803 0.569 0.408 0.490

2008 −0.736 −0.565 0.000 0.000 1.000 0.908 1.000 0.868 1.000 0.924 0.167 0.116 0.510 0.362 0.333 0.246 0.417 0.294 0.244 0.174 0.480 0.368 0.440 0.319 0.451 0.333 0.346 0.261 0.500 0.348 0.373 0.275 0.388 0.279 0.961 0.739 0.469 0.333 0.373

2009 −0.720 −0.538 0.102 0.076 0.965 0.875 0.932 0.864 0.964 0.903 0.120 0.091 0.551 0.415 0.429 0.318 0.480 0.364 0.261 0.182 0.529 0.424 0.510 0.385 0.480 0.364 0.440 0.333 0.560 0.424 0.447 0.339 0.429 0.323 0.920 0.727 0.520 0.394 0.440

2010 −0.708 −0.500 −0.347 −0.262 0.926 0.785 0.927 0.788 0.963 0.833 0.171 0.129 0.565 0.409 0.442 0.303 0.478 0.348 0.286 0.197 0.556 0.400 0.478 0.364 0.478 0.348 0.455 0.318 0.565 0.409 0.478 0.364 0.405 0.302 0.833 0.667 0.556 0.394 0.467

2012

−0.388 −0.297 −0.680 −0.531 0.926 0.836 0.964 0.875 0.962 0.850 0.149 0.109 0.500 0.349 0.409 0.281 0.422 0.297 0.302 0.203 0.556 0.406 0.455 0.328 0.442 0.317 0.333 0.238 0.545 0.375 0.422 0.313 0.442 0.297 0.913 0.688 0.476 0.313 0.409

2013

(continued on next page)

−0.608 −0.477 −0.320 −0.246 0.860 0.766 0.930 0.846 0.926 0.785 0.208 0.154 0.447 0.323 0.319 0.234 0.404 0.292 0.304 0.215 0.478 0.371 0.375 0.277 0.404 0.292 0.391 0.277 0.478 0.338 0.391 0.277 0.348 0.250 0.960 0.800 0.422 0.297 0.362

2011

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Pacific-Basin Finance Journal 51 (2018) 220–250

242

S2 S1 S2 S1 S2 S1 S2 S1 S2 S1 S2 S1 S2 S1 S2 S1 S2 S1 S2 S1 S2 S1 S2 S1 S2 S1 S2 S1 S2

0.403 1.000 0.866 0.925 0.731 0.925 0.769 0.947 0.650 0.838 0.531 0.696 0.507 0.887 0.746 1.000 0.821 1.000 0.866 1.000 0.776 1.000 0.896 1.000 0.848 1.000 0.894 0.708 0.507

2000

0.328 1.000 0.866 1.000 0.734 0.897 0.735 0.957 0.762 0.756 0.515 0.617 0.463 0.926 0.803 1.000 0.866 1.000 0.909 1.000 0.836 1.000 0.908 1.000 0.851 1.000 0.909 0.600 0.431

2001 0.394 1.000 0.875 1.000 0.903 1.000 0.824 0.962 0.746 0.660 0.465 0.673 0.486 0.931 0.806 1.000 0.889 1.000 0.917 1.000 0.847 1.000 0.917 1.000 0.889 1.000 0.917 0.720 0.521

2002 0.333 1.000 0.878 1.000 0.889 0.906 0.811 1.000 0.764 0.702 0.467 0.640 0.440 0.869 0.747 1.000 0.840 1.000 0.933 1.000 0.840 1.000 0.947 1.000 0.892 1.000 0.907 0.600 0.459

2003 0.338 1.000 0.899 1.000 0.900 0.966 0.826 0.965 0.841 0.625 0.451 0.560 0.408 0.966 0.817 1.000 0.831 1.000 0.901 1.000 0.857 0.969 0.901 0.967 0.857 1.000 0.915 0.564 0.451

2004 0.397 1.000 0.875 1.000 0.875 1.000 0.847 0.967 0.822 0.796 0.548 0.614 0.493 0.930 0.753 1.000 0.849 1.000 0.932 1.000 0.863 1.000 0.932 0.968 0.861 1.000 0.917 0.621 0.493

2005 0.424 0.973 0.867 1.000 0.893 0.973 0.847 0.943 0.827 0.645 0.494 0.576 0.471 0.944 0.821 0.973 0.835 1.000 0.941 1.000 0.859 1.000 0.929 0.973 0.881 1.000 0.953 0.600 0.459

