Does institutional quality reduce the impact of market concentration on bank stability? Evidence of developing countries

Abstract

This study investigates how market concentration (MC) and institutional quality (IQ) influence bank stability in developing nations, focusing on 80 banks in the ASEAN 4 countries (Indonesia, the Philippines, Malaysia, and Thailand) from 2006 to 2019. The study utilises the generalised method of moments technique to address concerns related to autocorrelation and endogeneity. The findings of the research are noteworthy. Firstly, a positive correlation between bank stability and market concentration is established, supporting the concentration-stability hypothesis. Banks operating in highly concentrated markets tend to exhibit higher stability compared to those in less concentrated markets. However, negative coefficients on square market concentration suggest a potential inverted U-shaped relationship, indicating that market concentration enhances bank stability up to a certain threshold. Secondly, the study highlights the significant impact of institutional quality on bank stability within the ASEAN 4 region. Furthermore, the study found that institutional quality might mitigate the influence of market concentration on bank stability. These results underscore the importance of a well-defined strategy for bank managers and bankers. When market concentration reaches a specific threshold, optimal bank stability is observed, and higher institutional quality contributes to improved bank stability. This research pioneers examining the effects of banking system market concentration and institutional quality on the stability of ASEAN 4 banks.

Keywords:

• ASEAN 4
• bank stability
• market concentration
• institution quality

JEL Classification:

• G21
• L22
• L25
• G33

1. Introduction

Banks must be financially stable to continue playing such roles and deal with various shocks. As a result, scholars and regulators must be interested in understanding the factors of a bank’s financial stability. The study was initiated due to a decline in the number of banks in many countries (Committee on the Global Financial System, 2018), resulting in higher concentration ratios. This situation raised concerns about whether the reduction in market concentration led banks to assume more risks. Additionally, there were uncertainties about whether a higher banking market concentration would have enhanced bank stability. Nevertheless, the impact of market structure on bank stability is influenced by two opposing theories: the concentration-stability hypothesis and the concentration-fragility hypothesis. According to the concentration-stability hypothesis, larger banks in a concentrated banking market will be more safe (Allen & Gale, 2004; Keeley,11990). Conversely, according to the concentration-fragility hypothesis, larger banks operating in a concentrated market may decrease stability (Boyd & De Nicolo, 2005; Schaeck et al., 2009; Uhde and Heimeshoff, 2009). Moreover, the influence of institutional quality on bank stability has become a focal point for researchers in recent times. The high and/or poor of a nation’s institutions is indicated by institutional quality. According to the available literature, good institutions reduce transaction costs and asymmetric information concerns, and hence enhance financial sector resource allocation (Ho & Michaely, 1988; Williamson, 1981). Improved institutional quality might substantially impact the bank’s financial stability. While the exploration of how institutional quality affects a bank’s financial stability is still in its early stages, there have been numerous studies conducted to comprehend the influence of institutional quality on a country’s economic and financial advancement. The investigation into how institutional quality influences bank stability has raised concerns among researchers (Canh et al., 2021), and those interested in this topic remain curious to learn more. Additionally, González-Rodríguez (2008) and Saif-Alyousfi et al. (2020) demonstrate that higher bank market concentration, which can lead to financial instability, tends to be associated with better institutional quality.

Overall, previous research has indicated a connection between market concentration and bank stability as well as between institution quality and bank stability. As a result, this research investigates the impact of market structure and institutional quality on bank stability. This research tries to fill that gap. The significance and scale of emerging markets, such as Southeast Asia, in the global economy make it noteworthy that there is a lack of empirical research exploring the effects of market concentration and institutional quality on bank stability in this particular region.

This research concentrates on the five members of the $AssociationofSoutheast$ $AsianNations$ $\left(ASEAN-4\right)$, namely, $Indonesia$, $Thailand$, $Malaysia$, and the $Philippines$. The study was specifically conducted in the ASEAN 4 countries for several reasons. Firstly, ASEAN has emerged as one of the most influential trading regions globally. With an average economic growth rate of 5.3% from 2006 to 2019, ASEAN-4 closely followed China in terms of economic expansion (WB, 2019a). The banking sector, as the cornerstone of the financial system, plays a crucial role in driving the remarkable economic accomplishments of ASEAN-4 countries (Fu et al., 2015; T. D. Le, 2019). In addition, any disruptions in the banking sectors of these nations could have a significant impact on the global financial system due to their extensive trading importance and open banking systems relative to other emerging regions (Ovi et al., 2020). These factors contribute to higher concentration ratios, which can have a variety of complex implications for the link among the concentration of markets (MC), quality of institutions (IQ), and stability of banks.

This paper makes several noteworthy contributions to the existing literature. Firstly, while Saha and Dutta (2022) investigated the link between governance quality and financial inclusion and stability, this research uniquely explores the impact of market concentration (MC), institutional quality (IQ), and $\mathrm{b}\mathrm{a}\mathrm{n}\mathrm{k}$ $\mathrm{s}\mathrm{t}\mathrm{a}\mathrm{b}\mathrm{i}\mathrm{l}\mathrm{i}\mathrm{t}\mathrm{y}$, specifically focusing on the ASEAN 4 countries. It addresses a crucial research gap by examining how of $\mathrm{M}\mathrm{C}$ and IQ interact to influence banking stability in this context, a topic that has not been previously explored. Secondly, the study’s significance lies in its examination of the moderating role, shedding light on how the influence of market structure on bank stability evolves alongside the development of institutions in emerging economies. Comprehending this connection is of utmost importance, particularly since financial systems in less developed markets tend to exhibit lower stability. Therefore, any evidence that supports this hypothesis would be highly valuable for policymakers aiming to strengthen financial stability in these economies.

The subsequent sections of our research are outlined as follows: Section 2 will provide an in-depth exploration of the theories, related studies, and assumptions pertinent to the topic. In Section 3, we will describe the research approach, encompassing the estimation method, models, and variable specifications. Moving on to Section 4, we will present the estimation findings and robustness tests employed to assess the hypotheses. Finally, Section 5 will offer the conclusion of our study.

2. Literature review

2.1. Market concentration and bank stability

The existing literature significantly centers on investigating the impact of MC on the stability of the banking sector. Supporters of the “concentration-stability” theory argue that market power is advantageous as it reduces information asymmetry and improves the quality of loan portfolios (Petersen & Rajan, 1995). Market strength, in the viewpoint of Hellmann et al. (2000), helps the bank to maintain their charter value, taking risks lower. According to Allen and Gale (2004), banks’ ability to gain from rising revenues prevents asset deterioration and improves the stability of the financial system. In numerous research studies, various samples, risk indicators, and concentration proxies have been employed to explore the relationship between concentration and bank stability. Fiordelisi and Mare (2014) use a sample of European cooperative banks to find a positive relationship between stability and competition (defined by the Lerner index). Second, the findings show that this crucial link remained constant during the 2007–2009 financial crisis. In five ASEAN nations, Sahul Hamid (2017) discovers that greater concentration boosts banking sector stability, hence corroborating the “concentration-stability” argument.

