Financial Liberalization and Bank Performance in GCC Countries: A Quantile Regression Analysis

Financial Liberalization and Bank Performance in GCC Countries: A Quantile Regression Analysis

Nur Afizah Muhamad Arifin^*, Norhasimah Shaharuddin, Idris Osman

Faculty of Business and Management, UiTM Cawangan Selangor, Kampus Puncak Alam, Bandar Puncak Alam, 42300, Puncak Alam, Selangor, Malaysia

^*Corresponding Author

DOI: https://dx.doi.org/10.47772/IJRISS.2025.905000463

Received: 16 May 2025; Accepted: 24 May 2025; Published: 23 June 2025

ABSTRACT

The banking sectors of Gulf Cooperation Council (GCC) countries have undergone significant financial reforms in recent decades. These reforms include the deregulation of financial markets, privatization of state-owned banks, and increased openness to foreign competition. While these liberalization efforts aim to enhance banking efficiency and attract foreign investment, they also introduce new risks and uncertainties, raising concerns about their overall impact on bank performance. This study investigates the effects of financial liberalization on the performance of banks operating in GCC countries. To achieve this, we analyze a panel dataset of GCC banks using quantile regression across five quantiles (10th, 25th, 50th, 75th, and 90th percentiles). This approach allows for a nuanced understanding of how liberalization affects banks at different performance levels. The findings reveal a consistent negative relationship between financial liberalization and return on assets (ROA) across all quantiles, with more significant adverse effects observed among the most profitable banks. These results indicate that while liberalization has facilitated growth and modernization in the banking sector, it also puts downward pressure on profit margins due to intensified competition and increased exposure to external market shocks. The study concludes that robust regulatory frameworks are essential to mitigate the risks associated with liberalization and ensure the long-term stability of the banking sector in the GCC region.

Keywords: financial liberalisation, bank performance, quantile.

INTRODUCTION

A robust financial system, supported by strong financial institutions, is crucial for sustaining long-term economic growth (McKinnon, 1973; Shaw, 1973). The liberalization of the banking sector has accelerated the integration of global financial markets by opening domestic markets to foreign banks and promoting international trade in financial goods and services (Unite & Sullivan, 2003; Li et al., 2024). Countries have liberalized their financial systems at different rates, influenced by their unique economic contexts and regulatory capacities (Claessens & van Horen, 2014; Shen et al., 2020). The pace and structure of financial liberalization, however, vary widely across countries, influenced by distinct economic conditions, regulatory capacities, and institutional maturity (Hamdi & Jlassi, 2014; Ma & Soh, 2024).

Proponents of financial liberalization argue that the entry of foreign banks enhances domestic financial development by increasing competition, reducing overhead costs, facilitating the privatization of inefficient state-owned banks, broadening the range of available financial services, and improving overall financial depth (Arifin et al., 2024; Unite & Sullivan, 2003; Bilal et al., 2024). These advancements are expected to help reduce systemic risk and foster sustainable economic growth (Abu-Abbas & Hassan, 2024; Hamdi & Jlassi, 2014; Alsulmi et al., 2024). However, liberalization has its drawbacks. Critics caution that heightened competition may destabilize domestic financial systems by exposing them to the cyclical fluctuations of global financial markets (Shen et al., 2020; Sahraoui & Alfadli, 2024).

Furthermore, the presence of foreign banks might diminish the profitability and market share of domestic institutions (Claessens & van Horen, 2014; Abdulkadhim et al., 2024). Liberalization is not without its risks. Critics warn that increased competition from foreign banks can erode the market share and profitability of domestic institutions, while greater exposure to international capital flows may amplify vulnerability to global financial shocks (Li et al., 2024). The 2008 global financial crisis underscored these concerns, highlighting how poorly regulated liberalized systems can propagate systemic risk rather than contain it (Shen et al., 2020).