2006 0.382 0.940 0.855 0.940 0.855 0.939 0.842 0.908 0.816 0.623 0.493 0.607 0.487 0.938 0.803 1.000 0.842 1.000 0.921 1.000 0.855 1.000 0.882 0.969 0.855 1.000 0.908 0.567 0.474

2007 0.371 0.966 0.814 0.968 0.845 0.931 0.771 0.900 0.800 0.652 0.471 0.680 0.514 0.929 0.757 1.000 0.800 1.000 0.886 1.000 0.845 1.000 0.887 0.966 0.826 1.000 0.901 0.615 0.457

2008 0.275 0.934 0.853 0.967 0.855 0.966 0.841 0.932 0.826 0.581 0.391 0.609 0.420 0.930 0.812 1.000 0.884 1.000 0.928 1.000 0.884 1.000 0.913 1.000 0.894 1.000 0.913 0.434 0.333

2009 0.333 0.964 0.873 0.966 0.877 1.000 0.877 0.930 0.844 0.628 0.453 0.600 0.470 0.925 0.788 1.000 0.879 1.000 0.955 1.000 0.894 1.000 0.939 0.966 0.864 1.000 0.955 0.509 0.409

2010 0.262 0.887 0.814 0.887 0.790 0.893 0.797 0.895 0.800 0.488 0.344 0.511 0.369 0.887 0.785 0.929 0.815 0.964 0.831 0.964 0.862 0.964 0.875 0.963 0.828 0.962 0.800 0.458 0.338

2011 0.333 1.000 0.800 0.962 0.785 0.962 0.813 0.925 0.781 0.632 0.409 0.619 0.424 0.961 0.803 0.962 0.773 0.964 0.818 1.000 0.803 0.964 0.848 0.962 0.773 0.963 0.818 0.574 0.424

2012 0.281 1.000 0.848 1.000 0.839 0.962 0.825 0.962 0.855 0.538 0.381 0.535 0.375 0.961 0.828 0.963 0.828 0.963 0.828 1.000 0.875 1.000 0.891 1.000 0.857 0.964 0.844 0.556 0.391

2013

The dyadic affinity score measures the bilateral political relations between China and the host country, ranging from −1 (least similar interests) to 1 (most similar interests). The Affinity data are coded with the “S1” indicator (“S1” is calculated as 1–2*(d)/dmax, where d is the sum of metric distances between votes by dyad members in a given year and dmax is the largest possible metric distance for those votes, see Signorino and Ritter, 1999), using 2 category vote data (1 = “yes” or approval for an issue; 2 = “no” or disapproval for an issue). “S2” classifies Yes as one, Abstain as two and No as three. Three host regions (Bermuda, Cayman Islands, Virgin Islands, British) are not listed in the table, for they are not members of UN and don't have voting rights. A comprehensive list of all UN General Assembly votes from 1946 to 2014 is provided by Erik Voeten's website http://dvn.iq.harvard.edu/dvn/dv/Voeten.

New Zealand

Indonesia

Philippines

Brunei Darussalam

Singapore

Malaysia

Thailand

India

Japan

Korea

Kazakhstan

Mauritius

South Africa

Ghana

Host country

Appendix A (continued)

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Pacific-Basin Finance Journal 51 (2018) 220–250

243 b

0.077

0.097

0.061

−0.033

0.064

0.016

1

0.007

0.001

−0.002

−0.005

with principal component analysis Extraction sums of squared loadings Cumulative Total % of Variance Cumulative % % 21.994 1.979 21.994 21.994 42.482 1.844 20.487 42.482

0.15

0.04

−0.001

0.011

0.02

0.001

−0.007

0.001

−0.004

0

0.042

1

0.031

−0.002

−0.005

0.004

0.004

−0.002

0.035

0.041

1

0.031

−0.002

−0.006

0.035

−0.002

0.007

1

0.9

0.041

Abnormal Abnormal return on assets return on equity

−0.006

Abnormal interest coverage ratio

0.001

0.9

Abnormal quick ratio

1

Panel B: Total variance explained Component Initial eigen values Total % of Variance 1 1.979 21.994 2 1.844 20.487

Abnormal Liquid Ratio Abnormal Quick Ratio Abnormal Interest Coverage Ratio Abnormal Return on Assets Abnormal Return on Equity Abnormal EPS Growth Rate Abnormal Net Income Growth Rate Abnormal Gross Profit Growth Rate Abnormal EPS

Abnormal liquid ratio

Panel A: Correlation matrix for performance ratiosa

Appendix B Factor analysis of long-term performance.