Additionally, greater concentration, in the viewpoint of proponents of “concentration-fragility,” raises the risk level of the financial system. According to Boyd and De Nicolo (2005), show that banks are more likely to increase loan interest rates in an uncompetitive or highly concentrated market in order to boost profits, which raises the likelihood of bankruptcy. Saha and Dutta (2021) demonstrate that competition fosters stability, but there is also evidence of fragility when concentration exists in the banking industry. Similar to Mirzaei et al. (2013), greater concentration can worsen the moral hazard difficulties associated with larger banks, driving banks to raise excessive rates of interest on loans, making clients more likely to default, and decreasing managerial effectiveness. According to Nicoló et al. (2004), greater concentration is associated with banking industry weakness. Uhde and Heimeshoff (2009) obtained similar results for the European banking system, employing three different concentration metrics. Similarly, using the 3-firm concentration ratio and the Lerner index, Fu et al. (2014) found comparable outcomes in 14 Asian Pacific nations. Kasman and Kasman (2015) analyzed the impact of market concentration on Turkish bank stability from 2002 to 2012, using the MC5 and the HHI, and they discovered that an increasingly concentrated market is less stable. Soedarmono et al. (2013) conduct a study on the effect of bank competition on the financial stability of emerging nations, considering crisis periods. Analyzing data from a substantial examples of commercial banks in Asia from 1994 – 2009, the findings reveal a correlation between higher market dominance in the banking industry and increased capital ratios, greater income volatility, and reduced bank illiquidity. Jiménez et al. (2013) suggest that market dominance serves as the primary source of charter value, and reduced competition in the banking market enhances banking stability. Furthermore, Saif-Alyousfi et al. (2020) discover that higher bank concentration leads to increased financial fragility, indicating that banks in highly concentrated markets are less secure.

In general, the association between the concentration and bank stability tends to produce a variety of results. Given the size and impact of developing nations like Southeast Asia on the global economy, there appears to be a gap in the finance literature. While numerous articles have examined the relationship between competition and bank stability, it is crucial not to directly compare these findings to those of $\mathrm{c}\mathrm{o}\mathrm{n}\mathrm{c}\mathrm{e}\mathrm{n}\mathrm{t}\mathrm{r}\mathrm{a}\mathrm{t}\mathrm{i}\mathrm{o}\mathrm{n}$ and $\mathrm{s}\mathrm{t}\mathrm{a}\mathrm{b}\mathrm{i}\mathrm{l}\mathrm{i}\mathrm{t}\mathrm{y}$, as concentration is not always a reliable proxy for competition (Beck et al., 2006; Calice et al., 2021).

Hypothesis 1:

$\text{market concentration has impact on banks stability}$

2.2. Institution quality and bank stability

Institutions play a critical role in a nation’s economy. Inadequate legal systems and governance structures, often due to corruption and ineffective rules, can lead to increased economic challenges (Levine, 1998; Porta et al., 1998). During financial crises, institutional integrity becomes even more crucial, as countries with good institutional quality are better equipped to respond effectively to disruptions (Klomp & De Haan, 2014). Strong institutional quality can reduce transaction costs and address the asymmetric information problem, influencing corporate behavior and overall economic performance (Gugler et al., 2013), while robust property rights and a functional legal system are vital for overall economic and financial development (Voghouei et al., 2011). Furthermore, improved institutional quality in an economic system facilitates better financial liberalization (Anginer et al., 2014; Delis, 2012; Gazdar & Cherif, 2015; Hoque et al., 2015). Furthermore, Klomp and De Haan (2012) highlight the significant impact of institutional environment on financial stability, emphasizing the crucial role of institutions in determining the stability of banks. Elfeituri (2022) and others find that IQ positively influences bank stability. Van Duuren et al. (2020) analyzed data from 110 countries between 2000 and 2011 and found that financial openness contributes to improved financial stability. However, they also observed a negative association between problematic loans and institutions of low quality. Hou and Wang (2016) conducted a study focusing on Chinese banks and concluded that enhancing institutional quality can counteract the negative effects of bank marketization on overall stability. Bermpei et al. (2018) demonstrated that a stable political environment enhances the positive impact of capital regulation and activity limits on financial stability. Similarly, Uddin et al. (2020) analyzed 730 banks from 19 emerging nations and found that financial stability is improved by factors such as government efficiency, corruption prevention regulations, agency trust, and rule of law compliance, which reduce banks’ risk. Albaity et al. (2021) examine the influence of dependability and quality of governance in the taking on risks decisions made by banks. By utilizing a database containing 202 organizations in 16 MENA countries from 2011 – 2017. The findings suggest that governance quality increases risk-taking. Shabir et al. (2021) discovered the nations with above-threshold quality of institutions mitigate the negative impact of unpredictability of policies on financial stability. Moreover, according to Q. K. Nguyen (2022), the link between bank stability and audit committee efficacy is highly dependent on the health of each bank and the country’s institutional quality. Albaity et al. (2022) show that the quality of regulation, reliability had an effect on the growth of bank credit in the countries of the Gulf Cooperation Council (GCC).

All in all, institutional quality has a positive impact on bank stability. Because when a country has good institutional quality can limit risky activities and improve stability. Based on previous studies provides evidence of the positive effects of institutional quality on bank stability. As a result, we suggest the following hypothesis.

Hypothesis 2:

Institutional quality has impact on bank stability

2.3. Institutional quality moderates the relationship between market concentration and bank stability

Prior study has concentrated on the relationship between institutional quality and bank stability, as well as the relationship between concentration and bank stability. According to Chan et al. (2015), institutional quality moderates the influence of market concentration on bank efficiency. Good institutional quality has been shown to be advantageous for financial institution supervision and surveillance (Anginer et al., 2014; Hoque et al., 2015). Moreover, González-Rodríguez (2008) and Saif-Alyousfi et al. (2020) suggest that higher institutional quality is more likely to be correlated with greater bank market concentration, which is associated with financial instability. Therefore, any negative effect should be mitigated, whereas the favorable impact of MC on bank stability should be amplified. Institutional development in developing and emergent nations is frequently inferior to that of developed nations. In addition, Tran et al. (2022) investigated the association between the structure of markets, quality of institutions, and financial stability in developing and emerging nations. Surprisingly, no studies have been conducted to examine the moderate influence of institutional quality on the relationship $\mathrm{b}\mathrm{e}\mathrm{t}\mathrm{w}\mathrm{e}\mathrm{e}\mathrm{n}$ $\mathrm{M}\mathrm{C}$ and banking stability in ASEAN 4. In summary, any progress made through institutional reform must be significant in improving financial stability in developing economies. As a result, we arrive at the following final hypothesis:

Hypothesis 3:

The association between market concentration and bank stability is mitigated by institutional quality.

3. Data and research methodology

3.1. Data

Our data was collected from numerous sources. However, the majority of bank-level data in five ASEAN countries were obtained from the Refinitiv Eikon database. As a consequence, an initial sample of 100 ASEAN-4-listed banks from the Eikon database was compiled. After excluding those with insufficient data when calculating ZSCORE and RISK, there were a total of 1,120 observations from 80 listed banks from 2006 to 2019, when data was available for the vast majority of listed banks. The State Bank of Vietnam has mandated audited financial information from commercial institutions since 2006 (T. D. Le, 2019). In addition, MC and macroeconomic variables data were obtained from the World Development Indicators dataset (WB, 2019b). Using the Worldwide Governance Indicators (WGI), institutional quality was measured. Finally, the Heritage Foundation provided information on the openness of the banking industry. Our choice of inquiry period was determined by the data availability.

