Bank return heterogeneity, do governance, sentiment, and uncertainty matter?

Shah, Syed Faisal; Mohamed Al-Baity Article Bank return heterogeneity, do governance, sentiment, and uncertainty matter? Cogent Economics & Finance Provided in Cooperation with: Taylor & Francis Group Suggested Citation: Shah, Syed Faisal; Mohamed Al-Baity (2022) : Bank return heterogeneity, do governance, sentiment, and uncertainty matter?, Cogent Economics & Finance, ISSN 2332-2039, Taylor & Francis, Abingdon, Vol. 10, Iss. 1, pp. 1-32, https://doi.org/10.1080/23322039.2022.2150133 This Version is available at: https://hdl.handle.net/10419/303883 Standard-Nutzungsbedingungen: Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden. Sie dürfen die Dokumente nicht für öffentliche oder kommerzielle Zwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglich machen, vertreiben oder anderweitig nutzen. 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Syed Faisal Shah & Mohamed Albaity To cite this article: Syed Faisal Shah & Mohamed Albaity (2022) Bank return heterogeneity, do governance, sentiment, and uncertainty matter?, Cogent Economics & Finance, 10:1, 2150133, DOI: 10.1080/23322039.2022.2150133 To link to this article: https://doi.org/10.1080/23322039.2022.2150133 © 2022 The Author(s). This open access article is distributed under a Creative Commons Attribution (CC-BY) 4.0 license. Published online: 29 Nov 2022. Submit your article to this journal Article views: 793 View related articles View Crossmark data Citing articles: 1 View citing articles Full Terms & Conditions of access and use can be found at https://www.tandfonline.com/action/journalInformation?journalCode=oaef20 FINANCIAL ECONOMICS | RESEARCH ARTICLE Bank return heterogeneity, do governance, sentiment, and uncertainty matter? Syed Faisal Shah 1 * and Mohamed Albaity 2 Abstract: This paper examined the impacts of; investor sentiment, governance, and uncertainty on bank stock returns in the Middle East and North Africa (MENA) and Gulf Cooperation Council (GCC) region countries. The sample consisted of 173 conventional and Islamic banks based in the MENA region and 68 conventional and Islamic banks based in the GCC region from 2010–2020. Also, this study employed the Two-step system Generalized Method of Moments (GMM) estimator. The selection of this estimator prevented endogeneity issues related to the variables used in this study. This research found that individual sentiment and uncertainty negatively affected bank stock returns while governance positively influenced bank stock returns. The regression coefficients from the interaction of the governance indicators and conventional banks variable showed a positive and significant effect on bank stock returns in the MENA region, except for the interaction of the rule of law and voice and accountability in conventional banks, showing a negative effect. The GCC countries showed similar results. However, the outcomes were insignificant. Regarding the control variables, the loan ratio and inflation were negative, and bank size and the GDP showed positive and significant effects on bank stock returns throughout all models, excluding the loan ratio and bank size in the GCC region. Overall, the banking sectors of the MENA region countries were sensitive to; investor sentiment, uncertainty, and country-level governance indicators. Subjects: Economics; Finance; Business, Management and Accounting Keywords: Investor sentiment; governance; uncertainty; MENA; stock return 1. Introduction As a result of the 2008 Global Financial Crisis, bank shareholders experienced severe losses on their investments. The collapse of the US stock market and banking industry during the credit crisis was partially blamed on poor bank governance (Berger & Bouwman, 2013; Fahlenbrach & Stulz, 2011). Recent studies have tried to explain the poor performance of bank stocks during the Global Financial Crisis. They have mainly concentrated on the differences in; business models, governance, regulations and capital structures of banks (Berger & Bouwman, 2013; Fahlenbrach & Stulz, 2011) and investor crisis sentiment (Irresberger et al., 2015). A lack of efficient country-level governance in monitoring and managing stock market volatility can cause increases in credit that might not be recovered promptly, potentially leading to a financial crisis. The most highlighted failure during the 2008 Global Financial Crisis was the collapse of Lehman Brothers due to the spread of credit from banks in the United States (Albaity et al., 2020). In addition, the more recent COVID-19 pandemic caused a massive financial shock to the economy of the United States (Mazur et al., 2021). Most financial sectors were not fully Shah & Albaity, Cogent Economics & Finance (2022), 10: 2150133 https://doi.org/10.1080/23322039.2022.2150133 Page 1 of 32 Received: 24 November 2021 Accepted: 17 November 2022 *Corresponding author: Syed Faisal Shah, College of Business, University of Sharjah, Sharjah, United Arab Emirates E-mail: [email protected] Reviewing editor: David McMillan, Department of Finance and Economics, University of Stirling, Stirling, United Kingdom Additional information is available at the end of the article © 2022 The Author(s). This open access article is distributed under a Creative Commons Attribution (CC-BY) 4.0 license. operational due to employees being quarantined, which resulted in a decline in stock returns. As a result, financial sector businesses laid off their labour forces to reduce costs. This situation led to a significant reduction in consumption and economic output (Mazur et al., 2021) and an increase in the saving behaviour of individuals who sought to reserve resources to deal with uncertainty and risk (Jin et al., 2021). Stock markets suffered (stock prices collapsed) from the uncertainty surrounding the COVID-19 pandemic (Mazur et al., 2021). Besides, banks’ financial positions suffered from the negative outcomes of the COVID-19 pandemic (Demir & Danisman, 2021). Moreover, Elnahass et al. (2021) revealed that the COVID-19 pandemic adversely affected; banks’ revenue, stock market valuations, and the overall economy. The pandemicfueled uncertainty had a negative and statistically significant effect on stock returns & risk (reward-to-variability ratio) in the stock markets of the G7 countries (Loudon, 2017). The most common and widely used factors used to measure uncertainty affecting stock returns comprise; economic uncertainty (Ahmad & Sharma, 2018), financial market uncertainty (Chiang et al., 2015), political uncertainty (Hillier & Loncan, 2019), policy uncertainty (Ftiti & Hadhri, 2019; Kang & Ratti, 2015; Xiong et al., 2018) and severe depressions causing uncertain shocks (Mathy, 2016). There is high responsiveness between stocks, economic uncertainty, and high trading due to overpricing, which may generate lower future stock returns (Yun et al., 2019). Investor sentiment is commonly explained as market participants’ expectations connected to a norm (Brown & Cliff, 2004; Singer et al., 2013). Baker and Wurgler (2007) stated that investor sentiment backed future cash flows and investment risk. Many studies have found that investor sentiment has had a positive and statistically significant impact on stock returns (Baker & Wurgler, 2006; Dash & Maitra, 2018; Gao & Yang, 2018; G. Wang et al., 2020; W. Y. Lee et al., 2002). At the same time, several papers have revealed that the spread in investor sentiment had an inverse relationship on future stock returns and realised volatility due to a lack of information about stock returns making it harder to predict stock returns (Fisher & Statman, 2000; See-To & Yang, 2017). Finally, country-level