1

0.079

0.089

0.152

0.061

0.15

0.001

0.077

0.097

Abnormal EPS

(continued on next page)

21.134 41.932

21.134 20.797

1.902 1.872

0.079

1

0.54

0.435

Cumulative %

0.089

0.54

1

0.306

−0.033

0.04

−0.007 0.064

−0.001

0.011

0.02

0.001

−0.004

0

Abnormal net Abnormal gross income growth rate profit growth rate

Rotation sums of squared loadings Total % of Variance

0.152

0.435

0.306

1

0.016

0.031

−0.002

0.031

0.042

Abnormal EPS growth rate

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Pacific-Basin Finance Journal 51 (2018) 220–250

1.122 1.006 1 0.837 0.689 0.423 0.1

12.472 11.173 11.107 9.302 7.653 4.697 1.113

54.954 66.127 77.234 86.537 94.19 98.887 100

Abnormal quick ratio

1.122 1.006

Abnormal interest coverage ratio

12.472 11.173

244

3 −0.014 −0.028 0.07 0.691 0.118 0.071 −0.08 −0.049 0.601

1.166 1.011

Abnormal EPS growth rate

12.957 11.238

4 0.011 0.013 −0.188 −0.246 0.93 −0.024 0.102 −0.094 0.092

54.889 66.127

Abnormal net Abnormal gross income growth rate profit growth rate

Abnormal EPS

This table reports the calculation process of bidders' long-term performance using factor analysis. The sample consists of 179 successful international acquisition deals announced over the 2000–2013 period for long-term analysis, with financial data available as identified in the CSMAR Database. a Panel A reports the Correlation Matrix for 9 fundamental financial ratios. Specifically, the industrial average is first calculated for each year. Then each abnormal value (e.g. abnormal liquid ratio) is calculated using each firm ratio minus industrial average in each year. b Panel B displays the results of the principal factor extraction. c Panel C displays the results of the Kaiser normalization rotation, showing the score coefficients of each factor.

2 −0.003 −0.008 −0.006 −0.073 −0.029 0.37 0.428 0.462 0.027

54.954 66.127

Abnormal Abnormal return on assets return on equity

Panel C: Kaiser normalization rotation result for principal component analysisc Component 1 Abnormal Liquid Ratio 0.512 Abnormal Quick Ratio 0.513 Abnormal Interest Coverage Ratio −0.013 Abnormal Return on Assets −0.048 Abnormal Return on Equity −0.031 Abnormal EPS Growth Rate 0.005 Abnormal Net Income Growth Rate −0.015 Abnormal Gross Profit Growth Rate −0.004 Abnormal EPS 0.001

3 4 5 6 7 8 9

Abnormal liquid ratio

Panel A: Correlation matrix for performance ratiosa

Appendix B (continued)

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Pacific-Basin Finance Journal 51 (2018) 220–250

Pacific-Basin Finance Journal 51 (2018) 220–250

W. Zhang, N. Mauck

Appendix C Variable definitions. Variable name

Definition

Source

Bilateral Political Relation (BPR)

The distance between UN General Assembly votes for a given bilateral pair and year. Specifically, we calculate BPR using BPR = 1 – [2 * d / dmax] where d is the sum of the distance between votes for a given bilateral pair and year, and dmax is the maximum possible distance between votes for a given bilateral pair and year. The measure “BPR” classifies Yes as one, Abstain as two and No as three. Then for each vote the distance is calculated as the absolute value of the difference in votes. Political risk of the host countries is measured using the Political Risk Index from International Country Risk Guide, which is calculated based on twelve components, including government stability, external conflict, ethnic tensions, socioeconomic, corruption, democratic, conditions, military in politics, accountability, investment profile, religious tensions, bureaucracy quality, internal conflict, law and order. The PR Index has a range of (0, 100). Higher value represents lower political risks. Transaction value of the deal(in billion dollars). The market-to-book equity ratio of the bidding firm. The leverage ratio of the bidding firm, measured as total debt over sales. A dummy variable, equal to 1 if the merger is an assets purchase deal. The annual difference in real stock market return between the target nation and China is gathered in the local currency and deflated using 2000 Constant Price Index (CPI). (From Karolyi and Liao (2017)) The difference of GDP per capita in thousands between the target nation and China. (From Karolyi and Liao (2017)) The difference of GDP growth between the target nation and China. (From Karolyi and Liao (2017)) A dummy variable equal to one if the target nation is identified as an “important” trade partner of China in the CIA World Factbook, and equal to zero otherwise. The great circle distance between the capitals of the target nation and China. We obtain latitude and longitude of capital cities of each country. We then apply the standard formula: 3963.0 * arccos[sin (lat1) * sin(lat2) + cos (lat1) * cos (lat2) * cos (lon2 - lon1)], where lon and lat are the longitudes and latitudes of the acquirer (“1” suffix) and the target country (“2” suffix) locations, respectively. Anti-Self Dealing Index Differences - differences between the target nation and China of domicile in the Anti-Self Dealing Index, a survey-based measure of legal protection of minority shareholders against expropriation by corporate insiders. Polity measures the democracy levels and differences, i.e., levels of and differences between the target nation and China of domicile in the measure of regime democracy and/or autocracy, ranging from −10 (high autocracy) to +10 (high democracy).