The theoretical foundation for financial liberalization was established by the seminal works of Shaw (1973) and McKinnon (1973), who asserted that financial repression hinders the development of the financial sector. They proposed that liberalization policies would enhance savings, investment, and ultimately economic growth. Hamdi and Jlassi (2014) define financial liberalization as the implementation of policy measures aimed at removing various government-imposed restrictions on the financial sector to stimulate economic development. Unite and Sullivan (2003) further explain that these reforms often include reducing barriers to foreign investment and facilitating the entry of foreign banks into domestic markets. Financial liberalization can take the form of deregulation allowing foreign-controlled banks to operate or regulatory reform providing incentives for partial foreign ownership of local banks (Bilal et al., 2024). Governments play a crucial role in supporting these processes through strategic policy reforms that align with global financial standards (Li et al., 2024).

Recent empirical research has investigated the effect of financial liberalisation on bank performance in greater detail. Houda Litimi, Ahmed BenSaïda, and Mohamed Mahees Raheem (2024), for example, examine how the rise of financial technology (FinTech) affects bank performance in the Gulf Cooperation Council (GCC). As determined by return on equity (ROE), return on assets (ROA), and net interest margin (NIM), the study concludes that the expansion of FinTech firms has a detrimental impact on bank performance. This shows that although market liberalisation may increase market accessibility, it also brings about new competitive pressures that may affect the profitability of conventional banks.

Further research by Hatem Abdulkadhim et al. (2024) uses a quantile regression technique to investigate the factors impacting the profitability of commercial banks in Iraq. The study concludes that while openness and inflation have a detrimental effect on bank profitability at higher quantiles, liquidity and the ratio of total debt to total shareholders’ equity have a strong positive association with ROA. Bank profitability was also shown to be positively and significantly impacted by institutional governance, underscoring the significance of regulatory frameworks in liberalised settings.

In recent decades, the banking sectors of the Gulf Cooperation Council (GCC) countries—including Saudi Arabia, Kuwait, the United Arab Emirates, Qatar, Oman, and Bahrain—have undergone significant transformations due to financial liberalization (Bilal et al., 2024; Alsulmi et al., 2024). These reforms have included interest rate deregulation, capital market liberalization, privatization of state-owned banks, and the opening of financial markets to foreign investors. The primary goal has been to enhance the efficiency of the financial system, promote competition, attract foreign investment, and diversify economies away from their traditional dependence on oil (Sahraoui & Alfadli, 2024).

While these liberalization efforts were expected to strengthen bank performance through improved competitiveness, access to capital, and advanced risk management practices, they have also raised concerns about the resilience of the banking system and the adequacy of regulatory oversight (Abu-Abbas & Hassan, 2024). This concern is particularly relevant in light of the global financial crisis, which exposed the vulnerabilities of liberalized financial systems (Shen et al., 2020). As the GCC banking sector continues to evolve within an increasingly interconnected global environment, a critical question arises: Has financial liberalization improved bank profitability and efficiency, or has it heightened exposure to volatility and financial instability?

Recent empirical studies have begun to address this issue. For instance, Shen, Chen, and Chen (2020) find that while financial liberalization can enhance profitability and operational efficiency, it may also promote excessive risk-taking among banks. Importantly, Claessens and van Horen (2014) demonstrate that the impact of liberalization is not uniform; their use of quantile regression reveals that the effects vary significantly across different levels of bank performance.

In response to this complexity, the present study investigates the effects of financial liberalization on the performance of 30 banks in the GCC from 2013 to 2022. Particular emphasis is placed on profitability, measured by Return on Assets (ROA), which is a key indicator in banking and financial economics. Understanding the determinants of ROA is crucial for policymakers, regulators, and bank managers, especially in emerging markets where liberalization policies are actively shaping the financial landscape.

Traditional analytical approaches, such as Ordinary Least Squares (OLS), assume a constant relationship across the conditional distribution of ROA, potentially overlooking important variations across performance levels. To address this limitation, the study employs quantile regression, a method that enables a more detailed examination of how financial liberalization and bank-specific factors, such as asset quality, liquidity, and capital adequacy, affect banks at different points along the performance spectrum (Abdulkadhim et al., 2024; Arifin et al., 2024; Ma & Soh, 2024).

