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In world economy stock market plays a very important role as it is considered as one of the most crucial microstructural building block of evolving financial sector liquidity. Recently the total market capitalization of the world stock market has become more than double in last 13 years. Total value has increased by almost 133% since 2003 (Iskyan, 2016) and peoples tendency to invest in stock market has become overwhelmed. But this path of gaining huge popularity of the stock market is not smooth because of different shock and volatility. Not only that, historically the stock market volatility has become roughly 20% in a year and 5.8% in a month (Ibbotson, 2011). In this situation investors often consider the stock market liquidity as the base for investing and observing company’s performance as it has important percussion for listed companies (Wuyts, 2017). Moreover in this volatile economic condition it has become so difficult to determine the exact value of a firm and investors often become so confound that they invest in wrong securities and that’s why investors and economist from all over the world are trying to determine a way that can make sense of these volatility, shock, liquidity consequences and firms’ value puzzle. In such a condition stock market liquidity has become one of the most important focuses of the investors for determining the firms’ value. Marketability or liquidity of a stock plays a central role in firms’ valuation because it is the life force of security market from the stand point of investors, traders and other parties (Ali, 2016). The higher the liquidity or return the higher the investors’ interest to invest in that stock. Akter and Mahmud (2014) also pointed out that the two very crucial issues in organization management are liquidity and profitability. Liquidity refers to the ease by which an asset can be converted into cash without losing its value or incurring any transaction cost (Dalgaard, 2009). On the other hand, share market liquidity refers to the ease by which shares can be traded at close to current market price where the word ease is substituted by speed and price (European Systematic Risk Board [ESRB], 2016). It is an important indicator of stock market development because it signifies how the market helps in improving the allocation of capital and thus enhancing the prospects of long term economic growth (Khaliq, 2013).
Though Bangladesh is an emerging economy, its stock markets are not that much efficient and stable in comparison to that of developed countries. According to World Bank (2017) statistics, stock market total value traded to GDP for Bangladesh is 1.86% in 2000, 0.17% in 2004, 3.02% in 2010 and 0.38 % in 2014 which indicates the unrestful behavior of the market. In this unrestful situation determining these markets’ listed firms liquidity and value is also a challenging thing. On the other hand, lots of researches regarding stock liquidity and firms’ value found controversial result. Some found positive, some negative or some found insignificant relationship, such that Fang, Neo and Tice (2009) has found that increase in liquidity decimalization has a positive effect on firms’ performance as it make available more information to the investors. Sidhu (2016) has found positive relationship between Amivest liquidity and Tobin’s Q. Zhang, Huang and Chen (2017) and Ali (2016) also found the same result where Zhang et al. (2017) addressed an exogenous shock to see the liquidity impact. Moreover there is also a strong relationship among firm’s value, stock liquidity and firm size which are explored by Ban (1981) and Amihud and Mendleson (1986). That means not only the risk matters in the market, but also other factors like liquidity, corporate profitability, social responsibility, company size, corporate governance also affect the overall performance (Moeljadi, 2014); (Jonathan, 2003). Purwohandoko (2017) and Putu, Moeljadi and Djazuli (2014) found that there is a positive relationship between firms’ size and firms’ value. On the other hand according to Setiadharma and Machali (2017) and Mule, Mukras and Nzioka (2015) firms’ size is not significant variable to explain firms’ value as the size is related with asset and all the time the asset may not be the quality asset which contributes to increase the firms’ value.
From the above discussion it is clear that liquidity and firms’ value is a big fact and it is still not introduced in perspective of Bangladesh and as the culture, value, way of behavior, monetary and fiscal policy, political condition are different in Bangladesh, other papers findings may not be applicable in perspective of Bangladesh. Not only that, none of the paper focused both on bank and NBFIs. So the researchers tried to focus on Sidhu (2016) and tried to observe liquidity impact on firms’ value in perspective of Bangladesh by introducing Banking and Non-Banking Financial Institutions (NBFI) and making a comparison of these firms analysis result in order to fulfill the gap. In DSE almost 16.81 % share are dominated by banking and non-banking financial institutions where this dominance makes the financial institutions more vulnerable in one hand and also highlights the crucial importance of the sector in resource allocation and mobilization of the economy on another hand (Khatun, 2017). After this part the researchers have focused on literature review, methodology and then analysis and findings part.
The primary objective of this paper is to observe the stock market liquidity and its impact on firms’ value. To attain these objectives the researchers have introduced some other specific objective:
-To determine the stock liquidity of the sample companies.
-To examine the effect of stock liquidity on firms’ (Banks and NBFIs) value.
