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Economic policy uncertainty and Mergers and Acquisitions in the
United Kingdom
Master’s thesis
MSc International Financial Management January 2021
Author: Maximilian Ehret Student number: S4217047 Supervisor: Dr. Nassima Selmane
Co-assessor: Halit Gonenc Abstract
This thesis examines how domestic and cross-border mergers and acquisitions (M&A) activity and M&A acquirer return are affected by economic policy uncertainty in the United Kingdom. The research comprises a sample of 7319 mergers from 1997 until 2019. The country-level empirical analysis shows that economic policy uncertainty reduces the number of deals and aggregated deal value in a given month. Abnormal acquirer shareholder returns for the M&A announcement window are positive and heightened during times of high economic policy uncertainty. The results do not show definite evidence whether the positive relationship between acquirer return and economic policy uncertainty is different for
domestic and cross-border deals.
Field Key Words: Mergers and Acquisitions, Economic Policy Uncertainty, Event study
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1.
Introduction
At the time of writing business in the United Kingdom (UK) is not only faced with the economic policy uncertainty (EPU) inducing global COVID-19 pandemic (Baker, Bloom, and Davis 2020) but is also faced with the aftermaths of the Brexit referendum. The UK left the European single market on December 31, 2020, and with only a few weeks to go the EU-UK trade negotiations were still “on a knife-edge”.1 The negotiators finally struck the eagerly awaited post-Brexit trade deal agreement2 on Christmas eve, the Czech Foreign Minister Tomas Petricek praised that “the end of the transition period means the end of uncertainty”. And while some uncertainty might be dispelled, the UK will still be faced with economic policy uncertainty. Economic policies have never been as uncertain as they currently are (Iyke 2020), thus it is more important than ever to understand how this uncertainty is affecting corporate investment activity and its profitability. This thesis is investigating how economic policy uncertainty is affecting domestic and international corporate mergers and acquisitions (M&A) in the UK. The relationship between M&A and economic policy uncertainty is unclear for scholars and practitioners, and the most recent findings have been showing conflicting evidence. While previous studies by Nguyen and Phan (2017) and Bonaime, Gulen, and Ion (2018) find that M&A activity in the US is negatively associated with economic policy uncertainty, Sha, Kang, and Wang (2020) observe the opposite relationship for Chinese mergers. Furthermore, Bonaime, Gulen, and Ion (2017) find evidence that mergers during times of heightened economic policy uncertainty are lost rather than delayed, which stresses
1 Micheál Martin - Irish prime minister in talks with RTÉ News, 6 Dec 2020 "Because even with a deal done it will be challenging in terms of the changes that will occur and the additional documentation. In the event of a no deal, it will be significantly onerous on businesses out there, so things are on a knife edge here and it is serious."
2 Czech politicians across the spectrum welcome EU–UK post-Brexit trade deal, 25 Dec 2020 -
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the relevance of understanding to what extent M&A activity is affected by economic policy uncertainty. Professionals and the public press share the common notion that uncertainty is leading to a slowdown in M&A deal-making. Deloitte’s (2016) snap pool of global M&A advisors after the Brexit referendum showed a “wait and see” approach to UK-related deals. The Birmingham Business Journal3 discussed how the COVID-19 pandemic will impact M&A with practitioners in the field, on one side they were stating that acquirers “are certainly more cautious, and discount rates are probably higher”, which insinuates increased acquirer returns. On the other hand, they were reporting that targets were at the same time “not considering their businesses beyond pre-COVID-19 valuations”. The question is, how do these contrasting views affect M&A? How will deal-making activity and abnormal acquirer returns be affected by it? The empirical analysis of M&A deal-making and the implications for acquirer shareholder return is providing clarification on these questions and is examining whether domestic and cross-border mergers behave differently. For example, cross-border deals can be a tool to diversify policy or price risk during times of economic policy uncertainty as described by Bonaime, Gulen, and Ion 2018. The importance of taking the economic environment into account when assessing the value implications of M&A has also been outlined by Beltratti and Paladino (2013), who found that M&A in the banking sector behaved differently during the financial crisis. The United Kingdom is chosen as the research subject because it has had to deal with distinctive policy uncertainty inducing events in the past and offers precise assessments, by practitioners, on how M&A should behave during heightened economic policy uncertainty. Because it is impossible to capture all exogenous shocks and crises that lead to heightened policy uncertainty this thesis make use of the economic policy uncertainty index a
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2.
Review of related literature
M&A is describing transactions or consolidations between firms that result in a business or ownership restructuring. Mergers are a strategic tool that can help a firm create value through external growth. The aim of M&A is to realize synergy through a larger asset base, access to new markets, a greater market share, additional manufacturing capacities, complementary strengths, or competencies (Gupta 2012). Different types of mergers are used as a means to diversify risk as a form of operational hedging. For example, cross-border mergers can be used to reduce exposure to domestic policy risk as shown by Bonaime, Gulen, and Ion 2018. In M&A, the acquirer faces the risk of overestimating the synergy effects which can lead to an unreasonable price premium and thus losses for the acquirer. (Roll 1986)
2.1.
