Impact of International Expansion Strategy on the Performance of Japanese Banks

doi:10.21203/rs.3.rs-1705840/v1

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Impact of International Expansion Strategy on the Performance of Japanese Banks

https://doi.org/10.21203/rs.3.rs-1705840/v1

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The effect of cross-border diversification on bank performance is part of the broader debate on how multinational banking and financial integration affect the global financial economy. Much of the empirical studies examining this relationship present mixed results globally. Studies on both sides of the expansion spectrum find that cross-border diversification improves bank performance but also increases bank risks leading to systemic failure. Even so, this line of debate has not been examined in the case of Japanese ‘city banks’ conducting international operations. The present study questions whether cross-border diversification improves the performance of Japanese banks and to what extent each cross-border expansion activity affects bank performance. The latter was largely ignored in previous studies. The study finds that cross-border diversification, in general, improves cost efficiency but decreases/harms profit efficiency of the banks analysed. Precisely, diversification activities such as foreign assets and foreign branch expansion present funding risks and operational inefficiency. The study concludes that, as a regional player, the current trend in cross-border expansion activities of Japanese banks requires close monitoring and supervision to prevent episodes of regional financial crises. Moreover, the current cross-border diversification strategies of these banks, especially the expansion and operation of overseas branch networks (retail banking) need to be re-examined.

International Economics

Bank performance

international expansion

overseas investment

systemic risk

Cross-border banking has grown significantly within the last four decades following the removal of investment restrictions on capital flows, deregulation, financial integration, technological progress, and financial innovations (de Jonghe et al., 2015; Gulamhussen et al., 2017; Mulder & Westerhuis, 2015). Consequently, the question of why and how banks internationalize, has received considerable attention in the literature (Batten & Szilagyi, 2011; Gulamhussen et al., 2016; He et al., 2019; Hsieh et al., 2010; Kabongo & Okpara, 2019; Mariotti & Piscitello, 2010; Mariscal et al., 2012; Mulder & Westerhuis, 2015; Qian & Delios, 2008; Yamori, 1998; Zapotichna, 2017). According to the resource exploitation theory (also called Dunning’s eclectic theory), multinational banks (MNBs) with superior technology and financial power expand to international markets to exploit location-specific, ownership-specific, and internalization-specific factors that enable them to maintain a competitive advantage over the financial institutions of host countries (He et al., 2019; Mariotti & Piscitello, 2010; Mariscal et al., 2012). However, leader-followership or defensive expansion theory suggests that MNBs follow their existing clients to overseas markets to service them and achieve economies of scale in their application of intangible assets (Mulder & Westerhuis, 2015; Qian & Delios, 2008; Yamori, 1997). However, some strands of the literature suggest that MNBs may expand to international markets to avoid strict domestic regulations, thereby engaging in regulatory arbitrage (Houston et al., 2012; Ongena et al., 2013; Yamori, 1997), or augment shrinking domestic financial markets (Lam, 2015; Mulder & Westerhuis, 2015). Existing empirical literature reveals four major channels for internationalization of MNBs: international investments (FDI), mergers and acquisitions, partial acquisition via shareholding, and direct establishment of international branch operations (Lopes et al., 2017).

The effects of cross-border diversification on the performance of MNBs are of practical significance in the cross-border banking literature. Many empirical studies have attempted to answer this question with diverse findings and conclusions. According to the diversification (expansion) hypothesis, cross-border diversification leads to improved earnings, shareholder value creation, reduced systemic risk exposure, and economies of scale and scope. Substantial empirical studies have confirmed some of these benefits.Demsetz and Strahan (1997) showed that cross-border diversification provides large bank holding companies (BHCs) with a size advantage, greater leverage, and reduced risks. In another study,(Deng & Elyasiani, 2008) it was found that cross-border diversification leads to value enhancement and risk reduction in BHCs and that the geographic proximity of branches relative to BHCs plays an important role in value-enhancing and risk-reducing potentials. Additionally, depending on the country’s local institutional setting, non-interest income was found to reduce the systemic risk exposure of diversified MNBs (de Jonghe et al., 2015). Moreover, a recent study of 384 banks in 56 countries shows that the cross-border activities of MNBs create shareholder value (Gulamhussen et al., 2017).

However, consistent with the market risk hypothesis, which suggests that diversification increases bank risk due to market-specific factors in host countries, some researchers have found that cross-border banking does not always provide the desired benefits. Battiston et al. (2012) studied the dynamics of default cascades in a network of credit interlinkages (hypothetical diversified credit institutions) and found that individual risk diversification across counterparties does not reduce systemic risks. A related study of twenty-two major banks in Asia and the Pacific after the global financial crises showed that the spillover effect of the crisis was driven mainly by heightened risk aversion and liquidity squeezed in the global financial markets originating from the US subprime crisis (Huang et al., 2012). The risk reduction from portfolio diversification by MNBs can be offset by incentives going in the opposite direction, leading to excessive risks (Gulamhussen et al., 2014). Using zscore, expected default frequency, and other measures of risk to analyse a sample of 384 banks across more than 50 countries, the authors find that international diversification increases bank risk. Finally, it was found that international banks in the US have a much higher risk than purely domestic banks because of lower lending standards for corporate borrowers (Berger et al., 2017). Moreover, the authors showed that a greater marginal degree of internationalisation within the subset of internationalised banks is associated with higher risk, concluding that internationalisation increases bank risk, especially during market crises.

