The banking sector plays a significant role in Southeast Asia's economic development for the past 40 years. An effective and secure financial system in the banking sector has attributed to an outstanding high record on East Asia economic growth in the mid-90s (World Bank, 1993). However, the Asian banking industry has undergone extensive financial transformation where many banks were forced to consolidate after an overwhelming impact during the previous financial crises. As a result, the number of commercial banks operating in Malaysia has reduced to 26. One common characteristic of previous financial crises incident is that banks have taken excessive risks and ended up with too many defaults. Governmental bailouts have also been blamed for creating incentives for banks to undertake excessive risks that contributed to these financial crises. Global Financial Development Report (2019/2020) claimed that there was extensive government intervention in backing up the national banking systems at that time. There is also no exception in Malaysia, where the Malaysian government has bailed out well-connected ailing, debt-ridden enterprises through GovernmentLinked Investment Companies (GLICs) and Government-Linked Companies (GLCs) (Gomez, Padmanabhan, Kamaruddin, Bhalla & Fisal, 2018). The act of government intervention to rescue financial institutions has direct and indirect economic costs that have long-lasting effects (Global Financial Development Report, 2019/2020). The government intervention is further concerned when its involvement in the banking sector has intensified after the financial crises, particularly Asian countries that have greater government ownership involvement compared to other regions (Hossain, Jain & Mitra, 2013). Prior studies claimed that the presence of moral hazard behaviour induced by government explicit and implicit protection leads to excessive risk-taking behaviour since it does not have to bear the costs (Jensen & Meckling, 1976; Global Financial Development Report, 2019/2020; Zhu & Yang, 2016). Excessive risk-taking behaviour in the banking industry could mark the onset of a banking crisis. Severe consequences on excessive risk-taking can be seen in the incident of Lehman Brothers bankruptcy during the Subprime crisis. They had taken excessive risks in creating loans, hence could not pay the excessive debts and faced unprecedented losses. In light of globalisation in the banking sector, the central bank of Malaysia (Bank Negara Malaysia) has taken the initiatives to strengthen Malaysia’s economies and boost the role of banking sector as a key element of national economic growth to compete in a more liberalised and challenging environment. Chin (2015) claimed that protracted affirmative action policies have led to the protectionism of local banks in Malaysia that favoured state-owned banks. The policies enforced by the government after the recent Global financial crisis has produced power shift which driven Malaysia’s state-owned banks to seize more opportunities where foreign banks were impotent and have to withdraw their business in Malaysia. This reveals that the state authority is strong in the Malaysian banking sector. In view of liberalisation in the banking sector, the risk exposure faced by banks nowadays has become greater with the expansion of the banks’ business scope and economic activities. This is predominantly important for commercial banks in Malaysia since they are the largest and most significant providers of funds in the Malaysian banking system. About 55.61 percent of the total loans in the banking system are derived from the household sector including mortgages, personal loans and hire purchase loans1. Currently, there are 26 commercial banks of which eight of them are local commercial banks. These commercial banks were governed by the Banking Act 1973 and Finance Companies Act 1969 under a single legislation. As indicated in the earlier paragraph on the Malaysian government bailout during previous financial crises, GLICs, indeed, play a significant part in the nation’s economic development. A key characteristic of Malaysia’s economy under GLIC-led framework is that government has substantial influence in intervening the economy through their ownership in business operations due to their political, social and economic interests (Gomez et al., 2018; Shleifer & Vishny, 1994) and these GLICs are closely tied with government policies (Abdul Razak, 2011). Following Haggarty and Shirley (1997) and OECD (2013), GLICs are defined as government-owned or -controlled investment companies that have a primary commercial objective. The Malaysian government has a direct controlling stake on the management decisions by virtue of its share ownership through GLCs or GLICs, that is, the ability to appoint senior management or make major decisions such as contract awards, strategy, restructuring, and financing, acquisitions and divestments. These GLICs constitute a significant part of the Malaysian economic structure as they hold more than 50 percent of the whole market capitalisation and majority ownership of Malaysia’s leading public listed firms. According to Gomez et al. (2018), GLICs hold majority ownership of 35 public listed firms from Malaysia’s Top 100 companies. This study focuses on five major GLICs that hold majority ownership in local commercial banks. They are known as Employees Provident Fund (EPF), Permodalan Nasional Berhad (PNB), Khazanah Nasional Berhad (KNB), Lembaga Tabung Angkatan Tentera (LTAT) and Lembaga Tabung Haji (LTH). Statistics show that EPF owns 45.34 percent of RHB Bank Berhad, PNB owns 42.96 percent shares in Malayan Banking Berhad, KNB owns 29.71 percent shares in CIMB Bank Berhad whereas LTAT has contributed to 35.42 percent of ownership in Affin Bank Berhad2. The existing literature on GLICs or state ownership were mainly focused on firm performance (see Abdul Razak, Ahmad & Aliahmed, 2008; Abdul Rahman & Rejab, 2013; Hamid, 2011; Najid & Rahman, 2011; Taufil-Mohd, Md-Rus & Musallam, 2013). Similarly, other studies conducted in the developed countries also shared such findings (see Berger, Clarke, Cull, Klapper & Udell,
2005; Cornett, Guo, Khaksari & Tehranian, 2010; Iannotta, Nocera & Sironi, 2007). However, the literature on the relationship between state ownership and risk-taking is scarce. Risk-taking is one important dimension for corporate success and it has an impact on national economic development, particularly the banking sector where the risk undertaking of a bank not only has an enormous impact on banks’ profitability but the national economy as a whole. In the event of excessive risk-taking, the impact of bank failure can spread to other banks and perhaps hitting the entire nation as well as the global economy (Stiglitz, 1993). Understanding the peril of excessive risk-taking, only a small number of recent studies examined the impact of state ownership on risk-taking. Notably, most of the extant studies were conducted in Western countries and developed countries such as Argentina, European countries, Middle East and North Africa (MENA) countries and the United States (see Brandao-Marques, Correa & Sapriza, 2020; Iannotta, Nocera & Sironi, 2013; Lassoued, Sassi & Ben Rejeb Attia, 2016; Uddin, 2016). The studies have also evolved into Asian countries lately (see Lee & Hooy, 2020; Vo, 2018; Zhu & Yang, 2016). Although there have been increasing empirical studies conducted in Western countries and developed countries, very little attention has been given to emerging market such as Malaysia in examining the impact of state ownership on the banks’ risktaking. It is relatively important to explore this issue in Malaysia where GLICs hold majority ownership in local commercial banks. Prior study claimed that banks have a greater propensity to undertake risky projects if they have a direct link with the government (Lassoued et al., 2016). This statement is further supported by the empirical findings where state ownership is found positively related to risk-taking (Brandao-Marques et al., 2020; Iannotta et al., 2013; Lassoued et al., 2016; Zhu & Yang, 2016). The existing literature also proves that state-owned banks (SOBs) tend to have poorer performance, are less efficient and less profitable and incur greater credit risks compared to private-owned banks (POBs). They claimed that these differences between SOBs and POBs are more eminent in countries that had greater government involvement in the banking system, particularly in Southeast Asia where SOBs are found underperforming (Cornett et al., 2010; Naima, Houda & Mouna, 2016). On the contrary, some other studies found a negative relationship between state ownership and risk-taking (see Vo, 2018) and the nonlinear relationship (see Uddin, 2016). The flaw in the functioning of corporate governance (CG) mechanisms has also played part of the root in previous financial crises that indirectly encourage government intervention in the banking sector. Gomez et al. (2018) argued that there is no effective body in Malaysia that can monitor the governance and performance of state-owned enterprises (SOEs) although there is a wellstructured governance mechanism in many other countries’ SOEs. Hence, having a sound CG mechanism in the Malaysian banking system is crucial in monitoring bank activities and investment decisions. Liang, Xu and Jiraporn (2013) and OECD (2006) claimed that the board of directors play an important role in establishing a sound bank governance. The finding from prior studies provides evidence that firms with more effective governance are associated with a lower level of risk-taking, particularly board of directors who play the effective monitoring role in reducing excessive risk-taking behaviour (Anderson, Mansi & Reeb, 2004; Jiraporn, Chatjuthamard, Tong & Kim, 2015; OECD, 2006). Hence, the fiduciary duty carried out by the board of directors is deemed important in the banking sector than any other industry since any shortcomings of bank governance could lead to potential financial system failure. Prior studies have highlighted the fact that banks tend to undertake excessive risk when there is state ownership in the bank. However, the existing studies do not examine CG mechanisms in moderating the impact of state ownership on the banks’ risktaking. Hence, this further motivates the exploration of CG mechanisms in this study. This study adds value to the existing literature in twofold. Firstly, this study contributes by being a pioneer study addressing state ownership through GLICs and their risk-taking behaviour in Malaysia’s banking industry. Malaysia, being one of the Southeast Asian countries, is the best teaching material for this research study. In addition, the Malaysian commercial bank is the largest and most significant provider of funds in the Malaysian banking system, the level of banks’ risk-taking could give a large impact on nation economic development. Secondly, most of the existing literature has largely ignored the role of corporate governance mechanisms while examining this relationship. Using Malaysia data, we look at how banks’ risk-taking behaviour is affected by the corporate governance mechanisms through different compositions of the board of directors such as board independence, foreign director and female director. The results of this study are important in providing insights into the role of government intervention and bank decision making through their understanding of the relationship between state ownership and risk-taking behaviour. We find that the role of state ownership is significant in determining the banks’ risk-taking behaviour in Malaysia. We also find that the composition of board of directors somehow plays a significant role in bank governance. This paper proceeds as follows. Hypotheses development is discussed in Section 2. Data, variables and summary statistics are presented in Section 3. Then, the results of data analysis are shared in Section 4 and the paper ends with the conclusion and implications in the last section (Section 5). 2.
Three theories have been widely used in the study of the relationship between action of financial bailouts by institutions, i.e., the government, would encourage riskier behaviour in the future if those risk-taking parties believe that they are not going to be responsible for the consequences as a result of excessive risk-taking behaviour (Krugman, 2009). Besides, the “too big to fail” perspective also leads to moral hazard as they believe that the government would not be willing to let larger banks fail and hence, is more likely to bail them out. Secondly, the agency theory. This theory focuses on the conflict of interest arises between the major and minor shareholders. Shareholders who have dominating power have the incentives to affect the banks’ decisions by undertaking riskier investments (Laeven & Levine, 2009; Shleifer & Vishny, 1986). Thirdly, the social lending theory. This theory shows the need of the government to accomplish social objectives. Prior studies claimed that SOBs tend to channel resources for socially beneficial projects that aim to increase job creations and developing nations where the social returns fail to be taken into account by the POBs (Berger et al., 2005; Stiglitz, 1993). In addition to the above theories, prior studies showed that state ownership encourages banks to take more risks. This is further supported by empirical findings where state ownership is positively associated with risk-taking. This leads SOBs to hold less core capital, less profitability and incur greater credit risk than POBs (Brandao-Marques et al., 2020; Iannotta et al., 2013; Lassoued et al., 2016; Zhu & Yang, 2016). These findings are consistent with the agency theory where banks controlled by major shareholders are found to have a tendency to undertake more risk. Since GLICs hold majority ownership in local commercial banks where the Malaysian government has a direct controlling stake, this study examines whether the state ownership and SOBs are associated with higher risktaking. Three risk-taking measurements are employed in this study; the credit risk proxy by non-performing loans (NPL) ratio, the capital adequacy proxy by capital adequacy ratio (CAR) and the liquidity risk proxy by liquidity ratio (LR). Koudstaal and Wijnbergen (2012) reported that the more troubled the loan portfolio, the greater the inclination for banks to take risks. A larger number of NPLs due to excessive risk-taking in lending could be used to mark the onset of a banking crisis (Reinhart & Rogoff, 2011; Jensen & Meckling, 1976). Moreover, capital adequacy plays an important role for banks solvency and their protection from untoward events which arise as a result of liquidity risk as well as the credit risk that banks are exposed to in the normal course of their business (Karim, Hassan, Hassan & Mohamad, 2014; Athanasoglou, Brissimis & Delis, 2008). Insufficient equity capital has also been partly blamed in the Global financial crisis and the Asian financial crisis. Moving on, LR could be used to measure a bank’s financial health and it presents a preliminary expectation regarding the solvency of a company (Imbierowicz & Rauch, 2014). A study from De Haan and Van Den End (2013) suggested that the extended liquidity support by the central bank might have been an incentive for banks to reduce their liquidity buffers. The subprime crisis that happened in 2008 has demonstrated how severely illiquidity can crystallise. To conclude, these three proxies are important in determining the banks’ risk-taking behaviour and therefore, are employed in this study. Based on the theoretical perspectives and empirical finding from prior studies, we proposed our first hypothesis as follows: H1a:
State-owned banks have higher risks than private-owned banks.
The degree of ownership by Malaysian government in banks significantly increases banks’ risk-taking behaviour.
