Stock Volatility and Liquidity During Covid-19 Pandemic:
Comparison of ESG Leader and Non ESG Leader Companies in Indonesia
Stefanus Marcello Aditya1*, Yunieta Anny Nainggolan1
1 School of Business and Management, Bandung Institute of Technology, Bandung, Indonesia
*Corresponding Author: [email protected] Accepted: 15 August 2022 | Published: 1 September 2022
DOI:https://doi.org/10.55057/ajafin.2022.4.3.1
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Abstract: The objectives of this paper are to find whether there are any differences between stock volatility and stock liquidity between ESG leader companies listed in IDXESGL index in Indonesia Stock Market and to find out whether stock volatility affects stock liquidity negatively during this Covid-19 pandemic times. Indonesia Stock Market recorded record in terms of increased number of investors. With the increased number of investors and unprecedented challenges caused by the pandemic, investment risks in the form of volatility and liquidity arose from that occasion. Moreover, the appliance of sustainable investment through investing into companies with good track of ESG records have proven to make companies become valued at premium and experienced a reduce in stock price volatility while also maintaining a greater excess return. With previous studies findings found volatility has a negative correlation with stock liquidity, and with reduced volatility exists within ESG companies, this could indicate that liquidities for companies with less volatility will increase. Thus, this paper will try to examine the samples which include ESG leader companies and Non ESG companies, totaling a sum of 60 samples within Indonesia Stock Market. Methods of research that have been utilized are descriptive statistics analysis and multiple linear regression. Moreover, the regression analysis method showcased that there is a positive relationship between stock volatility and stock liquidity. The relation of stock volatility and liquidity is categorized as high correlation with strong relationship. The findings of this research are hoped to be useful for investors, especially in the stock market. Applying a sustainable investment through investment of ESG companies could become investors’ consideration in order to avoid future potential risks. With a reduced volatility and higher liquidity for ESG companies, investors could benefit from their investment acitivty in the long – run.
Keywords: ESG leader companies, stock volatility, stock liquidity, covid-19 pandemic, investors
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1. Introduction
The current Covid - 19 pandemic has set numerous challenges Indonesia, including the recession that the country faced from Q2 2020 up until Q1 2021. However, the country was able to get out and rebound from recession in the Q2 of 2021. The economic recovery in Indonesia was mainly supported by the growth of several industries, including processing industry, agriculture industry, along with wholesale and retail industry, with each industry experiencing a growth of 4.92%, 2.28% and 5.56% year - on - year, respectively. Moreover,
funds raised within the country’s capital market have improved significantly. It reached 363.28 trillion rupiah in 2021, exceeding the 255.5 trillion rupiah reached in 2017 (Financial Service Authority, 2021). This increased fund has also been accompanied with an increase in investors number, reaching up to 3.4 million investors, a double sum from 2020. The uncertainty in this pandemic time also resulted in stagnant, even sluggish economic growth of the country. This condition pushed out the risk of investment in the stock market. Investment risks that may occur as a result from this event are stock volatility and stock liquidity.
The existence of fluctuation or the movement of stock price influenced investors’ behavior in the stock market (Fakhrunnas, 2020). According to Poon and Granger (2003), volatility becomes the primary consideration when making a decision in investment activity and the creation of a portfolio. The liquidity of a stock typically refers to how quickly shares of a stock may be purchased or sold without significantly affecting the stock price. Stocks with low liquidity may lead to the inability of transaction of that particular stock. The danger that investors may not find a market for their assets, preventing them from purchasing or selling when they wish, is known as liquidity risk. Farmer (2008) discovered that stock liquidity has a significant relation with stock volatility, with higher stock volatility being affected by low liquidity and lower stock volatility being affected by high stock liquidity.