Relationship of M&A and economic policy uncertainty
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contradictory finding with the real-options theory4 (Myers 1977) extended by competitive environment considerations (Grenadier 2002). The real-option theory is implying a “value of waiting” which in turn should lead to a slowdown in M&A. The extension by Grenadier (2002) states that high competition drastically erodes the “value of waiting” for the real-options theory. Sha, Kang, and Wang (2020) thus imply that the competitive environment in China is responsible for the positive relationship between policy uncertainty and M&A activity. One major shortcoming of this theory is that Sha, Kang, and Wang (2020) use papers published prior to 2007 to prove the competitiveness of the Chinese market. They also fail to address the fact that the opposing papers by Nguyen and Phan (2017) and Bonaime, Gulen, and Ion (2018) are examining the US market, which has had a vastly more competitive market in the past according to the national competitiveness index5 (World Economic Forum 2019). It is thus not theoretically resolved where the contradicting behavior stems from, and whether it could be addressed to structural changes in a transitioning economy like China, that have been left out in the robustness evaluation. When it comes to shareholder value creation Nguyen and Phan (2017), Bonaime, Gulen, and Ion (2018), and Sha, Kang, and Wang (2020) find ambiguous consequences for the firms involved in these mergers. Bonaime, Gulen, and Ion (2018) find no significant difference in abnormal returns between high and low uncertainty mergers. They further find support for the hypothesis that high policy uncertainty is increasing the negotiating power of the target. This contradicts with Nguyen and Phan (2017) who find evidence for a
4 Under uncertainty, the value of an investment project is determined by the expected present value of cash flows and additionally by the value of managerial flexibility when decisions are at least partially irreversible. A supposedly profitable investment project can become disadvantageous when market conditions turn out to be sufficiently unfavorable. The possibility to postpone a decision and wait for more information may help an investor to reduce the probability of loss-making decisions. (Sureth 2002)
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significant positive relationship between acquirer abnormal returns and economic policy uncertainty and a value transfer from financially constrained targets to acquirers. Nguyen and Phan (2017) see these findings as evidence for their hypothesis of increased acquirer prudence during times of heightened economic policy uncertainty. In addition to these findings, Nguyen and Phan (2017) find evidence that the merger process takes more time under heightened uncertainty, as well as a preference for stock rather than cash payments. Sha, Kang, and Wang (2020) investigate state-owned enterprises and find that they prefer stock payments and are less likely to conduct mergers during heightened uncertainty. Sha, Kang, and Wang (2020) interpret these results also as an indicator for heightened prudence at state-owned enterprises. Bonaime, Gulen, and Ion (2018) are further conceptualizing the incentives behind M&A. They identify channels that affect merger decisions and could be affected by policy uncertainty. Their results support the real options theory which predicts that the incentive to postpone acquisitions during times of high policy uncertainty is stronger for irreversible mergers and uncertainty-sensitive acquirers, but weaker for mergers that cannot easily be delayed. The second channel identified is the risk management channel, because the number of cross-border and vertical mergers are increasing during times of heightened economic policy uncertainty.
2.2.
Other determinants of M&A
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limited to 50 US mergers from the year 2015. Regarding the assessment of overall merger returns for both acquirer and target, Yilmaz and Tanyeri (2016) find a positive value creation, while the findings by Reddy et al. (2019) show no significant effect.
This insignificance can be further evaluated by distinguishing between target and acquirer abnormal returns. Yilmaz and Tanyeri (2015) and Kinateder, Fabich, and Wagner (2017) showcase that the acquirer is on average at a disadvantage, and Reddy et al. 2019, Kinateder, Fabich and Wagner (2017) even find a value-destroying effect for acquirers. The empirical research in this thesis solely focuses on the abnormal return of acquirers in the UK, discussed by the following papers. Giannopoulos, Khansalar, and Patel (2017) find a significant positive abnormal return for a sample of 1494 mergers from 2002 to 2006. The domestic mergers are insignificantly outperforming the cross-border mergers in their analysis. Opposed to this Sudarsanam and Mahate (2003) find, for a sample of solely domestic UK mergers from 1983 to 1995, a statistically significant negative merger announcement return. The negative return is also supported by Antoniou (2008). His sample consists of 396 domestic and cross-border mergers from 1985 to 2004.
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These fluctuations can be caused by various frequent and prolonged external shocks that would require polyadic classification that allows for several transition periods. The
aforementioned event studies largely focus on the announcement date effect of mergers. Cai, Song, and Walkling (2011) find evidence that the announcement period returns underestimate the wealth effects of mergers because the long-run anticipation of mergers is not factored in. Beltratti and Paladino (2013) claim that this effect of anticipation further strengthens the link between M&A and the economic environment. It is also necessary to mention that the event study design fails to identify sustainable long-run performance, Wan and Yiu (2009) carried out an accounting study on M&A before, during, and after the Asian Economic Crisis of 1997 and they highlight that the occurrence of an environmental jolt is an important factor when studying the long-term acquisition-performance relationship. Based on their findings, they concluded that firms should recognize new opportunities and can capitalize on them in the long-run if they change their strategies accordingly. The implications of Beltratti and Paladino (2013) and Wan and Yiu (2009) thus further stress the relevance of economic policy uncertainty when assessing M&A acquirer returns.
3.
Hypotheses development
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to increased economic policy uncertainty which is in contrast to the surge in deal-making in China (Sha 2020), thus the following hypotheses are proposed.
Hypothesis 1a: M&A deal-making activity is lower during periods of high economic policy
uncertainty.
Hypothesis 1b: M&A deal-making activity is higher during periods of high economic policy
uncertainty.
If Hypothesis 1a (H1a) holds, M&A in the UK is in accordance with the US findings and thus supports the two channels of M&A slowdown proposed by Nguyen and Phan (2017). The first one is the real-options theory. This theory is consistent with the findings by Bonaime, Gulen, and Ion (2018). And secondly, the prudence theory proposed by Nguyen and Phan (2017), which is consistent with the empirical analysis by Sha, Kang, and Wang (2020) for state-owned enterprises. If the empirical analysis shows support for Hypothesis (H1b), it can be seen as support for Sha, Kang, and Wang (2020), who argues that the real-options theory extended by competition considerations could be responsible for an increase in deal-making. But the UK, just like the US and China, is highly competitive based on the national competitiveness index (World Economic Forum 2019), which is why the theory offered by Sha, Kang, and Wang (2020) is not convincing. Bonaime, Gulen, and Ion (2018) find evidence that cross-border deal-making activity surges when economic policy uncertainty is high, which is consistent with the risk management channel that states that deal-making during times of heightened economic policy uncertainty could be a way of diversifying policy or price risks.
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Hypothesis 2: Acquisitions during periods of high economic policy uncertainty are associated
with greater acquirer shareholder returns.