LITERATURE REVIEW

The growing body of research on financial liberalization and bank performance highlights a complex and context-dependent relationship. While liberalization policies often lead to gains in efficiency and competitiveness, they can also increase the risk exposure of financial institutions, particularly in the absence of strong regulatory frameworks. Financial liberalization can enhance banking performance by improving cost efficiency, increasing allocative efficiency, and attracting foreign investment. For instance, Vega (2022), in a cross-country study involving 17 Central and Eastern European nations, found that liberalized banking markets could offer more competitively priced financial services, indicating improved cost efficiency.

Similarly, Chaudhary and Yadav (2023) demonstrated that liberalization improved allocative efficiency in emerging economies like Malaysia and India, as evidenced by reduced dispersion in Tobin’s Q, a proxy for capital allocation efficiency. However, liberalization also introduces significant risks; it may incentivize excessive risk-taking, particularly in countries with weak capital regulations (Zhang & Liu, 2023). The International Monetary Fund (IMF, 2023) cautions that while liberalization can spur growth, it often exacerbates financial fragility unless supported by robust institutional oversight.

A. Liberalisation in the GCC Context

The experience of the Gulf Cooperation Council (GCC) countries presents a unique case. Financial liberalization in this region through interest rate deregulation, capital account openness, and privatization has increased bank efficiency, competition, and profitability (Al-Khathlan, 2009; Al-Muharrami & Matthews, 2009). It has also expanded access to credit and improved financial inclusion, particularly in countries like the United Arab Emirates and Qatar (Erlanger & Ghosh, 2011). However, these reforms have exposed domestic banks to external shocks, including volatility from foreign financial crises and overexposure to high-risk sectors such as real estate (Abed & Detragiache, 2004). The rapid pace and depth of liberalization have posed challenges to regulatory capacity in the region, increasing systemic risks and heightening the need for effective supervisory frameworks.

Several scholars have examined the nuanced effects of liberalization across different bank types and profitability levels. Shen et al. (2020) conducted a meta-analysis and concluded that financial liberalization enhances efficiency but also requires stringent regulatory oversight to mitigate associated risks. Nguyen et al. (2021), using quantile regression analysis, found that liberalization significantly improved Return on Assets (ROA) among middle-performing banks in Asia, indicating heterogeneous impacts across the performance distribution. This aligns with the view that one-size-fits-all assessments using traditional regression models (e.g., OLS) may obscure important distributional differences an issue that quantile regression can address more effectively (Koenker, 2005).

B. Bank Specific Factors Affecting Performance

The literature emphasizes the crucial role of asset quality in shaping bank profitability and stability. Poor asset quality increases default risk, necessitates higher provisioning, and ultimately reduces profitability. In contrast, high-quality assets enhance financial performance and resilience. Aldizar and Agustina (2022) reported a significant negative relationship between asset quality deterioration and bank profitability. This finding is echoed by Samail et al. (2018), who linked strong asset quality to improved liquidity management and enhanced performance, especially within Islamic banking institutions in Malaysia.

Liquidity management is another critical determinant of bank performance. Yahaya et al. (2022) found a significant negative relationship between liquidity risk and bank performance, emphasizing the importance of maintaining optimal liquidity levels. While sufficient liquidity protects banks from solvency threats and supports customer confidence, excess liquidity may reduce returns due to lower yields on safe, liquid assets. Ahmad (2023) observed that banks with moderate liquidity thresholds manage risk better while remaining profitable. Similarly, Mohammad (2024) highlighted how liquid asset holdings allow South Asian banks to undertake higher-risk activities, thereby influencing performance variability. Ben-Ahmed (2023) focused on Tunisian banks and demonstrated that elevated liquidity risk hampers profitability. Yaseen and Farooq (2022) provided evidence that liquidity positively influences ROA, particularly for underperforming banks. This view is corroborated by Adebayo and Ajayi (2023), who stressed that while excess liquidity can stabilize, it may also indicate inefficiencies.

