The Shrinking Role of Foreign Operations at Global Financial Institutions and its Impact on Efficiency

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The Shrinking Role of Foreign Operations at Global Financial Institutions and its Impact on Efficiency Michael S. Pagano Professor of Finance , J. Robert , Mary Ellen Darretta PII: DOI: Reference:

S1544-6123(19)31150-X https://doi.org/10.1016/j.frl.2019.101419 FRL 101419

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Finance Research Letters

Received date: Revised date: Accepted date:

16 October 2019 14 November 2019 28 December 2019

Please cite this article as: Michael S. Pagano Professor of Finance , J. Robert , Mary Ellen Darretta , The Shrinking Role of Foreign Operations at Global Financial Institutions and its Impact on Efficiency, Finance Research Letters (2020), doi: https://doi.org/10.1016/j.frl.2019.101419

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Highlights: " Large global financial institutions (G-SIFIs) have dramatically reduced their foreign operations during 2013-2017. " This global retrenchment has coincided with increased inefficiency and has erased recent post-crisis improvements. " The firms' retreat to their home regions is nonlinearly related to inefficiency. " The most efficient approach for a G-SIFI might be a binary strategy of either high or very low reliance on foreign revenue. " These findings suggest that G-SIFIs are still adjusting to the "new normal" of the postcrisis period.

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The Shrinking Role of Foreign Operations at Global Financial Institutions and its Impact on Efficiency

Michael S. Pagano, Ph.D., CFA Professor of Finance The Robert J. and Mary Ellen Darretta Endowed Chair in Finance, Villanova University, 800 Lancaster Ave., PA. [email protected] 610-519-4389 November 14, 2019

In response to the U.S. and Eurozone financial crises of 2008-2009 and 2010-2012, global financial institutions are under greater scrutiny by investors and regulators. This study finds that these global financial institutions have dramatically reduced their foreign operations which, in turn, has coincided with increased inefficiency during 2014-2017. The firms’ retreat to their home regions is nonlinearly related to inefficiency and has led to inefficiency returning to levels last seen in 2009-2011. These findings indicate that the level of foreign operations is an important determinant that should be included when measuring a large financial firm’s efficiency.

Keywords: Foreign Operations, Large Financial Institutions; Efficiency; International Finance JEL Codes: G2, G21, G23 This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.

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The Shrinking Role of Foreign Operations at Global Financial Institutions and its Impact on Efficiency

In response to the U.S. and Eurozone financial crises of 2008-2009 and 2010-2012, global financial institutions are under greater scrutiny by investors and regulators. This study finds that these global financial institutions have dramatically reduced their foreign operations which, in turn, has coincided with increased inefficiency during 2014-2017. The firms’ retreat to their home regions is nonlinearly related to inefficiency and has led to inefficiency returning to levels last seen in 2009-2011. These findings indicate that the level of foreign operations is an important determinant that should be included when measuring a large financial firm’s efficiency.

Keywords: Foreign Operations, Large Financial Institutions; Efficiency; International Finance JEL Codes: G2, G21, G23 This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.

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Globally important financial institutions (referred to as “G-SIFIs”) have had their business models challenged by greater post-crisis regulation, unusually low interest rates (even negative in some countries), as well as increased competition from regional / local financial institutions (FIs) and “fintech” start-ups.1 Due to these changes, industry analysts such as Moody’s note that these “developments have triggered a series of strategic repositioning initiatives that are fundamentally reshaping several of the global investment banks.”2 One relatively common response to this “new normal” environment of increased regulatory and competitive pressures has been for these firms to shrink their global presence and retrench in their home country or region.3 This reaction to the competitive landscape of financial institutions in the aftermath of the U.S. and Eurozone financial crises is relatively unexplored. My main research question is: as the operating environment has dramatically changed for G-SIFIs, how has the efficiency of these firms been affected by the reduction in global operations of these firms? There are both advantages and disadvantages related to a G-SIFIs’ foreign operations. For example, a financial institution with a significant global presence can benefit from: 1) greater growth opportunities in faster growing areas, 2) increased profitability from larger economies of scale and/or more favorable competitive conditions, and 3) a better risk-return tradeoff due to enhanced diversification across many countries. Alternatively, a large financial firm with extensive foreign operations might also face additional costs because 1

Pagano and Sedunov (2016), among others, describes a systemically important financial institution as one that has strong inter-relations not only between firms within its home country but also with financial firms in other nations which, in the event of a crisis, can lead to international risk spillover effects. Brühl (2017) also explores the role of SIFIs and how to best define them within the current global financial system. 2

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See Moody’s Investor Services’ Special Comment dated May 14, 2014.