The flaw in the functioning of CG mechanisms has played part of the root in previous financial crises where there are massive government intervention and excessive risk-taking behaviour in the banking sector. Shareholder-friendly CG is also found connected with higher risk-taking (Anginer, Demirguc-Kunt, Huizinga & Ma, 2018). As indicated by Gomez (2005), the issue of ownership and the act of controlling a bank or enterprise is a key issue in the most definition of CG. This issue is even more crucial in developing countries such as Malaysia where the government has a dominant controlling stake and decision making in the national economy. Hence, it is important to establish sound CG in the Malaysian banking sector to ensure that the investment decisions or strategies made by banks are well-monitored to prevent excessive risk-taking behaviour. A good CG comes from the essential role and the fiduciary duty play by the board of directors (Gomez, 2005). Prior studies also showed that sound bank governance played by the role of the board of directors is relatively important in the banking sector as the effective monitoring roles by the board is connected with lower risk-taking (Anderson et al., 2004; Jiraporn et al., 2015; Liang et al., 2013; OECD, 2006). Thus, this study focuses on three CG mechanisms (board independence, foreign director, and female director) in moderating the impact of state ownership through GLICs on banks’ risk-taking. The earlier literature studies on corporate governance issues provide no definite findings on the role of independent directors. Advocates show that organisations will become more effective and efficient in reducing agency issues and moral hazard with the presence of independent directors on board (Rosenstein & Wyatt, 1990; Klein, 2002; Nguyen & Nielsen, 2010). However, another strand of literature argues that independent directors may lack sufficient knowledge of the firm-specific information and lead to sub-optimal decisions (Raheja, 2005). In the case of state ownership, OECD (2006) suggested that an adequate number of independent directors on the board of SOBs be included. This is to ensure that the decision made by the board is independent and does not interfere by the government. Based on the previous studies, we developed the second hypothesis as follows:
Board independence significantly affects the impact of ownership by Malaysian government on banks’ risk-taking behaviour.
Foreign directors are non-local directors who served on the board. The role of foreign director is ambiguous although many researchers have examined this in their studies for the past few decades. Statement shows that foreign directors bring new technology and managerial expertise which brings better supervision by reducing information asymmetry and agency cost (Ezat & ElMasry, 2008; Samaha, Dahawy, Hussainey & Stapleton, 2012). Berger, Hasan and Zhou (2009) asserted that minority foreign ownership takes positions on the board and “leverage” the positions to monitor and improve bank management. Others contend that foreign directors are likely to be less familiar with national accounting standard and management methods, making it more difficult for foreign directors to appraise managerial performance or challenge managerial decisions. Based on these vague statements, we examine the effect of foreign director in the Malaysian banking industry with the third hypothesis developed as follows: H3:
Foreign director significantly affects the impact of ownership by Malaysian government on banks’ risk-taking behaviour.
Recently, female directors have become the theme of corporate governance mechanisms that arouse wide concern in research worldwide. Adams and Ferreira (2009) found that female directors are more likely to join monitoring committees and improve firm performance by easing the weak governance in Chinese listed firms. For example, corporate governance could be strengthened through improved monitoring and greater oversight of management by female directors. However, some other literature suggests that female directors in statecontrolled firms are required to divert part of their efforts to non-profit related political and social activities. In other words, female directors in state-controlled firms are more likely charged with social or political tasks. Hence, it is vital for us to examine the role of female directors. The fourth hypothesis is developed as follows: H4:
Female director significantly affects the impact of ownership by Malaysian government on banks’ risk-taking behaviour. 3.
We first identified and obtained the list of banks involved in this study from Bank Negara Malaysia (BNM). We construct a balanced panel of 8 public listed local commercial banks in Malaysia from year 2011 to 2015. Foreign-owned commercial banks are excluded in the sample due to data limitations. Besides, the five major GLICs hold more than 50 percent of the whole market capitalisation and they have hold majority ownership in the local commercial banks. Hence, our final sample of this study focuses on local commercial banks. By referring to annual reports released from the Bursa Malaysia website, relevant data such as government shares ownership, bank’s liquidity and profitability, bank’s level of risk, bank’s size, and bank’s revenue growth could be identified. Hence, all the data employed in this study are hand collected. We have state-owned banks and private-owned banks in our sample. We define banks as state-owned banks if: (1) Malaysian government owns the largest percentage of ownership through five major GLICs, namely EPF, PNB, KNB, LTAT and LTH; (2) The degree of government ownership is equal to or more than 40 percent. Likewise, for the private-owned bank, it is defined as such if the private entity owns the largest percentage of ownership in the bank. 3.2
State ownership is the independent variable that will give an impact on the banks’ risk-taking behaviour. In this study, we focus on Malaysian government ownership through the five largest GLICs in Malaysia. They are Employees Provident Fund (EPF), Permodalan Nasional Bnd (PNB), Khazanah Nasional Bhd (KNB), Lembaga Tabung Angkatan Tentera (LTAT) and Lembaga Tabung Haji (LTH). These investment institutions have also hold majority ownership in the local commercial banks. Hence, we employ two state ownership measurements in this study. They are dummy of state ownership (dummySO) and degree of state ownership (SO). Dummy of state ownership takes a value of “1” if (1) Malaysian government owns the largest percentage of ownership; (2) the total of ownership is more than or equal to 40 percent or “0” otherwise. Besides, the degree of ownership by Malaysian government is obtained by taking the percentage of shares held by these five investment institutions in the banks.
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NPL ratio can be calculated by ratio asformula aresult resultof of ashown morebelow: risk-takingof ofaaabank. bank.NPL NPLratio ratiocan canbe becalculated calculatedby by ratio as aformula more risk-taking 𝑁𝑜𝑛 − 𝑝𝑒𝑟𝑓𝑜𝑟𝑚𝑖𝑛𝑔 𝑙𝑜𝑎𝑛𝑠 using the formula as shown below: usingthe theformula formulaasasshown shownbelow: below: using 𝑁𝑜𝑛 − 𝑝𝑒𝑟𝑓𝑜𝑟𝑚𝑖𝑛𝑔 𝑙𝑜𝑎𝑛𝑠 𝑁𝑜𝑛 𝑝𝑒𝑟𝑓𝑜𝑟𝑚𝑖𝑛𝑔 𝑙𝑜𝑎𝑛𝑠 𝑁𝑜𝑛 −−− 𝑝𝑒𝑟𝑓𝑜𝑟𝑚𝑖𝑛𝑔 𝑙𝑜𝑎𝑛𝑠 𝑁𝑜𝑛 𝑝𝑒𝑟𝑓𝑜𝑟𝑚𝑖𝑛𝑔 𝑙𝑜𝑎𝑛𝑠 (𝑁𝑃𝐿)𝑅𝑎𝑡𝑖𝑜 𝑁𝑜𝑛 𝑃𝑒𝑟𝑓𝑜𝑟𝑚𝑖𝑛𝑔 𝐿𝑜𝑎𝑛𝑠 𝑅𝑎𝑡𝑖𝑜== 𝑁𝑜𝑛 −𝑝𝑒𝑟𝑓𝑜𝑟𝑚𝑖𝑛𝑔 𝑝𝑒𝑟𝑓𝑜𝑟𝑚𝑖𝑛𝑔 𝑙𝑜𝑎𝑛𝑠 𝑁𝑜𝑛 𝑁𝑜𝑛 − − 𝑝𝑒𝑟𝑓𝑜𝑟𝑚𝑖𝑛𝑔 𝑙𝑜𝑎𝑛𝑠 𝑁𝑜𝑛 𝑃𝑒𝑟𝑓𝑜𝑟𝑚𝑖𝑛𝑔 𝐿𝑜𝑎𝑛𝑠 (𝑁𝑃𝐿) (𝑁𝑃𝐿) (𝑁𝑃𝐿) 𝑁𝑜𝑛 −− 𝑃𝑒𝑟𝑓𝑜𝑟𝑚𝑖𝑛𝑔 𝐿𝑜𝑎𝑛𝑠 𝑅𝑎𝑡𝑖𝑜 == 𝑁𝑜𝑛 −− 𝑃𝑒𝑟𝑓𝑜𝑟𝑚𝑖𝑛𝑔 𝐿𝑜𝑎𝑛𝑠 𝑅𝑎𝑡𝑖𝑜 (𝑁𝑃𝐿) 𝑁𝑜𝑛 𝑃𝑒𝑟𝑓𝑜𝑟𝑚𝑖𝑛𝑔 𝐿𝑜𝑎𝑛𝑠 𝑅𝑎𝑡𝑖𝑜= 𝑇𝑜𝑡𝑎𝑙 𝑙𝑜𝑎𝑛𝑠𝑙𝑜𝑎𝑛𝑠 (𝑁𝑃𝐿) (𝑁𝑃𝐿) (𝑁𝑃𝐿) 𝑁𝑜𝑛 −− 𝑃𝑒𝑟𝑓𝑜𝑟𝑚𝑖𝑛𝑔 𝐿𝑜𝑎𝑛𝑠 𝑅𝑎𝑡𝑖𝑜 == 𝑁𝑜𝑛 𝑁𝑜𝑛 − − 𝑃𝑒𝑟𝑓𝑜𝑟𝑚𝑖𝑛𝑔 𝑃𝑒𝑟𝑓𝑜𝑟𝑚𝑖𝑛𝑔 𝐿𝑜𝑎𝑛𝑠 𝐿𝑜𝑎𝑛𝑠 𝑅𝑎𝑡𝑖𝑜 𝑅𝑎𝑡𝑖𝑜 = 𝑇𝑜𝑡𝑎𝑙 𝑙𝑜𝑎𝑛𝑠 𝑇𝑜𝑡𝑎𝑙 𝑇𝑜𝑡𝑎𝑙 𝑙𝑜𝑎𝑛𝑠 𝑙𝑜𝑎𝑛𝑠 𝑇𝑜𝑡𝑎𝑙 𝑙𝑜𝑎𝑛𝑠 𝑁𝑜𝑛− −𝑝𝑒𝑟𝑓𝑜𝑟𝑚𝑖𝑛𝑔 𝑝𝑒𝑟𝑓𝑜𝑟𝑚𝑖𝑛𝑔 𝑙𝑜𝑎𝑛𝑠 𝑇𝑜𝑡𝑎𝑙 𝑙𝑜𝑎𝑛𝑠 𝑁𝑜𝑛 𝑁𝑜𝑛 − 𝑝𝑒𝑟𝑓𝑜𝑟𝑚𝑖𝑛𝑔 𝑙𝑜𝑎𝑛𝑠 𝑇𝑜𝑡𝑎𝑙𝑙𝑜𝑎𝑛𝑠 𝑙𝑜𝑎𝑛𝑠𝑙𝑜𝑎𝑛𝑠 𝑇𝑜𝑡𝑎𝑙 (𝑁𝑃𝐿)𝑅𝑎𝑡𝑖𝑜 (𝑁𝑃𝐿) 𝑁𝑜𝑛− −𝑃𝑒𝑟𝑓𝑜𝑟𝑚𝑖𝑛𝑔 𝑃𝑒𝑟𝑓𝑜𝑟𝑚𝑖𝑛𝑔𝐿𝑜𝑎𝑛𝑠 𝐿𝑜𝑎𝑛𝑠(𝑁𝑃𝐿) 𝑅𝑎𝑡𝑖𝑜= = 𝑁𝑜𝑛 𝑁𝑜𝑛 − 𝑃𝑒𝑟𝑓𝑜𝑟𝑚𝑖𝑛𝑔 𝐿𝑜𝑎𝑛𝑠 𝑅𝑎𝑡𝑖𝑜 = 𝑇𝑜𝑡𝑎𝑙ahave 𝑙𝑜𝑎𝑛𝑠 Empirical evidence suggests thatcapital capital requirements haveaaaasignificant significant 𝑇𝑜𝑡𝑎𝑙 𝑇𝑜𝑡𝑎𝑙 𝑙𝑜𝑎𝑛𝑠 𝑙𝑜𝑎𝑛𝑠 Empirical evidence suggests that capital requirements have significant impact Empirical evidence suggests that capital requirements have significant Empirical Empirical evidence evidence suggests suggests that that capital requirements requirements have significant Empirical evidence suggests thatcapital capital requirements haveaaaasignificant significant Empirical evidence suggests that capital requirements have significant impact Empirical Empirical evidence evidence suggests suggests that that capital capital requirements requirements have have aaasignificant significant impact impact Empirical evidence suggests that capital requirements have significant Empirical Empirical evidence evidence suggests suggests that that capital requirements requirements have have significant impact on deposit and lending behaviours of a bank (Karim et al., 2014). Prior on deposit and lending behaviours of a bank (Karim et al., 2014). Prior researchers impact on deposit and lending behaviours of a bank (Karim et al., 2014). Prior impact impact on on deposit deposit and and lending lending behaviours behaviours of of a a bank bank (Karim (Karim et et al., al., 2014). 2014). Prior Prior impact on deposit and lending behaviours of a bank (Karim et al., 2014). Prior ondeposit deposit and lending behaviours of abank bank (Karim etal., al., 2014). Prior researchers on on deposit and and lending lending behaviours behaviours of of athat athat bank (Karim (Karim etet al., 2014). 