The influence of consolidation and
internationalization on systemic risk in the
financial sector
Abstract
This paper analyses the impact of banking mergers on systemic risk, with in particular if
internationalization prior to acquisition increases systemic risk. By using the marginal expected shortfall methodology for an international sample of mergers, a significant increase in systemic risk is found as a result of mergers in the financial sector. Moreover, if a bank is operating internationally prior to acquisition, this increases systemic risk. Additionally, there is evidence of a too-big-to-fail motive for relatively smaller banks to use mergers to become systemically important. The results confirm that consolidation in the financial sector increases fragility of the financial system.
Keywords: Marginal Expected Shortfall, Internationalization, Mergers and Acquisitions, Banking, Consolidation
Author: Rinke Bakker
University: Rijksuniversiteit Groningen Faculty: Faculty of Economics and Business
Degree program: MSc. International Financial Management Thesis Supervisor: Martien Lamers
2
1. Introduction
The financial crisis that began in August 2007 reached systemic levels with the collapse of Lehman Brothers in September 2008. Systemic risk is now one of the key items for regulators on a
macroprudential level, as the aftermath of the financial crisis demonstrated there were severe financial instabilities that had a significant impact on the real economy (Berger, Molyneux and Wilson, 2014). As each bank has different strategies with respect to risk-taking and how they react to regulations (Laeven and Levine, 2009), it is important to study the many factors that influence systemic risk. One of the main concerns is that consolidation in the financial sector has resulted in a few systemically important banks (2014). The main method in which banks consolidate is through mergers and acquisitions (M&A) (hereafter used interchangeably). However, consolidation has not only been domestic, but also cross-border. In this case the regulatory framework in which banks operate changes, which could allow banks to shift risks to countries with less stringent regulations (Ongena, Popov and Udell, 2013).
Research on systemic risk has gained much traction, with various new methods for assessing systemic risk (Acharya, Pedersen, Philippon and Richardson, 2010; Brownlees and Engle, 2012). However, with these methods, there is no consensus in the literature about whether mergers result in higher levels of systemic risk (Karolyi and Taboada, 2015; Vallascas and Hagendorff, 2011; Weiss, Neumann and Bostandzic, 2014). Moreover, current research does not go in-depth on the influence of internationalization on systemic risk, as international banks could potentially make use of
regulatory arbitrage shifting risk to countries with less stringent regulations (Ongena, Popov and Udell, 2013).
To research how internationalization plays a role in systemic risk change as a result of mergers, this paper extends the work of Weiss, Neumann and Bostandzic (2014). They researched whether bank mergers increase the systemic risk of financial firms, and were concerned with mainly American and European banks, but did not explore how internationalization plays a role, nor why the international regulatory operating network is important. The main research question is how consolidation and internationalization of banks influences systemic risk.
3 this respect is of key importance, because cross-border acquisitions allow banks to make use of regulatory arbitrage, while an already international bank can make use of its extensive network to shift risks away from the regulator. This prediction is in line with research that demonstrates that banks use capital flows to shift funds to countries with less stringent regulations (Houston, Lin and Ma, 2012). This leads to the following questions: How does internationalization influence systemic risk of an acquiring bank as a result of acquisitions? Is there any interaction between
internationalization prior to acquisition and whether the new acquisition is domestic or cross-border? These questions should help regulators develop policies to govern mergers within the financial sector. Moreover, this paper adds to the literature that assesses how mergers influence systemic risk, as there is no clear consensus (Karolyi and Taboada, 2015).
A sample of 528 mergers worldwide announced between 1998 and 2016 was used. Following the popular methodology of Acharya, Pedersen, Philippon and Richardson (2010) on how to assess systemic risk, a significant impact of mergers on systemic risk is found. Moreover, interaction between internationalization and cross-border acquisitions, when controlling for factors such as size and leverage, significantly increases the impact of a bank merger on systemic risk. In other words, an international bank that acquires another bank cross-border will on average become more
systemically relevant, whereas a domestic bank that acquires cross-border does not experience an increase in systemic risk. Thus, the already established network of a bank is key to how systemic risk develops.
The implications of these results are that regulators should consider that mergers in the banking sector generally increase systemic risk, and thus increase the fragility of the financial sector. Cross-border mergers only increase systemic risk when the bank is international prior to acquisition, but when a domestic firm acquires cross-border, there is a reduction in systemic risk. There is evidence of a too-big-to-fail incentive for banks to become systemically important and make use of implicit government subsidies that could result in excessive risk-taking. For the management of banks, this is also useful to consider, as systemic risk is measured by the shortfall of equity, thus their decisions influence firm value during systemic shocks.
This paper is structured as follows. First, the literature on systemic risk and internationalization is assessed. A theoretical model on diversification versus diversity of banks is used to explore how diversification influences systemic risk (Wagner, 2010). The model is applied in the case of
4 to calculate systemic risk, and how this is implemented by Weiss, Neumann and Bostandzic (2014). By following Weiss, Neumann and Bostandzic (2014)in structure and method, the results can be compared, and this provides a strong foundation for exploring internationalization. The section is concluded with hypotheses based on the literature. Then, the sample is explored and the criteria for the final selection used is examined. This is followed by the results based on sub-samples and multivariate regressions. The findings are explored and linked back to the hypotheses and literature to investigate what these results imply. In the final section, the conclusion is followed by the limitations of the research, and directions for further research are explored.
2. Literature review
This section first explores how systemic risk and mergers are related, which is done by using the theoretical work of Wagner (2010) on how the diversification strategies of banks reduce diversity and increase systemic risk. This is caused by an increase in the probability of joint failure of both banks. Based on these insights, Wagner’s (2010) model is applied to mergers of banks and how these influence systemic risk. Next, the various ways in which regulations impact systemic risk are
explored, and similar research on systemic risk and mergers is summarized. The paper of Weiss, Neumann and Bostandzic (2014) is assessed in detail, as their structure forms the basis of this paper. Following this, the link between systemic risk changes due to mergers and internationalization is made. Theories on internal capital flows and cross-border regulations are used to identify in which ways the internationalization of a bank prior to acquisition influences systemic risk. The distinction is made between cross-border acquisitions and domestic acquisitions. Finally, the section concludes with three hypotheses, based on the work of Weiss, Neumann and Bostandzic (2014),
internationalization and the interaction between cross-border acquisitions and internationalization.
2.1 Systemic risk and mergers
5 the system, rather than eliminated (Weiss, Neumann and Bostandzic, 2014). Moreover, if the banks diversify their services, but offer these to the same clients, this is not true diversification, and increases the impact of shocks in these businesses (Berger, Molyneux and Wilson, 2014).
When analyzing a merger of banks using the model of Wagner (2010), this analysis involves two equally diversified (similar) banks. This is based on the theoretical insight of Markowitz (1991) that all investors will hold the same minimum variance portfolio with a riskless asset, with the
components based on individual risk appetite. This insight can be applied to banks also; if they all diversify in a similar manner, they will eventually hold the same portfolio of assets, which leads to zero diversity (Markowitz, 1991; Wagner, 2010). The outcome is that bank mergers can only lead to an increase in systemic risk.