According to Tracey (2018), U.S. bank regulators at the Federal Reserve Board have recently proposed “new regulatory categories of banks based on size as well as other risk factors, such as international activity, off-balancesheet exposures, and reliance on volatile forms of short-term funding.” Since G-SIFIs are active on a global basis, it suggests that it is important to see how changes in foreign operations affect these firms’ profitability, financing choices, and efficiency because international clientele typically require services related to currencies and derivatives.

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of: 1) increased scrutiny and compliance requirements from both domestic and foreign regulators, 2) greater organizational complexity and higher fixed costs to manage multiple foreign business units, and 3) larger capital requirements due to increased global systemic risk. In the post-crisis period, many G-SIFIs have reduced their foreign operations, presumably because the costs noted above have begun to outweigh the benefits of an increased global presence. As will be shown later, this global retrenchment by many G-SIFIs can have a significant effect on the efficiency of these firms. Based on Pagano (2017), “efficiency” can be defined in many ways and I employ a definition of this term which uses a traditional production function approach to see how well a firm’s managers can convert key inputs such as financial capital and labor into a “final output” which, for shareholders, is the market value of the firm’s equity (see also Hughes et al. (2003) in which they refer to it as “market value shortfall.”) The current study builds upon this prior research to estimate the efficiency of 34 current or former G-SIFIs and 14 North American SIFIs during 2005-2017 by calculating the difference between a “best-practice” market value for each firm (given the firm’s investment in capital / labor) and the firm’s actual market value of equity.4 SIFIs which have a greater difference between the best-practice value and the observed market value will have a larger market value shortfall and are therefore considered to be more “inefficient” in managing their resources than firms with smaller differences. In contrast to Pagano’s (2017) earlier finding that large financial firms reduced inefficiency after the U.S. financial crisis, I estimate that inefficiency 4

For the classification of G-SIFIs, we rely on the Financial Stability Board (FSB) and its recent report which currently identifies 30 such institutions along with 4 FIs that are no longer on this list. I also include 14 North American-focused firms which are viewed as systemically important within the North American region. In contrast to Pagano (2017), which used 46 firms (32 G-SIFIs and 14 regional SIFIs), the current analysis updates this universe by dropping 4 firms from the earlier G-SIFI list and adding 2 newly designated G-SIFIs. To avoid survivorship bias, we allow firms to enter and exit the sample based on their presence on the FSB list over time. Appendix A lists the 48 firms that are used in the empirical tests.

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has once again increased as many G-SIFIs shrank their exposure to foreign revenue sources. For example, G-SIFI inefficiency in 2017 increased to levels last seen during 2009-2011 while these firm’s average reliance on foreign revenue retreated from 40.9% to 29.2% of total revenue during 2013-2017. Panel A of Figure 1 shows the sizable drop in G-SIFIs’ foreign revenue exposure. As foreign revenue fell over 11 percentage points during this period, G-SIFIs’ inefficiency rose over 14%. These univariate statistics suggest that the reduction in foreign operations coincided with a return to less-efficient periods. Panel B of Figure 1 displays some weak evidence that regional SIFIs have started to increase their share of foreign revenue from 6.7% to 10.5% of total revenue during this same period, possibly to partially fill the void created by the G-SIFIs’ global pullback. The multivariate results also indicate that the reduction in foreign revenue by G-SIFIs is nonlinearly related to the efficient management of these large firms. For example, the percentage of total revenue from foreign sources (FR%) is positively related to inefficiency at low levels of foreign activity and then switches to a negative relationship at higher levels of foreign revenue. Overall, I find that G-SIFIs’ global retrenchment is related to less efficiency after controlling for G-SIFI profitability, productivity, and risk-taking, as well as changes in regulation, technology, and the macroeconomy. Thus, the level of foreign operations is an important factor that should be included in production models when measuring a large financial firm’s efficiency.