If Hypothesis 2 is supported it is in line with previous findings by Nguyen and Phan (2017) and Sha, Kang, and Wang (2020) which attribute this relationship to the prudence of acquiring firms. Sha, Kang, and Wang (2020) base this on the theory that state-owned enterprises are more prudent when making investment decisions and show that this heightened prudence of state-owned enterprises has a positive effect on the return of acquirers when compared to non-state-owned enterprises. The UK merger sample only comprises a handful of mergers that are including a state-owned enterprise. This is the reason why the empirical analysis is observing the relationship between acquirer return, economic policy uncertainty, and risk management. The risk management channel is investigated by observing the joint-effect of domestic mergers and economic policy uncertainty on the abnormal acquirer return. The theories and empirical evidence for a positive effect of corporate risk management on shareholder value creation are discussed by Bartram (2002), who attributes the positive effect of risk management on shareholder value to realistic capital market imperfections that cause agency costs, transaction costs, higher taxes, and expensive external finance. This empirical analysis thus presents cross-border mergers as a form of operational hedging that reduces and diversifies economic policy uncertainty. If the interaction between economic policy uncertainty and domestic mergers on acquirer return is significant and negative, it would support the value-creating effect of risk-management through cross-border deals during times of heightened policy uncertainty. This would assign domestic deal-making a worse performance, in terms of acquirer shareholder return, when economic policy uncertainty is high.
Hypothesis 3: The interaction effect of economic policy uncertainty and domestic merger
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4.
Data
The merger data for this analysis is collected from Eikon by Thomson Reuters. It consists of all completed domestic and cross-border mergers of British firms from Q3.1997 until the end of 2019. The timeframe is limited due to the data availability of the main independent variable for economic policy uncertainty (Baker, Bloom, and Davis 2016). The EPU index is a proxy for economic policy uncertainty provided by the researchers of “Economic Policy Uncertainty”. The data can be found on their website.6 Following common practice in the literature firms in the financial and utilities sector are excluded because they are highly regulated and their business models and financial figures deviate noticeably. The merger data is filtered based on sufficient data availability of the deal-specific financial figures and the acquirer identifier SEDOL (Stock Exchange Daily Official List). Duplicates were eliminated to prevent confounding information effects. The acquirer firms included must further provide enough stock price data around the M&A announcement date to ensure a reliable estimation window, for the estimation of the cumulative abnormal returns provided by Wharton International Event Studies based on Compustat Global and IHS Global Insight. When controlling for these criteria, I end up with a sample of 7319 mergers. The distribution of M&A deals is shown in Table 1. The distribution by year in Panel A shows that there are more domestic mergers than cross-border mergers. The years with the most mergers are 1999, 2000, 2001, and 2007. In Panel B the M&A deals are distributed by industry, the mergers are concentrated in the industries: Consumer Products and Services, High Technology, and Industrials. The frequency of industries by Acquirers and Targets is largely balanced.
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Table 1 The distribution of M&A deals
Panel A shows the distribution of the mergers sorted by year and differentiated by domestic and cross-border mergers. In Panel B the M&A Deal distribution for both Acquirer and Target by industry.
Panel A: M&A Sample Deal distribution by year Panel B: M&A Deal distribution by industry Year All Mergers Domestic
Mergers Cross-border Mergers Industry Acquiror Target Q3 and Q4 1997 246 158 88 Consumer Products
and Services 1215 1340 1998 485 310 175 Consumer Staples 386 364 1999 515 310 205 Government and Agencies 1 10
2000 630 401 229 Healthcare 413 481
2001 478 287 191 High Technology 1254 1392
2002 330 216 114 Industrials 1254 1184
2003 300 179 121 Materials 539 552
2004 358 244 114 Media and Entertainment 859 758
2005 381 248 133 Real Estate 679 546 2006 444 267 177 Retail 478 478 2007 480 304 176 Telecommunications 241 214 2008 311 172 139 Total 7319 7319 2009 193 115 78 2010 262 149 113 2011 227 123 104 2012 210 125 85 2013 212 132 80 2014 245 157 88 2015 244 148 96 2016 218 150 68 2017 190 124 66 2018 194 114 80 2019 166 104 62 Total 7319 4537 2782
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Table 2 Summary Statistics of individual M&A deals and acquirer financials in the previous
12 months
Table 2 reports the descriptive statistics of the full sample in Panel A, the sample in Panel B consists only of domestic mergers, and the sample in Panel C consists only of cross-border mergers. The monthly EPU variable is constructed using the natural logarithm of the previous month’s EPU index (Baker, Bloom, and Davis 2016) with the following weighting: EPUt=50%×EPU_Indext+33.33%×EPU_Indext-1+16.66%×EPU_Indext-2. The CAR variable shows the acquirer’s cumulative abnormal return with a 3- and 11-day event window, the abnormal return is based on the market-adjusted model. The Deal Value is the natural logarithm of the M&A Deal Value in Hundred Thousand GBP (Pound sterling). The Return on Asset is the ratio of acquirer net income to acquirer total assets in the previous 12 months. Total Assets is the natural logarithm of Total Assets in the previous 12 months. Book leverage is the ratio of acquirer total liabilities to acquirer total assets in the previous 12 months. Cash to Total Assets is the ratio of acquirer cash to acquirer total assets in the previous 12 months.
