Factors Influencing Malaysian State- Owned Enterprises’ Liquidity Position

Malaysian State-Owned Enterprises especially the Government-Linked Companies play a vital role in the business ecosystem in Malaysia, and also play a major role as an industry player in Malaysia. It is vital to study its liquidity position especially on how it handles in times of uncertainty. The aim of this study is to examine the determinants of liquidity position of Government-Linked Companies in Malaysia from 2011 to 2020. the liquidity position was evaluated by using internal variables, return on assets and size, and external variables, gross domestic product (GDP) and inflation. This study used Government-Linked Companies that were listed in Bursa Malaysia. The findings indicated that return on assets and size have a positive relationship with liquidity. Meanwhile, gross domestic product and inflation were shown to have negative relationship with liquidity. The result also indicated that return on assets and size have a statistically significant relationship with liquidity while gross domestic product and inflation have an insignificant relationship with liquidity. The result produced shed some lights on how government-linked companies could strengthen their liquidity position and avoid or minimize liquidity risk.


Introduction
State Owed Enterprises (SOE) can be referred to as entities or organizations that is owned by the government fully or partially and they operate on behalf of the government. Malaysian State-Owned Enterprises can be split into three categories, Government Linked Companies (GLC), Government Linked Investment Companies (GLIC), and Federal Statutory Bodies. For the purpose of this study, we will delve deeper into Government Linked Companies. Government Linked Companies are vital to the growth of the nation's economy as it provides an income for the country and also plays a vital role in certain industries as a key player. In determining the financial performance of the companies, liquidity plays a vital role in ensuring its survivability in a worst-case scenario of short-term unexpected events and also to meet the current cash needs. Liquidity refers to the amount of accessible cash for expenditure or investment activities, (Incekara and Cetinkaya, 2019).
Various research was carried out to determine the factors that influences a company's liquidity position. The research consists of various countries such as Vietnam, German, United Kingdom, and India, which also consists of various industries. Unfortunately, a scarcity of research occurred for topics that concerns on State Owned Enterprises, especially relating to financial performance particularly in Malaysia. Therefore, considering how State-Owned Enterprises should not only be considered as merely a political tool but also should be considered as a key player and contributor to the Malaysian economy, thus it is vital to understand the key elements that could affect the financial performance of State-Owned Enterprises. Ultimately, the aim of this research paper is to understand and analyze the factors affecting a State-Owned Enterprises' liquidity position.
Liquidity allows firms to pay its short-term obligations during Covid-19. Malaysia's GDP was seen to hit its worst performance since 1998 which shrank by 5.6 percent in 2020 according to a news report by (Anand, 2020). The cause can be seen due to low output in the economy where most of the drivers come from private corporations. According to The Straits Times (2020), about 30,000 business have shut down in 2020. Big corporations which are linked or funded or owned by the government were not sparked by Covid-19. Government Linked Companies such as the Malaysian Airlines were unable to generate revenue due to 75 percent of its planes were grounded (Babulal, 2020). Meanwhile, a multi-sector Government Linked Company, Boustead, also faced some setbacks in its hotel operation due to the movement control order (MCO) which catalyzes the sale of its famous hotel such as the Royale Chulan at a discounted price (Emmanuel, 2021). while its plantation sector was hugely affected by the palm oil import ban by the United States. Another famous Government Linked Company such as Petronas was also to be seen affected by . In addition to the global oil plunge, it was reported by The Star (2021), to have a profit after tax of 10.5 billion in 2020 as compared to 48.8 billion in 2019. The purpose of this study is to investigate the extent of internal and external factors on how it affects a company's liquidity.
The general objective of this study is to investigate the factors influencing liquidity position of Government Linked Companies in Malaysia. The specific objectives of the study are to: 1.
to investigate the impact of internal factors such as return on assets and size on liquidity position of government linked companies in Malaysia.

2.
To examine the impact of external factors such as gross domestic product and inflation on liquidity position of government linked companies in Malaysia.