The process of banks becoming more similar began with deregulation in the financial sector, which has allowed the banking sector to integrate financial services, resulting in financial conglomerates. This process in Europe began with the Second Banking Directive of 1989, and with the Gramm-Leach-Bliley Act of 1999 in the United States (US) (De Jonghe, 2010). Large conglomerates were formed through M&A, which led to more diversified banks on an individual level, but also to more similarity among them (Berger, Molyneux and Wilson, 2014.; De Nicolo and Kwast, 2002). Thus, based on the insights of Wagner (2010), these mergers increased systemic risk.
A nuance to the theory of Wagner (2010) is brought by Slijkerman, Schoenmaker and de Vries (2013), who demonstrate that, during systemic shocks, the diversification of banks into insurance activities can be useful, as insurance has different business models, and merging these activities can result in fewer liquidity problems during a shock, without influencing systemic risk.
Another way mergers can influence systemic risk is when the goal is to become too-big-to-fail and make use of implicit government guarantees to take on additional risk, with the public safety net covering the downside risks (Karolyi and Taboada, 2015). This moral hazard problem gained a great deal of attention in the aftermath of the financial crisis, when governments used public funds to bail-out banks. Carbo‐Valverde, Kane and Rodriguez‐Fernandez (2012) suggest that most mergers in the European Union (EU) between 1993 and 2004 were driven by acquirers’ incentive to make use of the safety nets.
6 (Berger, Molyneux and Wilson, 2014; Carbo‐Valverde, Kane and Rodriguez‐Fernandez, 2012).
Moreover, banks might be able to engage in activities that not permitted in their home country, which in turn increase systemic risk due to higher levels of risk involved.
There are some arguments that indicate that mergers decrease systemic risk, meaning consolidation in the financial sector has led to a more stable system. First, in a concentrated banking system, high market power can lead to higher profits. These profits can shield banks from shocks. Moreover, this can lead to a reduction in the incentives to take excessive risks (Beck, Demirguc-Kunt and Levine, 2006; Hellmann, Murdock and Stiglitz, 2000), which could lower systemic risk. Furthermore, a merger can create an internal money market that increases reserves against shocks (Carletti, Hartmann and Spagnolo, 2007). Second, with fewer banks, it is easier to monitor those in a concentrated banking system as opposed to many diverse banks. Carletti, Hartmann and Spagnolo (2007) argue that supervision in this situation might be more effective, which could result in fewer or less contagious systemic crises. Under the assumption of ceteris paribus, there will be larger banks in a more concentrated system, which in turn are better diversified and lead to a less fragile financial system. The problem is that, according to Wagner’s (2010) model, this can lead to lower
idiosyncratic risk, but does not lower the probability of multiple banks failing at the same time, thus resulting in higher systemic risk.
7 The structure and research design of Weiss, Neumann and Bostandzic (2014) as the basis for this paper. They use both MES and a lower tail-dependency measure to assess changes in systemic risk as the result of a merger. Their tail-dependency method was developed by themselves; however, this method only confirms that mergers lead to an increase in systemic risk. Management hubris, the presence of a permanent deposit insurance fund, and government-owned banks in the home
country increase the destabilizing effect of bank mergers. They do consider cross-border mergers, which they find are only significant for US banks, but do not explore internationalization prior to the merger. As regulatory arbitrage could play a significant role, this paper extends their original work to include internationalization.
2.2 Internationalization
This section initially focuses on how the internationalization of a bank, prior to acquisition, influences systemic risk. Next, it is assessed whether there is any difference between an
international bank acquiring domestically and cross-border. Mergers are not the only way for a bank to internationalize, other methods are greenfield projects, which means starting operations from scratch, and joint-ventures. For this paper, the focus is on mergers, as they result in a relatively big change in a small timeframe, that makes it possible to measure the change in systemic risk. One key aspect in this paper is that while cross-border mergers result in an international bank,
internationalization is defined as a bank already operating internationally prior to acquisition.
The link between internationalization (geographic diversification) and systemic risk has not yet been researched extensively; however, the impact of internationalization of banks on their total risk profile has been researched. Berger, El Ghoul, Guedhami and Roman (2016) find that
internationalization increases total risk, due to risks caused operating in foreign markets outweigh diversification benefits of these foreign operation. Banks internationalize to be able to diversify, enter new profitable countries with low competition and less stringent regulations, and make use of economies of scale (Nicolo, Bartholomew, Zaman and Zephirin, 2004). Moreover, banks might be able to gain access to a public safety net if they become too-big-to-fail, as liquidation due to bank failure can become too costly for society.
8 firms reduced cross-border lending following the collapse of Lehman Brothers (De Haas and Van Horen, 2012), indicating that they shifted capital to key locations.
Some nuance to this hypothesis is brought by Ivashina and Scharfstein (2010), who demonstrate that, depending on the stability of the deposits of the bank and the reliance on short-term debt, banks will cut their lending accordingly. Moreover, the withdrawal of cross-border lending depends on the closeness to the foreign country in terms of geography and lending relationships, as the nearer they are, the better the banks are able to judge risks (De Haas and Van Horen, 2012). The conclusion is that banks can use their geographic diversification to absorb shocks and retreat to their home country in times of stress.
However, if a large portion of banks uses similar internationalization strategies, the systemic risk can increase, according to Wagner’s (2010) model. A logical argument is that banks can transmit shocks in foreign countries across borders to their home country. First, if multiple banks use similar internationalization strategies, they increase systemic risk. Second, contagion through their lending portfolio channels can transmit the shock to the home country (Forbes and Warnock, 2012). Thus, international firms merging can lead to an increase in systemic risk as potential contagion increases.
Another way internationalization can impact systemic risk is the degree to which the acquirer can make use of regulatory arbitrage within their network. While international regulations have been converging over the years, and regulators understand the need to work together, the crisis of 2008 illustrated that problems remain. Acharya (2003) explains that the convergence of standards, such as capital requirements, can lead to a ‘race to the bottom’ if there is no consistency across borders concerning resolutions. The logic is that international banks will take more risks in countries with less stringent regulatory regimes, which forces their competitors to respond in similar fashion and the more country with more stringent regulations losing (large parts of) important banks. This can result in regulators converging on the lowest common denominator of regulations. Houston, Lin and Ma (2012) make the same conclusion. Moreover, they find strong evidence that banks have transferred capital to countries with fewer regulations. This can even be the case within the US and the EU, which have super-national regulator coordinating. In the US, regulators can implement identical rules in different ways, due to differences in their institutional design and incentives (Agarwal, Lucca, Seru and Trebbi, 2014). State regulators are less likely to enforce stringent regulations due to
concerns with the local economy, compared with federal regulators.
9 internationalization of bank holding companies and conclude that, based on location, level of activity and geographical distance, internationalization results in lower risk and higher value, but that more remote areas result in smaller risk reductions. Fang and van Lelyveld (2014) find similar results, using a correlation matrix approach for 49 large international banking groups, of lower credit risk as a result of internationalization. Lower individual risk of banks could lower the joint-probability of failure if these diversification strategies do not result in greater similarity among banks.