Observation Mean Standard Deviation Min Median Max Skewness Kurtosis
Panel A. Full Sample
EPU 7319 4.684 0.726 3.410 4.421 6.758 0.580 2.453 CAR [-1, +1] 7319 0.012 0.063 -0.566 0.004 1.157 3.621 52.959 CAR [-5, +5] 7319 0.021 0.112 -0.832 0.011 2.034 2.871 40.080 Deal Value 7319 4.339 1.902 0.000 4.245 12.902 0.395 3.324 Return on Assets 7319 0.014 0.246 -4.893 0.050 1.853 -8.442 118.234 Total Assets 7319 5.119 2.189 -2.293 5.018 12.053 0.222 2.958 Book Leverage 7319 0.540 0.260 0.000 0.531 3.261 1.695 12.426 Cash to Total Assets 7319 4.684 0.726 3.410 4.421 6.758 0.580 2.453
Panel B. Domestic Sample
EPU 4537 4.671 0.729 3.410 4.404 6.758 0.607 2.503 CAR [-1, +1] 4537 0.012 0.066 -0.566 0.003 1.157 4.021 60.140 CAR [-5, +5] 4537 0.021 0.116 -0.818 0.010 2.034 3.565 48.907 Deal Value 4537 4.043 1.776 0.000 3.959 11.717 0.319 3.258 Return on Assets 4537 0.009 0.244 -4.893 0.045 1.853 -7.579 104.262 Total Assets 4537 4.660 1.998 -2.293 4.596 11.995 0.163 2.937 Book Leverage 4537 0.540 0.267 0.000 0.527 3.261 1.956 14.011 Cash to Total Assets 4537 4.671 0.729 3.410 4.404 6.758 0.607 2.503
Panel C. Cross-border Sample
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Table 3 shows the summary statistics of the monthly country-level statistics. The aggregated natural logarithms for No. of M&A Deals and Value of M&A Deals of every month are calculated respectively for the full, domestic, and cross-border sample. The stock market data for the monthly country-level determinants is taken from Thompson Reuters Datastream and the annual British gross domestic product (GDP) to calculate the average monthly GDP growth is provided by the Office for National Statistics. The Pearson correlation matrix for and Table 3 can be found in Appendix A. The correlation among the independent variables for individual M&A deals in Table 2 stays below 0.6, for the country-level statistics in Table 3 a correlation of 0.5 is not exceeded. Possible multicollinearity issues are acknowledged and discussed in the empirical results section.
Table 3 Summary statistics of monthly country-level M&A activity
Table 3 reports the descriptive statistics of the monthly country-level statistics. The analysis includes 270 months from July 1997 until December 2019. No. of M&A Deals is the natural logarithm of the number of M&A deals in a given month for the full sample, domestic deals, and cross-border deals. Aggregated Value of M&A Deals is the natural logarithm of aggregated M&A deal volume in a given month for the full sample, domestic deals, and cross-border deals. The EPU variable in a given month is constructed using the natural logarithm of the previous month’s EPU index (Baker, Bloom, and Davis 2016) with the following weighting: EPUt=50%×EPU_Indext+33.33%×EPU_Indext-1+16.66%×EPU_Indext-2. GDP Growth is calculated using the average 6-month GDP growth based on the annual GDP at market prices. Yield Spread is the average 6-month difference between the central bank policy rate and the interbank offered rate. Market Return is the 6-month market return of the FTSE All-share index.
Observation Mean Standard Deviation Min Median Max Skewness Kurtosis
No. of M&A Deals
Full Sample 270 3.182 0.489 1.946 3.178 4.511 0.005 2.345 Domestic Deals 270 2.678 0.551 1.099 2.708 4.060 -0.241 2.621 Cross-border
Deals 270 2.176 0.583 0.000 2.197 3.497 -0.497 3.787
Value of M&A Deals
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In the following, I depict the British EPU index and the M&A deal-activity by the number of Deals and aggregated value of Deals in a given month for the full merger sample. Figure 1 shows the trend of decreasing M&A deal-making and growing economic policy uncertainty. Historic events and crises that could contribute to the rise in economic policy uncertainty are also included.
Figure 1. The economic policy uncertainty index and the number of M&A deals in the
United Kingdom
Figure 1 shows the monthly EPU index on the left axis as well as the absolute Number of M&A deals in a given month on the right axis. Notable events are added to illustrate how the EPU is behaving during or after a crisis.
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Figure 2. The economic policy uncertainty index and the aggregated value of M&A deals in
the United Kingdom
Figure 2 shows the monthly EPU index on the left axis as well as the aggregated value of M&A deals in a given month on the right axis. Notable events are added to illustrate how the EPU is behaving during or after a crisis.
5.
Methodology
The methodology used in this paper is detailed in the following section. Firstly, the main independent variable acting as a proxy for economic policy uncertainty is introduced. Secondly, the method for investigating the effect of economic policy uncertainty on M&A activity is described. Lastly, the specification of the acquirer return analysis and its key components are explained.
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months (Nguyen and Phan 2017).7 The use of this formula is also in accordance with Tampakoudis et al. (2019), who explains that the weighted average is used to assign heavier importance to the most recent months relative to the announcement. The monthly aggregated country-level impact of EPU on deal activity is investigated using the ordinary least square (OLS) regression design (I).In the case of evident heteroscedasticity, based on the Breusch-Pagan (1979) and Cook-Weisberg (1983) test, robust variance estimates are used to obtain unbiased standard errors.
𝑀𝑀&𝐴𝐴 𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝑡𝑡 = 𝛼𝛼𝑡𝑡+ 𝛽𝛽1𝐸𝐸𝐸𝐸𝐸𝐸𝑡𝑡−1+ 𝛽𝛽𝑛𝑛× 𝐶𝐶𝐶𝐶𝐶𝐶𝐴𝐴𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝑡𝑡−1+ +𝜀𝜀𝑡𝑡 (I)
The dependent variables which are being used as a proxy for monthly, country-level M&A activity are the natural logarithm of the number of M&A deals as well as the aggregated value of M&A deals. The aggregated M&A activity analysis is related to the industry-level aggregated M&A activity analysis done by Nguyen and Phan (2017) and the country-level aggregated M&A activity analysis by Bonaime, Gulen, and Ion (2018).
Following Nguyen and Phan (2017) and Bonaime, Gulen, and Ion (2018) I include a control variable for the 6-month average GDP growth as a measure of economic activity, the 6-month average Yield Spread between the central bank policy rate and the inter-bank offered rate (LIBOR) as a measure of market liquidity, and the 6-month market return of the FTSE All-Share index for the general market condition.