governance has received little attention in the banking industry, especially in the MENA region. Few studies have tried to enlarge on and test the influence of country-level governance on the efficiency of the banking sector (Chortareas et al., 2012; Kamarudin et al., 2016, 2018; Lensink et al., 2008). Good governance maintains and improves trust in the banking sector; therefore, improved levels of good governance promote bank stability and increase stock returns (Albaity et al., 2020). Macro governance elements, such as; political stability, regulatory quality, government effectiveness, control of corruption, the rule of law, and voice and accountability, form institutional quality and play an essential role in improving efficiency in the banking sector (Albaity et al., 2020; Chan et al., 2015; Uddin et al., 2020). Several studies have found that macro governance statistically impacted bank stock returns (Chortareas et al., 2012; Kamarudin et al., 2016, 2018). This paper was motivated by the research of (Kamarudin et al., 2018), which focused on governance in the banking sector, and (Di et al., 2021), which focused on investor sentiment in the banking sector. The present study used several sources for data collection (Appendix A). The sample data covered 17 countries from the MENA region, including six GCC countries. The MENA and GCC regions were selected due to their unique; social norms, state roles, geography, cultural identity, social conflicts and certain governance regulations (Albaity et al., 2020). Oil-producing countries comprised 53% of the sample (Mertzanis et al., 2019). The regions have also experienced their share of economic instability and political conflict (Arab spring, debt crisis and unemployment (Awartani et al., 2016). The sample consisted of 173 listed banks, 49 Islamic and 124 conventional banks in the MENA region. In addition, 40 conventional and 28 Islamic banks in GCC countries were part of the MENA region sample, using data from 2010 to 2020. This study offers an important contribution to the existing literature. This paper aimed to examine; investor sentiment, uncertainty, and governance indicators on bank stock returns in the MENA countries. In addition, this research investigated the interaction effects of governance indicators and conventional banks on bank stock returns in the MENA countries. Shah & Albaity, Cogent Economics & Finance (2022), 10: 2150133 https://doi.org/10.1080/23322039.2022.2150133 Page 2 of 32 The remainder of this paper is structured as follows. Section 2 provides a review of the existing literature related to the topic of this study. Section 3 provides specific details regarding the; sample data, variables, hypotheses, and the methodology employed in the analysis. Section 4 presents the results of the empirical findings and hypothesis testing. Finally, Section 5 concludes this paper and provides insights for possible future studies. 2. Related literature According to Fama (1970), the efficient market hypothesis reflects all available private and public information. While the alternative hypothesis (theory) of behavioural finance doesn’t signify all the available information, meaning that the market is inefficient (Fama, 2021). On the other hand, Daniel et al. (1998), Kahneman (2011), and Odean (1998) have challenged the efficient market hypothesis. Investor decision-making relies on the way that information is obtained and processed. Psychological biases (i.e., overconfidence, belief persistence, and over-optimism) negatively influence investment decisions (Daniel et al., 1998; Kahneman, 2011; Odean, 1998; Shah et al., 2020). Similarly, investors follow stocks under media attention and invest in a bid to make a profit (Kaniel et al., 2008). Kahneman (1973) stated that investors could not gather all information about all stocks. However, higher levels of reliable information clarify and reduce the differences between investors’ expected and actual returns and market inefficiency (H. K. Baker & Nofsinger, 2010). 2.1. Investor sentiment & stock returns Investor sentiment is investors’ belief concerning future cash flows and investment risk (Baker & Wurgler, 2007). Several strands of literature have investigated the association between investor sentiment and stock returns from various perspectives. Di et al. (2021) attempted to evaluate the stock performance sensitivity of Middle Eastern and Asian countries to changes in the size of the impact of investor sentiment. They obtained sentiment data from Google Trends and stock return data from the Bloomberg database. The outcome of their analysis showed that the impact of investor sentiment on bank stock performance was uneven. Di et al. (2021) and W. Y. Lee et al. (2002) also stated that investors’ optimism strongly influenced bank stock returns. Besides, Shah and Albaity (2022) conducted a study on the MENA and GCC countries and found that market sentiment had a positive and significant influence on bank stock returns, while individual sentiment had a negative and significant effect on bank stock returns in the MENA region. Investor sentiment had a positive and statistically significant relationship with stock returns in China (Gao & Yang, 2018; G. Wang et al., 2020), and another study used three indices DJIA, S&P500, and NASDAQ (W. Y. Lee et al., 2002). While, Albaity et al. (2022) examined the impact of COVID-19 investor sentiment on bank stock returns in 16 MENA region countries, and the result showed that COVID-19 investor sentiment had no significant effect on bank stock market returns. G. Wang et al. (2020) studied individual and institutional investors in the Shanghai and Shenzhen Stock Exchanges. They revealed that investor sentiment positively influenced stock returns, but only for individual investors. On the contrary, Huang et al. (2015) and Irresberger et al. (2015) found a negative and statistically significant influence of investor sentiment on stock returns. Similarly, Corredor et al. (2015) and Schmeling (2009) claimed that investor emotion negatively impacted stock returns. The negative relationship could be the reason for information spread, triggering investment momentum, and experience creating overreaction. As a result, investors bought too much stock (Di et al., 2021; Hong & Stein, 1999). Furthermore, investor pessimism negatively impacted the stock market while positively affecting stock market volatility and trading volume (Dimpfl & Kleiman, 2019). Sul et al. (2017) analysed investor sentiment data from Twitter and revealed that it significantly positively and negatively affected stock returns. Finally, Chu et al. (2016) showed that investor sentiment had no impact on stock returns. Shah & Albaity, Cogent Economics & Finance (2022), 10: 2150133 https://doi.org/10.1080/23322039.2022.2150133 Page 3 of 32 2.2. Governance and stock returns A country’s efficient governance system supports firms in improving their financial performance (Albaity et al., 2020). Macro governance elements, such as; political stability, regulatory quality, government effectiveness, control of corruption, the rule of law, and voice and accountability, form institutional quality and play an essential role in improving efficiency in the banking sector (Albaity et al., 2020; Chan et al., 2015; Uddin et al., 2020). Similarly, governance is one mechanism that promotes bank efficiency (W.