Specifically, cross-border acquisitions by international banks allow them to shift more risks to less strict environments. Research has shown that cross-border acquisitions and capital flows of banks are more likely to originate from countries that have stronger regulations (Houston, Lin and Ma, 2012; Karolyi and Taboada, 2015). Thus, this effect seems to be stronger for countries
headquartered in developed countries.
2.3 Hypotheses
Based on the literature review, three hypotheses are constructed in relation to changes in systemic risk due to acquisitions. First, the consensus in the literature following the financial crisis seems to suggest that M&A in the financial sector increase systemic risk of the acquirer. Weiss, Neumann and Bostandzic (2014) find confirmation of their concentration-fragility hypotheses (i.e. that mergers significantly contribute to systemic risk). This is in line with the theory of Wagner (2010), who argues that mergers might increase diversification, but that this can lead to greater similarity among banks, resulting in a higher probability of joint failure, thus a higher systemic risk. Therefore, the first hypothesis is that mergers significantly increases systemic risk of the acquirer. This hypothesis is tested by comparing the results of this paper with Weiss, Neumann and Bostandzic (2014), before extending their work.
Second, a link between internationalization (prior to acquisition) and systemic risk as a result of mergers is explored. The argument is that regulatory arbitrage allows international banks to shift risk to public safety nets or, on the other side of the spectrum, shift risks away from stringent regulators to countries with less stringent regulations. This is easier for banks who already have an established international network (Houston, Lin and Ma, 2012; Karolyi and Taboada, 2015). Differences in execution of regulations can even occur when different regulators are enforcing exactly the same regulations, as supervisors have different institutional designs and incentives (Agarwal, Lucca, Seru and Trebbi, 2014). Thus, the second hypothesis is that internationalization prior to acquisitions leads to a significant increase in systemic risk of banks following acquisitions.
Third, the interaction between cross-border mergers and internationalization prior to
cross-10 border, they can integrate the new target into their international structure. Houston, Lin and Ma (2012) find that banks have shifted capital flows to countries with less stringent regulations, giving merit to the regulatory arbitrage argument. Moreover, banks might transfer shocks in the foreign market to the home market through their internal capital markets (Forbes and Warnock, 2012). Therefore, the third hypothesis is that cross-border acquisitions of internationalized banks positively impact systemic risk after acquisition.
3. Methodology
This section discusses how the systemic risk contribution of an individual bank to the overall system is calculated. A similar method is used to calculate the expected shortfall of a market index, which is used to calculate the adjusted MES. Finally, the multivariate model is presented, which is used for further analysis.
By assessing the differences in MES, the differences in systemic risk that a bank contributes to the system as a result of a merger is identified. The method is identical to that of Weiss, Neumann and Bostandzic (2014), who calculate the delta of the MES based on the difference of post- and pre-MES. When controlling for various externalities, such as other mergers, changes in regulations, accounting variables and deal characteristics, the contribution of the merger that the individual bank adds to the financial system is isolated. The contribution to systemic risk is based on returns on equity, which results in how the market assesses the contribution to systemic risk.
This paper follows the theoretical method developed by Acharya, Pedersen, Philippon and Richardson (2010) on systemic expected shortfall and MES. This a well-used measure in the literature (Berger, Molyneux and Wilson, 2014; Karolyi and Taboada, 2015), and is also used by Weiss, Neumann and Bostandzic (2014). Systemic risk is used to measure the systemic fragility of an individual institution. The contribution of an individual bank is measured as the worst 5% daily equity returns. By using the market to define these days of extreme tail returns (one-sided), a period of general distress is captured. The systemic risk measure is used to identify the capital needs of a financial institution during distress in the market. These are the values greater than the variance at risk (VaR), which we set at 5%. This leads to the expected shortfall conditional to a return smaller than I, with the VaR level, as in Equation 1.
11 Based on these dates, we calculate the shortfall of company i, on date t. These dates are conditional on the return in the market being lower than I. MES is defined as the mean net return on equity during a market crash. This is shown in Equation 2, with return of firm I conditional on the worst 5% market returns. The sign flipped, as is standard notation for the shortfall.
𝑀𝐸𝑆𝑖,𝑡 = −𝐸𝑡(𝑟𝑖,𝑡+1|𝑟𝑚,𝑡+1 < 𝐼5%) (2)
This paper does not compute MES with a more advanced dynamical model to estimate the beta, which uses threshold autoregressive conditional heteroskedasticity (TARCH) and dynamic
conditional correlation models (DCC) based on the work of Brownlees and Engle (2012). This keeps the results comparable to Weiss, Neumann and Bostandzic (2014), who use a period of 180 days until 10 days prior to the merger announcement to calculate the pre-merger MES, and 10 days to 180 days after the merger completion to assess the post-merger MES. The change in MES is based on the difference between these two variables. The results are in Equation 3.
∆𝑀𝐸𝑆𝑖,𝑡5%= 𝑀𝐸𝑆𝑖,𝑡;[+11,+180]5% − 𝑀𝐸𝑆𝑖,𝑡;[−11,−180]5% (3)
To calculate the change in expected shortfall of the market for a specific region, used to compute the market-adjusted MES, we calculate the expected shortfall of the market during distress, with VaR at 5%, as in Equation 4.
𝐸𝑆𝑚 = 𝐸𝑚(𝑟𝑚+1|𝑟𝑚+1 < 𝐼5%) (4)
This equation is used to calculate the change in expected shortfall of the market when comparing the post-merger period returns. Equation 6 displays, for the post-merger period, the adjusted MES, which is the shortfall of the individual bank minus the shortfall in the market.
𝑎𝑑𝑗𝑢𝑠𝑡𝑒𝑑 𝑀𝐸𝑆𝑖,𝑡5%= 𝑀𝐸𝑆𝑖,𝑡;[+11,+180]5% − 𝐸𝑆𝑚,𝑡;[+11,+180]5% (5)
The main proxy used for internationalization is an international income dummy. Based on research by Berger, El Ghoul, Guedhami and Roman (2016), various factors should be able to capture internationalization. Therefore, foreign assets over total assets, foreign sales over total sales and dummy variables of both these are also explored regarding the degree internationalization and if they are international.
12 ∆𝑀𝐸𝑆𝑖,𝑡5%= 𝛼 + 𝛽1𝐼𝑛𝑡𝑒𝑟𝑛𝑎𝑡𝑖𝑜𝑛𝑎𝑙𝑖𝑧𝑎𝑡𝑖𝑜𝑛𝑖,𝑡−1+ 𝛽2𝐶𝑟𝑜𝑠𝑠𝑏𝑜𝑟𝑑𝑒𝑟𝑖,𝑡+ 𝛽3𝐼𝑛𝑡𝑒𝑟𝑛𝑎𝑡𝑖𝑜𝑛𝑎𝑙𝑖𝑧𝑎𝑡𝑖𝑜𝑛𝑖,𝑡−1
∗ 𝐶𝑟𝑜𝑠𝑠𝑏𝑜𝑟𝑑𝑒𝑟𝑖,𝑡+ 𝛽4𝐶𝑜𝑛𝑡𝑟𝑜𝑙 𝑣𝑎𝑟𝑖𝑎𝑏𝑙𝑒𝑠𝑖,𝑡−1+ 𝜖𝑖 (6)
4. Data and descriptive statistics
This section provides a detailed description of how the data sample and various sub-samples were acquired. Additional tables and statistics that are referred to are available in the appendix.