Gartzke (1998)

Political Risk of Host Country (PR Index)

Deal Size M/B Ratio Leverage Assets Purchase Return Diff

GDP Diff GDP Growth Diff Trade Partner

Distance

Anti-self Dealing Diff

Polity Diff

245

International Country Risk Guide

CSMAR CSMAR CSMAR CSMAR http://finance.sina.com.cn/ worldmac/indicator_CM.MKT.INDX. ZG.shtml World Development Indicator (WDI) World Development Indicator (WDI) CIA world Factbook

http://www.mapsofworld.com/ utilities/world-latitude-longitude. htm

Djankov et al. (2008)

http://www.systemicpeace.org/ p4creports.html

246

0.471 0.984 −1.421 −0.593 0.677

T-test

(5) Top 3 owner

0.700 0.434 −0.976 −0.240 0.409

2.007 1.72 −0.989 1.115 1.583

2.293** 1.427* −0.395 0.332 0.603

T-test

1.188 1.143 0.467 0.657 −0.485

T-test

3.818** 2.341 0.169 0.853** 0.814*

2.224 1.521 0.411 2.003 1.789

(6) Non-top 3 T-test owner

4.01 0.601 1.179 0.756 −0.491

(2) Unsuccessful deals (n = 12)

−3.119 −1.907 −1.145 −1.093* −0.405

−1.369 −1.149 −1.441 −1.853 −0.528

T-test

−0.394 0.261 −0.881 −0.373 0.737

−1.717 0.827 −1.574 −0.423 1.094 (5)–(6)

T-test

(1)–(2)

(7) With top management political connection (n = 109) 1.745 0.565 −0.494 −0.082 0.47

0.955 0.382 −0.998 −0.272 0.321

(3) SOE bidders (n = 95)

1.141 1.55 −0.849 −0.215 0.904

T-test

0.579 0.796 −1.336 −0.62 0.492

T-test

2.12 1.573 0.271 2.023 2.037

T-test

3.130* 2.67 −0.238 0.949** 0.83

1.832 1.365 −0.505 2.033 1.551

(8) With top management T-test political connection (n = 70)

3.339** 2.217 0.104 0.805** 0.863**

(4) Non-SOE bidders (n = 84)

−1.385 −2.105 −0.256 −1.031* −0.36

(7)–(8)

−2.385 −1.835 −1.102 −1.077* −0.543

(3)–(4)

−0.591 −1.249 −0.312 −1.711 −0.454

T-test

−1.036 −1.095 −1.38 −1.813 −0.709

T-test

This table reports the long-term performance of Chinese acquirers using principal component analysis. The sample consists of 179 (12) successful (unsuccessful) international acquisition deals announced over the 2000–2013 period for long-term analysis, with financial data available as identified in the CSMAR Database. Deals are grouped by whether the announced deal is completed or not, whether Chinese state is the biggest owner of the bidder, whether the Chinese state acts as the top3owner of the bidder, whether the bidder has top management with political connections. F(t)is the average total score in year t, measuring the bidders' performance. Calculation details are shown in Appendix B. T-statistics are reported as well. ***, ** and * denote statistical significance at the 1%, 5%, and 10% levels, respectively, using a two-tailed test.

F(−1) F(0) F(+1) F(+2) F(+3)

F(−1) F(0) F(+1) F(+2) F(+3)

(1) Successful deals (n = 179)

Appendix D Long-term performance of chinese bidders after mergers using factor analysis.