3.2. Methodology

According to the preceding arguments, a dynamic model of bank’s financial stability is used, one that appears such as this:

(1) $Zscor{e}_{i,t}=\alpha +{\alpha }_{1\hspace{0.17em}}Zscor{e}_{i,t-1}+{\alpha }_{2\hspace{0.17em}}M{C}_{i,t}+{\alpha }_{3\hspace{0.17em}}SQM{C}_{i,t}+{\alpha }_{4\hspace{0.17em}}I{Q}_{i,t}+{\alpha }_{5\hspace{0.17em}}Controlvariable{s}_{i,t}$(1)

In the following model, we also include MC*IQ to investigate the impact on bank stability ¹:

(2) $Zscor{e}_{i,t}=\beta +{\beta }_{1\hspace{0.17em}}Zscor{e}_{i,t-1}+{\beta }_{2\hspace{0.17em}}M{C}_{i,t}+{\beta }_{3}SQM{C}_{i,t}+{\beta }_{4}MC\mathit{\ast }I{Q}_{i,t}+{\beta }_{5\hspace{0.17em}}Controlvariable{s}_{i,t}$(2)

Following prior studies such as T. D. Le et al. (2020), Lepetit and Strobel (2013), We also utilise $\mathrm{Z}\mathrm{S}\mathrm{C}\mathrm{O}\mathrm{R}\mathrm{E}$ as a metric of $\mathrm{b}\mathrm{a}\mathrm{n}\mathrm{k}\mathrm{s}\mathrm{t}\mathrm{a}\mathrm{b}\mathrm{i}\mathrm{l}\mathrm{i}\mathrm{t}\mathrm{y}$. The $ZSCORE$ of a bank is measured as $\mathrm{Z}\mathrm{S}\mathrm{C}\mathrm{O}\mathrm{R}{\mathrm{E}}_{\mathrm{i},\mathrm{t}}=\frac{\mathrm{R}\mathrm{O}{\mathrm{A}}_{\mathrm{i},\mathrm{t}}+\mathrm{E}\mathrm{Q}\mathrm{U}\mathrm{I}\mathrm{T}{\mathrm{Y}}_{\mathrm{i},\mathrm{t}}}{{\sigma }_{\mathrm{R}\mathrm{O}{\mathrm{A}}_{\mathrm{i}}}}$ where $\mathrm{R}\mathrm{O}{\mathrm{A}}_{\mathrm{i},\mathrm{t}}$ and $EQUIT{Y}_{i,t}$ $\mathrm{a}\mathrm{r}\mathrm{e}\mathrm{t}\mathrm{h}\mathrm{e}\mathrm{c}\mathrm{u}\mathrm{r}\mathrm{r}\mathrm{e}\mathrm{n}\mathrm{t}\mathrm{v}\mathrm{a}\mathrm{l}\mathrm{u}\mathrm{e}\mathrm{o}\mathrm{f}\mathrm{R}\mathrm{O}\mathrm{A}$ and the $ratiooftotalequity/totalassets$, respectively while ${\sigma }_{\mathrm{R}\mathrm{O}{\mathrm{A}}_{\mathrm{i}}}$is $\mathrm{t}\mathrm{h}\mathrm{e}$ standard deviation of $\mathrm{R}\mathrm{O}\mathrm{A}$ $\mathrm{o}\mathrm{v}\mathrm{e}\mathrm{r}\mathrm{t}\mathrm{h}\mathrm{e}$ $\mathrm{s}\mathrm{a}\mathrm{m}\mathrm{p}\mathrm{l}\mathrm{e}$ $\mathrm{p}\mathrm{e}\mathrm{r}\mathrm{i}\mathrm{o}\mathrm{d}$. We employ the $\mathrm{n}\mathrm{a}\mathrm{t}\mathrm{u}\mathrm{r}\mathrm{a}\mathrm{l}$ $\mathrm{l}\mathrm{o}\mathrm{g}\mathrm{a}\mathrm{r}\mathrm{i}\mathrm{t}\mathrm{h}\mathrm{m}$ of $\mathrm{Z}\mathrm{S}\mathrm{C}\mathrm{O}\mathrm{R}\mathrm{E}$ to solve this problem due to its extremely skewed distribution. A higher $\mathrm{Z}\mathrm{S}\mathrm{C}\mathrm{O}\mathrm{R}\mathrm{E}$ number indicates greater $\mathrm{b}\mathrm{a}\mathrm{n}\mathrm{k}\mathrm{s}\mathrm{t}\mathrm{a}\mathrm{b}\mathrm{i}\mathrm{l}\mathrm{i}\mathrm{t}\mathrm{y}$

Market concentration (MC): market share, MC3 are used to estimate market concentration. Similar to Calice et al. (2021), Mirzaei et al. (2013). MC3 is calculated as a percentage of a country’s three largest banks’ total assets divided by all commercial banks’ total assets (Cihak et al., 2012). A higher MC3 indicates a greater concentration in banking.

Institution quality(IQ): Institutional quality is a wide notion that encompasses several elements, including the effectiveness of $\mathrm{t}\mathrm{h}\mathrm{e}\mathrm{g}\mathrm{o}\mathrm{v}\mathrm{e}\mathrm{r}\mathrm{n}\mathrm{m}\mathrm{e}\mathrm{n}{\mathrm{t}}^{\prime }\mathrm{s}\mathrm{p}\mathrm{l}\mathrm{a}\mathrm{n}\mathrm{n}\mathrm{i}\mathrm{n}\mathrm{g}$ and implementation, $\mathrm{f}\mathrm{r}\mathrm{e}\mathrm{e}\mathrm{d}\mathrm{o}\mathrm{m}\mathrm{o}\mathrm{f}$ speech $\mathrm{a}\mathrm{n}\mathrm{d}$ association, $\mathrm{a}\mathrm{c}\mathrm{c}\mathrm{o}\mathrm{u}\mathrm{n}\mathrm{t}\mathrm{a}\mathrm{b}\mathrm{i}\mathrm{l}\mathrm{i}\mathrm{t}\mathrm{y}$, the prevention of corruption, and regulatory quality. The most frequently used statistics in recent institutional literature are based on the worldwide governance indicators (WGI), developed by Kaufmann et al. (1999) to encompass social, political, economic, and legal factors. The WGI data consist of six broad elements: voice and accountability, government effectiveness, corruption control, regulatory quality, rule of law, and political stability and violence, ranging from −2.5 (weak) to 2.5 (strong). Since its introduction, the WGI data has become widely used as a proxy for institutional quality, following studies by Muizzuddin et al. (2021), Uddin et al. (2020), Etudaiye-Muhtar and Abdul-Baki (2020), and others. This dataset is favored for its coverage of political and non-political institutional characteristics, inclusion of perceptions from various stakeholders, and empirical applicability. Given its substantial significance, we will also construct the institutional quality index using the WGI dataset.

3.2.1. Control variables

Bank size is controlled by a natural logarithm of total assets, as measured by SIZE. The too-big-to-fail hypothesis demonstrates that larger banks are more inclined to take greater risks, leading to a higher rate of bank failure (Beck et al., 2006; T. D. Le, 2020, 2021). Others, however, find the opposite (Pennathur et al., 2012). $LATA,$ $Liquidassets$ to $totalasset$, is a measure used to manage $liquidityrisk$. The better the bank’s stability, the larger the liquidity ratio (Shim, 2013; Son & Liem, 2020; Vithessonthi, 2014), However, according to Delis and Staikouras (2011), contend that the dangers a bank faces increase with its liquidity ratio. Furthermore, ROA, $\mathrm{r}\mathrm{e}\mathrm{t}\mathrm{u}\mathrm{r}\mathrm{n}\mathrm{o}\mathrm{n}\mathrm{a}\mathrm{s}\mathrm{s}\mathrm{e}\mathrm{t}\mathrm{s}$, is established because $\mathrm{m}\mathrm{o}\mathrm{r}\mathrm{e}\mathrm{p}\mathrm{r}\mathrm{o}\mathrm{f}\mathrm{i}\mathrm{t}\mathrm{a}\mathrm{b}\mathrm{l}\mathrm{e}$ $\mathrm{b}\mathrm{a}\mathrm{n}\mathrm{k}\mathrm{s}\mathrm{c}\mathrm{a}\mathrm{n}$ $\mathrm{w}\mathrm{i}\mathrm{t}\mathrm{h}\mathrm{s}\mathrm{t}\mathrm{a}\mathrm{n}\mathrm{d}\mathrm{f}\mathrm{i}\mathrm{n}\mathrm{a}\mathrm{n}\mathrm{c}\mathrm{i}\mathrm{a}\mathrm{l}\mathrm{s}\mathrm{h}\mathrm{o}\mathrm{c}\mathrm{k}\mathrm{s}\mathrm{b}\mathrm{e}\mathrm{t}\mathrm{t}\mathrm{e}\mathrm{r}$, boosting $\mathrm{b}\mathrm{a}\mathrm{n}\mathrm{k}\mathrm{s}\mathrm{t}\mathrm{a}\mathrm{b}\mathrm{i}\mathrm{l}\mathrm{i}\mathrm{t}\mathrm{y}$ (Athanasoglou et al., 2008). Higher profitability, however, may imply high-risk premia when there is insufficient bank oversight and asymmetric information (Hellmann et al., 2000). Following (Barth et al., 2004; T. D. Q. Le & Nguyen, 2022; Mercieca et al., 2007). $FREE$, The banking freedom index is used to account for the impact of the banking system’s $\mathrm{o}\mathrm{p}\mathrm{e}\mathrm{n}\mathrm{n}\mathrm{e}\mathrm{s}\mathrm{s}$. Several studies indicate that a banking system that has functioned in a more competitive market because of a greater level of openness of the banking system appears to pursue $\mathrm{t}\mathrm{h}\mathrm{e}$ best appropriate risk-control strategies and objectives (Mercieca et al., 2007). This increased competition may result in lower interest margins, reducing banking system profitability and putting a risk to bank stability (T. D. Le, 2017). $GDP$ is to control for the effect of economic growth. And $INF$ is to control for the effect of inflation.