Literature Review 1.
Liquidity Liquidity is referred to as the ability for a company to pay its short-term obligations or utilizing its current assets to meet its current liabilities. Liquidity provides a liquid firm more confidence that their short-term claims which will be satisfied within a given time (Hussain et al., 2018). Liquidity have been used as a dependent variable in various research within various countries (Al-Harbi, 2017;Al-Homaidi et al., 2019;Sabki et al., 2019). The usage of liquidity is important as it provides information about the availability of assets that can be converted to meet a firm's short-term cash requirement and if it is not readily available, a firm would be facing liquidity problem or distress (Nguyen et al., 2019). Liquidity has been used as a method of measurement to pay its short-term obligations in which it was measured by using current ratio. The usage of current ratio can be seen in various studies that were conducted in the past such as, studies conducted by (Hussain et al., 2018;Nguyen et al., 2019;Al-Homaidi et al., 2020). Current ratio can be measured by dividing the total current assets with current liabilities which was done and used by (Vu et al., 2020;Dang, 2020). However, as indicated by Vu et al (2020), the current ratio does have a limitation that it may be not as productive to use it as a method of comparison for business operating in different industries. Based on the studies, apparently, liquidity is the most crucial factor that affects the performance of Government Linked companies especially, to maintain the solvency and short-term cash requirement.

Return on Assets
Various studies used ROA as one of the profitability ratios. In the most cases, Return on Assets does have a significant impact on the liquidity of a company which can be seen in research conducted by (Dang, 2020). The extent of ROA can also be seen to have an impact in different industries or sectors. It can be seen in the previous studies conducted by Trinh and Thuy Mai (2016) where the ROA explains the changes of cash holding level in real estate companies in Vietnam. Another research conducted by Nguyen et al (2019), ROA was shown to have a positive impact on the liquidity of steel firms in Vietnam. The impact of ROA can be seen to have a positive relationship with liquidity in research carried out by Al-Homaidi et al. (2020). However, some studies indicated that ROA shows a negative impact on a companies' liquidity. According to Vu et al (2020), the higher ROA is related to lower liquidity, which is explained by the fact that all enterprises must choose between profitability and liquidity. Similar result done by Dang (2020) shows that ROA has a negative effect on firm's liquidity. Opponent results done by Elahi (2017) suggested that profitability is not a significant factor in determining liquidity in both UK and Germany. As a summary, it can be concluded that return on assets seems to have significant factor on the liquidity performance of the company. Therefore, the present study proposes the following hypothesis (H1): H1: There is a significant relationship between return on assets (ROA) and liquidity.

Size
Size represents the amount of total assets of a company. The size of the company will be converted to a natural logarithm which is a common way of usage in multiple studies. Size of company can be used in various industries such as, private commercial bank, manufacturing firms and steel firms (Assfaw, 2019;Vu et al., 2020;Nguyen et al., 2019). Apart from that, it is also used in countries such in Malaysia, Vietnam and India as one of many independent variables (Sabki et al., 2019;Trinh & Mai, 2016;Assfaw, 2019). The impact of size towards the liquidity of a company varies across researches. A company's size was proven to be able to affect a company's cash conversion cycle or liquidity significantly and positively (Trinh & Mai, 2016). This is supported by Assfaw (2019), whereby the size of a company, in this case, a bank which has a positive impact and significant impact on liquidity at 1% significance as measured by loans to deposit ratio. However, different studies have produced inconsistency result in which, the size of steel firms in Vietnam has a negative effect on the liquidity specifically the current ratio and quick ratio of firms in the steel industry (Nguyen et al., 2019). This can also be seen in another research conducted in Vietnam, where the firm size has a negative relationship with liquidity in Vietnam's listed enterprises (Dang, 2020). In addition, Assfaw (2019) found that a firm's size has a negative impact on liquidity of banks as measured with liquid assets to the total deposit of bank. This is also supported by Vu et al. (2020), whereby a firm's size was shown to have a negative relationship with current ratio and quick ratio. Another study done by Sabki et al. (2019), the firm size was shown to have a positive relationship with liquidity. However, the regression result shows an insignificant finding. It can be concluded that size is an important factor which affects liquidity position of the companies. Therefore, the present study proposes the following hypothesis (H2): H2: There is a significant relationship between size and liquidity.

Gross Domestic Product
Gross Domestic Product (GDP)t is the total monetary of all the finished goods and services produced by a country during a specific time and it represents the country's economic condition. The state of economic condition can be seen to affect businesses. The research conducted by Laštuvková (2016) indicated that smaller banks reacted strongly to external changes. This can be resulted in high liquidity creation and higher GDP value. Not only this can be seen in banks, but also in research conducted by Rana et al (2018) in Pakistan which analyzes chemical products and pharmaceutical sector. The results shows that GDP has a positive and significant influence towards liquidity. A study done in Vietnam by Dang (2020) indicated that GDP was recognized as one major variable that positively affected liquidity, with a unit rise in GDP resulting in a 3.66 unit increase in liquidity. This is also supported by (Assfaw, 2019;Al-Homaidi et al., 2020). However, research conducted by Al-Homaidi et al (2019) revealed that GDP was indeed significant in influencing liquidity in Indian commercial banks, but it was concluded to have negative influence on liquidity. Based on the previous studies, apparently, GDP has positive significant relationship with liquidity. Therefore, the present study proposes the following hypothesis (H3): H3: There is a significant relationship between gross domestic product and liquidity.