The initial dataset was acquired from Bureau van Dijk’s Zephyr M&A database. From this, a global sample was acquired for the following Standard Industrial Classification SIC codes: 6000 to 6162, 6712 and 6719 for both the acquirer and target. This resulted in a sample of credit and depository institutions, as we aim to investigate whether internationalization (and not diversification) and consolidation in the banking sector impact the stability of the financial system. This is of interest as shocks in the financial system can lead to damage in the real economy (Haldane and May, 2011). The selection of SIC codes was made based on this paper being concerned with the detrimental effects of bank mergers on the stability of the financial system, and how internationalization influences this.
Deals with a value lower than 10 million USD, and an acquired stake below 50%, were excluded. The reasoning for this was to only include deals that result in full active control, rather than a potential financial investment, according to IFRS9 and U.S. GAAP. Moreover, this makes the results
comparable with both Vallascas and Hagendorf (2011) and Weiss, Neumann and Bostandzic (2014).
From the initial sample of 1199 firms, only 865 had stock data available from Thomson Reuters DataStream financial data for our required pre-and post-merger intervals. Next, we excluded variables that did not have a deal value from Zephyr available, or accounting variables from Worldscope available through DataStream. This reduced our sample to 675 deals. From these, we excluded deals involving the same company acquiring another company within 360 days. This further reduced our sample to 528, which is a similar number to Weiss, Neumann and Bostandzic (2014).
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Table 1. Distribution of mergers per region
Acquirer Target
Africa
Eastern Europe
Far East and Central Asia Middle East North America Oceania South and Central America Western Europe Sum Africa 9 1 10 Eastern Europe 14 14
Far East and Central Asia 1 28 1 2 32
Middle East 1 2 9 2 14
North America 327 2 1 330
Oceania 1 7 8
South and Central America 1 19 20
Western Europe 3 21 3 4 5 64 100
Sum 12 37 33 9 334 7 29 67 528
This table displays the distribution of acquisitions per region, based on the target region. The acquirer region is listed in the first column, while the target region is listed in the second row. This results in a total sample of 528 mergers, between 1998 and 2017. The sample was retrieved from Bureau van Dijk's M&A database, Zephyr. We kept companies with an SIC code between 6000 to 6162, 6712 and 6719. From these deals, those with a value below 10 million USD and with an acquired stake below 50% were excluded. Additionally, for these companies, stock prices and accounting variables from DataStream were required to be available. These DataStream variables came from Financial data and Worldscope from Thompson Reuters respectively
14 Table 2 presents the descriptive statistics for all the variables, including the various
internationalization proxies and control variables. All the accounting variables are stated in USD, based on the year prior to the merger announcement. Loan loss provision, total assets and deal value are transformed into the natural logarithm. To normalize the accounting variables, they are winzorized at a 1% and 99% level.
The market index on which both the MES and, similarly, the expected shortfall of the market is calculated as a value-weighted index based on the top 500 available public financial firms ranked according to total revenue in the latest available year. Due to data limitations regarding market values, this results in indices based on 486 US firms, 489 Western European firms, 273 Eastern European firms, 234 Middle Eastern firms, 484 firms from the Far East and Central Asia, 243 from Central and South America, 181 from Africa and 113 from Oceania.
0 10 20 30 40 50 60 Announced Completed
Figure 1. Distribution of mergers per year
15
Table 2. Descriptive statistics
Observations Mean Median Maximum Minimum Std. Dev.
ΔMES 528 0.21% 0.12% 10.12% -17.86% 1.97%
Acquirer and deal characteristics:
Equity ratio 528 57.166 57.860 93.781 8.223 21.590
Return on assets 528 1.439 1.310 5.482 -0.934 0.883
Non-interest income to total
income 528 0.350 0.275 1.689 0.028 0.281
Loan loss provisions (ln) 528 9.552 9.451 15.501 0.000 3.356
Market-to-book value 528 1.685 1.535 4.479 0.280 0.830
Too-big-to-fail motive 528 0.250 0.000 1.000 0.000 0.433
Pre-merger performance 528 0.075 0.078 1.871 -2.651 0.274
Relative deal value 528 29.828 14.027 918.077 0.009 55.635
Total assets (ln) 528 16.186 15.808 21.343 12.488 2.134
Deal value (ln) 528 18.521 18.257 22.884 16.165 1.554
Cross-border dummy 528 0.165 0.000 1.000 0.000 0.371
Internationalization variables
Foreign income dummy 305 0.167 0.000 1.000 0.000 0.374
Foreign asset dummy 329 0.246 0.000 1.000 0.000 0.431
Foreign assets over total assets 328 7.103 0.000 98.010 0.000 18.552
Foreign income over total income 305 5.646 0.000 155.250 -212.290 24.642
Foreign sales to total sales 350 5.321 0.000 79.600 0.000 12.734
Foreign sales dummy 350 0.289 0.000 1.000 0.000 0.454
Country control: GDP growth 528 2.547 2.596 19.592 -10.895 2.368 Political stability 528 0.367 0.509 1.610 -2.676 0.640 Rule of law 528 1.286 1.591 2.096 -1.114 0.659 Lerner index 524 0.256 0.280 0.590 -2.560 0.154 Regulatory environment: Anti-director rights index (ADRI) 497 3.574 3.000 5.000 0.000 1.053 Deposit insurance 515 349.303 262.000 8798.990 0.000 643.629
Foreign deposit coverage dummy 526 0.844 1.000 1.000 0.000 0.363
Capital regulatory index 515 5.900 6.000 9.000 0.000 1.916
Government-owned banks
dummy 486 0.214 0.000 1.000 0.000 0.411
Independence supervisory power 484 1.694 2.000 3.000 0.000 0.685
Official supervisory power 523 11.669 13.000 14.500 0.000 3.512
Moral hazard index post-crisis 505 3.037 4.618 4.618 -11.862 2.401
16
4.1 Control variables
To control for various factors that might influence systemic risk (as assessed by capital markets) of banks other than mergers, we control for a variety of firm-specific factors and country factors. Based on the initial sub-sample analysis, we conclude that there are many factors influencing systemic risk, such as concentration and profitability. Moreover, there is an indication that internationalization can influence systemic risk in a similar manner to a cross-border merger, as a domestic firm will also become international after the acquisition. Therefore, this is further explored in multivariate regressions with different control variables to test the hypotheses.