-K. Wang et al., 2012) and influences bank performance. Chortareas et al. (2012) pointed out that country governance (macro governance) in a particular country-level governance indicator scientifically impacted banks’ performance in 22 EU countries from 2000– 2008. In terms of macro governance elements, (Kamarudin et al., 2016) found positive effects of voice and accountability on bank performance in the GCC region countries and suggested that citizens and state institutions should promote democracy and mitigate poverty which would enhance bank performance. Kamarudin et al. (2016) claimed that citizens having greater freedom in selecting their government would improve bank efficiency. Voice and accountability tended to raise the level of media independence; as a result, information quality improved concerning local developments (Lensink et al., 2008). Information quality helps both foreign and local banks to increase their efficiency. Hwang and Akdede (2011) investigated the influence of governance effectiveness on efficiency in the public sector and found a positive relationship. Likewise, greater government credibility positively affected Islamic and conventional banks’ revenue efficiency in the GCC region (Kamarudin et al., 2016). Also, they spotted that government trustworthiness was gained through devising and applying policies and regulations for local and foreign businesses; eventually, it promoted bank performance. The banking sector needs regulatory quality to ensure the reliability of the financial system (Albaity et al., 2020). Regulatory quality, including formulating and implementing reliable bank-efficient policies and regulations, may improve and develop the banking sector (Albaity et al., 2020; Kamarudin et al., 2016). Furthermore, effective regulatory quality may provide the professional handling of bureaucracy and accountability of government employees, increasing bank performance. Therefore, better regulatory quality was positively correlated with bank performance when considering banks worldwide during Global Financial Crisis (2007–2008; Beltratti & Stulz, 2009). Kamarudin et al. (2018) claimed a positive association between the rule of law and bank efficiency levels in the Gulf countries regarding the impact of the rule of law. The quality of the rule of law affects the cost efficiency of banks where the judiciary is involved (Lensink et al., 2008). Also, they stated that judgment delays led to cost inefficiency; therefore, the quality of the rule of law promoted bank performance in 105 countries, including MENA region countries, between 1998–2003. The improved rule of law and a better judiciary system would benefit banks in the GCC region by reducing uncertainty and risk when starting businesses and raising private sector investment, helping to improve bank efficiency and the overall market (Kamarudin et al., 2016). Political stability and the absence of violence may improve bank performance due to politicians using their influence to promote social welfare. This situation might result in cost reductions and remove asymmetric information helping banks receive funds and provide loans efficiently (Kamarudin et al., 2016). Equally, political stability improved the banking sector’s efficiency level (Albaity et al., 2020). The positive relationship between political stability and bank performance may result in more efficient handling of deposit and loan transactions (Kamarudin et al., 2016). In terms of the control of corruption, banks perform better in non-corrupt markets (Chortareas et al., 2012). Equally, a higher level of control of corruption leads to better public sector efficiency in; administration, stability and infrastructure (Hwang & Akdede, 2011). Strong agency supervision reduces corruption, enhances monitoring, and discipline and, thus, improves overall bank efficiency (Kamarudin et al., 2016). On the other hand, banks based in highly corrupt countries suffer from Shah & Albaity, Cogent Economics & Finance (2022), 10: 2150133 https://doi.org/10.1080/23322039.2022.2150133 Page 4 of 32 high loan debts. Corruption also badly affects investment and financing decisions in the MENA region (Albaity et al., 2020) and ASEAN (Chan et al., 2015). 2.3. Uncertainty and stock returns The conditional volatility of a predictable disruption from an economic agent’s viewpoint is known as uncertainty (Jurado et al., 2015; Yun et al., 2019). The Great Depression was a disastrous time in American history that resulted in significant output, economic and employment declines. In addition, Mathy (2016) examined the key events of the depression that triggered a crisis in the banking sector. The results revealed that the Great Depression unfavourably affected households and businesses with a decline in income; however, uncertainty shocks may result in a return spread. As per Loudon (2017), considerable variability in stock returns was found in the G7 countries’ stock markets between 1973–2013 due to the level of uncertainty caused by different events. Different types of uncertainty may cause negative effects on stock returns, such as; stock market uncertainty (Loudon, 2017), economic uncertainty (Yun et al., 2019), political uncertainty (Hillier & Loncan, 2019), economic policy uncertainty (Ahmad & Sharma, 2018; Kang & Ratti, 2015; Xiong et al., 2018) and pandemics, such as COVID-19 (Xu, 2021). Political instability causes business uncertainty (Hillier & Loncan, 2019; Liu et al., 2017), which may cause capital flight (Alesina & Tabellini, 1989; Hillier & Loncan, 2019). The rise in COVID-19 cases negatively impacted Canada’s and the United States’ stock returns. Stock responses were asymmetric on the rise and decline in COVID-19 cases in Canada (Xu, 2021). According to Szczygielski et al. (2021), Asian markets showed resilience to COVID-19 uncertainty; on the other hand, European and North & Latin American markets experienced lowering effects from COVID-19 uncertainty over time. Besides, investors, risk-averse behaviour might enhance volatility in stock markets. Still, on the other hand (Chiang et al., 2015) found that a rise in risky behaviour may lead to positive results and investors expecting high returns. Moreover, the negative relationship between uncertainty and stock returns could result from behavioural factors, for instance, investor sentiment, overconfidence, and overoptimism (Loudon, 2017). Finally, Di et al. (2021) discovered that negative crisis sentiment terms caused statistically significant and negative effects on bank stock returns during financial crises. 2.4. Research gap Several studies have found that investor sentiment has influenced stock returns worldwide. Such affected countries include; some Middle Eastern and Asian countries (Di et al., 2021), China (Gao & Yang, 2018; Gong et al., 2022; G. G. Wang et al., 2020), India (Dash & Maitra, 2018), MENA countries (Albaity et al., 2022), United States (Gao et al., 2020; Renault, 2017). Therefore, this study identified the following research gaps. In the MENA region, there hasn’t been a reliable and available sentiment index that can be used to measure the impact of investor sentiment on bank returns. In addition, most MENA countries are Arab speaking, and they are challenged to find a reliable sentiment index. Thus, this paper has created a sentiment index for the MENA region using Google Trends comprising 89 terms, including Arabic terms, covering ten years. The index comprised 16,020 observations. Many studies have identified that governance indicators have a significant impact on stock returns in different countries, i.e. MENA countries (Albaity et al., 2020), GCC countries (Kamarudin et al., 2016), ASEAN-5 (Chan et al., 2015, Pakistan (Islam & Bilal, 2021), the United States (Krishnan & Wu, 2022). In terms of uncertainty, several studies have found that uncertainty has significantly impacted stock returns in various countries. The noted countries include; the G7 countries (Loudon, 2017), the United States (Escobari & Jafarinejad, 2019), South Korea (Yun et al., 2019), Brazil (Hillier & Loncan, 2019), China (Xiong et al., 2018), and the MENA countries (Albaity et al., 2022; Chau et al., 2014). Consequently, this study has identified the following research gaps. MENA countries have experienced much political turmoil and economic uncertainty since 2008 (e.g., Arab Spring, debt crisis, and oil price fluctuations; Awartani et al., 2016). Hence, governance institutions in MENA countries have developed to ensure that markets are governed, and information is Shah & Albaity, Cogent Economics & Finance (2022), 10: 2150133 https://doi.org/10.1080/23322039.2022.2150133 Page 5 of 32 transmitted accordingly. Thus, this paper has built on and expanded Albaity et al. (2020) and Albaity et al. (2022) by including more MENA countries and a different set of control variables over a longer period to confirm the previous results. 