Furthermore Baselga-Pascual et al. (2015) utilized the System Generalized Method of Moments (SGMM) estimator to study bank stability with dynamic models and panel data. They addressed endogeneity by using lagged values of the explained variable and other explanatory variables as instruments (Roodman, 2009). The two-step GMM estimator, known for its efficiency and robustness against heteroscedasticity and cross-correlation (Roodman, 2009), was employed. Effectiveness and reliability were ensured through the AR(2) test for second-order serial correlations and the Sargan/Hansen over-identifying tests to confirm instrument validity.

4. Empirical results

4.1. Descriptive statistics

Table 1 shows descriptive statistics of the variables in the study. The mean for $\mathrm{b}\mathrm{a}\mathrm{n}\mathrm{k}\mathrm{s}\mathrm{t}\mathrm{a}\mathrm{b}\mathrm{i}\mathrm{l}\mathrm{i}\mathrm{t}\mathrm{y}$ $\left(ZSCORE\right)$ is 27.219. Furthermore, the average values for $MC3$ is 46.045, respectively. $\mathrm{I}\mathrm{n}\mathrm{s}\mathrm{t}\mathrm{i}\mathrm{t}\mathrm{u}\mathrm{t}\mathrm{i}\mathrm{o}\mathrm{n}\mathrm{a}\mathrm{l}\mathrm{q}\mathrm{u}\mathrm{a}\mathrm{l}\mathrm{i}\mathrm{t}\mathrm{y}$ variables have value in the $\mathrm{r}\mathrm{a}\mathrm{n}\mathrm{g}\mathrm{e}\mathrm{f}\mathrm{r}\mathrm{o}\mathrm{m}$ -$2.5\hspace{0.17em}\left(\mathrm{w}\mathrm{e}\mathrm{a}\mathrm{k}\right)\mathrm{t}\mathrm{o}2.5\hspace{0.17em}\left(\mathrm{s}\mathrm{t}\mathrm{r}\mathrm{o}\mathrm{n}\mathrm{g}\right)$.

Table 1. The descriptive statistics of variables

Variable	Obs	Mean	Std. Dev.	Min	Max
ZSCORE	1,120	27.21	3.296	20.970	33.433
MC3	1,120	46.045	16.735	23.452	100.000
VAE	1,120	−0.275	0.510	−1.538	0.185
PVE	1,120	−0.650	0.587	−1.778	0.404
GEE	1,120	0.115	0.421	−0.332	1.267
RQE	1,120	−0.075	0.349	−0.669	0.838
RLE	1,120	−0.289	0.365	−0.719	0.623
CCE	1,120	−0.446	0.296	−0.839	0.411
SIZE	1,120	28.947	3.091	22.129	34.655
ROA	1,120	1.374	0.945	−2.357	3.847
LATA	1,120	0.127	0.075	0.004	0.336
FREE	1,120	49.358	11.177	30.000	70.000
GDP	1,120	5.289	1.574	−1.500	7.500
INF	1,120	5.350	4.993	−6.000	22.700

Notes: ZSCORE, the mean returns on assets and the mean standard deviation of ROA over the sample period, combined with the current period value of EQUITY; The three-market concentration (MC3). The structural indicator of the degree of concentration of banks within an economy, measured by the concentration of assets held by the three major banks in each economy. A higher value indicates a higher level of market concentration: Institutional quality proxies from WGI report on six dimensions of governance, voice and accountability(VAE), political stability(PVE), government effectiveness (GEE), regulatory quality (RQE), rules of law (RLE), and control of corruption (CCE). FREE, the financial freedom index, SIZE the natural logarithm of total assets. ROA, returns on assets; LATA, the ratio of liquid assets to total assets; GDP, the growth rate of gross domestic products; INF, the inflation. All variables have been weinsorized at the 1% and 99% levels (except for market concentration, Institutional quality and financial freedom and macroeconomic variables). (Robust standard errors are in parentheses. *,** and *** the Significant at 10, 5 and 1% levels, respectively)

Additionally, we also examine whether multicollinearity among independent variables may exist. Tables 2 and 3 present the results of the correlation matrix and $\mathrm{v}\mathrm{a}\mathrm{r}\mathrm{i}\mathrm{a}\mathrm{n}\mathrm{c}\mathrm{e}\mathrm{i}\mathrm{n}\mathrm{f}\mathrm{l}\mathrm{a}\mathrm{t}\mathrm{i}\mathrm{o}\mathrm{n}\mathrm{f}\mathrm{a}\mathrm{c}\mathrm{t}\mathrm{o}\mathrm{r}\left(VIF\right)$ values, respectively. There is a relatively high correlation between $ZSCORE$ and $SIZE,$ their VIF values are less than 10, suggesting that there is no multicollinearity among them.² Overall, market concentration and institution quality are positively associated with $\mathrm{b}\mathrm{a}\mathrm{n}\mathrm{k}\mathrm{s}\mathrm{t}\mathrm{a}\mathrm{b}\mathrm{i}\mathrm{l}\mathrm{i}\mathrm{t}\mathrm{y}$.

Table 2. The correlation matrix of variables

	ZSCORE	MC3	VAE	PVE	GEE	RQE	RLE	CCE	SIZE	ROA	LATA	FREE	GDP	INF
ZSCORE	1
MC3	0.503	1
VAE	0.085	−0.243	1
PVE	0.274	0.217	−0.469	1
GEE	0.417	0.667	−0.136	.416	1
RQE	0.479	0.563	0.095	.179	0.909	1
RLE	0.291	0.586	−0.270	.554	0.943	0.861	1
CCE	0.216	0.452	−0.092	.566	0.902	0.835	0.903	1
SIZE	0.878	−0.568	−0.125	.256	−0.561	−0.634	−0.422	−0.349	1
ROA	0.126	0.000	0.137	−.202	−0.051	0.007	−0.102	−0.110	0.054	1
LATA	−0.046	−0.067	0.431	−.191	−0.224	−0.215	−0.321	−0.249	−0.016	0.067	1
FREE	−0.133	−0.166	0.399	−.237	0.265	0.509	0.254	0.306	−0.260	0.075	−0.113	1
GDP	0.141	−0.146	0.031	.108	−0.228	−0.286	−0.217	−0.143	0.183	0.006	0.262	−0.276	1
INF	0.153	−0.096	−0.072	−.093	−0.459	−0.526	−0.491	−0.450	0.306	−0.009	0.119	−0.585	.293	1