The first control variable is the equity ratio. This factor is commonly expected to have a positive relation when assessing systemic risk (Huang, Zhou and Zhu, 2012), as highly leveraged firms with minimal reserves can face liquidity problems during a systemic shock, but one could argue that low leverage forces banks to increase profitability to survive competition. Another argument is banks with low leverage are preparing a takeover, which results in assuming or renegotiating the debt of the target, resulting in a more normal leverage level (Weiss, Neumann and Bostandzic, 2014). We control for profitability by using return on assets, as seen in Table 4, which can impact systemic risk. For example, low profitability could push banks to become more aggressive in risk-taking. Non-interest income to total income is to consider the impact of more volatile activities, such as investment banking and trading. Brunnermeier, Dong and Palia (2012) state that these non-core items contribute more to systemic risk compared with traditional items, such as liquidity creation. The market-to-book ratio is included to proxy for management hubris, which could lead to excessive risk-taking (Vallascas and Hagendorff, 2011). Pre-merger performance is also used to control for management hubris (Weiss, Neumann and Bostandzic, 2014), calculated as the buy-and-hold return during the pre-merger period. Too-big-to-fail is included as a variable to account for mergers that are motivated by an incentive to become systemically important, and which make use of
17 To control for differences between countries, macro-level factors and regulatory factors are
included. Gross domestic product (GDP) growth, political stability and rule of law are included to control for the operating environment (Weiss, Neumann and Bostandzic, 2014). Differences in the Lerner index are used to assess concentration and competition, as these can influence risk exposure through factors such as market power (Berger, Klapper and Turk-Ariss, 2009). An indication of market power influencing systemic risk is also observed in the sub-sample analysis; however, there are no control variables used.
The regulatory environment in which a bank operates is also important. The anti-director rights index (ADRI) by Porta, Lopez‐de‐Silanes, Shleifer and Vishny (1997), which was revised by Spamann (2009), has been used in various publications to assess shareholder rights. A lack of shareholder rights could allow management to avoid disclosure, which lowers the effectiveness of market discipline and allows management to take excessive risks (Hu and Black, 2008). Data on deposit insurance coverage (i.e. whether foreign deposits are covered, and whether a permanent fund exists) were gathered from the database of Beck, Demirguc-Kunt and Levine (2009). These factors can control for (implicit) government guarantees. If banks have these safety nets available, they can be inclined to take more risks (Karolyi and Taboada, 2015). Finally, to consider the stringency of the regulatory environment in the acquirer’s home market, data are used from Barth, Caprio and Levine (2008). The capital regulatory index, a dummy of government-owned banks, the independence of the supervisory authority, and the official supervisory power are all used to control for differences in the regulatory environment. In this, we follow the approach of (Weiss, Neumann and Bostandzic, 2014) for selection of variables. In general, the expectation is the more stringent the regulations, the lower systemic risk if banks are unable to shift risks to other countries. However, more stringent regulations could also be effective in lowering systemic risk. The existence of government owned banks can be used to control for willingness to bail-out banks. The independence and power of the supervisory authority can indicate the capability of the regulator to pursue a healthy financial system, thus this is expected to lower systemic risk.
5. Results
This section first discusses the MES per region, and how it compares to the market index. This is then used to compare results with Weiss, Neumann and Bostandzic (2014), and confirm the first
18 for multivariate regressions and the influence of various internationalization proxies are explored to assess whether methodological choices are driving the results.
Table 3. Changes in marginal expected shortfall
Acquirer systemic risk Expected shortfall market Market adj. expected shortfall
Acquirer region MES pre-merger MES post-merger ΔMES ES mrkt pre-merger MES mrkt post-merger ΔMES mrkt MES adj. pre-merger MES adj. post-merger ΔMES adj. North America 1.6% 1.8% 0.2%*** 2.6% 2.7% 0.1% -1.0%*** -0.9*%** 0.1% (0.002) (0.111) (0.000) (0.000) (0.159) Western Europe 2.8% 3.4% 0.6%*** 3.3% 3.6% 0.3%* -0.5%*** -0.2% 0.3%** (0.011) (0.085) (0.002) (0.157) (0.039)
Far East and
Central Asia 2.1% 1.7% -0.3% 2.6% 2.6% 0.0% -0.6%** -0.9%*** -0.3% (0.177) (0.443) (0.021) (0.000) (0.196) South and Central America 2.2% 2.2% 0.0% 2.8% 2.7% -0.1% -0.6% -0.5%* 0.1% (0.499) (0.440) (0.110) (0.099) (0.437) Eastern Europe 2.6% 2.9% 0.2% 3.3% 3.7% 0.4% -0.7%** -0.8%*** -0.1% (0.357) (0.234) (0.032) (0.006) (0.328) Middle East 0.8% 1.2% 0.5% 3.2% 2.3% -0.9% -2.4%*** -1.1%** 1.3%** (0.125) (0.103) (0.004) (0.018) (0.030) Africa 2.7% 0.5% -2.1% 3.2% 3.5% 0.2% -0.6% -2.9%*** -2.4% (0.136) (0.349) (0.379) (0.003) (0.148) Oceania 3.0% 3.6% 0.6% 3.7% 4.0% 0.3% -0.7%*** -0.3% 0.3% (0.305) (0.383) (0.008) (0.239) (0.225) Total 1.9% 2.1% 0.2%*** 2.8% 2.9% 0.1%* -0.9%*** -0.8%*** 0.1% (0.007) (0.063) (0.000) (0.000) (0.136)
*** is significant at 1%; ** is significant at 5%; * is significant at 10%. P-values are displayed between brackets. Significance calculated as changes in MES based on a paired Student’s t-test. MES is calculated as proposed by Acharya, Pedersen, Philippon and Richardson (2010). The pre-merger period is composed of -180 till -10 days till announcement. The post-merger period is designed as +10 till +180 after the completion period. The difference between post- and pre-merger period results in: ∆MESi,t5%=MESi;[+11,+180]5% -MESi;[-11,-180]5% The expected shortfall, calculated as the 5% worst market
outcomes during the event window, is composed of the top 500 financial institutions on revenue. These variables are all calculated based on a total international sample of 528 mergers of banks. Adj. is for the market adjusted expected shortfall. Table 3 presents the MES of the acquirer, the expected shortfall of the market index and the
19 post-merger MES are significant, with the total significance at 1%. This illustrates that the shocks in the market are similar, but slightly more severe than the merging banks’ shortfall. The final column displays the delta MES minus delta expected shortfall, with only the Middle East significant as a region. This could indicate that there is no increase in MES as a result of mergers, but that it is driven by the lack of announcements in more volatile periods.