3. Sampling and methodology This paper measured the influence of; investor sentiment, uncertainty, and governance on bank stock returns. In addition, the research has highlighted the effects of the interaction between a dummy variable (conventional banks) and selected governance indicators. The System Generalized Method of Moments (GMM) estimator was employed to examine a sample comprising 173 banks from the MENA region, including 68 banks in GCC countries, with data collected between 2010 to 2020. 3.1. Sampling Initially, a list of 534 banks’ data was gathered using consolidated statements in the MENA region. Upon further filtering, 361 banks were excluded from the sample due to missing data. Hence, the final sample comprised 173 banks operating between 2010–2020. 3.2. Two-step system GMM estimator The Generalized Method of Moments (GMM) approach is a well-known statistical technique developed by Arellano and Bond (1991). The selection of the two-step System (GMM) estimator prevented endogeneity issues related to the variables used in this study. The GMM estimator merges observed numerical economic data with population moment conditions to examine unknown parameters in an economic model (Albaity et al., 2020; Masoud & Albaity, 2021; Zsohar, 2012). The method’s reliability lies in implementing robustness measures to meet the assumption of errors in serially uncorrelated data (Abrigo & Love, 2016; Kamarudin et al., 2016; Masoud & Albaity, 2021). The models’ validity and freedom from misspecification were ensured through two tests. First, the Hansen test was used to examine the overidentifying restrictions of the instruments and ensuring that the procedures were followed (Hansen, 1982). The second test was the ArellanoBond (AR) test, which was employed to test the hypothesis of no correlation. The first order of the AR1 test of autocorrelation should not signal inconsistencies in the estimates, while the second order of the AR2 test confirmed the AR1 test. The analytical model is discussed below: BUHi;j;t¼α0þα1BUHi;j;t1ð Þ þα2ISEj;tþα3MSEj;tþα4UNCj;tþα5QoGj;tþα6ROEi;j;t þα7TLTAi;j;tþα8sizei;j;tþα9NIIi;j;tþα10OPRi;j;tþα11GLTAi;j;tþα12GDPj;t þα13INFj;tþα14Dumi;j;tþεi;j;t (1) The endogenous variable was bank buy and hold stock returns (BUH), the proxy for bank stock performance. The lagged buy and hold return as independent variable (BUH t1). Investor sentiment comprised individual (ISE) and market sentiment (MSE). The other independent variables were the quality of governance indicators (QoG) and uncertainty (UNC). Moreover, several control variables were included in this study, i.e., the return on equity (ROE), total liabilities (TLS), bank size (Size), non-interest income (NII), operating revenue (OPR), gross loans (GLS), GDP growth (GDP) and inflation (INF). A dummy control variable was created, with 1 signifying conventional banks and 0 signifying Islamic banks. The subscripts i, j and t referred to bank, country, and time, respectively. Lastly, α0andα15 were coefficients, and ε was an error term. BUHi;j;t¼α0þα1BUHi;j;t1ð Þ þα2ISEj;tþα3MSEj;tþα4UNCj;tþα5QoGj;tþα6ROEi;j;t þα7TLTAi;j;tþα8sizei;j;tþα9NIIi;j;tþα10OPRi;j;tþα11GLTAi;j;tþα12GDPj;t þα13INFj;tþα14Dumi;j;tþα15QoGj;t�Dumj;tþεi;j;t (2) Shah & Albaity, Cogent Economics & Finance (2022), 10: 2150133 https://doi.org/10.1080/23322039.2022.2150133 Page 6 of 32 Table 1. Descriptive statistics Country BUH ∆ISEN ∆MSEN ∆UNC RUL GOE REQ PSV COC VOA ROE TLTA GLTA size NII ∆OPR GDP INF United Arab Emirates 0.057 0.609 0.442 25.753 0.657 1.286 0.823 0.760 1.126 −1.037 7.520 0.804 0.661 16.399 40.070 0.001 3.576 1.143 0.370 1.560 1.444 80.484 0.163 0.193 0.250 0.109 0.097 0.079 14.902 0.175 0.091 1.322 19.623 0.002 1.794 1.784 Bahrain 0.022 0.659 0.245 0.000 0.403 0.424 0.618 −0.915 0.131 −1.302 7.073 0.827 0.528 15.500 52.795 0.001 3.375 1.939 0.335 1.181 1.039 0.000 0.066 0.152 0.132 0.228 0.195 0.125 8.996 0.106 0.143 1.242 22.803 0.005 1.219 1.027 Egypt 0.019 1.307 0.168 0.000 −0.489 −0.650 −0.671 −1.364 −0.624 −1.171 20.526 0.907 0.417 15.188 30.165 0.001 3.801 11.253 0.428 3.519 0.655 0.000 0.133 0.163 0.244 0.230 0.045 0.166 11.688 0.029 0.123 0.732 19.047 0.002 1.346 6.372 Israel 0.081 0.141 0.717 14.533 1.025 1.312 1.248 −1.022 0.880 0.682 8.126 0.935 0.671 17.560 38.591 0.001 3.728 1.064 .0247 0.788 1.702 42.184 0.073 0.068 0.050 0.172 0.119 0.056 2.254 0.008 0.065 1.114 6.206 0.001 0.970 1.262 Iraq −0.074 0.474 3.471 19.484 −1.542 −1.208 −1.179 −2.247 −1.312 −1.046 4.158 0.505 0.281 12.980 82.830 0.005 5.522 2.211 0.356 1.755 12.840 63.051 0.134 0.087 0.068 0.235 0.074 0.060 6.127 0.183 0.166 0.674 33.659 0.031 5.580 2.123 Iran 0.168 −0.137 2.987 6.195 −0.868 −0.412 −1.414 −1.221 −0.805 −1.482 11.013 0.939 0.634 16.652 75.460 0.002 0.843 20.255 0.911 0.450 6.210 60.513 0.144 0.158 0.142 0.305 0.144 0.127 26.931 0.037 0.060 1.228 41.100 0.007 6.272 11.213 Jordon −0.001 0.272 0.621 36.380 0.304 0.083 0.122 −0.486 0.148 −0.746 8.769 0.819 0.578 14.744 34.625 0.001 2.394 2.814 0.167 1.082 4.511 189.853 0.103 0.058 0.085 0.093 0.081 0.045 4.441 0.157 0.156 1.535 31.417 0.002 0.428 2.165 Kuwait 0.009 0.607 0.968 17.504 0.245 −0.054 −0.023 0.128 −0.148 −0.616 5.367 0.794 0.559 15.974 41.463 0.001 1.601 2.778 0.251 1.675 2.450 71.184 0.206 0.100 0.100 0.182 0.186 0.044 10.044 0.191 0.237 1.442 26.896 0.003 3.901 1.231 Lebanon −0.038 0.057 2.173 26.845 −0.779 −0.466 −0.226 −1.644 −0.973 −0.448 8.752 0.893 0.269 15.457 33.788 0.001 1.162 10.488 0.259 0.841 6.332 103.176 0.052 0.167 0.159 0.043 0.098 0.071 7.947 0.035 0.099 1.815 14.821 0.005 3.623 23.653 Morocco 0.022 1.659 3.510 20.760 −0.161 −0.111 −0.166 −0.392 −0.266 −0.660 10.132 0.898 0.691 16.651 28.552 0.001 3.475 1.157 0.195 4.343 10.836 164.496 0.055 0.056 0.068 0.057 0.101 0.036 3.270 0.017 0.104 0.919 9.178 0.003 1.187 0.569 Malta −0.051 0.060 −0.135 0.000 1.186 1.056 1.238 1.132 0.752 1.162 8.104 0.908 0.489 15.221 46.299 0.000 5.485 1.425 0.147 0.345 0.166 0.000 0.152 0.150 0.139 0.091 0.208 0.029 3.536 0.029 0.069 1.171 16.689 0.001 2.450 0.749 Oman −0.057 0.594 0.775 36.858 0.475 0.229 0.448 0.623 0.267 −1.060 8.783 0.818 0.736 15.389 28.600 0.001 3.017 1.610 0.152 1.519 2.151 211.301 0.055 0.073 0.149 0.130 0.090 0.032 5.245 0.134 0.134 1.190 17.060 0.004 2.890 1.291 (Continued) Shah & Albaity, Cogent Economics & Finance (2022), 10: 2150133 https://doi.org/10.1080/23322039.2022.2150133 Page 7 of 32 Table 2. Effect of Investor Sentiment, Uncertainty, and Governance on bank stock return in MENA region VARIABLES BUH BUH BUH BUH BUH BUH BUH BUH BUH BUH BUH BUH L.BUH (t-1) −0.5317*** −0.5762*** −0.5517*** −0.5835*** −0.6027*** −0.5514*** −0.5092** −0.5180*** −0.4661*** −0.6616*** −0.4211** −0.5963*** −0.133 −0.1429 −0.1435 −0.1455 −0.1382 −0.131 −0.2056 −0.1349 −0.1406 −0.1994 −0.1712 −0.1273 ∆ISEN −0.0434*** −0.0416*** −0.0489*** −0.0444*** −0.0579*** −0.0429*** −0.0797*** −0.0396*** −0.0393*** −0.0611*** −0.0731*** −0.0472*** −0.0111 −0.0123 −0.0133 −0.0135 −0.0123 −0.013 −0.0176 −0.0128 −0.0124 −0.0163 −0.0193 −0.0166 ∆MSEN 0.0039 0.005 0.0103 0.0059 0.0041 0.0089 −0.0004 0.0077 0.0085 0.0145* 0.0215** 0.0070** −0.0031 −0.0031 −0.0085 −0.0044 −0.0025 −0.0072 −0.0128 −0.0053 −0.006 −0.0076 −0.01 −0.0032 ∆UNC −0.0002** −0.0002** −0.0003** −0.0002** −0.0002** −0.0002** −0.0004** −0.0002* −0.0002** −0.0004** −0.0001 −0.0002 −0.0001 −0.0001 −0.0001 −0.0001 −0.0001 −0.0001 −0.0002 −0.0001 −0.0001 −0.0002 −0.0001 −0.0001 ROE 0.0088*** 0.0090*** 0.0079*** 0.0093*** 0.0092*** 0.0091*** 0.0045 0.0109*** 0.0113*** 0.0086** 0.0092*** 0.0087*** −0.0025 −0.0026 −0.0029 −0.0028 −0.0025 −0.0027 −0.0034 −0.0023 −0.0027 −0.0034 −0.0031 −0.0023 TLTA −0.1458 −0.1253 −0.0575 0.0418 −0.064 −0.0347 −0.3006 0.2457 0.0081 −0.5287 0.3332 0.2846 −0.13 −0.1212 −0.1267 −0.1445 −0.1283 −0.1622 −0.3209 −0.291 −0.1931 −0.3626 −0.312 −0.27 GLTA −0.1544** −0.1069 −0.0555 −0.0042 −0.1619** 0.1184 0.0331 −0.1910* −0.0732 0.2077 −0.2799** 0.0076 −0.0736 −0.0661 −0.0715 −0.0855 −0.0724 −0.0859 −0.1679 −0.113 −0.0804 −0.2472 −0.1359 −0.1141 size 0.0078 0.0004 0.0015 0.0147 0.0091 0.0234** 0.0409** −0.0128 −0.0143 0.034 −0.0072 0.0330* −0.008 −0.0071 −0.0079 −0.009 −0.0076 −0.0095 −0.0157 −0.0139 −0.0122 −0.0207 −0.0156 −0.0177 NII 0.0007 0 0.0005 0.0001 0.0004 −0.0006 0.0035* −0.0011 −0.0008 0.0025* −0.0012 0.001 −0.0006 −0.0005 −0.0006 −0.0006 −0.0005 −0.0006 −0.0018 −0.0009 −0.0009 −0.0014 −0.0012 −0.0014 ∆OPR −5.2153 −5.929 −3.8876 −4.6612 −5.5743 −5.0546 −4.5586 −6.3886 −7.0868 −5.374 −5.1689 −2.5704 −4.2632 −4.1292 −4.4323 −4.8409 −4.8588 −4.6203 −7.1398 −5.2334 −5.4913 −6.7586 −7.8831 −4.7645 Dum_co −0.0301 −0.0314 −0.0598** −0.0085 −0.016 −0.0242 0.1118 −0.1466 −0.0506 −0.3009* −0.0924 −1.2206* −0.0306 −0.0309 −0.0296 −0.0386 −0.0302 −0.0357 −0.105 −0.1017 −0.0614 −0.1592 −0.1091 −0.6341 GDP 0.0058 0.0025 0.0066 0.0032 0.0029 0.0053 0.0118* 0.0065 0.0056 0.0003 0.0162** 0.0132** −0.0051 −0.0046 −0.005 −0.0048 −0.0047 −0.0047 −0.0064 −0.0073 −0.0067 −0.0072 −0.0081 −0.0052 INF 0.0066 0.006 0.0079 0.0023 0.0073* 0.0019 0.0318** −0.0017 −0.0075 0.0244* −0.0074 0.0116 (Continued) Shah & Albaity, Cogent Economics & Finance (2022), 10: 2150133 https://doi.org/10.1080/23322039.2022.2150133 Page 14 of 32 Table 2. (Continued) VARIABLES BUH BUH BUH BUH BUH BUH BUH BUH BUH BUH BUH BUH −0.0048 −0.0041 −0.0053 −0.0041 −0.004 −0.004 −0.014 −0.0077 −0.0086 −0.0145 −0.0085 −0.0097 RUL 0.1707*** 1.0737** −0.0303 −0.4232 GOE 0.1695*** −0.1913 −0.0282 −0.262 REQ 0.1951*** −0.209 −0.036 −0.1593 PSV 0.0443** 0.8344** −0.0208 −0.4256 COC 0.1616*** −0.3025 −0.0256 −0.2769 VOA 0.0352* 1.0555* −0.0205 −0.5776 c.Dum_co#c. RUL −1.1528** −0.5086 c.Dum_co#c. GOE 0.4971* −0.2882 c.Dum_co#c. REQ 0.5221** −0.2222 c.Dum_co#c. PSV −0.9514* −0.5112 c.Dum_co#c. COC 0.5933* (Continued) Shah & Albaity, Cogent Economics & Finance (2022), 10: 2150133 https://doi.org/10.1080/23322039.2022.2150133 Page 15 of 32 Table 2. (Continued) VARIABLES BUH BUH BUH BUH BUH BUH BUH BUH BUH BUH BUH BUH −0.3381 c.Dum_co#c. VOA −1.1607* −0.6268 Observations 606 606 606 606 606 606 606 606 606 606 606 606 Number of ID 122 122 122 122 122 122 122 122 122 122 122 122 AR (1) 0.075 0.079 0.068 0.073 0.034 0.098 0.039 0.091 0.064 0.017 0.01 0.064 AR (2) 0.411 0.463 0.304 0.377 0.236 0.414 0.134 0.454 0.281 0.162 0.127 0.61 Hansen Test 0.142 0.144 0.181 0.134 0.129 0.347 0.477 0.515 0.154 0.424 0.187 0.107 VIF 1.43 1.39 1.44 1.4 1.4 1.35 1.91 1.94 1.92 1.95 1.95 3.52 The dependent variable is banks buy and hold stock return (BUH).The L. BUH t-1 is the lagged of buy and hold stock returns. Δ ISE and Δ MSE indicate the growth rate of individual sentiment and market sentiment, respectively. Δ UNC is the growth of uncertainty in the country. The QoG is each of the six quality of governance indicators namely the voice and accountability VOA, the political stability PSV, the government effectiveness GOE, the regulatory quality REQ, the control of corruption COC, and the rule of law RUL. ROE, GLTA, SIZE, NII and TLTA are the return of equity, gross loans by total assets, size of the bank non-interest income, total liabilities by total assets, respectively. Each of the QoG indicators interacted with conventional banks (Dum_co). The GDP and INF indicates the growth rate of GDP and inflation, respectively. Finally, Dum_co variable consist of conventional banks:1 and Islamic banks:0. Notes: ***, **, and * indicate statistical significance at the 1%, 5%, and 10% levels, respectively. Number in parentheses are p-values. Shah & Albaity, Cogent Economics & Finance (2022), 10: 2150133 https://doi.org/10.1080/23322039.2022.2150133 Page 16 of 32 result of the second order of the Arellano-Bond autocorrelation test was insignificant. This outcome meant there was no autocorrelation in the models. Also, the second-order Hansen test result was insignificant, which showed that the test statistics rejected the alternative hypothesis and accepted the null hypothesis that the instruments were exogenous in all cases. It can be seen in Table 2 that the lagged stock returns and current returns were negative and significant, hinting that the previous higher values of the lagged returns lowered the current returns. 4.2.1.1 Does investor sentiment influence banks’ performance?. The results of the Google Trends search on investor sentiment was the gauge of investor sentiment, which considered the growth of individual and market sentiment. Thus, investor sentiment and bank stock returns were expected to have a negative relationship. The results showed that the growth of individual sentiment had a weakly negative and significant relationship with bank stock returns, consistent with the results of (Petit et al., 2019; Shah & Albaity, 2022). This outcome meant that individual sentiment was sensitive throughout the models in the MENA region. At the same time, market sentiment was positive and insignificant in most models, except for Models 10, 11 and 12. Similarly, (Shah & Albaity, 2022) found positive but statistically significant results. As a result, investor sentiment had a negative and significant impact on stock returns, which was in line with the findings of (Di et al., 2021; Irresberger et al., 2015), while (Shah & Albaity, 2022) found mixed results. The results in the models supported the proposed hypotheses. Following the behavioural finance theory, cognition factors influence investors’ decision-making, and emotionally motivated investors can alter the price from its fundamental value (G. Wang et al., 2020). Such price alteration can lead to either an upward or downward trend of stock returns. In this paper, investor sentiment indicated a downward trend and a decline in stock returns. 