Inflation
Inflation can be explained as a rising cost in a basket of general items or household items that renders a value amount of money to be weaken. Based on the consumer price index, too high or too low-priced index is not good for the value that is stored in the money. Previous study done by Dang (2020) documented that inflation rate was deemed to not have any correlation with a company's liquidity in listed enterprises in Vietnam. Similar finding done by Al-Homaidi et al (2019), suggested that inflation rate has an insignificant effect on liquidity in India's listed commercial bank. However, this is inconsistent with the research conducted by Assfaw (2019) which revealed that inflation rate plays a positive and significant role in influencing bank's liquidity in Ethiopia. Although both studies showed inflation plays a role in creating a positive liquidity creation, Al-Harbi (2017), revealed that inflation has an inverse relationship with liquidity in banks from OIC countries, whereby an increase in inflation will cause a setback on liquidity. It can be summarized that inflation is the most crucial indicator to determine the liquidity position of the company. Therefore, the present study proposes the following hypothesis (H4): H4: There is a significant relationship between inflation and liquidity.
. Figure 1 shows the research framework which is used to examine the factors influencing liquidity (return on assets, size, gross domestic product, and inflation). Based on the framework, (4) hypotheses have been proposed and they would be examined in the study.

Regression Model
To confirm the hypotheses of the study, the empirical analysis carried out is based on the regression model as is shown in Equation (1).

Measurement of Variables
As for the measurement of variables, the study employed current ratio as a proxy of liquidity, which is the dependent variable. The four independent variables are firm return on assets, size, gross domestic product and inflation. Table 1 displays the measurement and variables for the study.

Independent variables (iv) Dependent variable (dv)
Return on Assets (  The sample of the study comprised three (3) selected government-linked companies in Malaysia which were UMW Holdings Berhad, Petroliam Nasional, and Felda Global Ventures. The selected companies have been chosen as the sample for this study because they showed good significant liquidity position and were listed in the top-ranked of their respective industries. Specifically, the study focuses on the factors affecting liquidity position of Malaysian state-owned enterprises.

Result and Discussion
The results of this study were obtained from different modes of analyses carried out, namely the descriptive analysis to determine minimum, maximum, mean and standard deviation, the diagnostic test to check the problem of research, the panel specification test to decide the final model, and the regression analysis to test model fitted the data well or not.  Table 2 presents the summary of descriptive analysis of dependent and independent variables over the sample period between the companies. The table shows an overall of 30 observations. The table shows the most optimal model predictor size which are Current Ratio, Return on Assets, Size, Gross Domestic Product, and Inflation. The variables describe the mean, standard deviation, minimum and maximum value. The analysis shows the highest mean, which is size, with a value of 24.56724, while the lowest mean is inflation, with a value of 0.2694. The highest standard deviation is size, which is 1.716541 while the lowest standard deviation is inflation, with a value of 0.0169908. Meanwhile, size has the highest maximum value of 27.18 and the lowest minimum value is Gross Domestic Product, with a value of -0.056.  Diagnostic Test was used to check the problem of research by using three tests which are multicollinearity, heteroscedasticity, and serial correlation. Multicollinearity was used to check a strongly correlated relationship between independent variables. The P-value must be less than ten to prevent an issue of multicollinearity. Based on table 5, it shows that there is no multicollinearity problem based on the result since the variance inflation factor is 1.22. Heteroscedasticity was performed to check the consistency of the data. To avoid this problem, the p-value must be more than 0.05. The result in the table shows that the p-value is less than 0.05, which is 0.000. This means it has heteroscedasticity problem. The serial correlation was used to verify autocorrelation. The p-value must be greater than 0.05 to avoid serial correlation problem. The result shows 0.4327 is more than 0.05. This research shows it has no serial correlation problem. It is suggested to rectify the problem of serial correlation is to perform Random Effect GLS Regression with Cluster Option. Notes: t statistics in parentheses *significant at 10% level **significant at 5% level ***significant at 1% level