W. Zhang, N. Mauck

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Appendix E Tobit regression: merger decisions and target nation characteristics. Panel A: Regressions on the annual number of mergers by Chinese bidders (1) All

−3.509*** [0.000] BPR Change 6.399* [0.069] Return Diff −0.009 [0.268] GDP Diff 0.000*** [0.001] −0.150* GDP Growth Diff [0.074] Distance 0.000** [0.023] Trade Partner

C

(2) Host countries with high political risk (PR Index < 81)

(3) Host countries with low political risk (PR Index > 81)

(4) All

(5) Host countries with high political risk (PR Index < 81)

(6) Host countries with low political risk (PR Index > 81)

−2.456*** [0.002] 4.261 [0.377] −0.025*** [0.007] 0.000*** [0.000] −0.085

−2.728* [0.087] 6.556 [0.155] 0.017 [0.271] 0.000 [0.300] 0.067

−2.356** [0.015] 5.778* [0.072] −0.007 [0.352] 0.000*** [0.001] −0.196**

−2.896** [0.035] 4.434 [0.325] −0.021** [0.011] 0.000*** [0.001] −0.122

−1.717 [0.275] 4.889 [0.226] 0.023 [0.108] 0.000** [0.015] −0.158

0.408 0.000*** [0.003]

[0.614] 0.000 [0.809]

[0.013] 0.000 [0.141] 2.719***

[0.210] 0.000** [0.019] 1.936***

[0.200] 0.000 [0.115] 2.987***

[0.000] 2.664***

[0.002] 2.210*

[0.000] 1.898

[0.003] −0.119* [0.078] 410 −308.152

[0.053] 0.025 [0.784] 213 −149.265

[0.185] −0.344*** [0.002] 170 −135.161

(5) Host countries with high political risk (PR Index < 81) −45.300** [0.038] 117.000 [0.114] −0.222* [0.088] 0.001* [0.091] −2.640*

(6) Host countries with low political risk (PR Index > 81) −3.330** [0.034] 5.560 [0.175] 0.011 [0.446] 0.000** [0.020] −0.217*

[0.089] −0.003 [0.138] 42.200**

[0.077] 0.000* [0.081] 2.110

Anti-self Dealing Diff Polity Diff N 410 Log −328.756 Likelihood

213 −154.825

170 −152.067

Panel B: Regression on the annual amount (in billions) of mergers by Chinese bidders (1) All (2) Host countries (4) All (3) Host countries with high political with low political risk (PR risk (PR Index > 81) Index < 81) C −34.700*** −49.100*** −3.810** −26.600*** [0.000] [0.000] [0.012] [0.005] BPR Change 60.600* 109.000 6.870 59.300* [0.057] [0.146] [0.117] [0.064] Return Diff −0.075 −0.244* 0.007 −0.058 [0.313] [0.069] [0.635] [0.439] GDP Diff 0.000 0.001** 0.000 0.000* [0.112] [0.032] [0.376] [0.086] GDP −1.690** −2.350 −0.001 −2.190*** Growth Diff [0.024] [0.134] [0.991] [0.005] Distance −0.002** −0.004** 0.000 −0.001 [0.042] [0.044] [0.511] [0.215] Trade 25.800*** Partner [0.004] −0.969

247

[0.024] −0.148

[0.141] −0.306*** (continued on next page)

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Appendix (continued) Panel A: Regressions on the annual number of mergers by Chinese bidders (1) All

(2) Host countries with high political risk (PR Index < 81)

(3) Host countries with low political risk (PR Index > 81)

(4) All

(5) Host countries with high political risk (PR Index < 81)

(6) Host countries with low political risk (PR Index > 81)