The role of capital adequacy in ensuring financial resilience is well-documented. Rahman and Lin (2023) found that higher capital adequacy ratios (CAR) help reduce financial risk, but may also constrain profitability by limiting banks’ ability to leverage. Uddin and Ahmed (2022) noted that the negative impact of CAR on return on assets (ROA) becomes more pronounced during economic downturns, particularly for less profitable firms. These findings support the hypothesis that while capital buffers stabilize the financial system, they may also create trade-offs between risk and return.

Alvarez and Larrain (2023) argue that financial liberalization enhances access to capital markets, but also increases volatility effects that are particularly pronounced among high-performing banks. Narayan and Zheng (2022) observed a nonlinear relationship in which modest levels of liberalization support firm performance, while excessive openness can diminish profit margins. These studies highlight the necessity of considering varied responses across the performance spectrum. The heterogeneity of these effects, whether linked to institutional robustness, bank-specific characteristics, or macroeconomic conditions, emphasizes the appropriateness of employing quantile regression to analyze differential impacts on ROA across various tiers of bank performance.

RESEARCH METHODOLOGY

This study utilizes panel data collected from 2012 to 2023, focusing on banking institutions within GCC countries. Additionally, the Malaysian banking sector offers an intriguing comparative context due to its regulatory maturity and emerging market characteristics, justifying its inclusion in this broader analysis. The study specifically targets all Malaysian commercial banks, applying thorough sampling criteria. Bank performance is measured using return on assets (ROA), which serves as the dependent variable. The primary independent variable is financial freedom, operationalized through the financial freedom index. This index ranges from 0 to 100 and reflects the extent of regulatory restrictions on financial institutions, with higher values indicating greater liberalization and fewer constraints (Berger et al., 2009; Sufian & Hassan, 2012). To account for other potential influences on bank performance, several control variables are incorporated into the analysis, including non-performing loans, capital adequacy, liquidity, and asset quality. The operational definitions and measurement methods for all variables used in this study are detailed in Table 1.

Table I Definition of Sources of Variables

This study employs an empirical methodology that combines quantile regression and panel data analysis to examine the varying effects of financial liberalization on bank performance. Quantile regression, introduced by Koenker and Bassett (1978), extends the traditional ordinary least squares (OLS) approach by estimating conditional quantiles of the dependent variable, rather than focusing solely on the conditional mean. While OLS regression approximates central tendencies like the mean or median, it only offers a limited understanding of the conditional distribution of the dependent variable (Mosteller & Tukey, 1977). In contrast, quantile regression allows for an examination of how explanatory variables affect the entire distribution of the outcome variable, thereby capturing distributional differences more effectively (Buchinsky, 1994, 1995; Eide & Showalter, 1997).

One of the key advantages of quantile regression is its robustness against outliers, as it minimizes a weighted sum of absolute residuals rather than the sum of squared residuals, which is more sensitive to extreme values. Furthermore, the estimation process utilizes linear programming techniques, improving computational efficiency. This methodology is particularly beneficial in cases where the conditional distribution of the dependent variable is skewed, heavy-tailed, or truncated—conditions under which OLS assumptions may not hold. By estimating the effects of covariates at different points in the conditional distribution, quantile regression provides a more comprehensive understanding of how financial liberalization and other bank-specific factors influence bank performance across various levels of profitability.