Table 3. Variance inflation Factor (VIF) results

Variable	VIF ZSCORE
MC3	1.97	2.31	3.01	2.88	2.85	2.39
VAE	1.82
PVE		1.73
GEE			2.92
RQE				3.77
RLE					2.59
CCE						1.84
SIZE	1.96	2.48	1.91	2.02	1.9	1.91
ROA	1.04	1.11	1.04	1.03	1.06	1.06
LATA	1.46	1.15	1.16	1.16	1.24	1.16
FREE	2.33	1.99	2.06	2.73	2.02	2.09
GDP	1.21	1.26	1.21	1.21	1.22	1.24
INF	1.71	1.86	1.84	1.73	1.96	1.8
Mean VIF	1.69	1.74	1.9	2.06	1.86	1.68

4.2. The base models

This section presents the empirical results of our research. Tables 4 display the results for our models. We constrain the lagged value of the dependent variable to one to minimize the number of moment conditions, in line with T. D. Le (2020) and T. D. Le and Ngo (2020). In all models, the coefficient of the lagged dependent variable (ZSCOREt-1) is positive and significant, falling between the fixed-effect coefficient (0.62) and pooled estimation³ (0.78), indicating the adequacy of the system GMM estimation. The p-value of the Hansen test is statistically insignificant, suggesting no rejection of the null hypothesis, which indicates no over-identification constraints and confirms the legitimacy of the instruments. While the null hypothesis of no first-order autocorrelation between first residual differences is rejected based on the significant p-value of the AR(1) test, the moment conditions of our model are still satisfied in all models due to the insignificant p-value of the AR(2) test. Therefore, diagnostic testing is justified based on the findings.

Table 4. Regression estimates

ZSCOREt-1	ZSCORE (1)	ZSCORE (2)	ZSCORE (3)	ZSCORE (4)	ZSCORE (5)	ZSCORE (6)	ZSCORE (7)	ZSCORE (8)	ZSCORE (9)	ZSCORE (10)	ZSCORE (11)	ZSCORE (12)
ZSCOREt-1	0.779*** (0.003)	0.777*** (0.004)	0.769*** (0.044)	0.727*** (0.045)	0.741*** (0.038)	0.752*** (0.021)	0.712*** (0.044)	0.694*** (0.051)	0.754*** (0.005)	0.705*** (0.043)	0.697*** (0.020)	0.729*** (0.005)
MC3	0.005*** (0.001)	0.004*** (0.001)	0.016** (0.006)	0.008* (0.005)	0.007** (0.003)	0.007* (0.004)	0.002*** (0.001)	0.007** (0.004)	0.005*** (0.002)	0.008** (0.005)	0.005* (0.003)	0.008** (0.002)
SQMC3	−0.002*** (0.001)	−0.003** (0.001)	−0.001** (0.000)	−0.000** (0.000)	−0.001*** (0.000)	−0.001*** (0.000)	−0.005** (0.002)	−0.012*** (0.002)	−0.001*** (0.000)	−0.002*** (0.000)	−0.001* (0.000)	−0.001** (0.000)
VAE	0.043** (0.019)
PVE			0.045*** (0.051)
GEE					0.446*** (0.012)
RQE							0.504*** (0.024)
RLE									0.376*** (0.010)
CCE											0.129* (0.071)
MC3*VAE		0.001** (0.001)
MC3*PVE				0.005*** (0.000)
MC3*GEE						0.002** (0.001)
MC3*RQE								0.001*** (0.001)
MC3*RLE										0.001*** (0.002)
MC3*CCE												0.002** (0.001)
SIZE	0.232*** (0.004)	0.229*** (0.004)	0.261*** (0.036)	0.291*** (0.042)	0.266*** (0.041)	0.216*** (0.028)	0.501*** (0.017)	0.315*** (0.050)	0.522*** (0.012)	0.340*** (0.045)	0.5310*** (0.021)	0.279*** (0.008)
ROA	0.053*** (0.011)	0.034** (0.013)	0.162*** (0.008)	0.062*** (0.006)	0.099** (0.043)	0.047 (0.033)	0.038*** (0.008)	−0.054 (0.040)	0.139*** (0.008)	0.094*** (0.008)	0.072*** (0.019)	0.124*** (0.019)
LATA	0.808*** (0.109)	1.114*** (0.126)	0.369 (0.375)	−0.159 (0.501)	0.917* (0.493)	1.590*** (0.360)	2.004*** (0.170)	0.851 (0.607)	1.579*** (0.149)	1.938*** (0.114)	1.130*** (0.272)	−0.202 (0.240)
FREE	0.001 (0.000)	0.000 (0.001)	0.004*** (0.000)	−0.005 (0.003)	0.001 (0.002)	0002 (0.001)	0.007*** (0.000)	−0.003 (0.004)	0.004*** (0.000)	0004*** (0.000)	0.003*** (0.001)	−0.001 (0.001)
GDP	0.002 (0.002)	0.008*** (0.003)	0.003 (0.005)	0.010 (0.007)	−0.001 (0.002)	−0.006 (0.004)	0.008*** (0.001)	0.001 (0.007)	0.006*** (0.001)	0.005*** (0.001)	−0.001 (0.004)	0.010*** (0.003)
INF	−0.004*** (0.001)	−0.005*** (0.001)	−0.011*** (0.003)	−0.010*** (0.003)	−0.003 (0.002)	−0.003 (0.002)	−0.018*** (0.001)	−0.009*** (0.003)	−0.011*** (0.001)	−0.012*** (0.000)	−0.005*** (0.001)	−0.006*** (0.001)
Constant	−0.866 (0.138)	−0.738 (0.142)	−1.169 (0.569)	−1.271 (0.111)	−1.022 (0.448)	−1.110 (0.423)	−1.679 (0.210)	−0.638 (0.510)	−2.081 (0.184)	−2.002 (0.199	−1.128 (0.287)	−0.857 (0.209)
No. of Obs	1027	1027	1027	1027	1027	1027	1027	1027	1027	1027	1027	1027
No. of Groups	80	80	80	80	80	80	80	80	80	80	80	80
AR(1) p-value	0.009	0.009	0.013	0.007	0.007	0.013	0.008	0.012	0.007	0.007	0.008	0.008
AR(2) p-value	0.432	0.415	0.505	0.385	0.422	0.369	0.347	0.454	0.346	0.326	0.415	0.534
Hansen test p-value	0.592	0.645	0.126	0.386	0.433	0.134	0.500	0.339	0.393	0.511	0.160	0.186

Notes: ZSCORE, the mean returns on assets and the mean standard deviation of ROA over the sample period, combined with the current period value of EQUITY; The three-market concentration (MC3). The structural indicator of the degree of concentration of banks within an economy, measured by the concentration of assets held by the three major banks in each economy. A higher value indicates a higher level of market concentration: Institutional quality proxies from WGI report on six dimensions of governance, voice and accountability (VAE), political stability(PVE), government effectiveness (GEE), regulatory quality (RQE), rules of law (RLE), and control of corruption (CCE). FREE, the financial freedom index, SIZE the natural logarithm of total assets. ROA, returns on assets; LATA, the ratio of liquid assets to total assets; GDP, the growth rate of gross domestic products; INF, the inflation. All variables have been weinsorized at the 1% and 99% levels (except for market concentration, Institutional quality and financial freedom and macroeconomic variables). (Robust standard errors are in parentheses. *,** and *** the Significant at 10, 5 and 1% levels, respectively)

Table 4 presents the coefficients of the market concentration variable (MC3), indicating a positive and significant relationship. This study supports the “concentration-stability” hypothesis, suggesting that higher market concentration is associated with increased stability in the banking sector. The higher market concentration enhances market power, reduces information asymmetry, and improves loan quality, thereby contributing to greater bank stability. This finding supports Hypothesis 1a and aligns with previous research (Beck et al., 2006; Schaeck et al., 2009). However, the negative coefficient on SQMC3 suggests that market concentration may lower bank stability up to a certain threshold. This could be due to banks in less competitive or highly concentrated markets increasing lending rates to boost profits, which raises the risk of bankruptcy. This result contrasts with Abdesslem et al. (2022). In summary, our findings support the theory that higher banking concentration leads to greater bank stability up to a specific threshold.