Corporate investment activity and the main independent variable EPU could jointly be correlated with non-policy related factors which leave the previous regressions with omitted variable bias. This endogeneity concern is addressed by implementing an instrumental variable
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(IV) model, the resolution methodology is initially proposed by Gulen and Ion (2016). The UK corporate environment is closely linked to the US through trade and business relationships, I thus assume that economic policy uncertainty in the US is spilling over to economic policy uncertainty in the UK. A one-year lag for the US EPU variable is introduced to resolve simultaneity concerns.
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market-adjusted model for shot-term event studies, based on Brown and Warner (1985) who showcase the reliability and validity of both of these models. Based on the market model the daily abnormal return (AR) is thus calculated by using the daily event window return and subtracting the return of the market portfolio as shown in equation (III).
𝐸𝐸(𝑅𝑅𝐴𝐴, 𝐴𝐴)=𝑅𝑅𝑚𝑚,𝑡𝑡 (II)
𝛢𝛢𝑅𝑅𝑖𝑖,𝑡𝑡 = 𝑅𝑅𝑖𝑖,𝑡𝑡 − 𝑅𝑅𝑚𝑚,𝑡𝑡 (III)
𝑅𝑅𝑖𝑖,𝑡𝑡= observed daily return of stock i at time t
𝑅𝑅𝑚𝑚,𝑡𝑡= observed daily return of the market portfolio t
The main dependent variable for calculating the cumulative abnormal return (CAR) is calculated by summing up the daily-abnormal returns (IV).
𝐶𝐶𝐴𝐴𝑅𝑅𝑖𝑖(𝐴𝐴1, 𝐴𝐴2) = ∑𝑡𝑡𝑡𝑡=𝑡𝑡2 1𝛢𝛢𝑅𝑅𝑖𝑖,𝑡𝑡 (IV)
As a next step, the sample is categorized into mergers during low policy EPU and high policy EPU based on the median EPU, to investigate the relationship between acquirer returns and economic policy uncertainty. A “one-sample” t-test is implemented to determine whether there is a statistically significant CAR different from 0 for the two groups over the event-window period. Further, a “difference between two means” t-test is conducted to check if there is a statistically significant difference between the two mean CAR.
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The interaction term can not be included in equation (VI) because the statistical-significance of lower-order coefficient are not applicable for main effect hypothesis testing. (Braumoeller 2004).
𝐶𝐶𝐴𝐴𝑅𝑅𝑖𝑖 = 𝛼𝛼𝑖𝑖 + 𝛽𝛽1𝐸𝐸𝐸𝐸𝐸𝐸𝑖𝑖+ 𝛽𝛽2𝐷𝐷𝐶𝐶𝐷𝐷𝐷𝐷𝐷𝐷𝐴𝐴𝐴𝐴𝐴𝐴𝑖𝑖 + 𝛽𝛽𝑛𝑛 × 𝐶𝐶𝐶𝐶𝐶𝐶𝐴𝐴𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝑖𝑖,𝑛𝑛+𝜀𝜀𝑖𝑖 (VI)
𝐶𝐶𝐴𝐴𝑅𝑅𝑖𝑖 = 𝛼𝛼𝑖𝑖+ 𝛽𝛽1𝐸𝐸𝐸𝐸𝐸𝐸𝑖𝑖+ 𝛽𝛽2𝐷𝐷𝐶𝐶𝐷𝐷𝐷𝐷𝐷𝐷𝐴𝐴𝐴𝐴𝐴𝐴𝑖𝑖+ 𝛽𝛽2𝐸𝐸𝐸𝐸𝐸𝐸𝑖𝑖 × 𝐷𝐷𝐶𝐶𝐷𝐷𝐷𝐷𝐷𝐷𝐴𝐴𝐴𝐴𝐴𝐴𝑖𝑖+ 𝛽𝛽𝑛𝑛× 𝐶𝐶𝐶𝐶𝐶𝐶𝐴𝐴𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝑖𝑖,𝑛𝑛+𝜀𝜀𝑖𝑖 (VII)
The choice of control variables is based on the literature examining the relationship between economic policy uncertainty and M&A. Deal Value is calculated using the natural logarithm of the M&A Deal Value in Hundred Thousand GBP. The Return on Asset is the ratio of acquirer net income divided by the acquirer total assets in the previous 12 months. Total Assets is stated as the natural logarithm of Total Assets in the previous 12 months. Book leverage is acquirer total liabilities to acquirer total assets in the previous 12 months. Cash to Total Assets is the ratio of acquirer cash to acquirer total assets in the previous 12 months. To ensure robustness an alternative calculation for the economic policy variable as proposed by Sha, Kang, and Wang (2020) is included, “EPU Sha” is defined as the 6-month average natural logarithm of the EPU index. Industry-fixed effects are included to control for common industry factors that could affect abnormal returns of acquirers.
6.
Empirical Results
6.1. M&A Activity
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uncertainty. The EPU coefficient (-0.403) in column 2 entails that, if the previous 3-month weighted EPU is increased by 1% this leads to a decrease in the number of M&A deals in a given month by 0,403%. These findings are economically significant given that the weighted, 3-month EPU fluctuates by 11,1% on average. The slowdown of merger activity due to economic policy uncertainty in the United Kingdom is in line with the results of the empirical research on US mergers done by Nguyen and Phan (2017) and Bonaime, Gulen, and Ion (2018) and supports H1a. GDP Growth is shown to have a significant positive effect on the number of mergers. If the average GDP growth is 1% higher, this leads to an increase of M&A deals by 0.14% (13.506 × 0.010 =0.135).
Table 4 Number of M&A Deals and Economic policy uncertainty
Table 4 reports the results of the OLS regression following equation (I). The dependent variable No. of M&A Deals is the natural logarithm of the aggregated number of M&A deals in a given month. The main independent variable EPU is calculated using the natural logarithm of the weighted EPU index of the previous 3 months. The control variables are based on the previous 6-month period. Three different merger samples are examined. The Full Sample comprises all mergers in a given month. The Domestic Sample comprises the domestic mergers in a given month. The Cross-border Sample comprises domestic mergers in a given month. The t -statistics are reported in parentheses. *, **, and *** indicate significance at the 10%, 5%, and 1% levels, respectively. Heteroscedasticity-robust standard errors are applied in the regressions where heteroscedasticity is evident based on the Breusch-Pagan (1979) and Cook-Weisberg (1983) test.