Some studies have analysed the motivation, location choice, and determinants of the internationalisation of Japanese banks. Yamori (1997, 1998) analysed whether Japanese banks lead or follow manufacturing industries in international markets. He also analysed the location choice of Japanese financial institutions. The author found that foreign direct investment (FDI) of manufacturing industries determined the location choice of Japanese financial institutions in the near east, whereas Japanese banks led manufacturing in the process of internationalisation in Asia and Oceania. (Qian and Delios (2008) examined the relationship between internalisation and experience of Japanese banks’ international expansion, focusing on the period between 1980 and 1998. The major finding was that Japanese banks undertake FDI to secure internalisation benefits by following their existing clients and to achieve economies of scale in their intangible assets. Batten and Szilagyi, (2011) examined the changing roles and direction of Japanese bank internationalisation between 1995 and 2008 and found that Japanese banks reduced their international presence mainly to mitigate the effect of failed loans in the Asia-Pacific region.

Lam (2015) examined the determinants of Japanese banks’ recent overseas diversification activities as well as the associated opportunities and risks, focusing on the period between 2000 and 2012. The author found that Japanese banks are well-positioned and have scaled up foreign exposure due to their resilient balance sheet, and that global and regional factors played prominent roles in the growth of cross-border claims. However, the author contended that increasing cross-border activities could lead to funding risks and supervisory challenges in the future.

The above can be summarised into two stylised facts: (1) considerable empirical studies have examined the trend, determinants, and effects of cross-border diversification on the performance of banks globally and (2) some empirical studies have also examined the cross-border diversification activities of Japanese banks. However, the effects of cross-border diversification on the performance of Japanese banks have not been precisely addressed. The recent rise in overseas diversification activities of Japanese banks, especially in the Asia-Pacific region in the post-GFC period, and the possible overseas market risk exposure of these banks (Lam, 2015), makes it imperative to systematically examine the relationship between overseas expansion activities and performance of Japanese banks under various diversification-specific activities.

Using relevant internationalisation measures, we examine the effects of cross-border expansion activities on the performance of Japanese city banks conducting international operations. This study shows that cross-border expansion activities improve cost efficiency but decrease or harm the profit efficiency of the banks. Specifically, expansion activities such as the foreign asset ratio and foreign branch ratio decrease bank-level stability and lending efficiency, indicating possible funding risks and operational inefficiency. These findings concur with those of previous studies that have found benefits in cross-border diversification. In addition, the findings show that cross-border expansion activities could harm bank performance and stability via funding risk. This study contributes to the literature in three ways: First, it covers the period from 2003 to 2020, providing the most recent empirical analysis of Japanese city banks’ international operations and performance. Therefore, the results may be useful to decision makers and evaluators in policy formulation. Second, to the best of our knowledge, this research is the first study, especially in post-GFC, to empirically examine the direct impact of overseas expansion activities on the performance of Japanese banks under various performance measures. Finally, this study empirically confirms the conjecture of an earlier study, which contended that the increasing overseas expansion activities of Japanese banks could lead to funding risks and supervisory challenges (Lam, 2015).

The remainder of this paper is organised as follows. Section two presents theoretical considerations and hypotheses development. Section three presents the empirical strategy, data, and summary statistics of the study. Section four presents the estimation results. Finally, a summary of the findings and concluding remarks are presented in Section five.

2.1 Measures of bank performance

This study adopts the resource exploitation theory to understand bank performance measures. The theory suggests that banks expand abroad to exploit market opportunities enabling them to maximise their revenue and profits. In line with this contention, and consistent with the empirical literature on banking, we consider cost efficiency and profit efficiency as important measures of bank performance to evaluate Japanese banks. Furthermore, bank stability is used in cross-border banking literature to measure the risk of international operations (Berger et al., 2017; Gulamhussen et al., 2014). Consistent with this argument, we employ the z-score to measure the risks of international expansion activities of the banks in our sample. Furthermore, we introduce a new measure of bank performance, lending efficiency, based on the financial intermediation services indirectly measured (FISIM) drawn from the system of national accounts (SNA). FISIM is the spread between the loan interest rate and deposit interest rate relative to the market spread rate, and focuses only on the productive activities of banks (Aduba & Izawa, 2021). This spread provides management efficiency for MNB in international markets as the bank is able to exploit interest rate differentials across various markets, transfer funds internally, and reap generous profits (Havrylchyk & Jurzyk, 2011; Qian & Delios, 2008).

2.2 Measures of international diversification (internationalization)

To examine the cross-border diversification of multinational enterprises or banks, researchers have derived different measures called the degree of internationalisation (DOI) (E., 1983; Sullivan, 1993). One of the most important DOIs used in the literature is the amount of overseas investment. This could be due to direct overseas investments or banks’ accumulated FDI in host countries. Direct overseas investment or banks’ FDI requires a more extensive and long-term transfer of resources to foreign countries and, therefore, shows deliberate overseas diversification activities of banks (Yamori, 1997, 1998). However, recent studies have considered more direct measures of MNBs’ overseas diversification. For example, Mulder & Westerhuis (2015) considered the foreign sales ratio, foreign assets ratio, and foreign employment ratio as measures of bank internationalisation. Besides the foreign asset ratio, Berger et al. (2017) also considered foreign loan ratio and foreign deposit ratio as important measures of bank internationalisation. In line with these empirical studies, this study considers six measures of international diversification: foreign investment ratio, foreign sales ratio, foreign loan ratio, foreign loan sales ratio, foreign assets ratio, and foreign branch ratio.