In terms of institution quality characteristics, this study investigated the influence of the institutional environment on the financial stability of emerging-market banks. Six WGI indicators, namely $\mathrm{v}\mathrm{o}\mathrm{i}\mathrm{c}\mathrm{e}\mathrm{a}\mathrm{n}\mathrm{d}\mathrm{a}\mathrm{c}\mathrm{c}\mathrm{o}\mathrm{u}\mathrm{n}\mathrm{t}\mathrm{a}\mathrm{b}\mathrm{i}\mathrm{l}\mathrm{i}\mathrm{t}\mathrm{y}$, $\mathrm{p}\mathrm{o}\mathrm{l}\mathrm{i}\mathrm{t}\mathrm{i}\mathrm{c}\mathrm{a}\mathrm{l}\mathrm{s}\mathrm{t}\mathrm{a}\mathrm{b}\mathrm{i}\mathrm{l}\mathrm{i}\mathrm{t}\mathrm{y}$, $\mathrm{g}\mathrm{o}\mathrm{v}\mathrm{e}\mathrm{r}\mathrm{n}\mathrm{m}\mathrm{e}\mathrm{n}\mathrm{t}$ $\mathrm{e}\mathrm{f}\mathrm{f}\mathrm{e}\mathrm{c}\mathrm{t}\mathrm{i}\mathrm{v}\mathrm{e}\mathrm{n}\mathrm{e}\mathrm{s}\mathrm{s}$, $\mathrm{r}\mathrm{e}\mathrm{g}\mathrm{u}\mathrm{l}\mathrm{a}\mathrm{t}\mathrm{o}\mathrm{r}\mathrm{y}\mathrm{q}\mathrm{u}\mathrm{a}\mathrm{l}\mathrm{i}\mathrm{t}\mathrm{y}$, $\mathrm{t}\mathrm{h}\mathrm{e}\mathrm{r}\mathrm{u}\mathrm{l}\mathrm{e}\mathrm{o}\mathrm{f}\mathrm{l}\mathrm{a}\mathrm{w}$, and $\mathrm{c}\mathrm{o}\mathrm{r}\mathrm{r}\mathrm{u}\mathrm{p}\mathrm{t}\mathrm{i}\mathrm{o}\mathrm{n}\mathrm{c}\mathrm{o}\mathrm{n}\mathrm{t}\mathrm{r}\mathrm{o}\mathrm{l}$, were used as proxies $\mathrm{f}\mathrm{o}\mathrm{r}\mathrm{i}\mathrm{n}\mathrm{s}\mathrm{t}\mathrm{i}\mathrm{t}\mathrm{u}\mathrm{t}\mathrm{i}\mathrm{o}\mathrm{n}\mathrm{a}\mathrm{l}\mathrm{q}\mathrm{u}\mathrm{a}\mathrm{l}\mathrm{i}\mathrm{t}\mathrm{y}$. Models $1,3,5,7,9,and11$ demonstrated that the quality of the institutional environment significantly affects the financial stability of emerging-market banks, leading to improved bank stability. This finding aligns with our Hypothesis 2 and is consistent with previous studies conducted in emerging and developing countries (Bermpei et al., 2018; Uddin et al., 2020). It is indicated by the results that financial liberalization is facilitated by better institutional quality in an economic system (Delis, 2012; Gazdar & Cherif, 2015). Additionally, good institutional quality can reduce transaction costs and address the asymmetric information problem, contributing to enhanced bank performance and stability (Gugler et al., 2013).

In Models 2, 4, 6, 8, 10, and 12 of Table 4, the interaction between market concentration and institutional quality demonstrate a positive and significant relationship. This suggests that in emerging countries, better institutional quality strengthens the positive influence of bank concentration on the bank’s financial stability. In other words, in less competitive markets, high institutional quality restricts banks from indulging in excessive risk-taking activities. This aligns with the argument that strong IQ enables better oversight (Anginer et al., 2014) and monitoring of financial institutions (Hoque et al., 2015), leading to a reduction in the likelihood of risky bank activities.

The positive effect of bank size (SIZE) on ZSCORE is significant, implying that larger banks tend to be more diversified and possess higher risk management expertise. This finding aligns with Son et al. (2016). Additionally, as expected, bank stability is positively associated with return on assets (ROA), consistent with the findings of L. T. Nguyen and Nguyen (2021) and Son et al. (2016). The liquidity ratio (LATA) also has a significant and positive effect on ZSCORE, indicating that higher liquidity ratios are linked to greater bank stability. This observation is in line with previous studies by Shim (2013), Son and Liem (2020), and Vithessonthi (2014). Furthermore, financial freedom (FREE) exhibits a significant and positive effect on ZSCORE, suggesting that banks operating in regions with higher financial freedom tend to have greater stability or lower risk levels. This finding is consistent with the results of Chortareas et al. (2013), who found that financial freedom enhances bank performance. When banks have more operational freedom, they may engage in more suitable, less risky activities than when they are restricted. Moreover, the positive relationship between GDP and ZSCORE confirms the traditional view that economic growth increases the demand for financial products and services offered by the banking system during cyclical upswings, leading to higher profitability and bank stability (Athanasoglou et al., 2008; Dietrich & Wanzenried, 2014). Ultimately, ZSCORE is adversely affected by inflation rates (INF), suggesting that they could elevate the risk of loan default by impacting borrowers’ financial situations, jeopardizing their liquidity, and diminishing their ability to repay (Pervan et al., 2015).

4.3. Robustness check

4.3.1. Alternative of bank stability

According to Galloway et al. (1997) and Hovakimian and Kane (2000), ZSCORE was replaced with RISK as a measure of bank risk, calculated by the yearly volatility of weekly stock returns. Higher volatility in banks’ stock returns indicates greater bank risk and lower bank stability.

The findings in Table 5 demonstrate that $\mathrm{m}\mathrm{a}\mathrm{r}\mathrm{k}\mathrm{e}\mathrm{t}\mathrm{c}\mathrm{o}\mathrm{n}\mathrm{c}\mathrm{e}\mathrm{n}\mathrm{t}\mathrm{r}\mathrm{a}\mathrm{t}\mathrm{i}\mathrm{o}\mathrm{n}$ $\left(\mathrm{M}\mathrm{C}3\right)$ and $\mathrm{i}\mathrm{n}\mathrm{s}\mathrm{t}\mathrm{i}\mathrm{t}\mathrm{u}\mathrm{t}\mathrm{i}\mathrm{o}\mathrm{n}\mathrm{a}\mathrm{l}\mathrm{q}\mathrm{u}\mathrm{a}\mathrm{l}\mathrm{i}\mathrm{t}\mathrm{y}\left(\mathrm{I}\mathrm{Q}\right)$ are negatively related to $\mathrm{R}\mathrm{I}\mathrm{S}\mathrm{K}$. Furthermore, $\mathrm{S}\mathrm{Q}\mathrm{M}\mathrm{C}3$ is positive and significant with $\mathrm{R}\mathrm{I}\mathrm{S}\mathrm{K}$. The results in Models 2, 4, 6, 8, 10, and 12 in Table 5 demonstrate the interaction terms between MC3 and IQ. The result in Table 5 is supported by the coefficient of interaction terms, which is negative and significant for $\mathrm{R}\mathrm{I}\mathrm{S}\mathrm{K}$. This suggests that in developing and emerging economies, institutions with higher $\mathrm{i}\mathrm{n}\mathrm{s}\mathrm{t}\mathrm{i}\mathrm{t}\mathrm{u}\mathrm{t}\mathrm{i}\mathrm{o}\mathrm{n}\mathrm{a}\mathrm{l}\mathrm{q}\mathrm{u}\mathrm{a}\mathrm{l}\mathrm{i}\mathrm{t}\mathrm{y}$ have a stronger positive impact on the financial stability of banks. This is consistent with González-Rodríguez (2008); Saif-Alyousfi et al. (2020)in conclusion, our $\mathrm{Z}\mathrm{S}\mathrm{C}\mathrm{O}\mathrm{R}\mathrm{E}$ model’s results are further supported by the $\mathrm{R}\mathrm{I}\mathrm{S}\mathrm{K}$ model’s robustness analysis.