23
24
Table 5 Value of M&A Deals and Economic policy uncertainty
Table 5 reports the results of the OLS regression following equation (I). The dependent variable Value of M&A Deals is the natural logarithm of the aggregated value of M&A deals in a given month. The main independent variable EPU is calculated using the natural logarithm of the weighted EPU index of the previous 3 months. The control variables are based on the previous 6-month period. Three different merger samples are examined. The Full Sample comprises all mergers in a given month. The Domestic Sample comprises the domestic mergers in a given month. The Cross-border Sample comprises domestic mergers in a given month. The t -statistics are reported in parentheses. *, **, and *** indicate significance at the 10%, 5%, and 1% levels, respectively. Heteroscedasticity-robust standard errors are applied in the regressions where heteroscedasticity is evident based on the Breusch-Pagan (1979) and Cook-Weisberg (1983) test.
Full Sample Domestic Sample Cross-border Sample Value of M&A Deals (1) (2) (3) (4) (5) (6) EPU -0.248*** -0.079 -0.429*** -0.249*** -0.241** -0.104 (-2.602) (-0.836) (-5.329) (-2.613) (-2.043) (-0.816) GDP Growth 38.241*** 38.359*** 32.179** (3.750) (3.747) (2.359) Yield Spread -0.175 -0.201 -0.134 (-0.908) (-1.042) (-0.522) Market Return 0.749 1.572** 0.336 (1.009) (2.110) (0.338) Intercept 10.515*** 9.002*** 10.239*** 8.665*** 9.708*** 8.454*** (22.732) (14.839) (26.076) (14.229) (16.430) (10.420) Observations 270 270 270 270 270 270 Adj. R² 0.02 0.10 0.07 0.15 0.01 0.03
25
true for the respective t-statistics of the EPU coefficient and thus the statistical significance of economic policy uncertainty.
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Table 6 Instrumental-variable (IV) approach for the M&A deal-making activity and
Economic policy uncertainty
Table 6 reports the results of the Two-Stage Least Squares (2SLS) regressions following equation (I). The instrumental variable used in the first stage regression to instrument the British EPU variable is the natural logarithm of the 3-month weighted EPU of the United States. The dependent variables in the second stage regressions are No. of M&A Deals and the Value of M&A Deals that show the natural logarithm of the number and aggregated value of M&A deals in a given month. The variable EPU is calculated using the natural logarithm of the weighted EPU index of the previous 3 months. The control variables are based on the previous 6-month period. Three different merger samples are examined. The Full Sample comprises all mergers in a given month. The Domestic Sample comprises the domestic mergers in a given month. The Cross-border Sample comprises domestic mergers in a given month. The t -statistics are reported in parentheses. *, **, and *** indicate significance at the 10%, 5%, and 1% levels, respectively. Heteroscedasticity-robust standard errors are applied in the regressions where heteroscedasticity is evident based on the Breusch-Pagan (1979) and Cook-Weisberg (1983) test.
Full
Sample Domestic Sample Cross-border Sample Sample Full Domestic Sample Cross-border Sample EPU No. of M&A Deals Value of M&A Deals
(1) (2) (3) (4) (5) (6) (7) EPU US 0.923*** (8.747) Instrumented EPU -0.571*** -0.540*** -0.648*** -0.189 -0.143 -0.345 (-7.880) (-6.503) (-6.236) (-0.834) (-0.626) (-1.132) GDP Growth -25.466*** 7.091* 10.461** 1.107 34.037*** 42.427*** 22.948 (-3.594) (1.728) (2.224) (0.188) (2.650) (3.292) (1.330) Yield Spread -0.103 -0.080 -0.090 -0.078 -0.184 -0.192 -0.155 (-0.950) (-1.305) (-1.281) (-0.888) (-0.958) (-0.999) (-0.600) Market Return -0.965** 0.036 0.284 -0.481 0.642 1.676** 0.102 (-2.295) (0.149) (1.013) (-1.374) (0.841) (2.185) (0.099) Intercept 1.197** 5.907*** 5.185*** 5.408*** 9.635*** 8.052*** 9.845*** (2.213) (13.866) (10.618) (8.854) (7.226) (6.017) (5.497) Observations 270 270 270 270 270 270 270 Adj. R² 0.29 0.50 0.49 0.28 0.10 0.16 0.03
27
compared to the EPU coefficient for the domestic sample in in column (3) and (6). The more pronounced effect that EPU has on domestic mergers, that was inferred from Table 4 and Table 5 is thus not supported when instrumenting the UK EPU with the one year lagged EPU. The results of the IV approach thus show that the differences between the statistical significance and size of the EPU coefficient for the different sub-samples could be subject to endogeneity caused by omitted variables. The overall negative impact of economic policy uncertainty on merger activity can be shown in all regression models. The empirical analysis does support H1a and H1b can be rejected.
6.2. M&A Acquirer Return
Table 7 depicts the full sample of 7319 individual mergers split along the median EPU and their respective average cumulative abnormal return (CAR) for the 3-day (-1, +1) and 11-day (-5, 5) event window.
Table 7 Acquirer return during times of low and high economic policy uncertainty
Table 7 presents t-test statistics of acquirer cumulative abnormal return (CAR) during times of low and high policy uncertainty, 3659 of 7319 mergers above the median EPU are categorized as “High Policy Uncertainty”. The t-statistics are reported in parentheses, respectively for the “one-sample” t-test and the “difference between two means” t-test. *, **, and *** indicate significance at the 10%, 5%, and 1% levels, respectively.