Foreign investment (including, but not limited to, accumulated FDI) is one of the major strategies used by banks to expand to international markets. Studies on cross-border banking find that direct foreign investments such as equity joint ventures, greenfield investments, direct acquisitions, and project finance have been used by MNBs to expand abroad with the primary goal of reaping potential benefits (higher returns) in host countries (Lopes et al., 2017; Mariscal et al., 2012). Similar to overseas investments, the amount of foreign assets in host countries provides MNBs with adequate footing and financial power to conduct international lending activities easily and effectively and exploit local liquidity markets (Moshirian, 2001). Moreover, the acquisition of local banks through mergers and acquisitions (M&As) and/or the establishment of new branch operations enables MNBs to benefit from local opportunities in foreign financial markets, where they maintain a competitive advantage (Clarke et al., 2003). Foreign branch operations and the acquisition of local banks increase the asset base of MNBs, which is expected to positively affect their performance.

The foregoing shows that there is an a priori expectation of a positive relationship between cross-border diversification and bank performance. This suggests that the motivation to expand abroad is largely related to resource exploitation, revenue generation, and profit maximisation. Therefore, to address the research questions on the effect of cross-border diversification on the performance of Japanese banks, we test the following four hypotheses by examining the relationship between overseas expansion activities and the four measures of bank performance.

Hypothesis 1

Overseas expansion activities have a positive effect on the cost efficiency of Japanese banks.

Hypothesis 2

Overseas expansion activities have a positive effect on the profit efficiency of Japanese banks.

Hypothesis 3

Overseas expansion activities have a positive effect on the z-score of Japanese banks.

Hypothesis 4

Overseas expansion activities have a positive effect on the lending efficiency of Japanese banks.

3.1 Measuring cross-border diversification: degree of internationalization

To measure the degree of internationalisation, we take the ratio of each bank’s cross-border activity (divestments or returns) to the total value of such activity (including the domestic value) on a bi-annual basis, as reported by each bank. Thus, the following estimation strategies emerged for each of the six identified DOIs:

$$Foreign invest. Ratio=FiR=\frac{Foreign invest.}{Foreign invest.+Domestic invest.} \left(1\right)$$

$$Foreign sales Ratio=FsR=\frac{Foreign sales\left(on invest\right)}{Foreign sales+Domestic sales} \left(2\right)$$

$$Foreign loan Ratio=FlR=\frac{Foreign loan}{Foreign loan+Domestic loan.} \left(3\right)$$

$$Foreign loan Interest.=FliR=\frac{Foreign loan Int.}{Foreign loan Int.+Domestic loan Int.} \left(4\right)$$

$$Foreign asset Ratio=FaR=\frac{Foreign ROA}{Foreign ROA+Domestic ROA} \left(5\right)$$

$$Foreign branch Ratio=FbR=\frac{Foreign branches}{Foreign branches+Domestic branches} \left(6\right)$$

Equations 1–6 estimate the ratios of individual cross-border diversification activities. However, following a procedure similar to that used by Mulder and Westerhuis (2015), we calculate the unweighted average of all six expansion activities (DOIs) using Eq. 7. This represents the overall diversification index used to address the research questions captured by the hypotheses.

$$Degree of internationalization={DOI}_{uwa}=\sum _{i=1}^{6}{FR}_{i}/6 \left(7\right)$$

Previous studies have used the unweighted average of DOIs (Eq. 7) to examine the effects of international diversification on bank performance. However, this approach does not address the effects of individual cross-border expansion activities. Therefore, this study tests the effect of individual cross-border diversification measures (Equations 1–6) on the performance of banks in our sample.

3.2 Measuring bank performance

The four bank performance measures identified in this study were estimated using different empirical strategies. The cost and profit efficiency scores were estimated using a stochastic frontier analysis (SFA) approach. Empirical studies suggest that SFA is better suited for efficiency estimation because it allows measurement errors in the data structure and provides firm-specific efficiency estimates (Yamori et al., 2017). Therefore, with the usual linear homogeneity restriction in input prices, we apply the standard trans-log function to estimate cost efficiency as

$$\text{l}\text{n}\left(\frac{{\text{C}}_{\text{i}\text{t}}}{{\text{p}}_{\text{l}\text{i}\text{t}}}\right)={{\theta }}_{0}+\sum _{\text{j}}{{\theta }}_{\text{j}}{\text{l}\text{n}\text{Y}}_{\text{j}\text{i}\text{t}}+\sum _{\text{k}\ne \text{l}}{{\beta }}_{\text{k}}\text{l}\text{n}\left(\frac{{\text{p}}_{\text{k}\text{i}\text{t}}}{{\text{p}}_{\text{l}\text{i}\text{t}}}\right)+{{\tau }}_{1}\text{T}+\frac{1}{2}\sum _{\text{j}}\sum _{\text{m}}{{\theta }}_{\text{j}\text{m}}{\text{l}\text{n}\text{Y}}_{\text{j}\text{i}\text{t}}{\text{l}\text{n}\text{Y}}_{\text{m}\text{i}\text{t}}+$$

$$\frac{1}{2}\sum _{\text{k}\ne \text{l}}{\sum }_{\text{n}\ne \text{l}}{{\beta }}_{\text{k}\text{n}}\text{l}\text{n}\left(\frac{{\text{p}}_{\text{k}\text{i}\text{t}}}{{\text{p}}_{\text{l}\text{i}\text{t}}}\right)\text{l}\text{n}\left(\frac{{\text{p}}_{\text{n}\text{i}\text{t}}}{{\text{p}}_{\text{l}\text{i}\text{t}}}\right)+\frac{1}{2}{{\tau }}_{2}{\text{T}}^{2}+\sum _{\text{j}}{\sum }_{\text{j}\ne \text{k}}{{\phi }}_{\text{j}\text{k}}{\text{l}\text{n}\text{Y}}_{\text{i}\text{j}\text{k}}\text{l}\text{n}\left(\frac{{\text{p}}_{\text{k}\text{i}\text{t}}}{{\text{p}}_{\text{l}\text{i}\text{t}}}\right)+{{\upsilon }}_{\text{i}\text{t}}+ {\text{u}}_{\text{i}\text{t}} \left(8\right)$$