4.2.1.2 The impact of uncertainty on bank stock returns. The result of the exogenous uncertainty variable showed that the percentage change in uncertainty was statistically significant and weakly but negatively influenced stock returns in all models, which supported the proposed hypothesis and was in line with the results of (Ahmad & Sharma, 2018; Di et al., 2021; Hillier & Loncan, 2019; Kang & Ratti, 2015; Loudon, 2017; Xiong et al., 2018; Yun et al., 2019). On the contrary, Shah and Albaity (2022) found uncertainty’s positive and significant effect on bank stock returns in the Middle East and North Africa region. According to Mathy (2016), uncertainty shocks represent an element of risk, and the inverse relationship between uncertainty and stock returns may result in risk-averse behaviour (Chiang et al., 2015). These results could be explained, in part, by the fact that MENA region investors are risk-averse relative to political and economic uncertainty and, as a result, avoid investing in the stock market when this risk rises. A similar explanation was given by (Hoque & Zaidi, 2020), referring to global geopolitical risk uncertainty. Policy uncertainty can raise the cost of capital, which in turn reduces output and investments, as demonstrated theoretically by (Al-Thaqeb & Algharabali, 2019). Even more so, this impact was enhanced over the longer term. Another Theoretical explanation is that the cost of borrowing money increases because of uncertainty, leading to less investment. That is to say, if borrowing costs are too high, potential investors may be dissuaded, resulting in a lesser return on their capital. Similarly, rising economic uncertainty may lead firms to; delay investments, hiring, and consumer purchases, which can negatively affect economic activity. A decrease in uncertainty increases economic and investment activities. 4.2.1.3 Does country governance promote banks’ revenue efficiency?. To address the issue of whether the six indicators of country governance mattered in determining the performance of banks’ operating in the MENA region, Equation (1) was estimated to include all six indicators of country governance. The six indicators comprised: the rule of law, government effectiveness, regulatory quality, political stability and the absence of violence, control of corruption, and voice and accountability. The impact of the six indicators of country governance and bank stock returns Shah & Albaity, Cogent Economics & Finance (2022), 10: 2150133 https://doi.org/10.1080/23322039.2022.2150133 Page 17 of 32 Table 3. Effect of investor sentiment, uncertainty, and governance on bank stock return in GCC countries VARIABLES BUH BUH BUH BUH BUH BUH BUH BUH BUH BUH BUH BUH L.BUH (t-1) −0.4482*** −0.4272*** −0.4009** 0.4892*** 0.4385*** 0.4169*** −0.3661* −0.4199** 0.4369*** 0.4893*** −0.4199** −0.4745** (0.1679) (0.1587) (0.1637) (0.1596) (0.1591) (0.1518) (0.1900) (0.1738) (0.1633) (0.1566) (0.1787) (0.2148) ∆ISEN −0.0615*** −0.0586*** −0.0713*** 0.0608*** 0.0586*** 0.0701*** 0.0712*** 0.0595*** 0.0619*** 0.0596*** 0.0569*** 0.0747*** (0.0157) (0.0163) (0.0180) (0.0203) (0.0158) (0.0170) (0.0149) (0.0169) (0.0186) (0.0192) (0.0156) (0.0160) ∆MSEN 0.0516*** 0.0554*** 0.0631*** 0.0445* 0.0515*** 0.0431** 0.0507*** 0.0583*** 0.0499** 0.0409* 0.0437** 0.0379 (0.0158) (0.0171) (0.0192) (0.0235) (0.0168) (0.0177) (0.0163) (0.0182) (0.0220) (0.0234) (0.0179) (0.0235) ∆UNC −0.0004*** −0.0003** −0.0004*** −0.0004** −0.0004** −0.0003** 0.0004*** −0.0003** 0.0004*** −0.0003** −0.0003** −0.0003** (0.0001) (0.0001) (0.0001) (0.0001) (0.0001) (0.0001) (0.0001) (0.0001) (0.0001) (0.0001) (0.0002) (0.0001) ROE 0.0016 0.0024 0.0017 0.0027 0.0015 0.0025 0.0018 0.0024 0.0027 0.0030 0.0015 0.0029 (0.0032) (0.0038) (0.0038) (0.0049) (0.0034) (0.0043) (0.0032) (0.0041) (0.0046) (0.0051) (0.0037) (0.0043) TLTA −0.7407*** −0.7170*** −0.7617*** −0.6569** 0.6725*** −0.2554 −0.6056 0.7166*** −0.6208** −0.6722** −0.6395** −0.1345 (0.2867) (0.2583) (0.2731) (0.3169) (0.2443) (0.2500) (0.3783) (0.2615) (0.2883) (0.2940) (0.2581) (0.5389) GLTA 0.0509 0.0206 0.0362 0.0192 0.0600 0.0396 0.0092 0.0599 −0.0676 0.0508 0.0152 −0.3429 (0.2515) (0.2371) (0.2536) (0.3175) (0.2112) (0.2777) (0.2947) (0.2563) (0.3302) (0.3158) (0.2402) (0.6219) size 0.0615*** 0.0556*** 0.0697*** 0.0671** 0.0579*** 0.0355 0.0555** 0.0603*** 0.0608** 0.0713*** 0.0595*** 0.0209 (0.0182) (0.0180) (0.0199) (0.0268) (0.0160) (0.0242) (0.0243) (0.0188) (0.0294) (0.0272) (0.0148) (0.0402) NII −0.0007 −0.0011 −0.0005 −0.0007 −0.0002 0.0010 −0.0009 −0.0007 −0.0011 −0.0002 −0.0005 −0.0000 (0.0022) (0.0021) (0.0023) (0.0028) (0.0020) (0.0020) (0.0026) (0.0023) (0.0028) (0.0029) (0.0022) (0.0033) ∆OPR −5.4440 −5.5382 −5.1275 −2.6979 −7.0889 −7.0516 −6.6767 −5.0867 −4.1896 −3.4250 −5.1255 −16.4341 (8.8405) (8.2632) (9.0046) (10.1690) (8.2428) (9.2737) (11.3932) (8.6132) (9.5829) (10.0142) (8.6195) (22.0783) Dum_co −0.1005*** −0.1130*** −0.1187*** −0.1109** 0.1192*** −0.0946** −0.0641 −0.1663 −0.1051 −0.1459* −0.1799* 1.1530 (0.0338) (0.0385) (0.0438) (0.0418) (0.0361) (0.0420) (0.1645) (0.1451) (0.1550) (0.0859) (0.0941) (1.7732) GDP 0.0355*** 0.0357*** 0.0362*** 0.0351*** 0.0323*** 0.0451*** 0.0370*** 0.0362*** 0.0323*** 0.0336*** 0.0334*** 0.0371* (0.0095) (0.0087) (0.0097) (0.0129) (0.0082) (0.0102) (0.0090) (0.0088) (0.0121) (0.0126) (0.0085) (0.0192) INF −0.0342** −0.0343** −0.0358** −0.0304 −0.0225* −0.0401** 0.0358*** −0.0363** −0.0308* −0.0275 −0.0287** −0.0408** (Continued) Shah & Albaity, Cogent Economics & Finance (2022), 10: 2150133 https://doi.org/10.1080/23322039.2022.2150133 Page 18 of 32 Table 3. (Continued) VARIABLES BUH BUH BUH BUH BUH BUH BUH BUH BUH BUH BUH BUH (0.0148) (0.0139) (0.0149) (0.0206) (0.0119) (0.0170) (0.0137) (0.0149) (0.0176) (0.0208) (0.0142) (0.0208) RUL 0.2719*** 0.3071 (0.0562) (0.2263) GOE 0.1745*** 0.0989 (0.0394) (0.2120) REQ 0.1952*** 0.2363 (0.0533) (0.2678) PSV 0.0929** 0.0295 (0.0443) (0.1344) COC 0.2046*** 0.0649 (0.0357) (0.1553) VOA 0.1452*** −0.8275 (0.0488) (1.0593) c.Dum_co#c. RUL −0.0841 (0.3622) c.Dum_co#c. GOE 0.1060 (0.2781) c.Dum_co#c. REQ −0.0587 (0.3765) c.Dum_co#c. PSV 0.1081 (0.2194) c.Dum_co#c. COC 0.1869 (Continued) Shah & Albaity, Cogent Economics & Finance (2022), 10: 2150133 https://doi.org/10.1080/23322039.2022.2150133 Page 19 of 32 Table 3. (Continued) VARIABLES BUH BUH BUH BUH BUH BUH BUH BUH BUH BUH BUH BUH (0.2177) c.Dum_co#c. VOA 1.1058 (1.5804) Observations 261 261 261 261 261 261 261 261 261 261 261 261 Number of ID 54 54 54 54 54 54 54 54 54 54 54 54 AR (1) 0.013 0.012 0.009 0.036 0.018 0.015 0.014 0.013 0.022 0.046 0.021 0.087 AR (2) 0.374 0.152 0.157 0.341 0.223 0.299 0.44 0.144 0.329 0.415 0.357 0.368 Hansen Test 0.268 0.178 0.115 0.165 0.306 0.155 0.193 0.111 0.111 0.144 0.132 0.137 VIF 1.42 1.41 1.43 1.44 1.42 1.46 2.0 1.86 1.83 1.77 1.82 2.94 The dependent variable is banks buy and hold stock return (BUH). The L. BUH t-1 is the lagged of buy and hold stock returns. Δ ISE and Δ MSE indicate the growth rate of individual sentiment and market sentiment, respectively. Δ UNC is the growth of uncertainty in the country. The QoG is each of the six quality of governance indicators namely the voice and accountability VOA, the political stability PSV, the government effectiveness GOE, the regulatory quality REQ, the control of corruption COC, and the rule of law RUL. ROE, GLTA, SIZE, NII and TLTA are the return of equity, gross loans by total assets, size of the bank non-interest income, total liabilities by total assets, respectively. Each of the QoG indicators interacted with conventional banks (Dum_co). The GDP and INF indicates the growth rate of GDP and inflation, respectively. Finally, Dum_co variable consist of conventional banks:1 and Islamic banks:0. Notes: ***, **, and * indicate statistical significance at the 1%, 5%, and 10% levels, respectively. Number in parentheses are p-values. Shah & Albaity, Cogent Economics & Finance (2022), 10: 2150133 https://doi.org/10.1080/23322039.2022.2150133 Page 20 of 32 was analysed individually due to the high correlation between the indicators (Kamarudin et al., 2016; Langbein & Knack, 2010) (Appendix B). The results are shown in Table 2. The rule of law showed a statistically significant and positive relationship with bank stock performance in the MENA region which was in line with the results of (MODUGU & DEMPERE, 2020) in the GCC countries during 2006–2017. The rule of law indicator denoted respect for law and order, the performance of the judiciary system and the effective implementation of contracts (Kamarudin