Methodology Hughes et al. (2003) takes a production function approach where the key inputs are an equity investor’s capital and physical labor (the latter can be measured by the number of employees). This method focuses on producing a primary “output” which is what financial researchers refer to as the main goal of the firm: i.e., the production function of the firm ultimately attempts to maximize shareholder value (the output). In this way, one can interpret a

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firm’s risky free cash flow (and its related growth rate) as “intermediate goods” that ultimately affect the “final output,” which is the market value of the equity investors’ investment. A stylized description of this study’s production function model of equity value closely follows Pagano (2017)5 and can be estimated by stochastic frontier analysis (SFA) based on a half-normal error distribution, as first described in Jondrow et al. (1982): Ve = g(K, L, T) = ln(MVEi,t) = 0 + 1 ln(BVEi,t) + 2 ln(Employeesi,t) + ∑

(1) (Time Effectst) + i,t

where, MVEi,t is the market value of equity for firm-i at the end of year-t and BVEi,t is the book value for this firm-year where the latter represents the firm’s capital investment (K), Employeesi,t is the total number of employees for this firm-year and proxies for labor (L), and Time Effectst represent annual fixed effects factors to control for changes in technology and regulation as well as the macroeconomy over time (denoted as T). In addition, i,t is a composite error term that equals (i,t – i,t) and distinguishes between the FI’s inefficiency (i,t) and random statistical noise (i,t). The inefficiency ratio equals the estimate of i,t divided by the firm’s potential market value of equity for each firm-year observation. Equation (1) can be viewed as a form of the classic Cobb-Douglas production function model and uses logarithms of the market and book values of equity, as well as the logarithm of the number of employees.6 I use Pagano’s (2017) three-step process to answer the main research question. This methodology is based on first using a Heckman selection model, 5

Note that the valuation model is described in general terms and can accommodate both constant growth and abnormal growth assumptions, as well as any type of asset pricing approach because the firm’s cash flows, their riskiness, and their required discount rate are viewed as “intermediate” goods and thus do not need to be explicitly modeled. In this way, we can directly equate the firm’s market value of equity to management’s initial choices related to investments in capital and labor. 6

The Cobb-Douglas (1928) production function defines output (Y) as a function of capital (K) and labor (L) inputs: Y = AKL. In logarithmic form, this function becomes: ln(Y) = A +  ln(K) +  ln(L) where A is a factor that can be viewed as a measure of a firm’s level of technology.

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then a stochastic frontier analysis (SFA), and in the third and final stage, a panel regression where the inefficiency estimates are regressed on SIFI-specific factors such as the firm’s reliance on foreign revenue (FR%) and other control variables. The advantage of this approach is the inefficiency ratio is estimated within a comprehensive framework which accounts for random effects, nonlinearity, and possible selection bias. To conserve space here, please see Pagano (2017) for more details on this estimation process.

Data I collect calendar year-end annual data during 2005-2017 for equity market capitalization, as well as various balance sheet and income statement items, for 48 SIFIs from Bloomberg.7 To conserve space, I report the summary statistics for the main variable of interest here in the text. The average Inefficiency Ratio is .399 with a standard deviation of .174 (i.e., the market value shortfall as a percentage of the best-practice market value). The standard deviation shows that there is considerable variation in efficiency across firms and over time. Figure A.1 displays this variation for the Inefficiency Ratio, Operating Margin, and ROA using equally weighted annual averages of all 48 firms in the sample. The overall average inefficiency spiked at over .44 during the 2008 crisis period, then fell below .37 in 2013 before reversing course and rising once again to over .41 in 2017.

Results Based on first-stage regressions described above and in Pagano (2017), I find that selection bias exists. These results hold even after controlling for time-varying fixed effects which take account of annual shifts in the industry’s production frontier due to changes in technology, regulation, and/or the

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For foreign revenue data and firm-level employment, I obtained data primarily from Bloomberg but supplemented this data source by obtaining information directly from firm’s financial disclosures whenever the data were not reported via Bloomberg. Our sample starts in 2005 because this is the earliest period for which reliable foreign revenue data are available.