2014). Prior researchers researchers Empirical evidence suggests capital requirements have significant impact on deposit and lending behaviours of bank (Karim et al., 2014). Prior Empirical Empirical evidence evidence suggests suggests that capital capital requirements requirements have have aaa2014). significant significant impact on deposit and lending behaviours of bank (Karim etPrior al., 2014). Prior impact on deposit and lending behaviours of aaabank (Karim et al., Prior researchers such as Shehzad, Haan and Scholtens (2010) and Zhu and Yang (2016) such as Shehzad, Haan and Scholtens (2010) and Zhu and Yang (2016) have researchers such as Shehzad, Haan and Scholtens (2010) and Zhu and Yang (2016) researchers researchers such such as as Shehzad, Shehzad, Haan Haan and and Scholtens Scholtens (2010) (2010) and and Zhu Zhu and and Yang Yang (2016) (2016) researchers such Shehzad, Haan and Scholtens (2010) and Zhu and Yang (2016) such ason Shehzad, Haan and Scholtens (2010) and Zhu and Yang (2016) have such such as as Shehzad, Shehzad, Haan Haan and and Scholtens Scholtens (2010) (2010) and and Zhu Zhu and and Yang Yang (2016) (2016) have have impact on deposit and lending behaviours ofaaabank bank (Karim et al., 2014). Prior impact impact on deposit deposit and and lending lending behaviours behaviours of of bank (Karim (Karim et et al., al., 2014). 2014). Prior Prior researchers such asas Shehzad, Haan and Scholtens (2010) and Zhu and Yang (2016) researchers researchers such such as as Shehzad, Shehzad, Haan Haan and and Scholtens Scholtens (2010) (2010) and and Zhu Zhu and and Yang Yang (2016) (2016) have examined CAR in the study of state ownership and ownership concentration examined CAR in the study of state ownership and ownership concentration have examined CAR in the study of state ownership and ownership concentration have have examined examined CAR CAR in in the the study study of of state state ownership ownership and and ownership ownership concentration concentration have examined CAR in the study of state ownership and ownership concentration examined CAR in the study of state ownership and ownership concentration researchers such as Shehzad, Haan and Scholtens (2010) and Zhu and Yang (2016) examined CAR in the study of state ownership and ownership concentration researchers researchers such such as as Shehzad, Shehzad, Haan Haan and and Scholtens Scholtens (2010) (2010) and and Zhu Zhu and and Yang Yang (2016) (2016) examined CAR in the study ofwe state ownership and ownership concentration have examined CAR inthe the study ofstate state ownership and ownership concentration have have examined examined CAR CAR in in the study study of of state ownership ownership and and ownership ownership concentration concentration intheir their studies. Inthis this paper, use total capital ratio asaaaaameasure measure ofcapital capital in their studies. In this paper, we use total capital ratio as measure of capital in their studies. In this paper, we use total capital ratio as measure of capital in their studies. In this paper, we use total capitalratio ratio as measure of capital in studies. In we use total capital as of in their studies. In this paper, we use total capital ratio as ameasure measure of capital have examined CAR inpaper, the study of state ownership and ownership concentration have have examined examined CAR CAR in in the the study study of of state state ownership ownership and and ownership ownership concentration concentration in their studies. In this paper, we use total capital ratio as a measure of capital in in their their studies. studies. In In this this paper, paper, we we use use total total capital capital ratio ratio as as a a measure measure of of capital capital in their studies. In this paper, we use total capital ratio as a measure of capital in in their their studies. studies. In In this this paper, paper, we we use use total total capital capital ratio ratio as as a a measure of of capital capital adequacy. We expect banks with agreater greaterpercentage percentageof ofMalaysian Malaysiangovernment government adequacy. We expect banks with a greater percentage of Malaysian government adequacy. adequacy. We We expect expect banks banks with with a a greater percentage of Malaysian government adequacy. We expect banks with a greater percentage of Malaysian government adequacy. We expect banks with a greater percentage of Malaysian government in their studies. In this paper, we use total capital ratio as a measure of capital in in their their studies. studies. In In this this paper, paper, we we use use total total capital capital ratio ratio as as a a measure measure of of capital capital adequacy. We expect banks withaaaaaaCAR, greater percentage of Malaysian government adequacy. adequacy. We We expect expect banks banks with with greater greater percentage percentage of of Malaysian government government adequacy. We expect banks with greater percentage ofMalaysian Malaysian government adequacy. adequacy. We We expect expect banks banks with with greater greater percentage percentage of of Malaysian Malaysian government government ownership would have lower which implies that thebank bank mayhave have ownership would have CAR, which implies that the bank may have ownership ownership would would have have aaaaaalower lower CAR, CAR, which which implies implies that that the the bank may may have ownership would have alower lower which that thebank bank may have ownership would have lower which implies bank may have adequacy. We expect banks with aCAR, greater percentage of Malaysian government adequacy. We expect banks with aCAR, greater percentage of Malaysian government adequacy. We expect banks with aCAR, greater percentage of Malaysian government implies bank ownership would have lower CAR, which that the bank may have which that the ownership ownership would would have have a a lower which which that that the the may may have have ownership would have a lower CAR, which implies bank may have ownership ownership would would have have a a lower lower CAR, CAR, which implies implies bank bank may have insufficient capital to absorb any potential losses. CAR can be calculated by using insufficient capital to absorb any potential losses. CAR can be calculated by using insufficient insufficient capital capital to to absorb absorb any any potential potential losses. losses. CAR CAR can can be be calculated calculated by by using using insufficient capital to absorb any potential losses. CAR can be calculated by ownership would have a lower CAR, which implies that the bank may have insufficient capital to absorb any potential losses. CAR can be calculated by using ownership would have a lower CAR, which implies that the bank may have ownership would have a lower CAR, which implies that the bank may insufficient capital to absorb any potential losses. CAR can be calculated by insufficient capital to absorb any potential losses. CAR can be calculated by using insufficient capital to absorb any potential losses. CAR can becalculated calculated by insufficient capital toto absorb any potential losses. CAR canbe be calculated byhave using insufficient capital absorb any potential losses. CAR can be by insufficient capital to absorb any potential losses. CAR can calculated by using the formula as shown below: the formula as shown below: the the formula formula as as shown shown below: below: insufficient capital to absorb any potential losses. CAR can be calculated by using insufficient capital to absorb any potential losses. CAR can be calculated by using the formula as shown below: insufficient capital to absorb any potential losses. CAR can be calculated by using using the formula shown below: the formula as shown below: using the formula asas shown below: the the formula formula as as shown shown below: below: using using the the formula formula as as shown shown below: below: the formula as shown below: theformula formulaas asshown shownbelow: below: the 𝑇𝑖𝑒𝑟 𝑐𝑎𝑝𝑖𝑡𝑎𝑙 𝑡𝑖𝑒𝑟 𝑐𝑎𝑝𝑖𝑡𝑎𝑙 𝑇𝑖𝑒𝑟 𝑐𝑎𝑝𝑖𝑡𝑎𝑙 ++ 𝑡𝑖𝑒𝑟 𝑐𝑎𝑝𝑖𝑡𝑎𝑙 𝑇𝑖𝑒𝑟 𝑇𝑖𝑒𝑟 1111 𝑐𝑎𝑝𝑖𝑡𝑎𝑙 𝑐𝑎𝑝𝑖𝑡𝑎𝑙 + + 𝑡𝑖𝑒𝑟 𝑡𝑖𝑒𝑟 2222 𝑐𝑎𝑝𝑖𝑡𝑎𝑙 𝑐𝑎𝑝𝑖𝑡𝑎𝑙 𝑇𝑖𝑒𝑟 𝑐𝑎𝑝𝑖𝑡𝑎𝑙 + 𝑡𝑖𝑒𝑟 𝑐𝑎𝑝𝑖𝑡𝑎𝑙 𝐶𝑎𝑝𝑖𝑡𝑎𝑙 𝐴𝑑𝑒𝑞𝑢𝑎𝑐𝑦 𝑅𝑎𝑡𝑖𝑜 (𝐶𝐴𝑅)= = 𝑇𝑖𝑒𝑟 𝑐𝑎𝑝𝑖𝑡𝑎𝑙 + 𝑡𝑖𝑒𝑟 𝑐𝑎𝑝𝑖𝑡𝑎𝑙 𝑇𝑖𝑒𝑟 𝑇𝑖𝑒𝑟 𝑐𝑎𝑝𝑖𝑡𝑎𝑙 𝑐𝑎𝑝𝑖𝑡𝑎𝑙 + + 𝑡𝑖𝑒𝑟 𝑡𝑖𝑒𝑟 𝑐𝑎𝑝𝑖𝑡𝑎𝑙 𝑐𝑎𝑝𝑖𝑡𝑎𝑙 𝐶𝑎𝑝𝑖𝑡𝑎𝑙 𝐴𝑑𝑒𝑞𝑢𝑎𝑐𝑦 𝑅𝑎𝑡𝑖𝑜 (𝐶𝐴𝑅) = 𝐶𝑎𝑝𝑖𝑡𝑎𝑙 𝐶𝑎𝑝𝑖𝑡𝑎𝑙 𝐴𝑑𝑒𝑞𝑢𝑎𝑐𝑦 𝐴𝑑𝑒𝑞𝑢𝑎𝑐𝑦 𝑅𝑎𝑡𝑖𝑜 𝑅𝑎𝑡𝑖𝑜 (𝐶𝐴𝑅) (𝐶𝐴𝑅) = 𝐶𝑎𝑝𝑖𝑡𝑎𝑙 𝐴𝑑𝑒𝑞𝑢𝑎𝑐𝑦 𝑅𝑎𝑡𝑖𝑜 (𝐶𝐴𝑅)= 𝑅𝑖𝑠𝑘 𝑤𝑒𝑖𝑔ℎ𝑒𝑑 𝑎𝑠𝑠𝑒𝑡𝑠 𝑇𝑖𝑒𝑟𝑅𝑖𝑠𝑘 1𝑐𝑎𝑝𝑖𝑡𝑎𝑙 𝑐𝑎𝑝𝑖𝑡𝑎𝑙 +𝑡𝑖𝑒𝑟 𝑡𝑖𝑒𝑟 2𝑐𝑎𝑝𝑖𝑡𝑎𝑙 𝑐𝑎𝑝𝑖𝑡𝑎𝑙 𝑇𝑖𝑒𝑟 1𝑅𝑖𝑠𝑘 1𝑅𝑖𝑠𝑘 𝑐𝑎𝑝𝑖𝑡𝑎𝑙 + + 𝑡𝑖𝑒𝑟 𝑐𝑎𝑝𝑖𝑡𝑎𝑙 𝐶𝑎𝑝𝑖𝑡𝑎𝑙 𝐴𝑑𝑒𝑞𝑢𝑎𝑐𝑦 𝑅𝑎𝑡𝑖𝑜 (𝐶𝐴𝑅) ==𝑇𝑖𝑒𝑟 𝐶𝑎𝑝𝑖𝑡𝑎𝑙 𝐶𝑎𝑝𝑖𝑡𝑎𝑙 𝐴𝑑𝑒𝑞𝑢𝑎𝑐𝑦 𝐴𝑑𝑒𝑞𝑢𝑎𝑐𝑦 𝑅𝑎𝑡𝑖𝑜 𝑅𝑎𝑡𝑖𝑜 (𝐶𝐴𝑅) (𝐶𝐴𝑅) = 𝑤𝑒𝑖𝑔ℎ𝑒𝑑 𝑎𝑠𝑠𝑒𝑡𝑠 𝑤𝑒𝑖𝑔ℎ𝑒𝑑 𝑤𝑒𝑖𝑔ℎ𝑒𝑑 𝑎𝑠𝑠𝑒𝑡𝑠 𝑎𝑠𝑠𝑒𝑡𝑠 𝑅𝑖𝑠𝑘 𝑤𝑒𝑖𝑔ℎ𝑒𝑑 𝑎𝑠𝑠𝑒𝑡𝑠 𝐶𝑎𝑝𝑖𝑡𝑎𝑙𝐴𝑑𝑒𝑞𝑢𝑎𝑐𝑦 𝐴𝑑𝑒𝑞𝑢𝑎𝑐𝑦𝑅𝑎𝑡𝑖𝑜 𝑅𝑎𝑡𝑖𝑜(𝐶𝐴𝑅) (𝐶𝐴𝑅)= = 𝐶𝑎𝑝𝑖𝑡𝑎𝑙 𝐶𝑎𝑝𝑖𝑡𝑎𝑙 𝐴𝑑𝑒𝑞𝑢𝑎𝑐𝑦 𝑅𝑎𝑡𝑖𝑜 (𝐶𝐴𝑅) = 𝑅𝑖𝑠𝑘𝑤𝑒𝑖𝑔ℎ𝑒𝑑 𝑤𝑒𝑖𝑔ℎ𝑒𝑑𝑎𝑠𝑠𝑒𝑡𝑠 𝑎𝑠𝑠𝑒𝑡𝑠 𝑅𝑖𝑠𝑘 𝑅𝑖𝑠𝑘 𝑤𝑒𝑖𝑔ℎ𝑒𝑑 𝑎𝑠𝑠𝑒𝑡𝑠 𝑅𝑖𝑠𝑘𝑤𝑒𝑖𝑔ℎ𝑒𝑑 𝑤𝑒𝑖𝑔ℎ𝑒𝑑𝑎𝑠𝑠𝑒𝑡𝑠 𝑎𝑠𝑠𝑒𝑡𝑠 𝑅𝑖𝑠𝑘 𝑅𝑖𝑠𝑘 𝑤𝑒𝑖𝑔ℎ𝑒𝑑 𝑎𝑠𝑠𝑒𝑡𝑠 Moreover, recent studies conducted by De Haan and Van Den End (2013), Moreover, recent studies conducted by De Haan and Van Den End (2013), Moreover, Moreover, recent recent studies studies conducted conducted by by De De Haan Haan and and Van Van Den Den End End (2013), (2013), Moreover, recent studies conducted by De Haan and Van Den End (2013), Moreover, recent studies conducted byDe De Haan and Van Den End (2013), Moreover, recent studies conducted by De Haan and Van Den End (2013), Moreover, Moreover, recent recent studies studies conducted conducted by by De De Haan Haan and and Van Van