20
Table 4. Sub-sample analysis
MES pre MES post ΔMES N MES pre MES post ΔMES N
Foreign assets
present 2.9% 3.4% 0.5%* 65 Foreign income 3.0% 4.0% 1.0%** 387
(0.079) (0.039) No foreign assets reported 2.1% 1.6% 0.1% 220 No foreign income reported 1.6% 1.7% 0.1% 240 (0.125) (0.190)
No data present 1.9% 2.2% 0.2%** 144 No data present 2.0% 2.2% 0.2%* 196
(0.042) (0.060)
MES pre MES post ΔMES N MES pre MES post ΔMES N
Cross-border 2.7% 3.2% 0.4%* 87 High relevance 2.5% 2.9% 0.4%** 176
(0.072) (0.021)
Domestic 1.7% 1.9% 0.2%** 441 Medium relevance 1.8% 1.9% 0.1% 175
(0.023) (0.132)
Low relevance 1.4% 1.5% 0.1% 177
(0.183)
MES pre MES post ΔMES N MES pre MES post ΔMES N
High
concentration 2.0% 1.8% -0.3% 117 High profitability 2.0% 2.2% 0.2% 175
(0.123) (0.124) Medium concentration 1.8% 2.3% 0.5%*** 188 Medium profitability 1.8% 2.1% 0.3%*** 173 (0.000) (0.006) Low
concentration 2.0% 1.8% -0.3%** 223 Low profitability 1.9% 2.1% 0.1% 180
(0.022) (0.204)
MES pre MES post ΔMES N MES pre MES post ΔMES N
High supervisory power 1.7% 1.8% 0.1% 137 Pre-crisis 1.6% 1.7% 0.2%* 284 (0.186) (0.056) Medium supervisory power 1.8% 1.9% 0.1% 208 During-crisis 4.0% 6.6% 2.6%*** 28 (0.190) (0.001) Low supervisory power 2.2% 2.6% 0.4%** 178 Post-crisis 2.1% 2.0% 0.1% 216 (0.017) (0.320)
*** is significant at 1%; ** is significant at 5%; * is significant at 10%. P-values are displayed within brackets.
The sub-samples are based on dummy variables for foreign assets, foreign income and cross-border acquisitions. The other factors are divided along a percentile distribution. Relevance is calculated as the cross-product of total assets and relative deal value. The crisis period is defined as the beginning of the sub-prime mortgage crisis, end of 2007; whereas post-crisis is defined as early 2009.
21 concentration and profitability are included based on the expectations of influence of control
variables. Regulation is included by splitting different levels of supervisory power. Finally, a sub-sample is included based on pre-, during and post-crisis MES, to investigate whether the timing of the merger is relevant for systemic risk.
We find a significant impact of reported foreign income, as it results in 1% higher MES, significant at 10%. Moreover, medium profitability and concentration significantly and positively indicate a higher delta MES. When comparing these result with Weiss, Neumann and Bostandzic (2014), we do not find a confirmation of the theory that high-profitability banks are able to offset the adverse effects of systemic risk. We did find, however, that cross-border mergers seem to result in higher MES compared with domestic mergers, at 10% and 5% significance respectively. Interestingly, low concentration seems to offset the impact of mergers on MES, as it is 0.3% lower at a significance level of 5%. Low supervisory power also increases MES, with 0.4% at 5% significance, indicating that weaker regulators are allowing mergers with detrimental effects to systemic risk.
The crisis period resulted in a significantly higher change in MES, with 2.6%, at 1% significance. A potential explanation is that these are distressed mergers, for example, under pressure from
regulators (Koetter, Bos, Heid, Kolari, Kool and Porath, 2007). The result is that these banks could be deemed to have a high systemic risk after completion (judged by the market) due to the acquisition of risky parts of a failing bank. To follow-up on these findings, control variables are included in the regression models.
5.1 Results multivariate regression
Table 5 presents the results of the first multivariate least squares regressions, focussing on the original model of Weiss, Neumann and Bostandzic (2014) and internationalization. All these
regressions use White’s adjusted standard errors to deal with heteroskedasticity. Regressions 1 to 4 all use region-fixed effects, which are unreported. Table 6 presents the multivariate least squares regressions that explore the influence of the interaction between internationalization and the cross-border dummy. In these regressions White’s adjusted standard errors are also used. Models 6 to 8 use region fixed effects, while these are not present in model 9. The inclusion of region-fixed effects rather than country-fixed effects is a result of the large number of countries with few mergers. In total, nine models are presented, with a different selection of the independent variables of foreign income, internationalization, cross-border acquisitions and the interaction between
internationalization and cross-border acquisitions. A major problem in all the regressions is
22 distinguish the individual effect of independent variables when multicollinearity is present. The problem generally becomes worse when including interaction effects, or a moderator. Moreover, there is in some cases a relatively high correlation between internationalization variables and control variables. These are presented in Appendix 5. In the most extreme cases, the problem results in abnormal standard errors (Farrar and Glauber, 1967). As it not only influences that control variables, but also the independent variables we wish to examine, some variables must be dropped to identify their impact. However, a potential problem with this approach is that biased results may be
presented due to an omitted variable bias.
The first model (1) is based on Weiss, Neumann and Bostandzic (2014). The model suggests that the too-big-to-fail motive (defined as systemically unimportant banks), the Lerner index and the
shareholder rights index ADRI significantly influence the change in MES, with 1%, 10% and 1% significance respectively. With stronger shareholder rights, on average, the MES drops by 0.29%; whereas a too-big-to-fail motive increases the systemic risk by 0.88%. For a more concentrated banking system, measured by the Lerner index, the systemic risk is lower with -1.03%. Next, we continue to investigate whether internationalization influences MES as a result of an acquisition.
23
Table 5. Multivariate regression models marginal expected shortfall
(1) Model reference paper (2) Internationalization w. accounting (3) Internationalization w. accounting and country (4) full model (5) full model w. cross-border Internationalization variables:
Foreign income dummy 0.78% 0.96% 1.36%** 1.70%**
(0.214) (0.133) (0.036) (0.019)
Cross-border dummy -0.25% -1.05%
(0.585) (0.213
Acquirer and deal characteristics:
Equity ratio 0.01% 0.01% 0.01% 0.01%** 0.01%*
(0.203) (0.171) (0.180) (0.043) (0.060)
Return on assets 0.23% 0.16% 0.40% 0.45%
(0.213) (0.405) (0.114) (0.041)
Non-interest income to total income -0.17% -0.82% -0.91% -0.65% -0.61%
(0.666) (0.132) (0.118) (0.320) (0.294)
loan loss provisions over interest income 0.73% 0.69% 0.54% 0.46% 0.27%
(0.203) (0.554) (0.649) (0.697) (0.792)
Market to book value -0.01% -0.04% 0.02% -0.11% -0.11%
(0.945) (0.868) (0.943) (0.663) (0.663)
Too-big-to fail-motive 0.88%*** 0.60%*** 0.75%** 0.73%*** 0.74%***
(0.000) (0.004) (0.013) (0.007) (0.007)
Pre-merger performance 0.24% -0.10% 0.25% -0.33% -0.29%
(0.724) (0.892) (0.735) (0.691) (0.723)
Relative deal value -0.01% -0.01% -0.01% -0.01%
(0.285) (0.434) (0.404) (0.334) Total Assets (ln) 0.00% (0.540) Country control GDP growth 0.00% -0.16%* -0.22% -0.26%* (0.960) (0.098) (0.151) (0.056) Political stability 0.00% 0.29% -0.19% -0.22% (1.000) (0.253) (0.590) (0.434) Rule of law 0.85% -0.28% -0.13% (0.152) (0.722) (0.903) Lerner index -1.03%* -0.16% -0.66% -0.82% (0.053) (0.781) (0.239) (0.148)
Regulatory environment and deposit schemes:
ADRI -0.29%*** -0.21% -0.19%
(0.006) (0.154) (0.169)
Deposit insurance
Foreign deposit coverage dummy 0.08% 1.57% 1.60%
(0.898) (0.167) (0.147)
Independence supervisory power -0.11% 0.29% 0.33%
(0.534) (0.163) (0.112)
Observations 461 305 305 272 272
Region fixed effects Yes Yes Yes Yes Yes
R-squared -0.102 0.119 0.149 0.240 0.250
Adjusted R-squared -0.059 0.080 0.095 0.176 0.187
*** is significant at 1%; ** is significant at 5%; * is significant at 10%. P-values are displayed within brackets. Dependent variable is acquirer change in MES due to acquisition, ΔMES. All models are estimated with White’s robust standard errors to adjust for heteroskedasticity. Models 1 to 4 use region-fixed effects. In regressions 1 to 4, various control variables are dropped due to correlation and the resulting multicollinearity with internationalization variables. A full description of each variable and its source is available in Appendix 1.