et al., 2016). It would be reasonable to say that the judicial institutions in the MENA region reduce uncertainty and risk in conducting business and improve bank stock performance, strengthening stock markets. The government effectiveness indicator showed a positive and statistically significant influence on stock performance in the MENA region countries, which was consistent with the results of (Chan et al., 2015) when investigating the ASEAN 5 countries, and (Kamarudin et al., 2016) in the GCC region countries. On the other hand, (MODUGU & DEMPERE, 2020) found negative and insignificant results in Gulf countries. According to Stevens and Cooper (2010), governance policies and actions demonstrated a higher level of commitment resulting in improved efficiency. Thus, the results indicated that better government credibility concerning the formulation and implementation of private sector policies and regulations positively affected the operations and the stock performance of the banking sector in the MENA region. The regulatory quality variable exhibited a positive sign and showed statistically significant effects on bank stock performance consistent with the results of (Kamarudin et al., 2016) in the GCC region, while (MODUGU & DEMPERE, 2020) found negative effects. The better regulatory quality improved revenue efficiency in the banking sectors of the GCC region countries (Kamarudin et al., 2016). Similarly, Albaity et al. (2020) claimed that regulatory quality negative influenced credit risk and insolvency risk while, on the other hand, it improved the revenue of the banks. The regulation theory specifies that regulatory system quality can be evaluated by measuring efficiency, effectiveness and good governance (Jalilian et al., 2007). It can be observed in Table 2 that political stability and the absence of violence exhibited a statistically significant and positive influence on stock returns in the banking sectors of the MENA region. This result signifies that countries with better political stability will experience improved bank revenue efficiency. A similar result was found by (Kamarudin et al., 2016). Also, it supports the theory that a more robust institutional framework accelerates more predictable bank performance. This paper found a positive and statistically significant impact of the control of corruption on bank stock performance, which was consistent with the result of (Chortareas et al., 2012; Kamarudin et al., 2016). Similarly, (MODUGU & DEMPERE, 2020) found a positive and insignificant influence of control of corruption on stock performance. According to Kamarudin et al. (2016), agency and strong supervision reduce corruption and improve; monitoring, discipline and bank performance. Besides, highly corrupt countries suffer from high debt due to capital flight, negatively affecting investment and financial decisions (Albaity et al., 2020; Chan et al., 2015). The supervision theory implies that a powerful supervisory agency directly disciplines and monitors banks leading to better performance and a fall in corruption regarding bank lending (Beck et al., 2006). It can be observed in Table 3 that voice and accountability exhibited positive and statistically significant effects on bank stock returns, which was in line with the results of (Chortareas et al., 2012; Kamarudin et al., 2016, 2018; Lensink et al., 2008). In contrast, voice and accountability showed a significant but negative association with stock market performance in the GCC countries (MODUGU & DEMPERE, 2020). Voice and accountability promote democracy and eliminate poverty through citizens’ influence and role in state institutions, leading to better bank performance in the MENA region (Albaity et al., 2020). 4.2.1.4 Conventional banks’ governance indicators and stock returns. It can be observed in Table 2 that the interaction between the governance indicators and conventional banks (dummy variable) had a statistically significant influence on stock returns throughout the models. First, the rule of law is a form of country governance indicator, and conventional banks were found to be Shah & Albaity, Cogent Economics & Finance (2022), 10: 2150133 https://doi.org/10.1080/23322039.2022.2150133 Page 21 of 32 negative, which meant that the rule of law in conventional banks weakened the relationship with bank stock returns in the MENA region. This result was consistent with the results of (Kamarudin et al., 2016) in conventional banks in the GCC region countries. Second, the interaction between government effectiveness and conventional banks was positive and statistically significant, indicating that government effectiveness in conventional banks strengthened bank stock returns. This outcome supported the results of (Chan et al., 2015; Kamarudin et al., 2016). Third, the interaction between regulatory quality and conventional banks showed a positive and significant relationship, which suggested that regulatory quality in conventional banks positively influenced bank stock returns, which was consistent with the results of (Albaity et al., 2020; Kamarudin et al., 2016). Fourth, the interaction between political stability and the absence of violence and conventional banks showed a negative and statistically significant relationship. This result signified that political stability and the absence of violence in conventional banks had a decreasing relationship with bank stock returns in the MENA region countries. In contrast, Kamarudin et al. (2016) found that political stability and the absence of violence in conventional banks in the GCC region countries had a positive and statistically significant relationship with bank stock returns. Fifth, the interaction of control of corruption and conventional banks of the MENA region was found to be positive, which suggested that better control of corruption in conventional banks led to improved bank stock returns. Similar results were found by (Chortareas et al., 2012; Kamarudin et al., 2016, 2018). Finally, voice and accountability’s interaction with conventional banks in the MENA region countries was negative, meaning that this interaction negatively influenced bank stock return. In contrast, several other studies have found voice and accountability’s influence in conventional banks to be positive, which was inconsistent with the results of (Chortareas et al. (2012); Kamarudin et al. (2016), (Kamarudin et al., 2018); Lensink et al., 2008). When looking at the control variables, a significant positive effect of the return on equity (ROE) on stock returns was found throughout all models, consistent with Kanas et al. (2019). A higher ROE specifies better bank performance and improved bank stability (Kanas et al., 2019). Also, a change in the ROE could change a bank’s degree of financial leverage (Di et al., 2021). In some of the models,’ the loans to assets ratio had a statistically significant and negative impact on bank stock returns. Bank liquidity can be measured through the loans to assets ratio, which might affect risk-taking behaviour in banks (Albaity et al., 2020). A higher loans-to-assets ratio signals low liquidity, which may lead banks to difficulty fulfilling their financial obligations and, thus, lowering stock returns (Bouheni & Hasnaoui, 2017). Similarly, the natural logarithm of bank total assets, representing bank size, had a weak positive and significant relationship with stock returns in the two models. According to Adusei and Elliott (2015), Masoud and Albaity (2021) and Simpasa et al. (2015), a positive bank size indicates financial stability providing more capital to finance banks’ business operations. The net interest income (NII) was found to have a positive but statistically insignificant effect on the endogenous variable, except for two models. Higher net interest income raises bank stock returns. The growth of gross loans was insignificant and directly related to the dependent variable. Finally, the Gross Domestic Product (GDP) and Inflation (INF) were positive and statistically significant in only a few models. The GDP positively impacted bank stock returns, consistent with Irresberger et al. (2015), while Chue et al. (2019) found an inverse relationship for the same situation. 