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macroeconomy. A panel data set is then used to identify the determinants of differences in SIFI inefficiency. By regressing the inefficiency estimates on a firm’s book value of assets (BVA) and its squared term, one can observe a nonlinear relationship between the SIFI’s size and inefficiency. In Table 1, column 1, the final stage’s model of the full sample’s inefficiency estimates (with firm-clustered standard errors) shows a clear nonlinear relationship between firm size and inefficiency.8 The negative coefficient on BVA and the positive coefficient on the squared form of BVA indicates that inefficiency first decreases (conversely, the firm’s efficiency increases), as firm size grows. At some point, the pattern reverses and further increases in firm size lead to greater inefficiency. This nonlinearity suggests the costs of becoming extremely large (e.g., over $1 trillion in assets) could outweigh the benefits for most firms.9 The results are robust to other potential explanatory factors such as the firm’s productivity (RevPerEmp), profitability (OperMargin), and risk-taking behavior (CashRatio).10 To conserve space, additional tests are not reported here but I confirm that the key findings of nonlinearity are robust after controlling for: 1) higher post-Dodd Frank Act capital requirements, 2) potential differences in geographic regions, 3) possible 2016-2017 Trump election effects, and 4) an alternative inefficiency measure based on the firm’s market-to-book value of equity. As the seventh and eighth rows of parameter estimates show in Table 1, a firm’s dependence on foreign revenue (FR%) also has a nonlinear effect on 8

The first column in Table 1 also includes interaction terms between the two BVA variables and a G-SIFI dummy variable to account for differences in the effect of firm size between G-SIFIs and regional SIFIs. 9

Interestingly, the nonlinear relationship shown in column 1 is confirmed for the regional SIFI sub-sample (in column 2) but the signs for the size-related variables flip for the G-SIFI sub-sample (in column 3). 10

Note that the parameter estimates of two of three of these additional control variables in column 1 are negative and only one of these three variables is statistically significant. This suggests that increases in profitability (OperMargin) are an important determinant in reducing a firm’s market value shortfall.

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inefficiency. The linear term (FR%) is positively related to inefficiency while the nonlinear term (FR% Squared) exhibits a negative relationship. This pattern is significant for the full sample and the G-SIFI sub-sample but not the regional SIFIs, which suggests that the global FIs are driving this finding. The “peak” level of inefficiency occurs at a foreign revenue percentage of 31.3% for G-SIFIs and 19.1% for regional SIFIs. This suggests that SIFIs experience more inefficiency as they approach these levels. G-SIFIs have reduced their foreign revenue from over 40% to below 30% while regional SIFIs increased this percentage from below 4% to over 10%. In both cases, FIs have moved closer to their most inefficient levels which helps explain why SIFIs have become more inefficient in recent years after first improving their efficiency in 2010-2013.

Conclusions Although there has been wide variation over time and across firms, GSIFIs are becoming less efficient as they shrink their foreign operations, even after controlling for changes in profitability, productivity, risk-taking, regulation, technology, and macroeconomic conditions. Inefficiency bottomed after the 2010-2012 Eurozone crisis period and has now increased to levels seen back in 2009-2011. In addition, the nonlinear pattern between inefficiency and a G-SIFIs’ foreign activities indicates that the most efficient approach might be a binary strategy of either high or very low reliance on foreign revenue. The results therefore suggest that the level of foreign operations is an important factor that should be included in production models to properly measure a large financial firm’s efficiency. Further study of the determinants of G-SIFI’s foreign operations and their implications for firm efficiency and risk-taking are potential areas for future research. There also are policy implications based on the above analysis. Given the current tension between U.S. and European financial regulators

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noted in Germain (2016), the differences between G-SIFIs and U.S.-focused SIFIs in my results suggest a greater need for transnational regulatory coordination to reduce the potential unintended consequences from regulations that might be applied unevenly across global FI’s based in different geographic areas.