Low Economic Policy
Uncertainty High Economic Policy Uncertainty Difference Low-High CAR (-1, +1) 0.0103***
(10.20) 0.0132*** (12.30) -0,0029** (-1.97) CAR (-5, +5) 0.0187***
(10.04) 0.0225*** (12.19) -0,0038 (-1,46)
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for the 3-day event window while the analysis for the 11-day event window is insignificant. One cause for the lower statistical significance of the 11-day event window models (4), (5), and (6) could be the heavier tails of the dependent variable, measured by the kurtosis (53.0 versus 40.1), and thus the higher number of outliers of the CAR (-5,+5) distribution when compared to CAR (-1,+1). The “difference between two means” t-test does not give a definite answer whether the samples differ due to economic policy uncertainty. The support for Hypothesis 2, stating that mergers during times of higher economic policy have a greater positive significant effect on acquirer return, is thus limited and will be further analyzed in Table 8 and Table 9. The results also show that the CAR for the 11-day event window is larger than for the 3-day event window. This difference in size is an indicator that the UK market is not efficient when reacting to the merger announcement, according to the semi-strong efficient-market hypothesis. In the following, the relationship between economic policy uncertainty and acquirer return is further investigated and the domestic versus cross-border deals dimension is incorporated.
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Table 8 Acquirer return, economic policy uncertainty, and domestic deals
Table 8 reports the results of the OLS regression following equation (VII). The dependent variable CAR is the acquirer’s cumulative abnormal return over the 3-day (-1, +1) and 11-day (-5, +5) announcement event window based on the market-adjusted model. The main independent variable EPU is calculated using the natural logarithm of the weighted EPU index of the previous 3 months. The moderator variable Domestic Dummy equals{1} for domestic deals between two companies headquartered in the UK and {0} otherwise. EPU × Domestic Dummy is the interaction term of EPU and Domestic Dummy. The Deal Value is the natural logarithm of the M&A Deal Value in Hundred Thousand GBP. The Return on Asset is the ratio of acquirer net income to acquirer total assets in the previous 12 months. Total Assets is the natural logarithm of Total Assets in the previous 12 months. Book leverage is the ratio of acquirer total liabilities to acquirer total assets in the previous 12 months. Cash to Total Assets is the ratio of acquirer cash to acquirer total assets in the previous 12 months. The High-Tech Dummy equals {1} if the acquirer’s macro industry is High Technology and {0} otherwise. The t -statistics are reported in parentheses. *, **, and *** indicate significance at the 10%, 5%, and 1% levels, respectively. Heteroscedasticity-robust standard errors are applied in the regressions where
heteroscedasticity is evident based on the Breusch-Pagan (1979) and Cook-Weisberg (1983) test. CAR (-1, +1) CAR (-5, +5) (1) (2) (3) (4) (5) (6) EPU 0.002** 0.003** 0.006*** 0.002 0.003* 0.006** (2.021) (2.574) (3.954) (0.830) (1.707) (2.018) Domestic Dummy -0.001 0.026*** -0.004 0.016 (-0.915) (2.723) (-1.546) (0.881)
EPU x Domestic Dummy -0.006*** -0.004
(-2.910) (-1.155) Deal Value 0.003*** 0.003*** 0.003*** 0.003*** (4.739) (4.619) (3.273) (3.223) Return on Assets -0.013 -0.013 -0.021 -0.021 (-1.368) (-1.377) (-1.621) (-1.626) Total Assets -0.004*** -0.004*** -0.007*** -0.006*** (-7.325) (-7.189) (-6.650) (-6.570) Book Leverage -0.003 -0.003 0.002 0.001 (-0.728) (-0.746) (0.217) (0.209) Cash to Total Assets 0.000 0.001 -0.012 -0.011
(0.009) (0.081) (-0.936) (-0.907) Intra-Industry Dummy -0.002 -0.002 -0.001 -0.001 (-1.026) (-1.035) (-0.255) (-0.259) High-Tech Dummy -0.001 -0.001 -0.003 -0.003 (-0.346) (-0.306) (-0.600) (-0.583) Intercept 0.002 0.011* -0.006 0.014 0.030*** 0.017 (0.517) (1.745) (-0.700) (1.533) (2.708) (1.079) Observations 7319 7319 7319 7319 7319 7319 Adj. R² 0.001 0.016 0.017 0.000 0.012 0.012
30
deals. In columns (3) and (6) the interaction term EPU × Domestic Dummy is introduced and shows a negative joint-effect of economic policy uncertainty on domestic mergers. This finding supports Hypothesis 3 and could be attributed to the wealth-creating effect of diversifying risk through cross-border mergers in times of heightened policy uncertainty. When doing the statistical hypothesis testing based on the significance of the coefficients, it shows that support of the hypothesis depends on the length of the event window. The support is highly significant for the 3-day event window in column (3) and insignificant for the 11-day event window in column (6). The results in column (3) postulate that if the logarithmic EPU variable is 0 domestic mergers cumulative abnormal return is 2.6% higher than cross-border returns. Domestic and cross-border deals equal the same acquirer returns when the logarithmic EPU variable takes on a value of 4.33 (0.026-4.33×0.006=0) which equals a 3-month weighted EPU of 76. This threshold level is exceeded in 224 of 270 (83%) months included in the sample. The merger Deal Value is positive and significant in all the models depicted in Table 8. Acquirer’s Total Assets are shown to have a highly significant negative effect on CAR which has also been found by Nguyen and Phan (2017) and Sha, Kang, and Wang (2020). The other control variables Book Leverage and High-tech industry as proposed by Nguyen and Phan (2017) as well as Cash to Total Assets by Sha, Kang, and Wang (2020) show no statistical significance in my observation. The Intra-Industry dummy is also shown to have no statistically significant effect on CAR.