Den Den End End (2013), (2013), Moreover, recent studies conducted by De Haan and Van Den End (2013), Moreover, Moreover, recent recent studies studies conducted conducted by by De Haan Haan and and Van Van Den Den End End (2013), (2013), Imbierowicz and Rauch (2014), and Zhu and Yang (2016) found that liquidity risk Moreover, recent studies conducted by DeHaan Haan and VanDen Den End (2013), Moreover, Moreover, recent recent studies studies conducted conducted by by De De Haan and and Van Van Den End End (2013), (2013), Imbierowicz and Rauch (2014), and Zhu and Yang (2016) found that liquidity risk Imbierowicz Imbierowicz and and Rauch Rauch (2014), (2014), and and Zhu Zhu and and Yang Yang (2016) (2016) found found that that liquidity liquidity risk risk Imbierowicz and Rauch (2014), and Zhu and Yang (2016) found that liquidity risk Imbierowicz and Rauch (2014), and Zhu and Yang (2016) found that liquidity Imbierowicz and Rauch (2014), and Zhu and Yang (2016) found that liquidity risk Imbierowicz Imbierowicz and and Rauch Rauch (2014), (2014), and and Zhu Zhu and and Yang Yang (2016) (2016) found found that that liquidity liquidity risk risk Imbierowicz and Rauch (2014), and Zhu and Yang (2016) found that liquidity Imbierowicz Imbierowicz and and Rauch Rauch (2014), (2014), and and Zhu Zhu and and Yang Yang (2016) (2016) found found that that liquidity liquidity has a significant impact on bank default probability. Basel III has introduced Imbierowicz andimpact Rauch (2014), and Zhuand and Yang(2016) (2016) found that liquidity risk Imbierowicz Imbierowicz and and Rauch Rauch (2014), (2014), and and Zhu Zhu and Yang Yang (2016) found found that that liquidity liquidity risk risk has significant impact on bank default probability. Basel III has introduced has significant impact on bank default probability. Basel III has introduced has aaaasignificant on bank default probability. Basel III has introduced has significant impact on bank default probability. Basel III has introduced risk has a significant impact on bank default probability. Basel III has introduced has impact on bank default probability. Basel III hasintroduced introduced has has aaahas significant impact impact on on bank bank default default probability. probability. Basel Basel III III has has introduced introduced risk significant impact on bank default probability. Basel III has introduced risk has significant impact on bank default probability. Basel III has introduced risk aaaCoverage significant impact on bank default probability. Basel III has introduced Liquidity Coverage Ratio (LCR) strengthen banks’ liquidity profiles. However, has asignificant significant impact on bank default probability. Basel III has introduced has has ahas asignificant significant significant impact impact on on bank bank default default probability. probability. Basel Basel III III has has introduced Liquidity Ratio (LCR) toto strengthen banks’ liquidity profiles. However, Liquidity Liquidity Coverage Coverage Ratio Ratio (LCR) (LCR) to to strengthen strengthen banks’ banks’ liquidity liquidity profiles. profiles. However, However, Liquidity Coverage Ratio (LCR) to strengthen banks’ liquidity profiles. However, Liquidity Coverage Ratio (LCR) to strengthen banks’ liquidity profiles. However, Liquidity Coverage Ratio (LCR) to strengthen banks’ liquidity profiles. However, Liquidity Liquidity Coverage Coverage Ratio Ratio (LCR) (LCR) to to strengthen strengthen banks’ banks’ liquidity liquidity profiles. profiles. However, However, Liquidity Coverage Ratio (LCR) to strengthen banks’ liquidity profiles. However, liquidity Liquidity Liquidity Coverage Coverage Ratio Ratio (LCR) (LCR) to to strengthen strengthen banks’ banks’ liquidity liquidity profiles. profiles. However, However, due to data limitation, we adopt a static measure for banks’ liquidity usingthe the Liquidity Coverage Ratio (LCR) strengthen banks’ liquidity profiles. However, Liquidity Liquidity Coverage Coverage Ratio Ratio (LCR) (LCR) to to strengthen strengthen banks’ liquidity profiles. profiles. However, However, due to data limitation, we adopt a static measure for banks’ liquidity using the due due to to data data limitation, limitation, we we adopt adopt a a static static measure measure for for banks’ banks’ liquidity liquidity using using the due to data limitation, we adopt a static measure for banks’ liquidity usingthe the due to data limitation, we adopt astatic static measure for banks’ liquidity using due to data limitation, we adopt static measure for banks’ liquidity using the due due to to data data limitation, limitation, we we adopt adopt aaaaaathe static measure measure for for banks’ banks’ liquidity liquidity using using the for due to data limitation, static measure banks’ liquidity using due due to to data data limitation, limitation, static static measure measure for banks’ liquidity using using the the due to data limitation, we adopt static measure for banks’ liquidity using the standard balance sheet data since detailed breakdown of off-balance sheets due due to to data data limitation, limitation, we we adopt adopt static static measure measure for for banks’ banks’ liquidity liquidity using using the the standard balance sheet data since the detailed breakdown of off-balance sheets standard standard balance balance sheet sheet data data since since the the detailed detailed breakdown breakdown of of off-balance off-balance sheets sheets is isisis standard balance sheet data since the detailed breakdown of off-balance sheets standard balance sheet data since the detailed breakdown of off-balance sheets standard balance sheet data since the detailed breakdown of off-balance sheets standard balance sheet data since the detailed breakdown of off-balance sheets isis standard balance sheet data since the detailed breakdown of off-balance sheets isis standard balance sheet data since the detailed breakdown of off-balance sheets standard standard balance balance sheet sheet data data since since the the detailed detailed breakdown breakdown of of off-balance off-balance sheets sheets isisisthat not available. Hence, the liquidity ratio is employed in this study. We expect standard balance sheet data since the detailed breakdown of off-balance sheets standard standard balance balance sheet sheet data data since since the the detailed detailed breakdown breakdown of of off-balance off-balance sheets sheets not available. Hence, the liquidity ratio is employed in this study. We expect that not not available. available. Hence, Hence, the the liquidity liquidity ratio ratio is is employed employed in in this this study. study. We We expect expect that that not available. Hence, theliquidity liquidity ratioisis employed this study. Weexpect expect that isnot not available. Hence, theliquidity liquidity ratio employed this study. We expect this not available. Hence, the liquidity ratio isisemployed employed inin this study. We expect that not not available. available. Hence, Hence, the the liquidity ratio ratio employed in in this study. study. We We expect that that is not available. Hence, thethe liquidity ratio employed this study. expect that not not available. available. Hence, Hence, the liquidity liquidity ratio employed employed in this study. study. We We expect expect that that banks with agreater greater percentage of Malaysian government ownership would have is not available. Hence, the liquidity ratio employed in this study. We expect is not available. Hence, the liquidity ratio isis employed inin this study. We expect is available. Hence, ratio isis employed in this study. We expect banks with a greater percentage of Malaysian government ownership would have banks banks with with a a greater percentage percentage of of Malaysian Malaysian government government ownership ownership would would have have banks with a greater percentage of Malaysian government ownership would have that banks with agreater greater percentage Malaysian government ownership would banks with percentage of Malaysian government ownership would have banks banks with with awith aawith greater percentage percentage of Malaysian government government ownership ownership would would have have banks with agreater greater percentage ofMalaysian Malaysian government ownership would have banks banks with with awith agreater greater greater percentage percentage of Malaysian Malaysian government government ownership ownership would would have have alower lower LR result higher risk-taking. LRcan can becalculated calculated by using the that banks aresult greater percentage ofof Malaysian government ownership would that banks banks aaaresult aresult greater percentage percentage of of Malaysian Malaysian government government ownership ownership would would lower LR as of higher risk-taking. LR can be calculated by using the lower LR asas ofof higher risk-taking. LR can be calculated by using the aaathat LR as aaaLR of higher risk-taking. LR be by using the alower lower LR as result of higher risk-taking. LR can be calculated by using the have a lower as a result of higher risk-taking. LR can be calculated by using LR a lower LR as a result of higher risk-taking. LR be calculated by using the lower LR as a result higher risk-taking. can be calculated by using the aformula a lower lower LR LR as as a a result of of higher risk-taking. risk-taking. LR LR can can be calculated calculated by by using using the the aaahave lower LR LR as as a a result