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References Brühl, V., 2017, How to define a systemically important financial institution -A new perspective, Intereconomics 52, 107-110. Cobb, C., and Douglas, P., 1928, Theory of Production, American Economic Review 18, 139-165. Germain, R., 2016, Locating authority: Resolution regimes, SIFIs and the enduring significance of financial great powers, Journal of Banking Regulation 17, 34-45. Heckman, J., 1979, Sample Selection Bias as a Specification Error, Econometrica, vol. 47: 153-161. Hughes, J., Lang, W., Mester, L., Moon, C., Pagano, M.S., 2003, “Do bankers sacrifice value to build empires? Managerial incentives, industry consolidation, and financial performance,” Journal of Banking and Finance 23: 291–324. Jondrow, J., Lovell, C., Materov, I., Schmidt, P., 1982, On the estimation of technical efficiency in the stochastic frontier production function model, Journal of Econometrics 19: 233–238. Moody’s Investor Service, 2014, Global Investment Banks: Competing Demands of Regulators and Shareholders Force Strategic Repositioning that may further Differentiate Credit Profiles, Special Comment: May 15. Pagano, M., 2017, “How have global financial institutions responded to the challenges of the post-crisis era?” Applied Economics 49: 1414-1425. Pagano, M., Sedunov, J., 2016, A Comprehensive Approach to Measuring the Relation Between Systemic Risk Exposure and Sovereign Debt, Journal of Financial Stability 23: 62-78.

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Tracey, R., 2018, Rules for Big Banks to Get a Rethink, Wall Street Journal, Oct. 30, A3.

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Table 1. Estimated Determinants of Inefficiency Ratios This table reports the estimates of Equation (A.3)’s panel regression models of inefficiency ratios (the dependent variable) on annual data for 48 large U.S. and international financial institutions. Time- and firm-specific fixed effects are included but not reported here to conserve space. t-statistics are reported in parentheses. Variable

Constant

(1) Full Sample

(2) N.A. SIFI

(3) Global SIFI

0.57462 (12.03) ***

0.73941

(12.88) ***

0.39362 (7.75) ***

BVA

-0.00039 (-3.70) ***

-0.00028962

(-5.28) ***

0.000146 (3.43) ***

BVA2

0.001186 (4.23) ***

0.00087154

(4.02) ***

-0.00038 (-2.93) ***

RevPerEmp

-0.00009 (-1.37)

-0.00058714

(-5.91) ***

-0.00016 (-3.66) ***

OperMargin

-0.20590 (-5.37) ***

-0.40782

(-5.37) ***

-0.31088 (-5.46) ***

CashRatio

0.079257 (1.29)

-0.06192

(-0.75)

0.29854 (3.81) ***

FR%

0.992779 (4.05) ***

0.44526

(1.48)

0.36433 (2.41) **

FR% Squared

-1.08566 (-4.14) ***

-1.16285

(-1.53)

-0.58154 (-3.03) ***

BVA x G-SIFI Dummy

0.000495 (4.35) ***

BVA2 x G-SIFI Dummy

-0.00132 (-4.27) ***

Number of Observations Adjusted R2

420

175

264

0.7634

0.4116

0.2729

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Figure 1. Trends in Foreign Revenue Activity for G-SIFIs and Regional SIFIs Panel A. Global SIFI Activity

Panel B. Reg. SIFI Activity

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Appendix A. Systemically Important Financial Institutions Table A.1 provides a list of G-SIFIs based on the latest classification by the Financial Stability Board, as well as 14 North American-focused SIFIs. Table A.1. List of SIFIs used in the analysis Company Agricultural Bank of China Bank of America Banco Santander Bank of China Banque Populaire Barclays BB&T Banco Bilbao Vizcaya Argentaria Bank of New York Mellon Bank of Montreal BNP Paribas China Construction Bank Citigroup Comerica Incorporated Commerzbank Credit Agricole Credit Suisse Deutsche Bank Dexia Fifth Third Bancorp Goldman Sachs Huntington Bancshares Incorporated HSBC Holdings PLC Industrial & Commercial Bank of China The ING Group J.P. Morgan Key Bancorp Lloyds Banking Group PLC Mitsubishi UFG Financial Group Mizuho Financial Morgan Stanley M&T Bank Nordea PNC Bank Regions Financial Corp Royal Bank of Canada Royal Bank of Scotland Societe Generale Standard Chartered State Street Corp. SunTrust Banks Inc. Sumitomo Mitsui Financial Goup Toronto Dominion Bank UBS Group AG Unicredit U.S. Bancorp Wells Fargo Corporation Zions Bancorporation

Global SIFI? Yes Yes Yes Yes Yes Yes No Yes Yes No Yes Yes Yes No Yes Yes Yes Yes Yes No Yes No Yes Yes Yes Yes No Yes Yes Yes Yes No Yes No No Yes Yes Yes Yes No Yes Yes No Yes Yes No Yes No