Regression Model LIQit = -5.2198 + 3.0129 ROAit + 0.2817 SIZEit + (2.3766) GDPit + 3.6763 INFit + εit
Based on table 6, the regression results suggested that the model fitted the data well at one percent level. The Adjusted R 2 is 53.46 percent. This indicates that the 53.46 percent variation in liquidity is explained by all the independent variables in the model. However, the other 46.56 percent can be explained by other variables which are not included in the study. The findings of this study suggested that return on assets (ROA) and Size have a statistically significant relationship with liquidity. The positive coefficient of return on assets (ROA) indicates that return on assets (ROA) has a significant positive relationship with liquidity. Thus, one unit increases in return on assets (ROA) will lead to an increase of 3.0129 unit in liquidity when other variables are constant. The result shown in this study is supported by Trinh & Thuy Mai (2016) in which return on assets (ROA) was shown as a motive of cash holding, which implies that a firm would hold cash or liquid assets intentionally for a smooth daily transaction. The positive relationship of return on assets in this study was also supported by Nguyen et al. (2019) when return on assets (ROA) increases, it proves that a firm has the capacity to pay its debt. Thus, this indicates a good financial performance. Meanwhile Vu et al. (2020) concluded that return on assets (ROA) positively affect cash and quick ratio which implies a profitable firm has a better position of cash holding to pay off their obligations. Thus, hypothesis 1 (H1) is accepted.
The regression results also indicated that size was the significant variable in influencing liquidity. The result was consistent with the previous studies which were conducted by (Trinh and Thuy Mai, 2016;Assfaw, 2019). The size of firms which was based on the total assets of the firm itself does have an influence on liquidity whereby, a larger firm size indicates that the firm might have an excess of current assets or non-current assets which provides a degree of control on liquidity. This was proven by Al-Homadi et al (2019) which can be concluded that bank size asset size does have a significant impact on liquidity. Therefore, the second hypothesis (H2) is accepted.
Previous research has shown that external factors do, to a certain degree have a significant effect on liquidity which can be seen on research conducted by Laštuvková (2016), in which different size of firm or banks have a different reaction or sensitivity from external factors. This was also supported by Al-Harbi (2017) in which both gross domestic product and inflation were proven to have a significant correlation with liquidity in banks from OIC countries. However, the result in this study was proven to be a contradictory to both studies. Both gross domestic products and inflation were shown to be insignificant. The result produced in this research instead, is consistent with Al-Homaidi et al (2019), in which Gross Domestic Product and Inflation were deemed to have an insignificant effect. This might be explained due to the selected companies since it might have a solid liquidity structure which is important in persevering its performance, stability, and creating a solid foundation to avoid liquidity crisis as explained by (Incekara and Çetinkaya, 2019). Therefore, both third and fourth hypothesis (H3) and (H4) are not accepted.

Discussion and Recommendation
Based on the findings, size and return on assets (ROA) were deemed to be statistically significant in influencing the liquidity position of Government-Linked Companies. The result also shows that size is the most significant variable as compared to others which means that an entity with large asset size will lead to an increase of liquidity position. In the event of a pandemic such as the Covid-19, large financial or asset size does cushion the financial strain that is certainly being faced by many companies. This certainly would allow companies with an excess of cash to experience a lower risk of default, thus, allowing them to pay off their obligations. The return on assets (ROA) on the other hand, was proven to have a statistically significant positive result. This shows that a company that was able to utilize its assets to generate income would catapult the liquidity position of the company. Both size and return on assets (ROA) can be seen to be working in tandem, in which as return on assets (ROA) increases, it would contribute to the increase of size, in terms of current assets. Thus, it would result in strengthening the Liquidity position of the company. Lastly, a robust liquidity structure would also allow companies to minimize the adverse effect of external factors such as gross domestic product (GDP) and inflation (INF). However, this kind of situation would also depend on the type of the industries or sectors in which the companies reside in.
For future research, the study should employ more sets or different combinations of variables for independent and dependent variables to get a detailed and comprehensive result which covers both internal and external factors. Such example can be seen in Hussain et al (2019), which have separated the economic variables into two while the remaining independent variables and dependent variables stay constant. It is also recommended to include other state-owned enterprises which cover Federal Statutory Bodies and Government-Linked Companies. By doing this, it would certainly produce a near accurate result supported by data and numbers which would certainly be beneficial in tracking the performance of Malaysian state-owned enterprises.

Conclusion
Government Linked Companies play a vital role in the private sector of the Malaysian economy. Unlike any other private companies, Government Linked Companies have been operate not only limited to the best interest of the company itself but also at the best interest of the country. As of 2017, Government Linked Investment Companies or Government Linked Investment Companies controlled around 42% of listed companies in Bursa Malaysia as stated by Gomez, T.E, Free Malaysia Today (2017). Thus, to understand more about Government Linked Companies' financial performance was used as the basis for this study in which three (3) Government Linked Companies were identified from Bursa Malaysia and the financial data was collected from 2011 to 2020 to carry out this study.