When accounting and country controls are included, as in Model 3, we find that the too-big-to-fail-motive is significant at 5% and GPD growth at 10%. These variables have a coefficient of 0.75% and -0.16% respectively. However, GDP growth is barely significant, with a very small economic impact.
24 income dummy is significant at 5%, with a 1.36% increase in systemic risk as a result of a merger if the acquirer is international prior to acquisition. The equity ratio is statistically significant, but economically insignificant at 0.01%. Similar all the other models, the too-big-to-fail motive is significant, at 10%.
The final model (5) of Table 5 also includes the cross-border dummy. This results in slightly higher impact of internationalization at 1.70%. Moreover, GDP growth is significant at 10%, with a coefficient of -0.26. This model results in the best fit, measured a by R-squared of 0.187.
To summarize, table 5 shows that an international bank will on average have a higher increase in systemic risk and thus a destabilizing effect on the financial system. This gives merit to the theory that internal capital markets can cause shocks in foreign countries by influencing the company through internal capital markets (Forbes and Warnock, 2012). Another explanation is that companies make use of regulatory arbitrage to transfer risk across borders to countries with less stringent regulations, which in turn increases systemic risk. However, none of the included regulatory
variables are significant. The too-big-to-fail-motive is significant for all models, with a value of 0.75% on average in models 3 to 5. Thus, relatively less systemically risky banks will on average result in a higher systemic risk after acquisition. By increasing their size, banks can increase implicit subsidies, such as deposit insurance (John, John and Senbet, 1991). This finding is different from both Buch and DeLong (2008) and Karolyi and Taboada (2015), who find a limited impact of this incentive on risk and systemic risk respectively. Recent developments, such as the European Bank Recovery and Resolution Directive, which aims to make use of the bail-in of creditors and shareholders to bear the costs of bank failure, lower the incentive of attempting to make use of implicit government subsidies (Berger, Molyneux and Wilson, 2014). However, Acharya, Anginer and Warburton (2013) state that the Dodd-Frank act did not lower the expectation of investors that the government will shield them from significant losses. Similarly, the market is expected to adjust its expectation, which can result in a higher delta MES. Therefore, harmonization of regulations could be one of the items to improve financial stability and offset the increase in systemic risk.
Table 6 present the multivariate regressions for hypotheses 3, that the interaction of
25
Table 6. Multivariate regressions interaction internationalization
(6) Interaction Foreign sales dummy (7) Interaction Foreign sales ratio (8) Interaction Foreign asset dummy (9) Interaction Foreign asset ratio Internationalization variables:
Foreign sales dummy 0.67%
(0.216)
Foreign sales ratio -0.01%
(0.278)
Foreign asset dummy 0.19%
(0.700)
Foreign asset ratio 0.02%
(0.178)
Cross-border dummy -2.33%*** -0.34% -3.46%*** 0.09%
(0.000) (0.654) (0.000) (0.867)
Internationalization*cross border 2.50%*** 0.00% 2.89%*** -0.01%
(0.001) (0.971) (0.000) (0.597)
Acquirer and deal characteristics:
Equity ratio 0.01% 0.01%* 0.01%** 0.02%**
(0.318) (0.056) (0.049) (0.013)
Return on assets 0.05% 0.23% 0.13% -0.01%
(0.749) (0.291) (0.488) (0.976)
Non-interest income to total income -0.49% -0.10% -0.10% -0.59%
(0.263) (0.854) (0.833) (0.298)
loan loss provisions over interest income 0.22% -2.25% -2.11% 0.34%
(0.733) (0.056) (0.134) (0.640)
Market to book value 0.16% 0.11%
(0.516) (0.506)
Too big to fail motive 0.72%*** 0.36%** 0.36%** 0.63%***
(0.001) (0.047) (0.038) (0.001) Pre-merger performance -0.60% -0.35% 0.23% 0.02% (0.404) (0.634) (0.750) (0.973) Country control GDP growth 0.14% (0.136) Political stability -0.18% (0.418) Lerner index -0.81%* -0.82%** (0.063) (0.039)
Regulatory environment and deposit schemes:
ADRI -0.27%** -0.18%
(0.029) (0.136)
Foreign deposit coverage dummy 0.01%
(0.982)
Capital regulatory index -0.02% -0.02%
(0.748) (0.444)
Government owned banks dummy -1.20% -1.63%*
(0.109) (0.088)
Observations 350 309 288 328
Region fixed effects Yes Yes Yes No
R-squared 0.102 0.143 0.164 0.062
Adjusted R-squared 0.062 0.080 0.105 0.036
*** is significant at 1%; ** is significant at 5%; * is significant at 10%. P-values are displayed within brackets. Dependent variable is acquirer change in MES due to acquisition, ΔMES. All models are estimated with White’s robust standard errors to adjust for heteroskedasticity. Internationalization is defined as the previously used foreign ratio posted above. Models 6 to 8 use region-fixed effects. In regressions 6 to 9, various control variables are dropped due to correlation and the resulting multicollinearity with internationalization variables. A full description of each variable and its source is available in Appendix 1.
26 acquisition, as the coefficient of the foreign sales dummy times the cross-border dummy is equal to 2.50%.
When foreign sales ratio is used as internationalization proxy in model 7, the multicollinearity problem is less severe, allowing the inclusion of additional country controls and regulatory controls. In this case cross-border and interaction term is no longer significant. The equity ratio is significant at 10%, but economically insignificant at 1%. As with all models, the too-big-to-fail motive is
significant. Additionally, the anti-director rights index and Lerner index are significant at 5% and 10% respectively. These results indicate that a more concentrated banking system and better shareholder rights lead to a reduction of systemic risk after acquisition.