result of of higher higher risk-taking. risk-taking. can can be calculated calculated by using using the the as shown below: haveaaaas lower LRbelow: as resultof ofhigher higherrisk-taking. risk-taking.LR LRcan canbe becalculated calculatedby byusing using have lower lower LR LR as as aaaresult result of higher risk-taking. LR can be calculated by using formula as shown below: formula formula as shown shown below: formula as shown below: formula as shown below: formula formula as as shown shown below: the formula shown below: formula as shown below: formula formula as as shown shown below: below: the formula asas shown below: the the formula formula as as shown shown below: below: (𝐿𝑅) 𝐿𝑖𝑞𝑢𝑖𝑑𝑖𝑡𝑦 𝑅𝑎𝑡𝑖𝑜 (𝐿𝑅) (𝐿𝑅) (𝐿𝑅) 𝐿𝑖𝑞𝑢𝑖𝑑𝑖𝑡𝑦 𝑅𝑎𝑡𝑖𝑜 𝐿𝑖𝑞𝑢𝑖𝑑𝑖𝑡𝑦 𝐿𝑖𝑞𝑢𝑖𝑑𝑖𝑡𝑦 𝑅𝑎𝑡𝑖𝑜 𝑅𝑎𝑡𝑖𝑜 (𝐿𝑅) 𝐿𝑖𝑞𝑢𝑖𝑑𝑖𝑡𝑦 𝑅𝑎𝑡𝑖𝑜 𝐿𝑖𝑞𝑢𝑖𝑑𝑎𝑠𝑠𝑒𝑡𝑠 𝑎𝑠𝑠𝑒𝑡𝑠 𝐿𝑖𝑞𝑢𝑖𝑑 𝐿𝑖𝑞𝑢𝑖𝑑 𝑎𝑠𝑠𝑒𝑡𝑠 𝐿𝑖𝑞𝑢𝑖𝑑𝑖𝑡𝑦 𝑅𝑎𝑡𝑖𝑜 (𝐿𝑅) (𝐿𝑅) 𝐿𝑖𝑞𝑢𝑖𝑑𝑖𝑡𝑦 𝑅𝑎𝑡𝑖𝑜 𝐿𝑖𝑞𝑢𝑖𝑑𝑖𝑡𝑦 𝐿𝑖𝑞𝑢𝑖𝑑 𝑎𝑠𝑠𝑒𝑡𝑠 ൌ (𝐿𝑅) ൌ ൌ (𝐿𝑅) 𝑅𝑎𝑡𝑖𝑜 (𝐿𝑅) (𝐿𝑅) 𝐿𝑖𝑞𝑢𝑖𝑑𝑖𝑡𝑦𝑅𝑎𝑡𝑖𝑜 𝑅𝑎𝑡𝑖𝑜 𝐿𝑖𝑞𝑢𝑖𝑑𝑖𝑡𝑦 𝐿𝑖𝑞𝑢𝑖𝑑𝑖𝑡𝑦 𝑅𝑎𝑡𝑖𝑜 𝐿𝑖𝑞𝑢𝑖𝑑 𝑎𝑠𝑠𝑒𝑡𝑠 𝐿𝑖𝑞𝑢𝑖𝑑 𝐿𝑖𝑞𝑢𝑖𝑑 𝑎𝑠𝑠𝑒𝑡𝑠 𝑎𝑠𝑠𝑒𝑡𝑠 𝐿𝑖𝑞𝑢𝑖𝑑 𝑎𝑠𝑠𝑒𝑡𝑠 = 𝐿𝑖𝑞𝑢𝑖𝑑 𝑎𝑠𝑠𝑒𝑡𝑠 𝐿𝑖𝑞𝑢𝑖𝑑 𝐿𝑖𝑞𝑢𝑖𝑑 𝑎𝑠𝑠𝑒𝑡𝑠 𝑎𝑠𝑠𝑒𝑡𝑠 𝐷𝑒𝑝𝑜 𝑠𝑖𝑡𝑠𝑓𝑟𝑜𝑚 𝑓𝑟𝑜𝑚𝑐𝑢𝑠𝑡𝑜𝑚𝑒𝑟𝑠 𝑐𝑢𝑠𝑡𝑜𝑚𝑒𝑟𝑠 𝑎𝑛𝑑𝑝𝑙𝑎𝑐𝑒𝑚𝑒𝑛𝑡𝑠 𝑝𝑙𝑎𝑐𝑒𝑚𝑒𝑛𝑡𝑠 𝐷𝑒𝑝𝑜 𝑠𝑖𝑡𝑠 𝑠𝑖𝑡𝑠 𝑓𝑟𝑜𝑚 𝑐𝑢𝑠𝑡𝑜𝑚𝑒𝑟𝑠 𝑎𝑛𝑑 𝑎𝑛𝑑 𝑝𝑙𝑎𝑐𝑒𝑚𝑒𝑛𝑡𝑠 == 𝐷𝑒𝑝𝑜𝑠𝑖𝑡𝑠 𝑓𝑟𝑜𝑚 𝑐𝑢𝑠𝑡𝑜𝑚𝑒𝑟𝑠𝐷𝑒𝑝𝑜 = = 𝑎𝑛𝑑 𝑝𝑙𝑎𝑐𝑒𝑚𝑒𝑛𝑡𝑠 𝑓𝑟𝑜𝑚 𝑓𝑖𝑛𝑎𝑛𝑐𝑖𝑎𝑙 𝑖𝑛𝑠𝑡𝑖𝑡𝑢𝑡𝑖𝑜𝑛𝑠 =𝐷𝑒𝑝𝑜𝑠𝑖𝑡𝑠 = = 𝐷𝑒𝑝𝑜𝑠𝑖𝑡𝑠 𝑓𝑟𝑜𝑚 𝑐𝑢𝑠𝑡𝑜𝑚𝑒𝑟𝑠 𝑎𝑛𝑑 𝑝𝑙𝑎𝑐𝑒𝑚𝑒𝑛𝑡𝑠 𝑓𝑟𝑜𝑚 𝑓𝑖𝑛𝑎𝑛𝑐𝑖𝑎𝑙 𝑖𝑛𝑠𝑡𝑖𝑡𝑢𝑡𝑖𝑜𝑛𝑠 𝐷𝑒𝑝𝑜𝑠𝑖𝑡𝑠 𝑓𝑟𝑜𝑚 𝑐𝑢𝑠𝑡𝑜𝑚𝑒𝑟𝑠 𝑎𝑛𝑑 𝑝𝑙𝑎𝑐𝑒𝑚𝑒𝑛𝑡𝑠 𝑓𝑟𝑜𝑚 𝑓𝑖𝑛𝑎𝑛𝑐𝑖𝑎𝑙 𝑖𝑛𝑠𝑡𝑖𝑡𝑢𝑡𝑖𝑜𝑛𝑠 𝑓𝑟𝑜𝑚 𝑐𝑢𝑠𝑡𝑜𝑚𝑒𝑟𝑠 𝑎𝑛𝑑 𝑝𝑙𝑎𝑐𝑒𝑚𝑒𝑛𝑡𝑠 𝑓𝑟𝑜𝑚 𝑓𝑖𝑛𝑎𝑛𝑐𝑖𝑎𝑙 𝑖𝑛𝑠𝑡𝑖𝑡𝑢𝑡𝑖𝑜𝑛𝑠 𝐷𝑒𝑝𝑜𝑠𝑖𝑡𝑠 𝑓𝑟𝑜𝑚 𝑐𝑢𝑠𝑡𝑜𝑚𝑒𝑟𝑠 𝑎𝑛𝑑 𝑝𝑙𝑎𝑐𝑒𝑚𝑒𝑛𝑡𝑠 𝑓𝑟𝑜𝑚 𝑓𝑖𝑛𝑎𝑛𝑐𝑖𝑎𝑙 𝑖𝑛𝑠𝑡𝑖𝑡𝑢𝑡𝑖𝑜𝑛𝑠 𝑓𝑟𝑜𝑚 𝑓𝑖𝑛𝑎𝑛𝑐𝑖𝑎𝑙 𝑖𝑛𝑠𝑡𝑖𝑡𝑢𝑡𝑖𝑜𝑛𝑠 𝑓𝑟𝑜𝑚 𝑓𝑟𝑜𝑚 𝑓𝑖𝑛𝑎𝑛𝑐𝑖𝑎𝑙 𝑓𝑖𝑛𝑎𝑛𝑐𝑖𝑎𝑙 𝑖𝑛𝑠𝑡𝑖𝑡𝑢𝑡𝑖𝑜𝑛𝑠 𝑖𝑛𝑠𝑡𝑖𝑡𝑢𝑡𝑖𝑜𝑛𝑠 𝐷𝑒𝑝𝑜𝑠𝑖𝑡𝑠 𝑓𝑟𝑜𝑚 𝑐𝑢𝑠𝑡𝑜𝑚𝑒𝑟𝑠 𝑎𝑛𝑑 𝑝𝑙𝑎𝑐𝑒𝑚𝑒𝑛𝑡𝑠 𝑓𝑟𝑜𝑚 𝑓𝑖𝑛𝑎𝑛𝑐𝑖𝑎𝑙 𝑖𝑛𝑠𝑡𝑖𝑡𝑢𝑡𝑖𝑜𝑛𝑠 𝐷𝑒𝑝𝑜𝑠𝑖𝑡𝑠 𝐷𝑒𝑝𝑜𝑠𝑖𝑡𝑠 𝑓𝑟𝑜𝑚 𝑓𝑟𝑜𝑚 𝑐𝑢𝑠𝑡𝑜𝑚𝑒𝑟𝑠 𝑐𝑢𝑠𝑡𝑜𝑚𝑒𝑟𝑠 𝑎𝑛𝑑 𝑎𝑛𝑑 𝑝𝑙𝑎𝑐𝑒𝑚𝑒𝑛𝑡𝑠 𝑝𝑙𝑎𝑐𝑒𝑚𝑒𝑛𝑡𝑠 𝑓𝑟𝑜𝑚 𝑓𝑟𝑜𝑚 𝑓𝑖𝑛𝑎𝑛𝑐𝑖𝑎𝑙 𝑓𝑖𝑛𝑎𝑛𝑐𝑖𝑎𝑙 𝑖𝑛𝑠𝑡𝑖𝑡𝑢𝑡𝑖𝑜𝑛𝑠 𝑖𝑛𝑠𝑡𝑖𝑡𝑢𝑡𝑖𝑜𝑛𝑠
Moderating variables 3.2.3. Moderating variables 3.2.3 variables 3.2.3 Moderating variables 3.2.3 variables 3.2.3. Moderating variables 3.2.3. 3.2.3. Moderating Moderating variables variables 3.2.3. Moderating variables 3.2.3.Moderating Moderating variables 3.2.3. Moderating Moderating variables variables 3.2.3. Moderating variables 3.2.3. 3.2.3. Moderating Moderating variables variables Three board characteristics variables have been chosen to further investigate the Three board characteristics variables have been chosen further investigate the Three board characteristics variables have been chosen to further investigate the Three Three board board characteristics characteristics variables variables have have been been chosen chosen to to further further investigate investigate the the to Three board characteristics variables have been chosen toto further investigate the Three Three board board characteristics characteristics variables have been chosen chosen to further further investigate investigate the the Three board characteristicsvariables variables have chosen to further investigate the Three Three board board characteristics characteristics variables have have chosen chosen to further further investigate investigate the the board characteristics variableshave havebeen been chosen to further investigate the Three board characteristics variables have been chosen to further investigate the Three Three board board characteristics characteristics variables variables have have been been chosen chosen to to further further investigate investigate the the effect of corporate governance aspects in the study of the relationship between effectof ofcorporate corporategovernance governanceaspects aspectsin inthe thestudy studyof ofthe therelationship relationshipbetween between effect effect of corporate governance aspects in the study of the relationship between state ownership and bank’s risk-taking behaviour. They are known as the board independence, foreign director and female director. One of the most used board characteristics variable by previous researchers is board independence. The independence of directors is the central theme in governance issues. Bursa Malaysia main market listing requirements Chapter 3: Admission part 3.04 mentioned that (1) an applicant must ensure that at least 2 directors or 1/3 of the board of directors of the applicant, whichever is higher, are independent directors. (2) If the number of directors of the applicant is not 3 or a multiple of 3, then the number nearest 1/3 must be used. Realising the importance of quality corporate governance, we introduce dummy as adopted in Zhu, Ye, Tucker and Chan (2016). Theoretically, banks with board independence ratio more than or equal to 0.33 should take a value of “1” or “0” otherwise. However, we find that most of the sample banks in our study have made an effort in adhering to the above requirements by having more than 0.33 independent directors in their board structure. To study the differences in board independence composition in banks’ risk-taking behaviour, we have identified banks with board independence ratio more than or equal to 0.50 should take a value of “1” or “0” otherwise. This is to the extent of studying whether a larger proportion of independent directors have a greater impact on the banks’ risk-taking. Other than board independence, the dummy of foreign directors on board is also employed as one of the moderating variables in studying the relationship between state ownership and bank’s risk-taking behaviour. Following Du, Jian and Lai (2017), we take a value of “1” if one or more directors come(s) from other countries or “0” otherwise. Besides, one of the utmost popular issues examined by researchers Liu, Wei and Xie (2014), Gulamhussen and Santa (2015) and Zhu and Yang (2016) in their research study was gender diversity. We would like to know whether female director helps in bank monitoring and reduce the likelihood of excessive risk-taking. Hence, we employ the dummy of female directors as one of the moderating variables in this study. We take a value of “1” if there is at least one woman on the board of directors of the bank or “0” otherwise.
There are three control variables employed in this study. They are: bank size, bank revenue growth and return on assets. The bank size is measured by the logarithm of the bank’s total assets. This has been adopted in the studies by Anginer et al. (2018), Brandao-Marques et al. (2020), Laeven and Levine (2009), Lassoued et al. (2016), and Zhu and Yang (2016) while investigating the bank’s risk-taking. The reason for controlling this variable is bank size may influence a bank’s level of risk-taking. There is a possibility that larger banks may have better risk diversification and greater capacity to absorb their risk-taking.
The revenue growth informs us how much additional annual revenue that a business can handle according to the resources in the balance sheet. Bank revenue growth is adopted as one of the control variables since every bank has a different revenue growth rate and it is believed that large banks may have higher revenue growth as they are accessible to greater resources that are available. 11 growth are to resources that available. To To increase thethey reliability of the result conducted, we control bank revenue growth as as they are accessible accessible to greater greater resources that are arethe available. To increase increase the conducted, we growth. growth. The bank of revenue growth can be calculated by the taking therevenue first difference the reliability reliability of the the result result conducted, we control control the bank bank revenue growth. The The bank revenue growth can be calculated by taking the first difference of the logarithm total operating bank revenueof growth can be income. calculated by taking the first difference of of the the logarithm of income. The return on operating assets (ROA) is another control variable employed in this logarithm of total total operating income. The on assets control employed The return return on widely assets (ROA) (ROA) is another another control variable employed in in this this study. The ROA has been used in is the previous studiesvariable while investigating study. The ROA has been widely used in the previous studies while investigating The ROA has been widely used the previous whilesuch investigating the study. relationship between the bank’s state in ownership and studies risk-taking, as the between bank’s state ownership and as the relationship relationship between the bank’s state ownership and risk-taking, risk-taking, such as Dong, Dong, Dong, Liu, Shen and Sun’sthe (2016), and Lassoued et al.’s (2016). Thesuch reason Liu, Shen and Sun’s (2016), and Lassoued et al.’s (2016). The reason Liu, Shen this and variable Sun’s (2016), andtheLassoued et al.’s The reason for for controlling is because bank’s total assets(2016). may influence the for controlling this variable is because the bank’s total assets may influence controlling this and variable because the bank’s assets may influence the incomes generated it alsoisgives an impact on the total bank’s risk-taking. There the generated itit also an impact on bank’s risk-taking. incomes generated and also gives anmore impact on the the bank’s risk-taking. There is aa is aincomes possibility that the and banks whogives owned assets will earn higher ROAThere and is possibility that the banks who owned more assets will earn higher ROA and possibility thatrisks. the banks who owned more assets will earn higher ROA and exposed to greater exposed exposed to to greater greater risks. risks. 3.3
Model 3.3.