where ${\theta }_{jm}={\theta }_{mj} for all j and m, and {\beta }_{kn}={\beta }_{nk} for all k and n$, ${C}_{it}$ is the observed total cost, ${p}_{i} \left(i=1, 2, 3\right)$denotes the three input prices: labour, deposit, and capital, ${\text{Y}}_{j}\left(j=\text{1,2}\right)$ are two outputs: total loans and total security investments. T represents the time trend. $\theta$, $\beta$, $\tau$, and ${\phi }$ are parameters to be estimated. v_it is a standard statistical error term, independently and identically distributed as N(0, σ_v²), and u_it is a non-negative error term that represents technical inefficiency. Regarding the a priori distributional assumptions for the inefficiency term, we employ half-normal and exponential distributions.

From Eq. (8), the profit efficiency score can be estimated by making the necessary transformation in the dependent variable and the error structure, known as the alternative profit function specification (Berger & Mester, 1997). Thus, the profit efficiency score can be estimated as

$$\text{l}\text{n}\left({\tilde{{\pi }}}_{\text{i}\text{t}}\right)={{\theta }}_{0}+\sum _{\text{j}}{{\theta }}_{\text{j}}{\text{l}\text{n}\text{Y}}_{\text{j}\text{i}\text{t}}+\sum _{\text{k}\ne \text{l}}{{\beta }}_{\text{k}}\text{l}\text{n}\left(\frac{{\text{p}}_{\text{k}\text{i}\text{t}}}{{\text{p}}_{\text{l}\text{i}\text{t}}}\right)+{{\tau }}_{1}\text{T}+\frac{1}{2}\sum _{\text{j}}\sum _{\text{m}}{{\theta }}_{\text{j}\text{m}}{\text{l}\text{n}\text{Y}}_{\text{j}\text{i}\text{t}}{\text{l}\text{n}\text{Y}}_{\text{m}\text{i}\text{t}}+$$

where ${\tilde{{\pi }}}_{\text{i}\text{t}}=({\pi }+{\left|{\pi }\right|}^{min}+1)$, and ${\left|{\pi }\right|}^{min}$ is the absolute value of the minimum profit for all banks in the sample. This transformation allows for a nonzero value of the natural log of the dependent variable ${{\pi }}_{\text{i}\text{t}}$. All other variables are as defined.

We consider lending efficiency, based on the FISIM framework, as an important measure of bank performance, where lending efficiency is the residual earnings resulting from the spread between interest on loans and interest paid on deposits relative to interbank rates. The lending efficiency can be estimated as follows:

$LE=fisim=\left({r}_{l}-{r}_{r}\right)*{G}_{L}+\left({r}_{r}-{r}_{d}\right)*{G}_{D} \left(10\right)$ Where ${r}_{l}$ ${r}_{d}$ and ${r}_{r}$ are loan, deposit, and reference rate, respectively. ${G}_{L}$ and ${G}_{D}$ are total loans and total deposits, respectively. The reference rate ${r}_{r}$ is taken as the interbank lending rate. The loan interest rate refers to the ratio of loan interest to total loans, and the deposit interest rate is the ratio of interest paid to loanable funds divided by the total deposit.

Finally, our measure of bank soundness or stability (z-score) was calculated following the strategies employed in empirical studies on cross-border banking (Gulamhussen et al., 2014; Laeven & Levine, 2009). We computed zscore as follows:

$$zscore=\frac{ROA+EQT}{\sigma \left(ROA\right)} \left(11\right)$$

where ROA and EQT denote three years moving average (equivalent to six consecutive periods for biannual data) of returns on total assets and the equity-to-asset ratio, respectively. σ represents the standard deviation of returns on total assets during the same period. For the equity-to-asset ratio, we consider the BIS capital-risk-asset ratio, calculated as Basel capital regulations. Since z-score represents a bank’s distance from insolvency, a higher zscore indicates greater banking stability.

3.3 Econometric model

To investigate the effect of cross-border diversification on bank performance, we use the following empirical model for estimation.

$${{\Pi }}_{it}=\alpha +\beta .{\text{D}\text{O}\text{I}}_{it}+\delta .{controls}_{it}+{\epsilon }_{it} \left(12\right)$$

where ${\varPi }_{it}$ is the performance of bank i at time $t,$ ${DOI}_{it}$is the diversification index of bank i in time $t$, and $\alpha , \beta ,$ and $\delta$ are the parameters to be estimated. The model controls for each bank’s financial condition using bad-loan-ratio (BLR), capital adequacy ratio (BIS), and non-interest income ratio (NIIR).

The bad loan ratio is an important quasi-fixed input often used to control for asset quality in empirical banking studies. The level of non-performing assets is known to have a significant impact on bank performance, especially in cross-border banking, and not controlling for this in the empirical model could bias the results (Hughes & Mester, 2014). The capital adequacy ratio enables banks to absorb losses from credit, markets, and operational risks. Beyond being a regulatory requirement, the bank capital adequacy ratio affects lending, investment drive, and bank outputs (Hassan et al., 2016) and, as a result, provides a good control measure in our econometric model. The definition of BIS is identical to EQT used in computing the z-score in Eq. (11). Moreover, ‘non-interest income’ is the return from non-interest-bearing assets and other bank services which is an important measure of non-risky divestment that contributes to bank performance. These control variables are crucial for avoiding omitted variable bias in our econometric estimation results.