Throughout this study, the researcher would like to compare the volatility and liquidity performance of ESG (Environmental, Social, and Governance) leader companies in the Indonesia Stock Exchange with its matching companies. Environmental, social and governance are standards of business operations that are often used as indicators/screeners for new potential investments, with each aspect having its own indicators to assess (Environmental: how it performs as a steward of nature, Social: how it manages the relationship with employees, suppliers and other stakeholders in the company, Governance: how the management deals with leadership, audits, internal controls and shareholder rights) (Courage, 2022).
Companies with high ESG ratings were said to be more sustainable and ESG itself was said to be part of the risk management that a company owns. ESG ratings of a company are believed as a channel to expose the company’s measurement to risk and its future financial and company performance (Gorley, 2022). ESG reportings are, up until now, not yet recorded with a standardized procedure, and hence the ratings of ESG would be able to temporarily reflect the company’s nonfinancial performance and give investors tools to evaluate the company. A study found that companies with good ESG performance have lower volatility than companies with bad or poor ESG performance (Zhou & Zhou, 2021). The other result also showed that companies with high ESG scores and their increase in stock price volatility could be considered small, despite the ongoing Covid-19 pandemic (Zhou & Zhou, 2021), this was due to the ability of high ESG - scored companies to help their stock price become steadier and resilient during Covid-19 times. A previous study in India also found that high ESG - scores companies were chosen more often to be the choice of investments rather than lower ESG - scores companies (Meher et.al., 2020).
The presence of ESG enterprises has disproved the hypothesis that the flow of capital into the financial sector ceased during the early epidemic. Sonobe and Nemoto (2022) discovered that ESG enterprises have seen an increase in cash flow rather than a decrease. Furthermore, Sonobe and Nemoto (2022) discovered that sustainable investment that relates to ESG factors from the standpoint of investors and people is still low, with developed markets still in the early phases of data collecting and data analysis of enterprises. This has sparked an interest towards
sustainable investment whereas a result of the pandemic, the world has been more conscious and aware towards company’s non-financial performance which is represented by ESG factors.
2. Literature Review
2.1. Efficient Market Theory
Fama (1970) claimed that empirical studies on the concept of efficient markets have focused on whether certain subsets of accessible information are "completely reflected" in pricing. The efficient market model is based on the premise that asset prices "completely represent" all available information at any one moment. According to the EMH, equities always trade at their fair value on exchanges, preventing investors from buying undervalued stocks or selling them at inflated prices. As a result, it should be difficult to beat the market as a whole via stock selection or market timing, and the only method for an investor to earn a greater rate of return is to invest in riskier assets (Downey, 2021). Fama (1970) further explained that the theory has three variants: weak, semi-strong, and strong, which reflect three distinct expected degrees of market efficiency. As previously mentioned in the theory of Efficient Market Hypothesis, newly added information that has been received by the market will be immediately reflected in the stock prices. Technical analysis (the study of past stock prices in an attempt to predict future prices) and fundamental analysis (the study of financial information) may not help an investor make more money than if they just bought a random set of stocks (Malkiel, 2003).
2.2. Stock Volatility and Stock Liquidity
Stock volatility can be defined as the size of the difference between the opening and closing prices of a company's stock. It has been present in the stock market for some time and may influence how investors react when investing in the stock market. A more steady stock price may indicate the presence of low stock volatility, whereas the opposite circumstance, where the stock price undergoes greater movement, resulting in an unstable price, may indicate the presence of higher stock volatility. As volatility indicates a movement between up and down from stock, higher volatility is deemed to be harder to predict because the price movement occurs more often than stocks with lower volatility (Hashemijoo et.al., 2012). As stated before that stock volatility affect the stock price movement, investors may reconsider upon making investment because a company’s stock price has been the main concern for investors to make their investment since it could reflect the company’s value (Theresia and Arilyn, 2015) in Selpiana and Badjra (2018).