Table 5. Robustness test using the bank risk

RISKt-1	RISK (1)	RISK (2)	RISK (3)	RISK (4)	RISK (5)	RISK (6)	RISK (7)	RISK (8)	RISK (9)	RISK (10)	RISK (11)	RISK (12)
RISKt-1	0.236*** (0.017)	0.231*** (0.016)	0.248*** (0.006)	0.254*** (0.006)	0.292*** (0.003)	0.287*** (0.003)	0.249*** (0.007)	0.238*** (0.003)	0.233*** (0.003)	0.231*** (0.003)	0.240*** (0.004)	0.235*** (0.024)
MC3	−0.035*** (0.019)	−0.029*** (0.001)	−0.027*** (0.009)	−0022** (0.008)	−0.012*** (0.001)	−0.032*** (0.001)	−0.046*** (0.011)	−0.057*** (0.005)	−0.062*** (0.004)	−0.076*** (0.004)	−0.017*** (0.003)	−0.068*** (0.032)
SQMC3	0.001*** (0.000)	0.003** (0.000)	0.001*** (0.001)	0.001*** (0.000)	0.010*** (0.001)	0.020*** (0.006)	0.001*** (0.000)	0.001*** (0.000)	0.001*** (0.000)	0.002*** (0.000)	0.003*** (0.000)	0.001*** (0.000)
VAE	−0.829*** (0.216)
PVE			−0.221*** (0.034)
GEE					−0.966*** (0.017)
RQE							−0.261*** (0.013)
RLE									−0.668*** (0.038)
CCE											−1.651*** (0.033)
MC3*VAE		−0.047*** (0.008)
MC3*PVE				−0.015*** (0.000)
MC3*GEE						−0.019*** (0.000)
MC3*RQE								0.008 (0.001)
MC3*RLE										−0.010*** (0.001)
MC3*CCE												0.010 (0.014)
SIZE	−0.144*** (0.002)	−0.036*** (0.002)	0.124 (0.010)	−0.038*** (0.002)	−0.161*** (0.004)	−0.161*** (0.005)	−0.017*** (0.001)	−0.015 (0.002)	−0.052*** (0.006)	−0.046*** (0.007)	−0.053*** (0.009)	0.038 (0.057)
ROA	−0.199*** (0.006)	−0.396** (0.009)	−0.266*** (0.008)	−0.249*** (0.008)	−0.133*** (0.012)	−0.191*** (0.010)	−0.102*** (0.005)	−0.124*** (0.006)	−0.324*** (0.008)	−0.392*** (0.010)	0.031 (0.031)	0.303 (0.250)
LATA	−0.088 (0.083)	−4.091*** (0.155)	−0.149*** (0.049)	−0.010*** (0.043)	−2.252*** (0.148)	3.320 (0.207)	−0.377*** (0.107)	−1.667*** (0.292)	−1.767*** (0.068)	−1.606*** (0.068)	−3.212*** (0.301)	−9.972*** (0.306)
FREE	−0.019*** (0.000)	−0.026*** (0.000)	−0.023*** (0.000)	−0.022*** (0.000)	−0.021*** (0.000)	−0.022*** (0.001)	−0.005*** (0.000)	−0.036*** (0.001)	−0.027*** (0.000)	−0.032*** (0.000)	−0.028*** (0.001)	−0.030*** (0.001)
GDP	−0.060*** (0.001)	−0.086*** (0.001)	−0.0073*** (0.001)	−0.069*** (0.001)	−0.118*** (0.001)	−0.114*** (0.001)	−0.069*** (0.002)	−0.032*** (0.001)	−0.032*** (0.001)	−0.066*** (0.001)	−0.056*** (0.003)	−0.061*** (0.003)
INF	0.127*** (0.001	0.139*** (0.002)	0.184*** (0.001)	0.185*** (0.001)	0.194*** (0.002)	0.193*** (0.003)	0.036*** (0.001)	0.069*** (0.003)	0.069*** (0.002)	0.184*** (0.002)	0.203*** (0.004)	0.200*** (0.004)
Constant	−3.739 (1.336)	−5.783 (1.280)	−0.478 (0.603)	2.812 (0.054)	6.102 (0.133)	6.466 (0.194)	0.436 (0.060)	1.340 (0.370)	8.333 (0.161)	9.127 (0.166)	4.981 (0.304)	5.941 (0.229)
No.of Obs	1027	1027	1027	1027	1027	1027	1027	1027	1027	1027	1027	1027
No.of Groups	80	80	80	80	80	80	80	80	80	80	80	80
AR(1) p-value	0.004	0.000	0.000	0.000	0.000	0.000	0.000	0.000	0.000	0.000	0.004	0.000
AR(2) p-value	0.134	0.148	0.112	0.108	0.488	0.391	0.143	0.109	0.213	0.670	0.574	0.481
Hansen test p-value	0.721	0.798	0.487	0.491	0.491	0.505	0.106	0.106	0.725	0.730	0.131	0.130

Notes: RISK, the yearly volatility of weekly stock returns; The three-market concentration (MC3). The structural indicator of the degree of concentration of banks within an economy, measured by the concentration of assets held by the three major banks in each economy. A higher value indicates a higher level of market concentration: Institutional quality proxies from WGI report on six dimensions of governance, voice and accountability (VAE), political stability (PVE), government effectiveness (GEE), regulatory quality (RQE), rules of law (RLE), and control of corruption (CCE). FREE, the financial freedom index, SIZE the natural logarithm of total assets. ROA, returns on assets; LATA, the ratio of liquid assets to total assets; GDP, the growth rate of gross domestic products; INF, the inflation. All variables have been weinsorized at the 1% and 99% levels (except for market concentration, Institutional quality and financial freedom and macroeconomic variables). (Robust standard errors are in parentheses. *,** and *** the Significant at 10, 5 and 1% levels, respectively).

4.3.2. Alternative of market concentration

We were replacing three market concentration (MC3) with $fivemarketconcentration$ $\left(MC5\right)$ variable, the concentration of assets held by the five major banks in each economy—to test whether $fivemarketconcentration$ $\left(MC5\right)$ positive the stability of developing and emerging countries banks in Table 6. The results were consistent, significant, and continue to show their signs. According to prior findings that more market concentration and greater institutional quality improve the stability of the banking industry in developing and emerging nations, the MC5 and $institutional$ $quality$ variables demonstrate a positive connection with $bankstability$. Additionally, the results in Models 2, 4, 6, 8, 10, and 12 in Table 6 demonstrate that the coefficients of interaction between $institutionalquality$ and market concentration are positive and significant, respectively, supporting the main conclusions we presented above.