3.4 Data, variables, and summary statistics

The data used in this study were obtained from the bi-annual financial reports of five Japanese city banks conducting international operations. Although two of the banks are not subject to Basel capital standards, they conduct international operations. The reports of these banks provide information on both domestic and overseas activities between 2003 and 2020, about two-decades-long data, which provide a systematic view of the international activities of these banks before and after the GFC. These reports contain the aggregated data of all international operations and do not present any data according to specific geographical location of business operations. However, it separates domestic operations from overseas operations for all variables of interest, enabling us to investigate the effect of international operations on domestic operations and bank performance. Three of the banks are multinational banks, while two are considered megabanks conducting international operations such as investment banking. We used data on total assets, average domestic and overseas loans and securities, outstanding domestic and foreign investment management accounts, non-performing loans, capital adequacy ratio, non-interest income, and number of overseas branches. All financial data are deflated using the gross domestic product deflator published by the Japan Statistical Agency. Panel A of Table 1 presents the summary statistics of both domestic and overseas bank operations.

Using our empirical strategies, we calculate all expansion variables (DOIs) and bank performance measures from relevant bank operations. The summary statistics of all the dependent and predictor variables are presented in Panels B and C of Table 1. We examined the trend analysis of the expansion variables using a graphical presentation (see Fig. 1). The unweighted average of all diversification activities (DOI) suggests a sharp increase in international activities by most banks between 2013 and 2019, historically above the pre-GFC. This result was confirmed by individual diversification activities. For example, the foreign investment and foreign sales ratios exhibit a sharp upward trend between 2013 and 2019, similar to the trends in the foreign loan ratio, foreign loan sales ratio, and foreign asset ratio. These upward trends in all the expansion variables imply an increase in international activities, such as investment, lending, and other cross-border banking operations of Japanese banks in the last decade. A similar trend in cross-border diversification activities of Japanese financial institutions was noted by Lam (2015).

Table 1

Summary statistics of variables
Panel A. Summary of banking operations (million yen)
		Mean		Std. Dev.		min		max
Domestic investment.		60924.36		39812.66		8394.27		151500.00
Sales on domestic investment		364.04		242.44		3090.00		1235.65
Domestic lending		33163.47		19769.19		4624.52		67116.64
Sales on domestic lending		239.84		159.61		1.60		908.73
Domestic security investment		18525.54		14900.10		710.20		59445.16
Sales on domestic Sec. investment		90.81		75.37		1.58		312.35
Overseas investment		17019.12		19867.18		41.38		70537.34
Overseas sales on investment		227.61		272.00		0.13		1173.06
Overseas lending		10130.20		12266.26		3.20		44760.34
Sales on overseas lending		15.56		186.22		0.02		797.31
Overseas security investment		1654.60		2045.01		0.00		8596.43
Sales on overseas security investment		20.50		23.52		-0.08		93.47
Total Assets		86509.16		64140.11		9037.39		251500.00
Bad loan ratio		1.98		1.61		0.52		13.24
BIS		12.57		2.85		2.27		23.05
Panel B. Dependent variables
Variables	Unit	Mean	Std. Dev.		Min		Max
ceffn	score	0.8509	0.0717		0.5964		0.9228
peffn	score	0.8178	0.1074		0.4161		0.9795
zscore	score	88.1104	44.1521		8.4606		150.7169
fisim	bil¥	47.8092	10.0301		27.8679		60.5581
Panel C. Predictor variables
Variables	Unit	Mean	Std. Dev.		Min		Max
DOI	ratio	16.3402	4.4332		9.9941		24.2069
FiR	ratio	14.3741	3.7019		8.9380		20.1570
FsR	ratio	24.8329	7.1940		15.8876		39.4508
FlR	ratio	15.4204	4.5014		8.5315		21.7649
FliR	ratio	26.3886	7.1666		15.1128		39.4736
FaR	ratio	0.6847	0.0601		0.6011		0.8013
FbR	ratio	11.1141	6.0267		3.8830		20.4820
BLR	ratio	1.9450	1.3487		0.7863		7.7984
BIS	ratio	12.5373	1.9099		8.4760		17.0750
NIIR	ratio	19.0241	3.2615		13.4605		24.4054
D-domestic, O-overseas

4.1 Effect of overall cross-border diversification on bank performance

Table 2 presents the empirical results on the effects of cross-border expansion activities on bank performance. First, we estimate a baseline regression using pooled OLS (1). The results show that the overall diversification measure (DOI) has a significant effect on all performance measures. However, based on model specification tests and robustness checks, we re-estimate our model using the feasible generalised least square regression method, controlling for the bank fixed effect (2), bank and time fixed effect (3), and bank fixed effect with time trend (4)[1]. Again, the results show robust estimates, largely confirming the OLS results. We adopt the latter empirical results hereafter.

Table 2 shows that the control variables exhibit the expected signs, depending on the association of output with a risk measurement index. For example, the bad loan ratio (BLR) has a significant negative effect on all outputs except lending efficiency, whereas the capital adequacy ratio (BIS) has a significantly negative effect on cost and profit efficiency. Interestingly, none interest income (NIIR) has significant positive effects on all outputs.

Panel A of Table 2 reveals that overall cross-border diversification measure (DOI) has a significantly positive effect on cost efficiency. Similarly, the results in Panel B show that overall cross-border diversification has a significantly positive effect on lending efficiency. Both results imply that cross-border expansion activities improve the overall cost and lending efficiency.