[0.146] 16.500*** [0.001] 410 −2200.995

[0.919] 18.100* [0.081] 213 −1176.455

[0.007] 2.940*** [0.000] 170 −936.677

Anti-self Dealing Diff Polity Diff N 410 Log −2212.131 Likelihood

213 −1179.818

170 −952.510

We specify the following tobit model:MergerInvti = αi, 0 + β1BPRti Change + βi, 2Xi, t + εi, t.The dependent variable, MergerInvtiis either the annual number of cross-border mergers (Panel A)or the annual amount (in billions) of cross-border mergers (Panel B) conducted by Chinese bidding firms in target country I and year t. The whole sample is also split into two groups according to the median political risk of the host countries, which is measured using the PR Index from International Country Risk Guide, with a range of (0, 100). The model uses UN voting records to calculate the political relations proxy as the distance between votes for a given country pair (i.e, China and the target country) and year, labeled BPR and estimates a basic tobit model. Specifically, BPR = 1 – [2 * d / dmax], where d is the sum of the distance between votes and dmax is the maximum possible distance between votes for them in a given year. BPR Change measures the improvement of China's political relation with the host county, and equals to BPR(t) - BPR(t-1). Return Diff is the difference in real local stock market returns between the target nation and China for a given year. GDP Diff is the GDP per capita difference (in thousands) between the target nation and China. GDP Growth Diff is the GDP growth rate difference between the target nation and China. Distance is the great circle distance between the capitals of the target nation and China. Trade Partner is a dummy that takes the value of one if the potential target is identified as a major trade partner with China. Anti-Self Dealing Diff is the differences between the target nation and China of domicile in the Anti-Self Dealing Index, a survey-based measure of legal protection of minority shareholders against expropriation by corporate insiders. Polity Diff is the democracy level difference between the target nation and China as defined by the Polity IV database. Detailed variable definitions and sources are in Appendix C·P-values are reported in the brackets under each coefficient.***, **, *denote statistical significance at the 1%, 5%, and 10% level, respectively.

Appendix F Granger causality results. Host country

Australia Barbados Bermuda Bolivia Brazil Brunei Darussalam Bulgaria Canada Cayman Islands Denmark France Germany Ghana India Indonesia

BPR Granger-causes intl mergers

Intl mergers granger- Host country causes BPR

BPR granger-causes intl mergers

Intl mergers grangercauses BPR

(1) Fstatistic

(2) Prob.

(3) Fstatistic

(4) Prob.

(1) Fstatistic

(2) Prob.

(3) Fstatistic

(4) Prob.

0.824 0.517

0.473 0.615

0.995 23.57***

0.411 0.000

0.388 2.664

0.691 0.130

0.127 0.354

0.883 0.713

Korea Luxembourg Malaysia Mauritius Netherlands New Zealand

0.427 0.595 0.156 1.562 4.258* 0.490

0.666 0.574 0.858 0.268 0.055 0.630

0.185 0.207 190.402*** 0.150 1.053 3.251*

0.835 0.817 0.000 0.863 0.393 0.093

2.664 2.682

0.130 0.128

0.354 1.879

0.713 0.214

1.052 1.303 3.292* 0.570 1.352

0.393 0.324 0.091 0.587 0.312

1.401 0.615 3.103 4.176* 2.284

0.301 0.565 0.101 0.057 0.164

Philippines Portugal Russian Federation Singapore South Africa Spain Sweden Switzerland Thailand

0.195 0.652 0.529 0.535 0.367 3.577* 1.611 0.833 0.880

0.827 0.547 0.608 0.605 0.704 0.078 0.258 0.479 0.451

0.920 0.027 0.556 2.096 0.064 0.304 0.213 0.123 20.072*** (continued on

248

0.437 0.973 0.594 0.185 0.938 0.746 0.812 0.887 0.001 next page)

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Appendix F (continued) Host country

BPR Granger-causes intl mergers

Intl mergers granger- Host country causes BPR

BPR granger-causes intl mergers

Intl mergers grangercauses BPR

(1) Fstatistic

(2) Prob.

(3) Fstatistic

(4) Prob.

(1) Fstatistic

(2) Prob.

(3) Fstatistic

(4) Prob.

Ireland Italy Japan

1.268 1.223 0.191

0.332 0.344 0.830

0.895 0.127 1.713

0.446 0.883 0.240

0.659 0.626

0.543 0.559

1.357 12.018***

0.311 0.004

Kazakhstan

1.210

0.348

1.420

0.297

9.364***

0.000

4.108**

0.017

United Kingdom United States Virgin Islands, British All

In Table 7, we specify the following VAR model: [PRtMergerInvt] = [A21(L)A22(L)A11(L)A12(L)][PRt−1MergerInvt−1] + [e2te1t]. The results testing if political relations Granger-cause merger investment are in the first two columns, and the results testing if merger investment Granger-causes political relations are in the last two columns. MergerInvtis the level of merger investment in year t where level is measured as the number of cross-border mergers conducted by Chinese listed firms. BPRt is the bilateral political relations score for year t. F-statistics for testing the null hypothesis and its pvalue are reported. ***, **, *denote statistical significance at the 1%, 5%, and 10% level, respectively.

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