The estimation model’s specification is used to examine the impact of financial liberalisation on bank performance, particularly for a subset of Malaysia’s commercial banks. The following are the estimating models used in this study:

\[ \text{ROA} = \sqrt{ \beta_0 + \beta_1 flindex_{i,t} + \beta_2 asqual_{i,t} + \beta_3 lratio_{i,t} + \beta_4 car_{i,t} + \varepsilon_{i,t} } \tag{1} \]

Quantile regression is used in this investigation. Financial liberalisation and other factors influencing bank performance are measured using quantile regression (QR) analysis and return on assets (ROA). Developed by Koenker and Bassett (1978), quantile regression (QR) is an extension of ordinary least squares (OLS) estimation of the conditional mean to a variety of models for different conditional quantile functions and purposes. Furthermore, the quantile regression estimator can offer a more dependable and efficient alternative to OLS in cases when the error term is non-normal (Buchinsky, 1995). This study expanded the Koenker and Bassett (1978) model based on equations (1) to estimate the model independently for ROA using the QR estimator in the manner described below:

\[ \gamma = V_t’ \beta_0 + \mu_{\theta t}; \quad Quant_{\theta} \left( \frac{\gamma_t}{V_t’} \right) = V_t’ \beta_{\theta} \tag{2} \]

where μ is the error term, Bθ is the slope coefficient that measures the impact of financial liberalisation on bank performance in quantile θ, the conditional quantile of bank performance, and V’ is the regressor set comprising the financial liberalisation and other control variables. According to equations (3) and (4), the QR estimator minimises the sample size β and the weighted absolute values of the residuals utilising all available data (Buchinsky, 1995; Koenker & Bassett, 1978). The θ-th quantile regression produces 0 < θ < 1.

\[ \min \sum_{\gamma_t \geq V_t’ \beta_{\theta}} \theta \gamma_t – V_t’ \beta_{\theta} \sum_{\gamma_t < V_t’ \beta_{\theta}} (1 – \theta) \gamma_t \tag{3} \]

\[ \beta \quad \begin{cases} \gamma_t \geq V_t’ \beta & \gamma_t \\ \gamma_t < V_t’ \beta & \end{cases} \tag{4} \]

where the indicator functions γ > V’β and γ ˂ V’β represent both positive and negative residual values depending on the value of θ. The whole conditional distribution of profitability based on bank performance, which is dependent on the regressor group of the financial liberalisation, can be found as a quantile θ rises from 0 to 1. This method assigns a weight of (1-θ) to positive residuals and a weight of (1-θ) to negative residuals, rather than squaring all errors. Based on the 10th, 25th, 50th (median), 75th, and 90th percentiles of the profitability spill over distribution of bank performance, regression estimate was carried out for five distinct quantiles in this study. The response of bank performance spillovers varies according to the degree of financial liberalisation, even within a given conditional quantile, as implied by the use of a proxy for return on asset in the collection of regressors.

RESULTS AND DISCUSSION

This section presents the empirical results derived from both quantile regression and Ordinary Least Squares (OLS) estimations, focusing on the relationship between bank performance—as measured by Return on Assets (ROA)—and a set of key explanatory variables: the financial liberalisation index (FLINDEX), asset quality (ASQUAL), liquidity ratio (LRATIO), and capital adequacy ratio (CAR). The application of quantile regression is particularly motivated by the need to account for potential heterogeneity in these relationships across different points of the conditional ROA distribution. This approach provides deeper insights beyond the conditional mean estimated by OLS, making it especially valuable in financial economics where the effects of explanatory variables may vary across performance levels (Koenker, 2005). Table 4 reports the estimated coefficients across selected quantiles of ROA, highlighting how the impact of financial liberalisation and bank-specific factors differs for banks with varying profitability levels. For comparative purposes, the final columns of Table 2 present the corresponding OLS estimates, enabling a direct contrast between mean-based and distributional perspectives.