Model
Based on the above variables, Model 1 and 2 are the baseline models used in Model 11 and the baseline used this Based study on to the regress onvariables, the relationship state and banks’ Based on the above above variables, Model between and 22 are are theownership baseline models models used in in this this study to regress on the relationship between state ownership and banks’ risk-taking study tobehaviour. regress onModel the relationship between state ownership and banks’ risk-taking risk-taking 1 is used to test hypothesis 1a in which to capture behaviour. Model 11 is used to capture behaviour. Model isthe used to test test hypothesis hypothesis 1a in in which which to tobanks. capture the risk risk the risk difference between state-owned banks and 1a private-owned Thethe difference between the state-owned banks and private-owned banks. The degree of difference between the state-owned banks and private-owned banks. The degree of degree of state ownership, SO is introduced in model 2 to test the degree of state ownership, SO is introduced in model to test the degree of state ownership state ownership, SO is introduced in model to test the degree of state ownership state ownership through GLICs on the banks’ risk-taking behaviour, as shown through through GLICs GLICs on the the banks’ banks’ risk-taking risk-taking behaviour, behaviour, as as shown shown in in hypothesis hypothesis 1b: 1b: in hypothesis 1b: on Risk + 𝛽 LTA + 𝛽 RG + 𝛽 ROA + 𝜀 Risk���� = = 𝛼𝛼 + +𝛽 𝛽��Dummy Dummy���� ���� + 𝛽�� LTA�� �� + 𝛽�� RG�� �� + 𝛽�� ROA�� �� + 𝜀�� �� ) + Risk 𝛽 Dummy + (𝑆𝑂 × Risk���� = = 𝛼𝛼 + +𝛽 𝛽��𝑆𝑂 𝑆𝑂���� + +𝛽 𝛽��(𝑆𝑂 × 𝐷𝑢𝑚𝑚𝑦 𝐷𝑢𝑚𝑚𝑦�� �� )�� �� + 𝛽�� Dummy���� ���� + 𝛽 LTA + 𝛽 RG + 𝛽 ROA + 𝜀 𝛽�� LTA���� + 𝛽�� RG���� + 𝛽�� ROA���� + 𝜀���� testing for the effect governance mechanisms on banks’ In testing forIn effect governance mechanisms on banks’ Inthe testing forof thecorporate effect of of corporate corporate governance mechanisms onriskbanks’ riskrisktaking behaviour, we develop six models as follows: To test for the interaction taking behaviour, we develop six six models as as follows: ToTo testtest forfor thetheinteraction taking behaviour, we develop models follows: interaction effect effect of hypothesis 2, we add board independence variable in Model 33 and Model effect hypothesis add board independence variable Model 3and and Model4. ofof hypothesis 2, 2, wewe add board independence variable inin Model Model 4. Model Model is used to investigate the interaction between board independence and 3 is used to investigate the the interaction between board independence and state state 4. Model 3 is used to investigate interaction between board independence ownership on banks’ risk-taking whereas the interaction between on on banks’ whereasthethe interaction between board and ownership state ownership banks’ risk-taking whereas interaction between board board independence the of ownership is in independence and the degree degree of state state ownership is tested tested in Model Model 4: independence and and the degree of state ownership is tested in Model 4: 4: Risk ) + Risk���� = = 𝛼𝛼 + +𝛽 𝛽��𝐵𝑜𝑎𝑟𝑑_𝐼 𝐵𝑜𝑎𝑟𝑑_𝐼���� + +𝛽 𝛽��(𝐵𝑜𝑎𝑟𝑑_𝐼 (𝐵𝑜𝑎𝑟𝑑_𝐼 × × 𝐷𝑢𝑚𝑚𝑦 𝐷𝑢𝑚𝑚𝑦�� �� )�� �� + 𝛽 Dummy + 𝛽 LTA + 𝛽 RG + 𝛽 ROA +𝜀 𝛽�� Dummy���� ���� + 𝛽�� LTA�� �� + 𝛽�� RG�� �� + 𝛽�� ROA�� �� + 𝜀�� ��
Risk Risk���� = = 𝛼𝛼 + +𝛽 𝛽��𝐵𝑜𝑎𝑟𝑑_𝐼 𝐵𝑜𝑎𝑟𝑑_𝐼���� + +𝛽 𝛽��(𝐵𝑜𝑎𝑟𝑑_𝐼 (𝐵𝑜𝑎𝑟𝑑_𝐼 × × 𝑆𝑂) 𝑆𝑂)���� + +𝛽 𝛽��SO SO���� + + 𝛽 LTA + 𝛽 RG + 𝛽 ROA + 𝜀 𝛽�� LTA���� + 𝛽�� RG���� + 𝛽�� ROA���� + 𝜀����
To To test test for for the the interaction interaction effect effect of of hypothesis hypothesis 3, 3, we we add add foreign foreign director director independence and the degree of state ownership is tested in Model 4: Risk �� = 𝛼 + 𝛽� 𝐵𝑜𝑎𝑟𝑑_𝐼�� + 𝛽� (𝐵𝑜𝑎𝑟𝑑_𝐼 × 𝐷𝑢𝑚𝑚𝑦�� )�� + 𝛽� Dummy���� + 𝛽� LTA�� + 𝛽� RG�� + 𝛽� ROA�� + 𝜀��
Risk �� = 𝛼 + 𝛽� 𝐵𝑜𝑎𝑟𝑑_𝐼�� + 𝛽� (𝐵𝑜𝑎𝑟𝑑_𝐼 × 𝑆𝑂)�� + 𝛽� SO�� + 𝛽� LTA�� + 𝛽� RG�� + 𝛽� ROA�� + 𝜀�� (4)(4) To test for interaction effect of hypothesis we add foreign director variable Risk = 𝛼 𝛽� 𝐹𝑜𝑟𝑒𝑖𝑔𝑛_𝐷 + 3, 𝛽of � (𝐹𝑜𝑟𝑒𝑖𝑔𝑛_𝐷 �� )�� + 12 the To��test for+the interaction effect hypothesis 3, × we𝐷𝑢𝑚𝑚𝑦 add foreign director �� in Model and Model 6. Model is used to investigate the interaction between (𝐹𝑜𝑟𝑒𝑖𝑔𝑛_𝐷 ) Risk �� = 𝛼 + 𝛽𝛽��𝐹𝑜𝑟𝑒𝑖𝑔𝑛_𝐷 + 𝛽 × 𝐷𝑢𝑚𝑚𝑦 + 𝛽 LTA + 𝛽 RG + 𝛽 ROA + 𝜀 variable in Model 5Dummy and Model 6. Model is used to investigate the interaction � �� �� ���� + � �� � �� � �� �� �� foreign director and state ownership on banks’ risk-taking whereas the interaction between foreign director and ownership whereas the (5) 𝛽� Dummy + 𝛽�state LTA�� + 𝛽 RG��on+ banks’ 𝛽� ROArisk-taking 𝜀�� ���� �� + in between is tested Model 6: 12foreign director and the degree of state� ownership interaction between foreign director and the degree of state ownership is tested (5)in Risk �� = 𝛼 + 𝛽� 𝐹𝑜𝑟𝑒𝑖𝑔𝑛_𝐷 �� + 𝛽� (𝐹𝑜𝑟𝑒𝑖𝑔𝑛_𝐷 × 𝐷𝑢𝑚𝑚𝑦�� )�� + Model Risk 6: �� = 𝛼 + 𝛽� 𝐹𝑜𝑟𝑒𝑖𝑔𝑛_𝐷�� + 𝛽� (𝐹𝑜𝑟𝑒𝑖𝑔𝑛_𝐷 × 𝑆𝑂)�� + )��𝜀+ Risk �� = 𝛼 + 𝛽𝛽 × 𝐷𝑢𝑚𝑚𝑦�� + (5) � 𝐹𝑜𝑟𝑒𝑖𝑔𝑛_𝐷 � (𝐹𝑜𝑟𝑒𝑖𝑔𝑛_𝐷 Dummy���� ��++𝛽�𝛽LTA �� + 𝛽� RG�� + 𝛽� ROA�� �� SO 𝛽 Risk �� = 𝛼 +�𝛽𝛽��𝐹𝑜𝑟𝑒𝑖𝑔𝑛_𝐷 𝑆𝑂)���� + + 𝜀�� �� + 𝛽� LTA � RG�� + 𝛽× � ROA
�� +��𝛽+ � (𝐹𝑜𝑟𝑒𝑖𝑔𝑛_𝐷 𝛽� Dummy���� + 𝛽� LTA�� + 𝛽� RG�� + 𝛽� ROA�� + 𝜀�� (5) (6) 𝛽� SO�� + 𝛽� LTA�� + 𝛽� RG�� + 𝛽� ROA�� + 𝜀�� (5) (6) Risk �� = 𝛼 + 𝛽� 𝐹𝑜𝑟𝑒𝑖𝑔𝑛_𝐷�� + 𝛽� (𝐹𝑜𝑟𝑒𝑖𝑔𝑛_𝐷 × 𝑆𝑂)�� + To test for the interaction effect of hypothesis 4, we add(6)female director Risk �� = 𝛼 + 𝛽𝛽 𝑆𝑂)���� + +(𝐹𝑜𝑟𝑒𝑖𝑔𝑛_𝐷 𝛽� RG�� + 𝛽×� ROA +𝜀 � 𝐹𝑜𝑟𝑒𝑖𝑔𝑛_𝐷 �� +��𝛽� � SO �� + 𝛽�7LTA variable in Model and Model 8. Model 7 is used4,��toweinvestigate interaction To test for the interaction effect of hypothesis add femalethedirector + 𝛽 LTA�� + 𝛽� RG�� + 4, 𝛽� we ROA +female 𝜀�� director variable (6) the � SO�� female To testvariable for between the𝛽interaction of hypothesis director and8. state ownership onto banks’ risk-taking whereas in Model �7effect and Model Model 7 add is��used investigate the interaction (6) in Model 7interaction andfemale Modelbetween 8. Modelfemale 7 is state used to investigate the interaction director and the ofrisk-taking state between ownership is tested between director and ownership ondegree banks’ whereas the in To test for the interaction effect of hypothesis 4, we add female director femaleinteraction director and state ownership on banks’ risk-taking whereas the interaction Model 8: between female director and the degree of state ownership is tested in between female director and of8.state is4,tested in Model 8:director variable in for Model 7 the anddegree Model Model 7 is used to interaction To the interaction effect of ownership hypothesis weinvestigate add femalethe Model 8: test between female director and state ownership on banks’ risk-taking whereas the variable in Model and Model 8. Model is used to investigate the interaction Risk �� = 𝛼 + 𝛽� 𝐹𝑒𝑚𝑎𝑙𝑒_𝐷�� + 𝛽� (𝐹𝑒𝑚𝑎𝑙𝑒_𝐷 × 𝐷𝑢𝑚𝑚𝑦�� )�� + interaction between female director and the degree of state ownership is tested in between female director and state ownership on banks’ risk-taking whereas the Dummy���� + 𝛽𝛽��(𝐹𝑒𝑚𝑎𝑙𝑒_𝐷 LTA�� + 𝛽� RG Risk �� = 𝛼 + 𝛽𝛽��𝐹𝑒𝑚𝑎𝑙𝑒_𝐷 × 𝐷𝑢𝑚𝑚𝑦 �� + 𝛽� ROA �� + �� )����++ 𝜀�� (7) Modelbetween 8: interaction female director and the degree of state ownership is tested in (7) 𝛽� Dummy���� + 𝛽� LTA�� + 𝛽� RG�� + 𝛽� ROA�� + 𝜀�� Model 8: (7) Risk �� = 𝛼 + 𝛽� 𝐹𝑒𝑚𝑎𝑙𝑒_𝐷�� + 𝛽� (𝐹𝑒𝑚𝑎𝑙𝑒_𝐷 × 𝐷𝑢𝑚𝑚𝑦�� )�� + Risk �� = 𝛼 + 𝛽� 𝐹𝑒𝑚𝑎𝑙𝑒_𝐷�� + 𝛽� (𝐹𝑒𝑚𝑎𝑙𝑒_𝐷 × 𝑆𝑂)�� + (8) Risk �� = 𝛼 + 𝛽𝛽 𝐷𝑢𝑚𝑚𝑦 )�� ��++ 𝜀�� Dummy���� LTA�� + 𝛽�×RG ROA � 𝐹𝑒𝑚𝑎𝑙𝑒_𝐷 �� ++𝛽𝛽 ��(𝐹𝑒𝑚𝑎𝑙𝑒_𝐷 �� + 𝛽��� SO�� + 𝛽� LTA + 𝛽 RG + 𝛽 ROA + 𝜀 Risk �� = 𝛼 +�𝛽𝛽��𝐹𝑒𝑚𝑎𝑙𝑒_𝐷 + 𝛽 (𝐹𝑒𝑚𝑎𝑙𝑒_𝐷 × 𝑆𝑂) + �� � �� � �� �� �� � �� 𝛽� Dummy���� + 𝛽� LTA (7) �� + 𝛽� RG�� + 𝛽� ROA �� + 𝜀�� (8) 𝛽� SO�� + 𝛽� LTA�� + 𝛽� RG�� + 𝛽� ROA�� + 𝜀�� (7) where Riskit is the risk measures for bank i in year t. The risk measurements (8) Risk �� = 𝛼 + 𝛽� 𝐹𝑒𝑚𝑎𝑙𝑒_𝐷�� + 𝛽� (𝐹𝑒𝑚𝑎𝑙𝑒_𝐷 × 𝑆𝑂)�� + where Riskit is consist the riskofmeasures for CAR, bank iand in year The risk measurements in the in the above regressions NPL ratio, LR. t.Dummy is the SOit Risk 𝛼+𝛽 +i 𝛽 (𝐹𝑒𝑚𝑎𝑙𝑒_𝐷 +the 𝛽 +𝛽 LTA 𝛽NPL + ×𝛽𝑆𝑂) ROA + 𝜀�� �� = � 𝐹𝑒𝑚𝑎𝑙𝑒_𝐷 �� �+ ��is � SO��for �consist �� � RG ��ratio, �SO �� state ownership bank in year t whereas state ownership above regressions of CAR, and LR. Dummy is the state SOit where Riskdummy is the risk measures for bank i in year t. The risk measurements in the it it 𝛽�bank SO�� + 𝛽�year LTAfor + 𝛽� RG +the𝛽�tboard ROA + SO 𝜀�� it is the state (8) �� ��isyear ��independence percentage for i in t. Board_I dummy for ownership dummy bank i in whereas ownership percentage above regressions consist of NPLit ratio, CAR, and LR. DummySOit is the state (8) i in year t. bank iownership in year t. dummy Foreign_D thei in foreign forstate bank i in year for bank i in year t.isBoard_I boarddummy independence dummy for t. bank it is the foritbank year tdirector whereas SOit is the ownership percentage the riskforeign measures forforbank i in year t. dummy The risk measurements initt.is thethe Female_D is the director dummy bank i in t. LTA ,for RG andi in year it is director dummy foryear bank i in t. it is forwhere bank iRisk in female year t. the Board_I board independence bank itForeign_D ityear it, Female_D it is the above regressions consist of NPL ratio, CAR, and LR. Dummy is the state where Risk is the risk measures for bank i in year t. The risk measurements in ROA are the control variables for bank i in year t. SOit it femaleit is director dummy for bank i in year t. LTA , RG ROAit areit the control it, and itForeign_D the foreign director dummy for bank i itin year t. Female_D is the ownership dummy bank i in t CAR, whereas SO,LR. state ownership above regressions consist NPL and Dummy state it is the SOit is the percentage variables fordummy bankfori of in year t. ratio, female director for bank iyear in year t. LTA it RGit, and ROAit are the control for bank year t. Board_I boardSO independence forpercentage bank i in year t. ownership dummy fori bank i int.year whereas ownership it istthe it is the state dummy variables fori in bank in year 4. Results Foreign_D is the foreign director dummy for bank i in year t. Female_D for bank i in year t. Board_I is the board independence dummy for bank i in year itt.is the it it Statistics
female director dummy for bank i in year t. LTA ROAit areit is thethe control Foreign_D the foreign director dummy for bank i in yearit, and t. Female_D it, RG it is 3.4. Descriptive Statistics 4.1female Descriptive Statistics variables bank ifor in year directorfor dummy bankt.i in year t. LTAit, RGit, and ROAit are the control Table 1 below provides the descriptive statistics for all the variables used in the variables for bank i in year t. model of this study. In descriptive the statistics contextstatistics of have in anthe average of 2.16 Table 1 below provides the forvariables allbanks the variables used in the
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a lower CAR (13.26%) in China. Similar statistics are found in Shaban and James’s (2018) study in Indonesia. The statistics show higher NPL (3.01%) and LR (25.75%), and lower CAR (11.91%). This enlightens that Malaysia’s banks have substantially lower default loans and a higher level of capital to absorb any potential losses. However, Malaysia’s banks are found to have less liquid assets. Another interesting variable worth mention is state ownership (SO). SO has a range from 12.12 percent to 66.87 percent. On average, SO owns 33.14 percent in Malaysia’s banking industry. Lassoued et al. (2016) found an average of 14.49 percent SO in MENA countries whereas an average of 11.09 percent SO is found in Vietnam by Vo (2018). These numbers illuminate that the government involvement in Malaysia’s banking industry is fairly high. This further motivates the researcher to examine the impact of state ownership on banks’ risk-taking.