-To show a comparative picture of both Bank and NBFI of Bangladesh in terms of liquidity impact on firms’ value.
There are very few studies that have focused on stock market liquidity and its impact on firms’ value all over the world where most of the papers have found controversial result and some also have introduced an exogenous shock to determine the liquidity effect on firms’ value.
Fang et al. (2009) tried to show the causal relationship of stock market liquidity and firms’ performance by exploring an exogenous shock and observed the volatility in firms’ performance measured by market to book value ratio where it was found that increase in liquidity decimalization has a positive effect on firms’ performance as it make available more information to the investors. On the other hand momentum trading, analyst coverage, investor overreaction and the effect on discount rate do not have any effect on firms’ value. Moreover stock liquidity has the direct time series relationship with the return and it improves operating performance of the company.
Sidhu (2016) examined the relationship of stock market liquidity on the firms’ value on Indian manufacturing firm and random effects panel regression has been run to analyze the relationship where it found positive relationship between Amivest liquidity and Tobin’s Q. The researcher also found positive relationship between size, age and firms’ value. The article by Zhang et al. (2017) has used the non-tradable share reform in China as a quasi-natural experiment to test the effect of stock liquidity on firms’ value by addressing an exogenous positive liquidity shock and found positive relationship. Ali, Mahmud and Lima (2016) has explored the effect of the stocks liquidity on the firm value in Iraq by considering 65 companies listed in Iraqi Stock Exchange and has found that firms’ with liquid stocks have better firm value as measured by Tobin’s Q as a function of firms’ value. This result also hold even when introduced firm fixed effect, control for idiosyncratic risk and control for endogenous risk.
Banz (1981), in his paper has tried to examine the relation between stock return and market value of common stock where it has been found that the size effect is not linear with the market value. Where Amihud and Mendleson (1986) found strong relationship among firms’ value, stock liquidity and firm size that means not only the risk matters in the market, but also other factors like liquidity, corporate profitability, social responsibility, company size, corporate governance, innovation ability can also affect the overall performance (Moeljadi, 2014); (Jonathan, 2003).
Kausar, Nazir and Butt (2014) has focused on determining which capital structure has greater firms’ value by introducing multiple regression and panel regression focusing on 197 companies from Pakistan where they found a negative relationship among the capital structure represented by total liability divided by total equity and firms’ value. On the other hand, Ali (2016) and Sumiati and Manihuruk (2016) have found insignificant relationship between these two variables.
Nguyen, Duong and Singh (2016) examine the stock market liquidity, measured by Tobin’s Q where this is represented by three components namely operating income to price, leverage, operating income to assets and firms’ value by addressing broker anonymity as an exogenous shock where they found that increase in liquidity around the shock leads to increase in firms’ value. On the other hand Arian, Galdipur and Kiamehr (2014) tried to focus on determining the impact of the gap between supply and demand index prices and turnover volume on Tobin’s Q in Tehran stock exchange through Pearson correlation and multiple regression analysis where they found that there is no statistically significant relationship between liquidity and firms’ value. On the other hand, turnover volume and firms value has direct and significant relationship. Some research papers have been done on stock liquidity and firms’ performance in Bangladesh. Among them Uddin and Moniruzzaman (2017) examined liquidity and profitability relationship by using CCC, LR, CR, TCR, ROA, ICP and ROE variables by introducing Pearson correlation where they found that there is no statistically significant relationship between liquidity and profitability in the textile sector in Bangladesh.
From the above discussion it is clear that, though lots of research works have been done regarding stock liquidity and firms’ value, none of the papers of Bangladesh address the issue. Not only that most of the research are either based on different sectors like manufacturing or textile sector or considered the whole stock market firms where there are very few papers that has worked based on Banking sector but none of them considered both bank and NBFI, as well the work that are done on banking sector are based on other countries. In Bangladesh most of the work addressed stock liquidity and firms’ performance issue but none considered the value term. So, the researchers here find a gap to analyze weather there is really any effect of liquidity on these institutions firms’ value.
Sample Selection and Data Collection
This is an explanatory research in terms of applied purposes, conducted based on secondary data. The research is basically a quantitative research as the research objectives and outcomes require quantitative analysis for the assessment, analysis and getting the optimum and expected result. Here the direct research objective is to determine the liquidity of the stock market and to address its impact on the value of the firm by focusing on the banking and non-banking financial institutions of Bangladesh and also to observe the impact of some control variables on firms’ value. To visualize the overall picture, the researchers have used some statistical test like descriptive statistics, fixed effect regression which require quantitative rather than qualitative data and these tests are used because most of the research paper (Sidhu, 2016) and (Ali, 2016) that the researchers have followed regarding the subject matter have run these tests. Not only that, to address the liquidity impact on firms’ value, all these tests are also required to do.