Table II Regression Results

Notes: t statistics in parentheses, p<0.10* p<0.05**, p<0.01***

The regression results reveal a consistently negative relationship between financial liberalisation, as measured by FLINDEX, and Return on Assets (ROA) across all quantiles, as well as in the OLS estimates. Notably, the statistical significance of this relationship intensifies at higher quantiles, with the coefficient for FLINDEX becoming more negative and strongly significant at the 75th and 90th percentiles. This pattern suggests that highly profitable firms experience a more pronounced adverse effect from financial liberalisation. These findings corroborate recent studies indicating that although financial liberalisation can facilitate credit expansion and investment opportunities, it simultaneously amplifies competitive pressures and financial volatility, especially in emerging and developing markets (Khan et al., 2022; Arestis & Caner, 2021). The liberalisation process may incentivize excessive risk-taking or heightened exposure to global economic shocks, which ultimately undermines firm-level profitability.

Regarding asset quality, proxied by ASQUAL, the results show a negative and statistically significant impact on ROA across most quantiles and the OLS model. The effect is particularly pronounced at the median and lower quantiles, indicating that firms with weaker profitability are more susceptible to declines in asset quality. This aligns with Zhang and Li (2023), who documented that worsening asset quality—often due to rising non-performing loans—directly diminishes the earnings capacity of financial institutions. Poor asset quality exacerbates credit risk and increases provisioning requirements, thereby constraining both profitability and capital adequacy.

The liquidity ratio (LRATIO) exhibits a positive and statistically significant association with ROA at the 10th, 50th, and 75th percentiles, though this relationship is not significant in the OLS regression. This suggests that liquidity’s benefits are more salient among low- to mid-performing firms. Banks with higher liquidity are better equipped to meet short-term obligations, reducing operational risk and bolstering investor confidence (Alshatti, 2020). However, excessive liquidity could indicate underutilized capital, which may explain the diminished effect observed at higher profitability levels.

The capital adequacy ratio (CAR) shows a negative and significant relationship with ROA at the lower quantiles and in the OLS model. This finding implies that higher capital buffers may reduce profitability, particularly for firms operating at lower efficiency or scale. It supports the argument by Diallo and Tapsoba (2021) that while capital requirements are vital for financial stability, they may also restrict risk-taking and leverage, limiting short-term earnings. This effect is most pronounced among firms with low to moderate profitability, where capital is managed more conservatively.

CONCLUSIONS

This study highlights the varying impacts of financial liberalization and important bank-specific indicators on profitability, emphasizing the complex relationships that differ based on levels of firm performance. By using a quantile regression approach, the analysis shows that these effects are not consistent but vary significantly across different levels of profitability.

Specifically, financial liberalization tends to have a more pronounced effect on highly profitable firms, where increased competition and financial volatility may more significantly erode profit margins. In contrast, factors such as asset quality and capital adequacy have a greater negative impact on lower-performing firms. This suggests that banks in weaker financial positions are more susceptible to credit risks and capital constraints, which can limit their profitability and growth potential.

Liquidity is identified as a key factor, offering substantial benefits to firms in the lower quantiles of profitability by enhancing their ability to meet short-term obligations and stabilize their operations. However, this benefit decreases at higher profitability levels, where excessive liquidity might suggest inefficient capital utilization, thereby reducing its positive impact. These differing effects underscore the need for a tailored approach in both regulatory oversight and internal risk management practices. Regulators should take these variations into account when creating policies aimed at promoting financial stability, ensuring that capital and liquidity requirements support banks across the entire performance spectrum without hindering growth or encouraging excessive risk-taking.

Furthermore, the findings stress the importance for banks to implement dynamic risk management strategies that reflect their specific position within the profitability distribution. Highly profitable banks may need to focus on mitigating the negative impacts of increased competition and financial liberalization by improving operational efficiency and diversifying risk exposures. Conversely, lower-performing banks should prioritize enhancing asset quality and maintaining sufficient capital buffers to protect against vulnerabilities that could threaten their viability.

Overall, this study contributes to a better understanding of how financial liberalization and bank-specific characteristics interact to influence profitability, offering valuable insights for policymakers and financial institutions seeking to balance growth, stability, and risk in evolving financial markets.

ACKNOWLEDGMENT

We would like to thank to the reviewers for the thoughtful comments and suggestions for the improvement of this paper.

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