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Note: Obs. = Observations, Std. Dev. = Standard deviation, NPL = Non-performing loans ratio, CAR = Capital adequacy ratio, LR = Liquidity ratio, DummySO = State ownership dummy, SO = State ownership percentage, Board_I = Board independence dummy, Foreign_D = Foreign director dummy, Female_D = Female director dummy, TA = Total assets (in trillions), RG = Revenue growth and ROA = Return on assets.
| Variables | NPL | CAR | LR | |||
|---|---|---|---|---|---|---|
| (1) | (2) | (3) | (4) | (5) | (6) | |
| Dummyso | 0.0096** 0.0003 (0.0138) | (0.9810) | -0.0015 (0.6569) | -0.0554*** 0.0195 (0.0043) | (0.1451) | -0.0059 (0.9650) |
| Dummyso* | 0.1201 | 0.2226* | 1.2327 | |||
| SO | (0.2890) | (0.0530) | (0.2272) | |||
| SO | -0.1455 (0.2071) | -0.1533 (0.1471) | -1.7126 (0.1345) | |||
| LTA | -0.0055 (0.1037) | -0.0020 (0.5886) | 0.0176*** (0.0011) | 0.0098* (0.0593) | 0.0602** (0.0185) | 0.1228* (0.0600) |
| RG | -0.0096 (0.1371) | -0.0152* 0.0142 (0.0735) | (0.1674) | 0.01427 (0.1528) | -0.0858 (0.5160) | -0.1623 (0.4765) |
| ROA | -0.0039 (0.5459) | -0.0014 (0.8822) | 0.0040 (0.6131) | 0.01165 (0.3292) | 0.0812* (0.0652) | 0.0610 (0.1854) |
| Constant | 0.0832** 0.0634 (0.0114) | (0.1301) | -0.0508 (0.2943) | 0.0489 (0.3926) | -0.4079** (0.0477) | -0.8683 (0.1169) |
| Observation | 40 | 40 | 40 | 40 | 40 | 40 |
| R2 | 0.2886 | 0.3260 | 0.2991 | 0.3880 | 0.2126 | 0.3096 |
Note: ***, **, * denote significance at 1%, 5% and 10% significant levels respectively. NPL = Non-performing loans ratio, CAR = Capital adequacy ratio, LR = Liquidity ratio, DummySO = State ownership dummy, DummySO * SO = Interaction of state ownership dummy and state ownership percentage, SO = State ownership percentage, LTA = Logarithm of total assets, RG = Revenue growth and ROA = Return on assets.
Table 2 presents the regression results for the baseline models, model 1 and 2. The regression includes all control variables and R2 is reported at the bottom of the table. Column 1, 3, 5 in Table 2 shows the SOBs’ baseline result as compared with the POBs. Based on column 1 in Table 2, SOBs are associated with significantly higher non-performing loans (NPL) ratio of 0.0096 percent compared with the POBs, at 95 percent confidence level. The other two risktaking measurements, however, are not significant. Hence, we do not find strong evidence to support hypothesis 1a. Column 2, 4 and 6 is the model used to test the interaction of dummy and the degree of state ownership. The results show that the interaction of dummy and state ownership only significant in terms of Capital Adequacy Ratio (CAR). The significant positive coefficient of 0.2226 in column 4 suggests that a higher degree of state ownership tends to improve CAR. In addition, the results are found to have no significant impact on banks’ NPLs and LR. Thus, hypothesis 1b is rejected. As for the bank size, the results reveal that larger banks tend to have higher capital adequacy and liquidity, hence lowering the risk-taking behaviour. This is according to the literature that larger banks have better risk diversification and able to reduce high risk-taking activities. 4.3
Table 3 presents the regression result of the effect of board independence in the relationship between state ownership and banks’ risk-taking. The result shows that when more than half of the board of directors were independent directors, it has significant positive impacts on NPL ratio and liquidity ratio (LR) with a coefficient of 0.0112 and 0.1856 at 95 percent confidence level and 99 percent confidence level respectively.
| Risk-Taking | |||
|---|---|---|---|
| Variables | NPL | CAR | LR |
| Board_I | -0.0093** (0.0135) | 0.0004 (0.9061) | -0.1273*** (0.0024) |
| Board_I * Dummyso | 0.0112** (0.0168) | -0.0085 (0.2437) | 0.1856*** (0.0000) |
| Dummyso | 0.0051 (0.2381) | 0.0051 (0.4389) | -0.0674*** (0.0000) |
| LTA | -0.0076* (0.0888) | 0.0182*** (0.0001) | 0.0284* (0.0891) |
| RG | -0.0145** (0.0476) | 0.0153 (0.2258) | -0.1563 (0.1315) |
| ROA | 0.0018 (0.8283) | 0.0030 (0.6030) | -0.0002 (0.9957) |
| Constant | 0.1049** (0.0169) | -0.0562 (0.1894) | -0.0925 (0.5024) |
| Observation | 40 | 40 | 40 |
| R2 | 0.3706 | 0.3273 | 0.4530 |
Note: ***, **, * denote significance at 1%, 5% and 10% significant levels respectively. NPL = Non-performing loans ratio, CAR = Capital adequacy ratio, LR = Liquidity ratio,
Board_I = Board independence dummy, DummySO = State ownership dummy, Board_I * DummySO = Interaction of board independence dummy and state ownership dummy, LTA = Logarithm of total assets, RG = Revenue growth and ROA = Return on assets.
However, the board independence is found to have no significant impact on CAR. Hypothesis 2 of this study could not be rejected as the board independence significantly affects the impact of ownership by Malaysian government on banks’ risk-taking behaviour. Evidently, the risk-taking measurements show that the two risk-taking proxies out of the three are significant. The impact of board independence and the degree of state ownership is presented in Table 4. The finding in Table 4 is consistent with Table 3 where greater state ownership involvement in banks is associated with a statistically significant higher LR of 0.5133 percent.
| Banks’ Risk-Taking | |||
|---|---|---|---|
| Variables | NPL | CAR | LR |
| Board_I | -0.0111** (0.0476) | 0.0020 (0.7130) | -0.1960*** (0.0004) |
| Board_I * SO | 0.0237 (0.1018) | -0.0195 (0.2498) | 0.5133*** (0.0000) |
| SO | 0.0146 (0.2556) | 0.0196 (0.2483) | -0.2491*** (0.0000) |
| LTA | -0.0104* (0.0792) | 0.0165*** (0.0003) | 0.0130 (0.6215) |
| RG | -0.0118 (0.2786) | 0.0160 (0.1517) | -0.1545** (0.0430) |
| ROA | -0.0012 (0.8911) | 0.0063 (0.2855) | -0.0257 (0.5562) |
| Constant | 0.1369** (0.0163) 40 | -0.0451 (0.2525) 40 | 0.1541 (0.5269) 40 |
| Observation | 0.3061 | 0.3252 | 0.4167 |
| R2 | Note: ***, **, * denote significance at 1%, 5% and 10% significant levels respectively. NPL = Non-performing loans ratio, CAR = Capital adequacy ratio, LR = Liquidity ratio, Board_I = Board independence dummy, SO = State ownership percentage, Board_I * SO = Interaction of board independence dummy and state ownership percentage, LTA = | ||
Note: ***, **, * denote significance at 1%, 5% and 10% significant levels respectively. NPL = Non-performing loans ratio, CAR = Capital adequacy ratio, LR = Liquidity ratio, Board_I = Board independence dummy, SO = State ownership percentage, Board_I * SO = Interaction of board independence dummy and state ownership percentage, LTA = Logarithm of total assets, RG = Revenue growth and ROA = Return on assets.
Table 5 presents the regression result of the effect of foreign directors on the relationship between state ownership and banks’ risk-taking. The result shows that SOBs, on average, have a lower NPL ratio compared to the POBs by a magnitude of 0.0230 percent when there is the presence of foreign director on board. However, the foreign director is found to have no significant impact on CAR and LR. Since the findings of two risk-taking proxies out of the three are not significant, hence Hypothesis 3 is rejected. The impact of foreign director and the degree of state ownership is presented in Table 6. The finding in Table 6 is consistent with Table 5 where greater state ownership involvement in banks is associated with a statistically significant lower NPL ratio of 0.0719 percent.
| Taking | |||
|---|---|---|---|
| Variables | NPL | CAR | LR |
| Foreign_D | 0.0168** (0.0345) | 0.0152 (0.1930) | 0.0805*** (0.0033) |
| Foreign_D* DummySO | -0.0230*** (0.0007) | -0.0169 (0.2376) | -0.0641 (0.2883) |
| DummySO | 0.0313*** (0.0006) | 0.0152 (0.2930) | 0.0882 (0.1633) |
| LTA | -0.0021 (0.3946) | 0.0203*** (0.0000) | 0.0716*** (0.0063) |
| RG | -0.0121 (0.1764) | 0.0135** (0.0127) | -0.0782 (0.4354) |
| ROA | 0.0081 (0.4246) | 0.0153 (0.3339) | -0.0178 (0.6625) |
| Constant | 0.0158 (0.2143) | -0.1080*** (0.0000) | -0.6863*** (0.0058) |
| Observation | 40 | 40 | 40 |
| R2 | 0.4348 | 0.3732 | 0.2545 |
Note: ***, **, * denote significance at 1%, 5% and 10% significant levels respectively. NPL = Non-performing loans ratio, CAR = Capital adequacy ratio, LR = Liquidity ratio, Foreign_D = Foreign director dummy, DummySO = State ownership dummy, Foreign_D * DummySO = Interaction of foreign director dummy and state ownership dummy, LTA = Logarithm of total assets, RG = Revenue growth and ROA = Return on assets.
| Banks’ Risk-Taking | |||
|---|---|---|---|
| Variables | NPL | CAR | LR |
| Foreign_D | 0.0279** (0.0171) | 0.0241 (0.1972) | 0.1188* (0.0660) |
| Foreign_D* SO | -0.0719*** (0.0011) | -0.0515 (0.2391) | -0.2306 (0.2641) |
| SO | 0.0866*** (0.0011) | 0.0506 (0.2361) | 0.2043 (0.3008) |
| LTA | -0.0020 (0.4287) | 0.0196*** (0.0000) | 0.0929*** (0.0093) |
| RG | -0.0128 (0.1948) | 0.0140*** (0.0013) | -0.0966 (0.4293) |
| ROA | 0.0039 (0.6866) | 0.0170 (0.2675) | -0.0522 (0.1860) |
| Constant | 0.0077 (0.5498) | -0.1119*** (0.0000) | -0.9005** (0.0168) |
| Observation | 40 | 40 | 40 |
| R2 | 0.3943 Note: ***, **, * denote significance at 1%, 5% and 10% significant levels respectively. NPL = Non-performing loans ratio, CAR = Capital adequacy ratio, LR = Liquidity ratio, Foreign_D = Foreign director dummy, SO = State ownership percentage, Foreign_D * SO = Interaction of foreign director dummy and state ownership percentage, LTA = | 0.3731 | 0.2471 |
Note: ***, **, * denote significance at 1%, 5% and 10% significant levels respectively. NPL = Non-performing loans ratio, CAR = Capital adequacy ratio, LR = Liquidity ratio, Foreign_D = Foreign director dummy, SO = State ownership percentage, Foreign_D * SO = Interaction of foreign director dummy and state ownership percentage, LTA = Logarithm of total assets, RG = Revenue growth and ROA = Return on assets.