Concerning cross-border diversification effect on bank-level stability, Panel C shows a significant positive effect only under the bank fixed effect model (2) which implies that an increase in overall cross-border diversification results in positive bank-level stability. However, when controlled for bank and time fixed effect, and bank fixed effect and time trend, the effect disappears. It implies that the positive effect of cross-border expansion on bank stability is endogenous to time. Moreover, the overall expansion index (the unweighted average of the six expansion activities) may be casting a shadow on the effect of expansion activities.

These results are consistent with empirical findings on the effects of cross-border diversification on bank performance. For example, (Deng & Elyasiani, 2008) found that overseas (geographical) diversification is associated with value enhancement and overall risk reduction. Several other studies, including a recent one, (Gulamhussen et al., 2017) also agree with our results that banks enjoy a diversification advantage that comes with improved earnings (including excess value).

Panel D of Table 2 shows a significant negative effect of overseas diversification on profit efficiency. The results are consistent and robust across all empirical tests. This implies that an increase in overseas diversification significantly decreases or harms banks’ profit efficiency. This is consistent with findings from a recent study that showed lower mean profitability for internationalised banks operating in the US (Berger et al., 2017). However, our results contradict those of previous studies(Havrylchyk & Jurzyk, 2011). Using a different measure of profit, the authors find that diversification provides leverage to foreign banks operating in Central and Eastern Europe to earn a higher average ROA due to interest rate differentials in foreign markets.

However, there is a caveat to the results presented above. For instance, the effect of the overall diversification measure on bank performance suggests that cross-border expansion improves cost efficiency. This result, however, does not reveal the effect or contribution of individual cross-border expansion activities on cost efficiency. Therefore, we examine the effect of individual cross-border expansion activities on bank performance.

Table 2. Effect of overall diversification on bank performance

	Panel A. Dependent variable: Cost efficiency				Panel B. Dependent variable: FISIM
	(1)	(2)	(3)	(4)	(1)	(2)	(3)	(4)
BLR	-.033329***	-.032804***	-.042847***	-.036354***	.114975***	.04194**	.10925***	.020772
	(.010849)	(.004822)	(.006616)	(.005129)	(.034641)	(.021304)	(.03537)	(.023149)
BIS	-.005579**	-.005857**	-.00083	-.004719*	.039296**	.059183***	.050158***	.060941***
	(.002373)	(.002562)	(.002805)	(.002555)	(.017831)	(.014581)	(.018943)	(.014306)
NIIR	.002963**	.003411**	.000316	.006313***	.036907***	-.001735	.050199***	.015993
	(.001471)	(.001655)	(.002142)	(.002155)	(.013576)	(.009163)	(.014654)	(.012198)
DOI	.001178**	.001135**	.000416	.001596***	.039867***	.036533***	.045608***	.039098***
	(.000508)	(.000507)	(.000488)	(.000548)	(.002731)	(.002633)	(.003058)	(.002828)
tm				-.002055**				-.012878**
				(.001023)				(.00568)
_cons	.910007***	.904378***	.91631***	.872477***	15.31726***	16.15647***	14.551183***	16.049881***
	(.061478)	(.05388)	(.076729)	(.054593)	(.390616)	(.30033)	(.485651)	(.30011)
Observations	200	200	200	200	200	200	200	200
F-stat	11.116121	8.76429	13.35451	43.11306	63.882544	422.97762	25.120936	24.2240
Adj R²	.280952	.326954	.357375	.481568	.43076	.273867	.302146	.292146
Bank FE	No	Yes	Yes	Yes	No	Yes	Yes	Yes
Time FE	No	No	Yes	No	No	No	Yes	No
	Panel C. Dependent variable: Zscore				Panel D. Dependent variable: Profit efficiency
	(1)	(2)	(3)	(4)	(1)	(2)	(3)	(4)
BLR	-.312382***	-.305507***	-.149014***	-.208119***	-.045278***	-.048734***	-.023002**	-.042891***
	(.043323)	(.03823)	(.045635)	(.036994)	(.010496)	(.00632)	(.009427)	(.006772)
BIS	.062166***	.057057***	.021805	.052115***	-.008157*	-.004001	-.01037***	-.006183**
	(.019468)	(.018586)	(.020241)	(.015733)	(.004264)	(.00307)	(.003653)	(.003131)
NIIR	.076272***	.060399***	.018902	.007798	.013373***	.006702***	.00468	.002166
	(.015623)	(.014065)	(.016043)	(.015262)	(.002448)	(.002021)	(.002977)	(.002443)
DOI	.007966**	.010887***	.000589	-.001781	-.002888***	-.002346***	-.002337***	-.003092***
	(.00351)	(.003882)	(.00398)	(.004202)	(.000732)	(.000597)	(.000673)	(.000621)
tm				.043214***				.003446***
				(.007071)				(.001137)
_cons	2.341634***	2.657322***	2.96554***	2.953195***	.800362***	.890482***	.669374***	.939998***
	(.498321)	(.424502)	(.547566)	(.376717)	(.084507)	(.062328)	(.104278)	(.063224)
Observations	200	200	200	200	200	200	200	200
F-stat	43.11306	42.360899	37.5289	36.52891	25.005743	9.775174	13.35451	8.425065
Adj R²	.481568	.512758	.546846	.556846	.42664	.373242	.357375	.370017
Bank FE	No	Yes	Yes	Yes	No	Yes	Yes	Yes
Time FE	No	No	Yes	No	No	No	Yes	No
Robust standard errors in parentheses, p<.01, p<.05, * p<.1, (1)≈Pooled OLS estimation, (2)-(4)≈FE FGLS estimation*

4.2 Effect of individual cross-border diversification activities on bank performance

We examine the effects of individual diversification activities on bank performance. We re-estimate our model by regressing the bank performance measures for each of the six cross-border diversification activities under different empirical tests. The new estimates presented in Table 3 are robust, largely confirm the earlier results, and provide additional insights[2]. Panel A of Table 3 shows the cost efficiency results. All diversification activities, namely, foreign investment ratio, foreign sales ratio, foreign loan ratio, foreign loan sales ratio, foreign asset ratio, and foreign branch ratio, have significantly positive effects on cost efficiency. These results support earlier results that cross-border diversification has a positive and significant effect on cost efficiency. Similarly, the profit efficiency results show that all cross-border diversification measures have significantly negative effects on profit efficiency, implying that an increase in each diversification activity decreases or harms the profit efficiency of the banks analysed (see Table 3, Panel D).