31
significance of the weighted 3-month EPU variable in column (2) and (5) in Table 9 with the significance of the average 6-month EPU variable, it supports the notion that the alternative EPU variable can also be used as a highly significant determinant of acquirer’s cumulative abnormal returns. The joint-effect of economic policy uncertainty and domestic deals for the short-event window in column (2) and (6) is statistically significant and negative which is in accordance with the findings of Table 8. Besides positively affecting the adjusted coefficient of determination including the industry fixed effects in the regression does not affect the statistical significance of other control variables, which shows that the previous findings are robust. Summarizing the empirical results of the main regression model and the robustness checks shows reliant support for Hypothesis 2, which postulates a positive significant relationship between economic policy uncertainty and acquirer returns. Statistical hypothesis testing of Hypothesis 3 is showing that the statistically significant negative joint-effect of economic policy uncertainty and domestic mergers is solely present for the 3-day event window.
Thus, I do not find sufficient support for Hypothesis 3 that domestic mergers during times of high economic policy uncertainty are associated with a lower increase in shareholder value than cross-border mergers.
Table 9 Robustness of Acquirer return, economic policy uncertainty, and domestic deals
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Heteroscedasticity-robust standard errors are applied in the regressions where heteroscedasticity is evident based on the Breusch-Pagan (1979) and Cook-Weisberg (1983) test.
CAR (-1, +1) CAR (-5, +5) CAR (-1, +1) CAR (-5, +5)
(1) (2) (3) (4) (5) (6) (7) (8) EPU Sha 0.003*** 0.006*** 0.005*** 0.008*** (3.231) (4.031) (2.796) (2.698) EPU 0.003*** 0.007*** 0.004** 0.006** (2.942) (4.010) (1.982) (2.066) Domestic -0.001 0.022** -0.004 0.014 -0.001 0.025*** -0.002 0.014 (-0.934) (2.260) (-1.574) (0.739) (-0.422) (2.618) (-0.808) (0.796) EPU Sha x Domestic -0.005** -0.004
(-2.439) (-1.007) EPU x Domestic -0.005*** -0.004 Deal Value 0.003*** 0.003*** 0.003*** 0.003*** 0.003*** 0.003*** 0.003*** 0.003*** (4.720) (4.619) (3.241) (3.197) (4.708) (4.595) (3.226) (3.187) Return on Assets -0.013 -0.013 -0.021 -0.021 -0.013 -0.013 -0.021 -0.021 (-1.366) (-1.377) (-1.612) (-1.620) (-1.367) (-1.372) (-1.592) (-1.595) Total Assets -0.004*** -0.004*** -0.007*** -0.007*** -0.004*** -0.004*** -0.007*** -0.007*** (-7.371) (-7.235) (-6.723) (-6.632) (-7.273) (-7.190) (-6.573) (-6.524) Book Leverage -0.003 -0.003 0.002 0.002 -0.004 -0.004 0.002 0.002 (-0.655) (-0.674) (0.348) (0.340) (-0.957) (-0.958) (0.220) (0.220) Cash to Total Assets 0.000 0.001 -0.011 -0.011 0.000 0.001 -0.011 -0.011
(0.034) (0.092) (-0.897) (-0.872) (0.014) (0.097) (-0.886) (-0.856) Intra Industry Dummy -0.002 -0.002 -0.001 -0.001 -0.001 -0.001 -0.001 -0.001
(-1.001) (-1.003) (-0.218) (-0.218) (-0.818) (-0.826) (-0.243) (-0.246) High-Tech Dummy -0.001 -0.001 -0.003 -0.003
(-0.385) (-0.352) (-0.655) (-0.640)
Industry FE Yes Yes Yes Yes
Intercept 0.008 -0.007 0.019* 0.008 0.007 -0.009 0.023** 0.013 (1.184) (-0.776) (1.757) (0.528) (1.103) (-0.981) (2.028) (0.793) Observations 7319 7319 7319 7319 7319 7319 7319 7319 Adj. R² 0.017 0.017 0.013 0.013 0.019 0.019 0.014 0.014
7.
Conclusion
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economic policy uncertainty. This effect can be observed for both domestic and cross-border deals. In the acquirer return analysis, it is shown that mergers are overall a shareholder value-creating activity and economic policy uncertainty is shown to have a positive effect on the acquirer return. When comparing domestic versus cross-border deals the regression model shows that the acquirer return is not affected by the type of merger. The joint-effect between economic policy uncertainty and domestic deals is negative and thus suggests that during times of high economic policy uncertainty domestic mergers are less profitable when compared to cross-border mergers. The statistical significance of this relationship holds for the 3-day and not the 11-day event and is thus not reliant for making a generalized acquirer return evaluation.
The first limitation that event studies are not able to include numerous qualitative M&A motives and are also falling victim to a sampling bias because studies based on stock market returns are only feasible for public companies.(Adnan et al. 2016) The accuracy of the risk model used for calculating the abnormal returns is also a limitation of this study, although the market adjusted model is shown to be reliable and valid (Brown and Warner 1985) most of the previous studies use abnormal returns that are calculated with the market model based on asset pricing models such as CAPM or Fama-French. Using the market model could lead to higher accuracy of the abnormal return calculations and thus more explicit regression results.
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Appendix
Appendix A: Pearson correlation matrix
Appendix A presents a Pearson correlation matrix including the variables depicted in Table 2 and Table 3 Full Sample Correlation Matrix. Individual M&A Deals
1 2 3 4 5 6 7 8 EPU 1 CAR [-1, +1] 0.023 1 CAR [-5, +5] 0.010 0.634 1 Deal Value 0.095 0.001 -0.021 1 Return on Assets 0.022 -0.074 -0.069 0.138 1 Total Assets 0.124 -0.094 -0.093 0.594 0.273 1 Book Leverage -0.126 -0.027 -0.005 0.034 -0.041 0.113 1 CashtoTotalAssets -0.039 0.043 0.022 -0.100 -0.195 -0.296 -0.243 1 Full Sample Correlation Matrix: Country-level M&A activity
1 2 3 4 5 6
EPU 1
Value of M&A Deals -0.714 1
No. of M&A Deals -0.165 0.437 1
GDP Growth -0.372 0.470 0.308 1
Yield Spread 0.145 -0.244 -0.191 -0.458 1