Table 7 presents the regression result of the effect of female director in the relationship between state ownership and banks’ risk-taking. The result shows that when there is presence of female director on board, it has a significant negative impact on LR with a coefficient of 0.0859 at 95 percent confidence level. However, the female director is found to have no significant impact on NPL and CAR when it interacts with state ownership. On the other hand, the result in Table 8 shows that greater state ownership involvement in banks is associated with a significantly higher CAR of 0.0287 percent. Hypothesis 4 of this study is rejected as the evidence by the risk-taking measurements shows that the two risk-taking proxies out of the three are not significant.
From the regression results shown above, firstly, we find significant and positive relationships between the state ownership and non-performing loans (NPL) ratio. As compared to the POBs, SOBs are associated with a significantly higher NPL ratio. This indicates that the SOBs tend to take higher risks in providing loans. This finding is consistent with the social lending view where the SOBs tend to focus on developing nations and allocating too much funds to projects that comes with high social returns and improving general welfare. However, we could see a significant positive impact on capital adequacy ratio (CAR) when there is interaction from the degree of state ownership. This finding suggests that a higher degree of state ownership tends to improve the level of capital in banks. On the other hand, we do not find significant impact on banks’ liquidity ratio (LR). This finding is unexpected and suggests that the Malaysian government ownership in banks does not have a direct relationship with the banks’ liquidity risk. One possible explanation is that the state shareholders concern more on the banks’ solvency rather than the banks’ liquidity position. While the shareholders are pursuing on pecuniary interests, they may have overlooked the banks’ liquidity position. This might increase any potential financial distress in the future. Secondly, we expand the study to examine the effect of corporate governance mechanisms in the relationship between state ownership and banks’ risk-taking through board independence. The results show that SOBs with board independence ratio more than or equal to 0.50 are associated with higher NPL ratio. Consistent with the social lending view, the presence of ownership by the Malaysian government significantly affects the decision making made by the board, incurring higher risk in lending out loans. Although the ownership by the Malaysian government has no direct impact on banks’ LR, it has impacts on banks’ LR through the channel of board independence. The evidence shows that independent directors in SOBs attribute to lower liquidity risk. We further test and find that a higher degree of state ownership has a greater impact on LR. This finding is supported by Rosenstein and Wyatt (1990), Klein (2002) and Nguyen and Nielsen (2010), suggesting that the intervention from Malaysian government and outside directors has foster efficiency in supervising and advising functions in terms of liquidity assets and liabilities. Hence, reduce the likelihood of liquidity risk. Thirdly, we examine the effect of foreign director in the relationship between state ownership and risk-taking behaviour. The results show that the presence of foreign director in SOBs is associated with lower credit risk due to the need to adhere to risk management policies and procedures designed and implemented by the banks. Gillian and Starks (2003) revealed that foreign director plays a more active role in upholding better firm-level governance in reducing information asymmetry and strengthening a firm’s transparency which may have an impact on the firm’s investment policy. Moreover, we could see risk reduction to a greater extent when the Malaysian government ownership is higher. Although we do not find evidence on the interaction of foreign director and state ownership on LR, we find similar finding in the individual effect of foreign director. The result suggests that the presence of foreign director tends to improve the banks’ liquidity. Lastly, we examine the effect of female director and the results show that SOBs are associated with higher liquidity risk with the presence of female director. However, there is evidence that banks’ capital adequacy tends to improve when there is a higher degree of intervention by the Malaysian government. A possible explanation is the adoption of Basel III Accords in Malaysia. Although the implementation only begins in 2013, the Malaysian government has imposed strict regulations to ensure banks’ managers have attended to the requirements increment and started to adhere in placing a better capital prior to the full adoption of Basel III Accords. Being comply with stricter capital requirements, it shows that the CAR in Malaysian banks can be further improved in years to come. In short, SOBs’ risk-taking could be mitigated through CG mechanisms. Although there is no strong evidence to support across all three risk-taking proxies in this study, we find that the banks’ liquidity is strengthened with the presence of independent directors. Moreover, the credit risk is reduced with the presence of foreign director on board. This enlightens the fact that effective bank governance from the board of directors’ supervision is somehow crucial in mitigating Malaysian banks’ risk-taking.
| Taking | |||
|---|---|---|---|
| Variables | NPL | CAR | LR |
| Female_D | -0.0002 (0.9433) | -0.0110** (0.0104) | 0.0804*** (0.0000) |
| Female_D * DummySO | 0.0022 (0.5430) | 0.0101 (0.1753) | -0.0859** (0.0493) |
| DummySO | 0.0082 (0.1278) | -0.0103* (0.0577) | 0.0910*** (0.0006) |
| LTA | -0.0061 (0.2693) | 0.0231*** (0.0000) | 0.0247 (0.4224) |
| RG | -0.0097 (0.1317) | 0.0159* (0.0907) | -0.0980 (0.5013) |
| ROA | -0.0034 (0.6499) | 0.0027 (0.6559) | -0.0750* (0.0953) |
| Constant | 0.0902* (0.0932) | -0.1020*** (0.0100) | -0.0784 (0.7815) |
| Observation | 40 | 40 | 40 |
| R2 | 0.2912 | 0.3645 | 0.2858 |
Note: ***, **, * denote significance at 1%, 5% and 10% significant levels respectively. NPL = Non-performing loans ratio, CAR = Capital adequacy ratio, LR = Liquidity ratio, Female_D = Female director dummy, DummySO = State ownership dummy, Female_D * DummySO = Interaction of female director dummy and state ownership dummy, LTA = Logarithm of total assets, RG = Revenue growth and ROA = Return on assets.
| Banks’ Risk-Taking | |||
|---|---|---|---|
| Variables | NPL | CAR | LR |
| Female_D | 0.0012 (0.7712) | -0.0147** (0.0116) | 0.1158*** (0.0011) |
| Female_D * SO | -0.0007 (0.9496) | 0.0287* (0.0906) | -0.2603 (0.1015) |
| SO | 0.0241 (0.2196) | -0.0276 (0.1239) | 0.2427*** (0.0036) |
| LTA | -0.0078 (0.2329) | 0.0229*** (0.0000) | 0.0304 (0.4801) |
| RG | -0.0086 (0.3111) | 0.0162* (0.0578) | -0.1025 (0.5284) |
| ROA | -0.0055 (0.4784) | 0.0036 (0.5743) | -0.0887* (0.0861) |
| Constant | 0.1078* (0.0750) | -0.0977** (0.0299) | -0.1524 (0.7081) |
| Observation | 40 | 40 | 40 |
| R2 | 0.2546 of total assets, RG = Revenue growth and ROA = Return on assets. | 0.3549 Note: ***, **, * denote significance at 1%, 5% and 10% significant levels respectively. NPL = Non-performing loans ratio, CAR = Capital adequacy ratio, LR = Liquidity ratio, Female_D = Female director dummy, SO = State ownership percentage, Female_D * SO = Interaction of female director dummy and state ownership percentage, LTA = Logarithm | 0.2745 |
Note: ***, **, * denote significance at 1%, 5% and 10% significant levels respectively. NPL = Non-performing loans ratio, CAR = Capital adequacy ratio, LR = Liquidity ratio, Female_D = Female director dummy, SO = State ownership percentage, Female_D * SO = Interaction of female director dummy and state ownership percentage, LTA = Logarithm of total assets, RG = Revenue growth and ROA = Return on assets.
The panel regression model is reexamined using the Generalized Method of Moments (GMM) estimation since the results presented earlier may subject to endogeneity and unobservable time-invariant individual effects. Following Roodman (2009), this paper employs the two-step system GMM. The robustness result is presented in Table 9. The GMM result shows that most of the signs are consistent with the baseline result presented in Table 2, only the level of significance differs. The robustness result strengthens the finding of hypothesis
1a. This is evidence by greater credit risk and lower level of capital. Besides, the control variables become more significant after the inclusion of lag variables. The result highlights that the banks that are larger in size and the banks that are more capable at earning the revenue using assets, are exposed to lower risks.
| Variables | NPL | CAR | LR |
|---|---|---|---|
| (1) (2) (3) | (4) | (5) (6) | |
| Dummyso 0.0669*** 0.0130 | -0.0413*** -0.0847 | 0.3682*** -0.7731 | |
| (0.0000) (0.9112) (0.0006) | (0.4103) | (0.0020) (0.3867) | |
| Dummyso* SO 0.1925 | 0.5267 | 8.6280 | |
| (0.8311) | (0.2373) | (0.1410) | |
| SO -0.2441 | -0.4523 | -9.8971* | |
| (0.8070) | (0.1775) | (0.0861) | |
| LTA -0.0375*** -0.0177 0.0622*** | 0.0613*** | -0.0670 0.0704 | |
| (0.0023) (0.6614) (0.0001) | (0.0021) | (0.6179) (0.7541) | |
| RG -0.1048 -0.1206 0.1160 | 0.5014*** | -0.1961 -1.2725 | |
| (0.4863) (0.4594) (0.4422) | (0.0009) | (0.6989) (0.4215) | |
| ROA -0.0483** -0.0245 0.0341** | 0.0096 | 0.3999*** 0.0503 | |
| (0.0114) (0.6869) (0.0364) | (0.8045) | (0.0022) (0.8697) | |
| Constant 0.3460*** 0.2161 | -0.4866*** -0.5948*** 0.1933 | 0.8273 | |
| (0.0041) (0.5591) (0.0086) | (0.0084) | (0.9020) (0.7642) | |
| AR(1) test | |||
| -0.5200* -0.7985** -0.2983* | -1.4643* | -0.6734* -1.5671* | |
| statistic | |||
| (0.0603) (0.0425) (0.0766) | (0.0943) | (0.0501) (0.0871) | |
| AR(2) test | |||
| -1.1620 -0.2008 -1.2582 | -1.0004 | -0.0872 -0.5830 | |
| statistic | |||
| (0.2452) (0.8409) (0.2083) | (0.3171) | (0.9305) (0.5599) | |
| Hansen test of 2.4087 4.1272 2.3782 | 1.7791 | 1.5028 3.3094 | |
| over- | |||
| (0.6610) (0.8455) (0.6666) | (0.9871) | (0.8262) (0.9135) | |
| identification |
Note: ***, **, * denote significance at 1%, 5% and 10% significant levels respectively. NPL = Non-performing loans ratio, CAR = Capital adequacy ratio, LR = Liquidity ratio, DummySO = State ownership dummy, DummySO *SO = Interaction of state ownership dummy and state ownership percentage, SO = State ownership percentage, LTA = Logarithm of total assets, RG = Revenue growth and ROA = Return on assets.
The objective of this study is to investigate how state ownership affects banks’ risk-taking behaviour with a sample of banks in Malaysia. In summary, we find that state government incurred higher risk-taking behaviour in creating and lending out loans. This is consistent with the social lending view which shows that state-owned banks are more likely to focus on developing nations and lending out loans to project that comes with high social returns. The robustness result strengthens this finding, indicating that state-owned banks are associated with higher risk-taking behaviour. However, the banks’ risk-taking can be lower with more effective governance from the board of directors. Also, there is evidence that a higher degree of state ownership has a more significant impact on the banks’ risk-taking behaviour. In facing poor earnings prospects in the current challenging economic environment, this paper shares important implications for Malaysia’s banking industry. The government involvement through GLICs ownership in local commercial banks was seen as an important move to mitigate the increasing downside risks to Malaysia’s economic development. This can be done through maintaining an optimal level of decision making. In relation to the significant impact of how excessive risk-taking behaviour by banks has an effect on the national economy in the previous financial crises, the Malaysian banks should mitigate high risk-taking particularly in relative to loans portfolio. On the other hand, the bank management should remain focused on maintaining a healthy liquid position by building an efficient capital structure and liquidity management in banks. Even the findings show that state shareholders concern more on banks’ solvency, thus, the importance of liquidity position placed in banks should not be neglected. The ease availability of liquid funds is crucial at all times. Last but not least, the findings of this paper suggest that the supervision from the board of directors, somehow plays a crucial role in reducing the banks’ risk-taking behaviour. Reviewing the board of directors’ structure can help to monitor the banks’ activities and tackle the flaw of corporate governance mechanisms which is very relevant in the context of Malaysia where state-owned banks are one of the drivers of the economic development.
The data are extracted from Malaysia Banking Industry Report 1H2011 published by Emerging Markets Direct.
Shares ownership data are extracted from Bursa Malaysia Banks’ 2015 Annual Report.
This work was supported by the USM Fellowship provided by the Institute of Postgraduate Studies, Universiti Sains Malaysia.