Through its published research, Amihud (2012) stated there are several things that relates with liquidity in the capital market. Firstly, in accordance with the Amihud-Mendelson hypothesis of the link between liquidity and price, investors' expectations about market liquidity may be tied to a certain level of prices for any given condition of fundamental values. Secondly, even in the absence of a change in the underlying fundamentals, stock prices would see a big decrease when liquidity in a given time is significantly reduced. When pricing stocks, liquidity is considered a factor to be taken into consideration. The larger the liquidity risk of an asset, the lower its cost and the higher the projected return that investors get as compensation, with the transaction volume of a security demonstrating the significance of liquidity in asset pricing (Pederson et.al., 2012). With the growing number of investors in the Indonesia Stock Exchange, understanding stock liquidity within the capital market could be beneficial in order to avoid risk and understand how the market reacts to specific unprecedented events such as the Covid-19 pandemic. Furthermore, liquidity is also related to stock volatility in the capital market. Based on a study by Farmer et al., (2004) in Mike and Farmer (2007), liquidity is considered as the
most important factor of volatility, especially for short time periods. This fits with the current research limitation period where the period is estimated around a year.
2.3. Environmental, Social and Governance
According to Kocmanova (2011) in Husada and Handayani (2021), the prior company’s objective was to achieve high net profit in its business operation, but it seems that companies have tried to be more aware of environmental, social and governance (ESG) practices to reach sustainability. Environmental, Social and Governance, or commonly known as ESG, are factors that have been applied by investors upon investing in the stock market in order to acknowledge opportunities and risk from a company. According to IDX, ESG investing is an investment strategy that takes into account both financial measurements and ESG issues when making investment decisions. To evaluate a company's risk considerations, sustainability, and development potential, it is crucial that we take into account the aforementioned aspects in our investment choice.
As a firm does not function independently, its financial success is highly correlated with the ESG risks it confronts. A firm cannot expect to function sustainably over the long run if it does not practice excellent corporate governance and consider the impact of its everyday activities on the environment and society. Information on ESG elements of Indonesian Publicly Listed Companies is available in their Sustainability Report, which may be produced as a separate report or incorporated within the company's annual report.
2.4. IDXESGL
According to CNBC Indonesia (2020) , in December 2020, the Indonesian Stock Exchange (IDX) or Bursa Efek Indonesia (BEI) released a new index called IDX ESG (Environmental, Social, Governance) Leaders, or IDXESGL, which was hoped to serve as a baseline for other companies outside of the index to begin implementing environmental, social, and governance practices in order to achieve sustainability in the future. The company selection process which would enter the index were done by the Indonesian Stock Exchange (IDX) or BEI. It followed a process that included IDX selecting companies from IDX80 that had ESG risk scores from Sustainalytics, then removing companies with high controversial and ESG risk scores categorized as high and severe, leaving only companies with low ESG risk scores with a minimum of 15 companies and a maximum of 30 companies in the index. IDXESGL, according to IDX, is an index that measures the price performance of stocks that become leaders in ESG ratings and do not have substantial disputes. These stocks are chosen from firms with strong trading liquidity and solid financial performance. Sustainalytics created the ESG rating and dispute analysis.
2.5. Research Hypothesis
The researcher would like to build a hypothesis in the hypothesis development sub-chapter based on the previously given background and relevant aspects. Furthermore, the topics chosen for this research, which are IDXESGL and non IDXESGL firms, will be discussed in order to build the research hypothesis. The connection between stock volatility and stock liquidity owned by firms listed in IDXESGL and firms not listed in IDXESGL in Indonesia Stock Exchange.
H1: ESG leader companies have lower volatility than those which are non ESG leader companies
H2 : ESG leader companies have higher liquidity than those which are non ESG leader companies
H3 : There is a negative relationship between stock volatility and stock liquidity
3. Research Methodology
3.1. Research Design
This chapter will discuss the research design, or how the study will be carried out. The research will start with problem identification, which will determine what type of problem this research hopes to answer. The following phase is a study of the literature from various publications and past studies that may aid the research in any manner. The following phase would be data collection, which would be immediately followed by data analysis. After data analysis, the result of the analysis is known as the outcome of the analysis, and the researcher can draw conclusions based on the data's results.
3.2. Population and Sample
This study has its own population and sample that has been selected and utilized for this study.