Regarding the effect of individual diversification activities on bank stability, the results presented in Panel C show that foreign investment ratio, foreign sale ratio, foreign loan ratio and foreign loan sales ratio have significantly positive effects on bank stability. However, foreign asset ratio and foreign branch ratio have significantly negative effects on bank stability. These results imply that, on the one hand, foreign investment, and foreign loan ratio (including their sales) significantly improve bank-level stability. On the other hand, the foreign asset and foreign branch ratios decrease bank-level stability. The negative relationships between the foreign assets ratio and bank stability, and the foreign branch ratio and bank stability suggest possible funding risks. Regarding this, Lam (2015) pointed out that the rapid expansion activities of Japanese banks through takeovers and lending in the Asian region could present funding risk, in addition to imposing cross-border risk monitoring supervision.

Finally, the results of individual diversification activities on lending efficiency show that except for the foreign branch ratio, all overseas diversification activities have a significantly positive effect on lending efficiency. This implies that an increase in overseas branches decreases lending efficiency by at least one percentage point. Several factors could account for this finding, including operational inefficiency, host country infrastructure, human capital skills, technology, and the local institutional environment.

Table 3. Effect of individual diversification activities on bank performance (1)

	Panel A. Dependent variable: Cost efficiency				Panel B. Dependent variable: Lending efficiency
	(1)	(2)	(3)	(4)	(1)	(2)	(3)	(4)
FiR	.00146**	.001416**	.000716	.001944***	.042513***	.040467***	.050923***	.043464***
	(.000591)	(.000576)	(.000571)	(.000621)	(.004069)	(.003085)	(.003713)	(.003275)
FsR	.000836**	.000821**	.000319	.00114***	.025907***	.024032***	.03111***	.025793***
	(.000347)	(.000335)	(.000332)	(.000363)	(.002334)	(.00178)	(.002118)	(.001901)
FlR	.000982*	.000915*	.000277	.001324**	.041429***	.036926***	.043411***	.039091***
	(.000524)	(.000507)	(.000465)	(.000549)	(.003348)	(.00246)	(.002745)	(.00265)
FlsR	.000684**	.000656**	.000197	.000923***	.024166***	.021924***	.027118***	.023313***
	(.00032)	(.000308)	(.000294)	(.000333)	(.002124)	(.0016)	(.001846)	(.001723)
FaR	.092559*	.110689***	.105498***	.118996***	1.733291***	.515743	1.054415**	.437651
	(.049203)	(.041454)	(.035951)	(.04122)	(.401265)	(.435368)	(.45359)	(.440701)
FbR	.000953**	.001027***	.000707**	.001012***	-.013515***	-.012037***	-.017879***	-.012717***
	(.000382)	(.000384)	(.000355)	(.000379)	(.003136)	(.002248)	(.002401)	(.002165)
Time trend	No	No	No	Yes	No	No	No	Yes
Bank FE	No	Yes	Yes	Yes	No	Yes	Yes	Yes
Time FE	No	No	Yes	No	No	No	Yes	No
	Panel C. Dependent variable: Zscore				Panel D. Dependent variable: Profit efficiency
	(1)	(2)	(3)	(4)	(1)	(2)	(3)	(4)
FiR	.007628	.011026**	-.000456	-.002997	-.003527***	-.002726***	-.002718***	-.003614***
	(.004901)	(.004396)	(.004441)	(.004581)	(.000809)	(.000687)	(.00077)	(.000716)
FsR	.005286*	.007129***	.00079	-.001349	-.001732***	-.001506***	-.001531***	-.001993***
	(.002869)	(.002571)	(.002704)	(.002783)	(.000482)	(.000394)	(.000456)	(.000409)
FlR	.008093*	.010667***	.000356	-.001365	-.002876***	-.002316***	-.002263***	-.003042***
	(.004303)	(.003864)	(.003811)	(.004209)	(.000717)	(.000601)	(.000648)	(.000626)
FlsR	.005103*	.006984***	.000543	-.000652	-.001793***	-.00146***	-.001445***	-.001904***
	(.002634)	(.002366)	(.002426)	(.002579)	(.000439)	(.000364)	(.00041)	(.000379)
FaR	-.721649*	-.658101*	-1.221867***	-1.059916***	-.047428	-.080546*	-.075788	-.086432*
	(.404344)	(.392293)	(.293306)	(.346299)	(.070003)	(.045699)	(.050331)	(.045122)
FbR	-.007703**	-.006346*	-.007259***	-.006504*	-.004729***	-.004686***	-.004261***	-.004678***
	(.003137)	(.003837)	(.002358)	(.003398)	(.00043)	(.000462)	(.00046)	(.000464)
Time trend	No	No	No	Yes	No	No	No	Yes
Bank FE	No	Yes	Yes	Yes	No	Yes	Yes	Yes
Time FE	No	No	Yes	No	No	No	Yes	No
Robust standard errors in parentheses, p<.01, p<.05, * p<.1, (1)≈Pooled OLS estimation, (2)-(4)≈FGLS estimation*

[1] Our small panel data, with the number of cross-sections (N) < number of time (T), suffered from autocorrelation, heteroscedastic and cross-sectional dependence (AHCD). As suggested by relevant literature, we applied appropriate corrections for N<T using feasible generalized least square regression (Moundigbaye et al., 2018; Reed & Ye, 2011)

[2] To simplify Table 3, we excluded control variables and other regression statistics. The full regression results are presented in appendixes B1-B4.