4.3. Results for the Gulf Cooperation Council (GCC) region In Table 3, the lagged returns can be seen as negative and significant, indicating that the previous higher values of the lagged returns reduced the current returns. The investor sentiment indicator (see Appendix A) reflected individual and market sentiments. Hence, a negative relationship was expected between investor sentiment and bank stock returns in the GCC countries. The results showed that growth in individual sentiment (ISEN) had a statistically significant negative impact. In contrast, growth in market sentiment revealed a significant and positive impact, consistent with the results of G. G. Wang Shah & Albaity, Cogent Economics & Finance (2022), 10: 2150133 https://doi.org/10.1080/23322039.2022.2150133 Page 22 of 32 et al. (2020). Overall, investor sentiment reflected a negative relationship, which was consistent with the results of the MENA region models and those of (Di et al., 2021; Irresberger et al., 2015). The relationship between the uncertainty variable and bank stock returns in the GCC region countries was negative and statistically significant in all models, which was the same as the results of the MENA region. The analysis of the six country-level governance indicators showed that all the indicators had a statistically significant and positive impact on bank stock returns in the GCC region countries, except voice and accountability, which were found to be negative and significant. In contrast, all six indicators positively correlated with bank stock returns in the analysed MENA region countries. Looking at the control variables, an insignificant and positive impact was found for the return on equity (ROE) and gross loans by total assets (GLTA) on bank stock returns, while Non-Interest Income (NII) and growth in operating revenue (NII) had a negative and insignificant influence on bank stock returns. The related country-level control variable gross domestic product (GDP) was positive. In contrast, inflation (INF) was negative, while both variables were weak and statistically significant. Overall, the baseline results of the main variables in the GCC region countries were consistent with the MENA region. However, market sentiment (MSEN), voice and accountability (VOA), and interaction of governance indicators and conventional bank stock returns in the GCC region countries were not statistically significant (Tables 2 and 3). 5. Conclusion This paper examined how; investor sentiment, uncertainty, and governance indicators impacted bank stock returns. The sample covered 173 banks operating in 17 countries across the MENA region, including 68 banks based in GCC countries, using data between 2010 to 2020. Information concerning the variables and the data sources used in this study are in the appendix A. The GMM regression of banks’ yearly stock buy-and-hold returns provided convincing evidence that; investor sentiment, uncertainty, and governance drove bank performance between 2010 to 2020. The baseline findings revealed that investor sentiment and uncertainty adversely affected bank stock returns. In contrast, overall, governance indicators positively influenced bank stock returns. In addition, voice and accountability negatively influenced GCC countries’ stock returns. Also, the interaction of the rule of law and voice and accountability with conventional banks was negative in the MENA region. Regarding the control variables, loan ratio and inflation were negative, and bank size and the GDP revealed a positive and statistically significant effect on the endogenous variable in all models in the GCC region countries, excluding loan ratio and bank size. This novel paper also added an interaction term in the regression to determine the impact of governance indicators on the relationship between conventional banks and bank stock returns. The regression coefficient for the product of the governance indicators and conventional bank variables showed a positive and significant impact on bank stock returns in the MENA region. In contrast, the GCC region countries showed similar but statistically insignificant results. Overall, the banking sectors of the MENA region countries were sensitive to investor sentiment, uncertainty, and country-level governance indicators. The primary limitation of this paper was the lack of weekly, monthly, or quarterly data availability for all variables. Future research studies should consider the UN’s 2030 sustainable development goals. The implication that the financial sector has suffered from a lack of country-level governance, uncertainty, and investor sentiment, affecting economies of scale, may bring new challenges to investment and the region’s policymakers. Although volatility is priced, investors may still be affected by the mood in these markets. Similarly to underdeveloped and emerging economies, the MENA nations have limited access to arbitrage opportunities due to information inefficiencies. As a result, their strategies should take sentiment into account when calculating overall risk. To be rewarded, investors must own well-diversified portfolios. Thus, it is necessary to develop trading techniques to Shah & Albaity, Cogent Economics & Finance (2022), 10: 2150133 https://doi.org/10.1080/23322039.2022.2150133 Page 23 of 32 Appendix A. (Continued) Variables Description Expected sign Data source Dum_co Dummy variable that takes a value of “1” if the bank is conventional, and “0”otherwise. (+) BankFocus Country Level Variables GDP growth rate % The growth rate of the GDP in percentage. (±) World Bank Inflation % Inflation calculated through consumer prices (±) World Bank Shah & Albaity, Cogent Economics & Finance (2022), 10: 2150133 https://doi.org/10.1080/23322039.2022.2150133 Page 30 of 32 Appendix B. Correlation matrix table Variables BUH ∆ISEN ∆MSEN ∆UNC RUL GOE REQ PSV COC VOA ROE TLTA GLTA size NII ∆OPR GDP INF BUH 1 ∆ISEN −0.133 1 ∆MSEN −0.030 −0.015 1 ∆UNC −0.034 0.039 −0.074 1 RUL 0.208 0.122 −0.222 0.058 1 GOE 0.219 0.076 −0.189 0.076 0.893 1 REQ 0.180 0.121 −0.193 0.112 0.913 0.906 1 PSV 0.138 0.169 −0.213 0.137 0.799 0.750 0.712 1 COC 0.232 0.085 −0.207 0.082 0.931 0.959 0.888 0.826 1 VOA 0.000 −0.017 0.013 −0.062 0.180 0.129 0.230 −0.200 0.073 1 ROE 0.230 0.034 −0.027 −0.004 0.076 0.068 0.029 0.086 0.075 −0.076 1 TLTA 0.094 0.069 −0.098 0.032 0.425 0.406 0.325 0.318 0.348 0.219 0.154 1 GLTA 0.090 0.121 −0.150 0.049 0.531 0.483 0.381 0.529 0.532 0.051 0.092 0.393 1 size 0.157 0.101 −0.109 0.048 0.432 0.497 0.405 0.384 0.419 −0.106 0.203 0.564 0.297 1 NII −0.013 −0.128 0.074 0.019 −0.496 −0.375 −0.489 −0.437 −0.422 −0.075 −0.076 −0.392 0.281 −0.225 1 ∆OPR −0.065 −0.052 −0.003 −0.010 −0.067 −0.026 −0.066 −0.051 −0.044 −0.089 0.050 −0.003 0.029 −0.023 0.033 1 GDP 0.002 0.229 −0.101 0.036 0.174 0.184 0.238 0.238 0.232 −0.042 0.044 −0.082 0.090 0.008 −0.147 −0.103 1 INF 0.061 −0.102 0.012 0.026 −0.296 −0.230 −0.468 −0.196 −0.267 −0.199 0.183 0.205 0.036 0.061 0.257 0.046 −0.256 1 Shah & Albaity, Cogent Economics & Finance (2022), 10: 2150133 https://doi.org/10.1080/23322039.2022.2150133 Page 31 of 32 © 2022 The Author(s). 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