The population for this study is all companies listed in Indonesia Stock Exchange and the samples will be selected using relevant sampling methods or techniques.
This research’s population will comprise stocks that were listed on the Indonesia Stock Exchange throughout the Covid-19 era, specifically from December 2020 to December 2021.
At the end of 2021, the overall population, which is the total number of listed firms in IDX, accounted for 766 companies. Among the population which sums a number of 766 companies, there will be 60 companies in total which would be selected as the sample for this research. The first 30 samples for this research are collected using a purposive sampling method. According to Lewis and Thornhill (2012), purposive sampling could be defined as a sampling strategy in which the researcher uses his or her own discretion in selecting individuals of the population to participate in his/her research. Purposive sampling can also be classified as non-probability sampling.
For the rest of 30 companies which are not listed in the index of IDXESGL in Indonesia Stock Exchange, the samples will be gathered and collected using a sample matching method. Sample matching method is a method which allows researchers determining a limited dataset can be transformed into a representative sample of the population (Zheng, 2019).
3.3. Data Collection
This study's data collection will include the acquisition of secondary data. Secondary data for this research would be gathered from relevant literature resources such as prior papers, publications, articles, journals, and news. To analyze the stock volatilities and liquidities of firms included in the IDXESGL index and companies not listed in the IDXESGL index, the researcher will first use published reports from the Indonesia Stock Exchange. The type of data for this research will be panel data. Panel data includes a dataset in which the behavior of entities is recorded over time; the dataset may contain data on corporations or persons, among others (Reyna, 2007) . This research will involve data from various companies which have 60 companies in total and in a multiple observation, specifically from 14th of December 2020 until 31st of December 2021.
Data regarding stock volatility for this research will be acquired from each of the companies’
published reports and its stock price. Moreover, to calculate the volatility, the most important aspect is to collect the highest and the lowest price of each stock on a daily basis. The volatility model will utilize Garman-Klass Volatility Measurement. The decision to collect volatility on a daily basis is rationalized by an action to understand the volatility for each sample from
December 2020 up to December 2021. Since the data collection for this period will be specifically done during Covid-19 Delta Variant, there could be differences during the peak of Delta Variant spread in Indonesia regarding stock volatility. Stock liquidity data will be collected using formulas of calculation that are relevant and proven from previous studies. The formula and methods will be explained later in the data analysis method section.
3.4. Operation Variable
This study will make use of two research variables: independent and dependent variables. The dependent variable is the variable being examined in the research or experiment, whereas the independent variable is the variable that influences or causes changes in the dependent variable in a study. In this study, the dependent variable would be stock liquidity between the 2 sample groups, namely IDXESGL listed companies and non IDXESGL listed companies. This dependent variable would be affected by the previously stated independent variables of this research.
Stock liquidity calculation for this research will apply the Amihud Illiquidity Measures (Amihud, 2002). The ILLIQ or illiquidity metric used here is the daily ratio for absolute stock return to dollar volume averaged across time. It is the daily price reaction associated with one dollar of trading volume, and so serves as a crude estimate of price effect. Since the sample for this study comes from the Indonesia Stock Exchange, the Dollar Volume will be represented in Indonesian Rupiah (IDR).
The stock volatility analysis will be conducted to exhibit the volatility between each sample group. The known method of analysis for calculating stock volatility is Parkinson Historical Volatility Model (Parkinson, 1980). However, the calculation of asset’s volatility using Parkinson historical price volatility model employs and takes into account the high and low price of an asset in an intraday trading. This means that Parkinson model has not applied the closing and opening price, which sometimes could indicate a price jump during the opening session of market. The model of Parkinson has been extended with improvement and generalisation within the Garman-Klass volatility estimator. Garman-Klass volatility price estimators use data similar to that seen in a newspaper's financial section: high, low, opening, and closing prices, as well as transaction volume. Estimators seem to have much greater relative efficiency than the usual estimators (Garman & Klass, 1980).