Model 8 uses the foreign asset dummy as internationalization. While internationalization itself is not significant in this case, both the cross-border dummy and internationalization multiplied by the cross-border dummy are significant at 1%. In this case effect is similar as in model 6, an international firm will, on average, experience a 2.1% increase in systemic risk; while a firm that diversifies
internationally (i.e. a cross-border acquisition) could lower systemic risk by 2.3% on average. There is a significant negative relation between delta MES and the Lerner index and between delta MES and the government owned bank dummy. In both cases these lower on average the systemic risk result of an acquisition. A potential explanation for the fact that the presence of government owned banks reduces systemic changes of an acquisition, is that the market could expect the government to bail-out banks in the worst-case scenario.
The final model (9) in Table 6 uses the foreign asset ratio as proxy for internationalization. Berger, El Ghoul, Guedhami and Roman (2016) use this as key-variable to proxy for internationalization. The fact that it is not significant is a strong indicator against the hypothesis that internationalization positively influences delta MES. In this regression no country fixed effects are included and only accounting and deal characteristics are used, to reduce the impact of multicollinearity. The only economically and statistically significant factor is the too-big-to-fail motive at 1%.
27 the overall joint-likelihood a systemic shock (Wagner, 2010). Moreover, the increase can also be caused by the additional exposure in a foreign country, which can transfer a shock through internal capital markets (Forbes and Warnock, 2012).When looking at our third hypothesis, the relationship between systemic risk and internationalization is affected by whether it is a cross-border acquisition. If it is an international firm is acquiring cross-border, there is an indication that systemic risk
increases. However, we conclude these findings are inconclusive due to measurement problems when using an interaction term.
5.2 Robustness analysis
To test whether methodological choice affect the results, sub-sample analyses are used to compare pre- and post-crisis results. Moreover, as the sample consist of a large number of US banks, there is a split between US and non-US banks in Table 7. Moreover, this provides additional tests for
whether internationalization is a source of strength or weakness, and whether this has changed over time. In multivariate regressions in Table 7 only region fixed effects are used in the pre-financial period.
For model 10 based on only US banks, deal and accounting characteristics are used, as the country and regulatory controls are similar over time. This results in a sample of 232 mergers, with
internationalization and the cross-border dummy being significant at both 10%. Internationalization increases systemic fragility as a result of a banking merger, whereas a cross-border merger results in a decrease. This confirms the second hypothesis that internationalization significantly influences systemic risk.
For non-US mergers in model 11, internationalization prior to acquisition and cross-border mergers both aren’t significant. The loan loss provision, GDP growth, foreign deposit coverage dummy variable and the government owned bank variable are significant. Interesting here is that loan loss provision ratio is significant and reducing systemic risk. This could be caused by more conservative estimates for the required provision are valued by the market, which lowers systemic risk. However, for this theory additional evidence is required.
28 internationalization was not impacting systemic risk prior to the financial crisis, whereas after and during the financial crisis internationalization increased the fragility of the financial system.
Table 7. Robustness analysis multivariate regressions
(10) US only (11) Non-US (12) During and
post-financial crisis period
(13) Pre-financial crisis period
Internationalization variables
Foreign income dummy 1.93%* -0.02% 2.74%*** -0.38%
(0.052) (0.363) (0.001) (0.576)
Cross-border dummy -1.83%* -0.83% -1.99%**
(0.083) (0.290) (0.020)
Acquirer and deal characteristics:
Equity ratio 0.01% 0.04%* 0.02%** 0.00%
(0.133) (0.066) (0.015) (0.933)
Return on assets 0.02% 0.02%
(0.907) (0.945)
Non-interest income to total income -0.55% -0.81% 0.03% -0.38%
(0.314) (0.446) (0.963) (0.604)
loan loss provisions over interest income -1.76% -2.89%** 0.07% -5.91%
(0.273) (0.041) (0.946) (0.041)
Market to book value 0.01% 0.70% 0.15% -0.04%
(0.925) (0.136) (0.743) (0.856)
Too big to fail motive 0.40%** -1.39% 0.83%** 0.66%**
(0.017) (0.100) (0.026) (0.043)
Pre-merger performance 0.31% -1.13% 1.09% -0.45%
(0.607) (0.415) (0.222) (0.664)
Relative deal value 0.00% 0.01% 0.00% -0.01%
(0.725) (0.521) (0.966) (0.186) Country control GDP growth 0.30%*** (0.010) Lerner index 1.02% (0.788)
Regulatory environment and deposit schemes
ADRI -0.11%
(0.738)
Deposit insurance -0.11%
(0.738)
Foreign deposit coverage dummy 1.84%***
(0.025)
Capital regulatory index 0.16%
(0.287)
Government owned banks dummy -0.04%**
(0.013)
Observations 232 71 113 192
Region fixed effects No No No Yes
R-squared 0.097 0.324 0.319 0.226
Adjusted R-squared 0.056 0.124 0.252 0.169
*** is significant at 1%; ** is significant at 5%; * is significant at 10%. P-values are displayed within brackets. Dependent variable is acquirer change in MES due to acquisition, ΔMES. All models are estimated with White’s robust standard errors to adjust for heteroskedasticity. Model 13 uses region-fixed effects. In regressions 10, 12 and 13, various control variables are dropped due to correlation and the resulting multicollinearity with internationalization variables. A full description of each variable and its source is available in Appendix 1.
This section has provided additional checks on whether the results are driven by sample selection rather than the actual data. However, there are many other items that could be of influence, such as the measurement for systemic risk. Therefore, a future direction for research is to perform
29 extension on the MES. It considers leverage and size next to risk. The CoVaR methodology measures how the value of risk of the financial system changes as an individual institution becomes stressed. By employing different methods, the results of this paper could be validated.
6. Conclusion
By building on the paper of Weiss, Neumann and Bostandzic (2014), this paper has explored the intricacies of internationalization in the context of systemic risk a result of mergers. By extending their paper, a clear contribution to the literature is made. Internationalization significantly, in the economic and statistical sense, increases systemic risk when a bank is acquiring. A cross-border acquisition on the other hand seems to indicate a reduction in systemic fragility. The results for the interaction term, when an international bank acquirers cross-border, are inconclusive to whether this increases systemic risk. Moreover, the too-big-to-fail motive of banks positively increases systemic risk.
The implications for practice is that regulators should consider the potential increase in systemic fragility from international banks acquiring other banks. Moreover, in general mergers in the financial sector on average increase systemic risk, thus consolidation is detrimental to financial stability. Moreover, as there is evidence of an too-big-to-fail motive, harmonization of regulations in a cross-border setting could be very important for a stable financial system. For the management of banks, this is also useful to consider, as systemic risk is measured by the shortfall of equity, thus their decisions influence firm value during systemic shocks. This is key for risk management, as it can have a large impact on the debt to equity ratio.
There is also criticism to the marginal expected shortfall and systemic risk methodology. First, the channels through which spill over or contagion happens are not identified. Second, it is based on market efficiency, so could be impacted by bubbles, bail-outs or lack of market discipline (Berger, Molyneux and Wilson, 2014). Third, a better understanding and more robust results could be achieved by including various other methods, such as a left tail returns that also capture individual bank failure (Acharya, Anginer and Warburton, 2013). Another method interesting method to included would be he systemic risk variable of (Brownlees and Engle, 2012) which also considers leverage and capital shortage.
30
7. References
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