In recent years, Japanese banks have increased their cross-border expansion activities, especially in the Asia-Pacific region due, in part, to the withdrawal of other megabanks from this region post-GFC (Yaguchi & Yamaguchi, 2018). Unlike previous episodes of expansion, the recent expansion of Japanese banks is linked to shrinking portfolio markets caused by socio-demographic structural changes and financial regulatory frameworks, such as low-interest rate regimes, among others. It has been contended that this recent aggressive overseas expansion trend resembles the trend observed in the late 1980s and 1990 during which Japanese banks were exposed to foreign market risks and incurred heavy losses (Lam, 2015). Moreover, recent reports on the cross-border activities of Japanese banks suggest declining profits and increasing overall funding costs (NRI, 2020).

The findings of this study provide empirical evidence on the effect of recent cross-border expansion activities on the performance of Japanese banks. Specifically, the findings on the effect of expansion-specific activity on bank performance, which have been largely ignored in previous studies, provide interesting insights. Corroborating previous studies, we find that cross-border expansion, in general, is desirable for improving the cost efficiency of Japanese banks. However, the profit efficiency of Japanese banks appears to be negatively affected by increasing cross-border activities. This empirical evidence is applicable irrespective of the expansion-specific activity investigated. Profit efficiency is an important measure of bank performance because it closely aligns with the goal of profit maximisation. This performance measure requires that adequate managerial attention should be paid to raise a marginal dollar of revenue and decrease a marginal dollar of cost’ (Schaeck & Martin Čihák, 2008). It has also been noted that deregulations and business environment are known to have a significant effect on profit (Humphrey & Pulley, 1997). In this case, it can be concluded that both factors (the local regulatory framework and host countries’ business environment) influence the profit efficiency of Japanese banks conducting international operations. Given the recent events, such as the decision to sell out US retail banking by a Japanese megabank, it can be argued that the overseas business environment of Japanese banks has become increasingly turbulent, thereby affecting their profit efficiency.

The results also indicate that increasing foreign assets and branch networks negatively affect bank soundness (as measured by z-score). While the acquisition of foreign assets and foreign branch networks, in general, could provide banks with a strong footing and greater advantage in carrying out overseas operations and earning revenue, these activities could be offset by overly priced overseas assets and funding risk (Lam, 2015). Furthermore, overseas branch operations are subject to the host countries’ local financial market risk, infrastructure, regulatory framework, technology, and technical expertise. Hence, it has been argued that Japanese banks conducting international operations must improve their risk assessment and management approach to enable them to withstand overseas market stress, including infrastructural upgrades and human resources (Aduba & Izawa, 2021).

This study examined the impact of overseas diversification activities on bank performance using bi-annual data on the overseas operations of five Japanese banks between 2003 and 2020. It empirically examined the effect of six measures of the overseas diversification index in the domain of foreign investment, sales, loans, assets, and branch networks on four measures of bank performance: cost efficiency, profit efficiency, bank stability, and lending efficiency.

The findings show that cross-border diversification generally improves cost efficiency, bank stability, and lending efficiency. Conversely, cross-border diversification tends to decrease or harm the profit efficiency of the analysed banks.

Regarding the effect of expansion-specific activity, there was evidence of possible funding risks, as suggested by the negative effect of foreign assets and bank-level stability, as well as foreign branch networks and bank-level stability. Moreover, the study found that a growing number of foreign branches present operational inefficiency, which can ultimately harm lending efficiency.

Consistent with previous studies, this study shows that both benefits and risks are associated with cross-border expansion activities of Japanese banks. However, the study showed that expansion-specific activity matters in banks’ cross-border diversification strategies. We conclude that, as a major financial regional player, the current trend in the cross-border expansion activities of these banks may require close monitoring and supervision to prevent episodes of regional financial crises. Additionally, the current cross-border expansion strategies of Japanese banks, especially the expansion and operation of overseas branch networks (retail banking), need re-examination.

The study has some limitations mainly related to the data used. First, it used aggregated data from all overseas transactions. Hence, the effect of the host countries’ specific characteristics on the results could not be tested. Second, Japanese firms (including banks) are known for their long-term investment horizons. However, our data covers approximately two decades which might not be sufficient to reflect the potential gain of a long-term investment horizon beyond two decades. Future studies with similar research questions should address these limitations.

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Impact of International Expansion Strategy on the Performance of Japanese Banks

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Abstract

Figures

1. Introduction

2. Theoretical Consideration And Hypotheses Development

2.1 Measures of bank performance

2.2 Measures of international diversification (internationalization)

3. Empirical Strategy And Econometric Model

3.1 Measuring cross-border diversification: degree of internationalization

3.2 Measuring bank performance

3.3 Econometric model

3.4 Data, variables, and summary statistics

4. Empirical Results And Discussions

4.1 Effect of overall cross-border diversification on bank performance

4.2 Effect of individual cross-border diversification activities on bank performance

5. Discussion And Implication

6. Summary, Conclusion, And Caveat

References

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