To fulfill the research objectives, the target population is defined as the entire group of companies in which the researchers are interested that means all the banks and non-banking financial institutions of the country that are listed in DSE. There are 30 banks and 23 non-banking financial institutions listed in DSE. Among them 27 banks and 11 financial institutions data are available from 2007 to 2016. So the sample size is 270 annual reports for banks, 110 annual reports for NBFIs and the stock market data for the year of 2007-2016, where a balanced panel data has been created for the considered time. This sample size is supported by Sidhu (2016) where the researcher used 147 financial reports and the stock market data was for 2009 to 2012. Here, the researchers haven’t considered the other banks and non-banking financial institution, which are also listed in DSE, because of data unavailability and company’s listing after 2007. Only DSE has been considered because most of the companies that are listed under CSE also listed in DSE and DSE is the biggest and worldly known stock market of Bangladesh.
On the other hand, other sectors are excluded because in terms of economic effect and growth prospect, banking and non-banking institutions has the enormous effect on the overall economy and these firms stock performance are more volatile with the macroeconomic changes. Not only that the investors are prone to invest more in these firms, though there is no proper regulation and the rate of volatility and default is more. Moreover there is no supporting organization for the investors so that they can get the proper information about the stock performance and the firms’ true value as well people sometimes become so confused about the stock liquidity and value effect of these two sectors rather than others. So the researchers have shown up a picture of liquidity and firms’ value of the sample companies in these two types of financial institutions.
The researchers have picked the firm level data of the companies from 270 annual reports of the banks and 110 annual reports of non-banking financial institutions. All these annual reports have been collected from Lankabangla financial portal and the companies’ respective websites. Here as all companies are listed and transacted in DSE and as all these companies are regulated under the same regulation, conducted by the similar mannered management and all are from similar culture, background and country, so the sample will represent the whole population. On the other hand, for the calculation of liquidity the cross sectional daily shares traded and absolute return of the selected sectors companies are used. All these data are collected from DSE, CSV Data for AmiBroker.
Lots of researches found positive relationship between stock liquidity and firms’ value but it is very difficult to precisely measure firm value and liquidity and also measure and determine its variables.
Tobin’s Q (Dependent Variables)
Tobin’s Q is commonly used to proxy for firms’ value as used by Eaton (2015), Ali (2016), Zhang et al. (2017) etc. However valuation is subject to measurement error. Here Tobin’s Q has been calculated according to Ali et al. (2016). To determine the Tobin’s Q this paper is followed because they have calculated the variable based on banking sector of Bangladesh and as the researchers also focusing on the financial institutions of Bangladesh as well.
Liquidity (Independent Variable)
There is no universally accepted measure for stock market liquidity. Different researchers used different terms like high frequency data by Amihud (2002) and Cooper, Groth and Avera (1985), Bid ask spread by Chung, Elder & Kim (2010) trading volume and turnover by Krishnan & Mishra (2013). But the researchers have used Amivest measure of liquidity as this is a worldly recognized way of measuring liquidity and the paper Sidhu (2016) also used the same measure. After calculating the liquidity in million (Tk), it is converted into natural log form.
There are some other variables that also may affect firms’ liquidity are supposed to be included here as this is also a fundamental issue.
a) Size: Natural logarithm of firms’ size (represented by total asset) has been taken to control for the size of sample companies. Size is used as a control variable because it is thought that, larger firm has better performance and stability than small firm. On the other hand, it also said that larger size may always not better as because of low quality asset. So to see which concept is applicable to Bangladesh this variable is used. Setiadharma and Machali (2017), Ali (2016) and Sidhu (2016) also have used this as a control variable. The measure of Size is Ln (Total Asset).
b) Age: Age is also very important control variable as because of the maturity of the companies, its liquidity may be affected and sometimes some matured company may have more liquidity or sometime the opposite may happen. Age is calculated as the natural logarithm of the total year from its inception. Setiadharma and Machali (2017), Ali (2016) and Sidhu (2016) used this variable.
Age = Ln (Total year of inception)
c) Leverage/Capital structure (TD_TE): The total value and liquidity both are affected by leverage. Optimal leverage brings higher firms’ value. It is calculated as total debt divided by total equity (Setiadharma & Machali, 2017).
Leverage/Capital Structure =
d) Asset Growth (AG): Asset growth is also important because it also may affect firms’ value. Sometimes it is seen that asset is increasing but the value is decreasing because there is less quality asset.
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