Table 6. Robustness test using the alternative of market concentration (MC5)

ZSCOREt-1	ZSCORE (1)	ZSCORE (2)	ZSCORE (3)	ZSCORE (4)	ZSCORE (5)	ZSCORE (6)	ZSCORE (7)	ZSCORE (8)	ZSCORE (9)	ZSCORE (10)	ZSCORE (11)	ZSCORE (12)
ZSCOREt-1	0.696*** (0.012)	0.687*** (0.009)	0.686*** (0.044)	0.688*** (0.046)	0.736*** (0.028)	0.705*** (0.024)	0.746*** (0.002)	0.742*** (0.001)	0.714*** (0.005)	0.709*** (0.017)	0.700*** (0.013)	0.775*** (0.020)
MC5	0.007* (0.004)	0.003*** (0.001)	0.032** (0.012)	0.033*** (0.012)	0.018** (0.006)	0.011** (0.005)	0.003** (0.001)	0.007*** (0.001)	0.008* (0.004)	0.012** (0.005)	0.006*** (0.001)	0.033*** (0.011)
SQMC5	−0.005*** (0.001)	−0.003** (0.000)	−0.002*** (0.001)	−0.002*** (0.001)	−0.001** (0.000)	−0.001** (0.000)	−0.008** (0.001)	−0.010** (0.000)	−0.003*** (0.003)	−0.002*** (0.012)	−0.002*** (0.0010)	−0.005*** (0.003)
VAE	0.127** (0.058)
PVE			−0.001 (0.069)
GEE					0.166** (0.070)
RQE							0.149*** (0.040)
RLE									0.233*** (0.062)
CCE											0.165*** (0.019)
MC5*VAE		0.000** (0.000)
MC5*PVE				0.002** (0.001)
MC5*GEE						0.002* (0.001)
MC5*RQE								0.002*** (0.001)
MC5*RLE										0.002*** (0.001)
MC5*CCE												0.003* (0.002)
SIZE	0.276*** (0.048)	0.292*** (0.022)	0.370** (0.049)	0.368*** (0.053)	0.304*** (0.029)	0.306*** (0.026)	0.254*** (0.022)	0.259*** (0.021)	0.303*** (0.017)	0.287*** (0.014)	0.309*** (0.004)	0.266*** (0.077)
ROA	−0.087 (0.045)	−0.067 (0.009)	−0.092 (0.116)	−0.064 (0.062)	0.056*** (0.005)	−0.064 (0.063)	−0.034 (0.015)	0.041** (0.016)	0.070** (0.027)	0.038* (0.022)	0.056*** (0.004)	−0.031 (0.059)
LATA	−0.629 (0.380)	0.535*** (0.131)	1.792** (0.848)	1.842*** (0.612)	1.258*** (0.119)	2.414*** (0.768)	0.209 (0.160)	0.155 (0.163)	1.478*** (0.263)	1.582*** (0.286)	0.695*** (0.601)	2.223*** (0.804)
FREE	−0.001 (0.002)	−0.000 (0.001)	0.004 (0.005)	0.005 (0.003)	0.000 (0.000)	0.007** (0.003)	0.002*** (0.001)	0.001*** (0.000)	0.001 (0.001)	0.005*** (0.001)	0.002*** (0.000)	0.004 (0.003)
GDP	0.022*** (0.007)	0.024*** (0.003)	0.005 (0.010)	0.004 (0.008)	0.011*** (0.001)	0.001 (0.005)	0.012*** (0.003)	0.013*** (0.003)	−0.002 (0.003)	0.002 (0.003)	0.005*** (0.002)	−0.001 (0.007)
INF	−0.012*** (0.003)	−0.008*** (0.001)	−0.002 (0.005)	−0.004 (0.004)	−0.017*** (0.001)	−0.005 (0.005)	−0.004*** (0.002)	−0.005*** (0.001)	−0.001 (0.002)	−0.002 (0.002)	−0.009*** (0.001)	−0.007*** (0.006)
Constant	0.542 (0.476)	0.269 (0.190)	−1.901 (1.611)	−1.895 (1.025)	−1.628 (0.104)	−3.540 (1.202)	−0.293 (0.158)	−0.446 (0.155)	−1.444 (0.363)	−1.118 (0.407)	−0.874*** (0.076)	−2.875 (1.048)
No.of Obs	994	994	994	994	994	994	994	994	994	994	994	994
No.of Groups	80	80	80	80	80	80	80	80	80	80	80	80
AR(1) p-value	0.007	0.007	0.013	0.008	0.006	0.010	0.008	0.008	0.008	0.008	0.008	0.009
AR(2)p-value	0.613	0.608	0.452	0.366	0.445	0.337	0.495	0.505	0.359	0.361	0.453	0.347
Hansen test p-value	0.170	0.209	0.147	0.595	0.660	0.771	0.258	0.356	0.225	0.520	0.938	0.525

Notes: ZSCORE, the mean returns on assets and the mean standard deviation of ROA over the sample period, combined with the current period value of EQUITY; The five-market concentration (MC5). The structural indicator of the degree of concentration of banks within an economy, measured by the concentration of assets held by the three major banks in each economy. A higher value indicates a higher level of market concentration: Institutional quality proxies from WGI report on six dimensions of governance, voice and accountability (VAE), political stability (PVE), government effectiveness (GEE), regulatory quality (RQE), rules of law (RLE), and control of corruption (CCE). FREE, the financial freedom index, SIZE the natural logarithm of total assets. ROA, returns on assets; LATA, the ratio of liquid assets to total assets; GDP, the growth rate of gross domestic products; INF, the inflation. All variables have been weinsorized at the 1% and 99% levels (except for market concentration, Institutional quality and financial freedom and macroeconomic variables). (Robust standard errors are in parentheses. *,** and *** the Significant at 10, 5 and 1% levels, respectively)

5. Conclusion

This study examines the relationship between market concentration, institutional quality, and a bank’s financial stability using data from 80 banks in ASEAN 4 countries spanning the period from 2006 to 2019. The research reveals intriguing findings. Firstly, it indicates a positive association between bank stability and market concentration. According to the concentration-stability hypothesis, banks operating in highly concentrated markets tend to be more stable than those in less concentrated ones. However, the negative coefficients on SQMC suggest a possible inverted U-shaped relationship, implying that market concentration enhances bank stability up to a certain threshold. Secondly, the study demonstrates the significant impact of the institutional environment on the bank stability of ASEAN 4 countries. Furthermore, it suggests that institutional development may mitigate the influence of market concentration on bank stability. The research also explores the interaction terms between MC and IQ, shedding light on how the effect of market concentration on bank stability evolves as institutions in emerging countries improve. The findings underscore the significant influence of higher institutional quality on bank financial stability in emerging countries. In essence, robust institutional quality deters banks from taking excessive risks in highly concentrated markets. These results provide the foundation for regression models that utilize bank stability and market concentration variables as replacements to further investigate the research objectives.

The findings indicate that a well-defined strategy is crucial for bank managers and bankers. Bank stability is observed to be higher when market concentration reaches a specific threshold. Additionally, higher institutional quality contributes to improved bank stability. However, the study may have certain limitations. The indicators used for market concentration and bank stability, like those in previous similar studies, may not fully reflect real-world practices. In the future, conducting in-depth interviews and surveys with bankers and regulatory bodies could provide better insights into these concerns.

Acknowledgments

The author is grateful to FPT University for funding the publication of this paper.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Additional information

Funding

The work was supported by the FPT University.

Notes on contributors

Hai-Tuan Nguyen

Hai-Tuan Nguyen is a lecturer in the Business Administration Department at FPT University - Can Tho campus. He holds a philosophy of doctorate in business administration from Asia University, Taiwan. He relishes teaching and interdisciplinary research. His main research areas are finance, marketing, management, customer behavior, and sustainable development.

Notes

1. (Because of the multi—collinearity, $Equation\left(2\right)$ $doesnot$ incorporate $institutionqualityvariables$, as evidenced by our VIF test (not tabulated here).

2. Any VIF exceeding 10 indicates aproblem of multicollinearity (Hair et al., 2010).

3. Results of fixed effect and pooled estimations are available on request.