3.5. Data Analysis
Data analysis in this research will involve several steps. Data analysis is the practice of methodically utilizing statistical and/or logical approaches to describe and demonstrate, compress and recapitulate, and assess data. The type of research design for this research will use descriptive statistics research. The features and value distribution of one or more datasets are summarized using descriptive statistics. The central tendency and degree of value dispersion in datasets can be quickly viewed by analysts using the traditional descriptive statistics.
Analysts can evaluate the central tendency and variation of data in a spatial environment using spatial descriptive statistics. The two categories of descriptive statistics work in tandem (Lee, 2020). The method of analysis in this research includes statistics descriptive analysis of the selected samples and multiple linear regression.
Descriptive analysis will be conducted to show a summary of the stock price between ESGL listed stocks and non ESGL stocks. The objective of this analysis is to describe the quantitative characteristics of each sample group. This descriptive analysis will cover various characteristics
between each sample group including the mean, median, maximum, minimum, standard deviation and more characteristics that may be relevant to be shown.
Regression that will be utilized in this research will use a multiple linear regression model.
Since this research utilized one dependent variable and more than one independent variables, alongside with several control variables, multiple linear regression model will be the most appropriate regression model. All of the priorly stated hypothesis will each be tested using a multiple regression model.
To understand the relationship between stock volatility and stock liquidity, a multiple linear regression will be applied within this research. Given a collection of data that contains observations for both of these variables for a specific sample, a simple linear regression calculates the connection between a response variable y and a single explanatory variable x. A statistical approach used to explain the connection between two variables is linear regression.
The predictor variable is denoted by x, while the response or outcome variable is represented s by y. A statistical approach used to explain the connection between two variables is linear regression. The degree of the correlation between the two variables determines the quality of the linear regression model (Prion and Haerling, 2020).
The objective of applying linear regression within this research is to make sure the results of the research could be interpreted and the relationship between stock volatility and liquidity could be determined. For this research, the following regression equation will be:
4. Data Analysis and Interpretation
4.1. Descriptive Statistics Analysis
In this section, the researcher will discuss about the descriptive statistics data that have been acquired using SPSS. The descriptive statistics method is selected in order to understand the characteristics between each sample group.
Table 4.1: ESG Companies Descriptive Statistics Analysis ESG Sample Group Descriptive Statistics
N Minimum Maximum Mean Std. Deviation Variance
Volatility 30 .0178 .0531 .031617 .0081371 .000
Liquidity 30 1.5795 4.6902 3.370620 .6388023 .408
FSIZE 30 29.18 34.36 31.1293 1.38220 1.910
LTD 30 .0084 .6185 .219863 .1565904 .025
GWTH 30 -.3156 .9220 .086690 .2054265 .042
ROA 30 -.0245 .1536 .029990 .0373648 .001
PBR 30 .53 57.96 4.1457 10.31571 106.414
SampleGroup 30 1 1 1.00 .000 .000
Valid N (listwise) 30
Table 4.2: Non ESG Companies Descriptive Statistics Analysis Non ESG Sample Group Descriptive Statistics
N Minimum Maximum Mean Std. Deviation Variance
Volatility 30 .0018 .0920 .042213 .0200868 .000
Liquidity 30 -.7536 4.6139 1.739050 1.4069681 1.980
FSIZE 30 26.96 33.89 29.5963 1.56103 2.437
LTD 30 .0170 .4627 .180503 .1348497 .018
GWTH 30 -.0934 1.1343 .134240 .2462576 .061
ROA 30 -.0837 .0500 .006090 .0257550 .001
PBR 30 .17 16.63 2.5423 3.23440 10.461
SampleGroup 30 0 0 .00 .000 .000
Valid N (listwise) 30
Table 4.1 and table 4.2 exhibit descriptive statistics for each sample group. The descriptive statistics were conducted separately based on each sample group in order to understand and see whether there are any differences between each sample group in all variables used. It could be seen that from Volatility variable, non ESG sample group has a highest maximum value and a higher mean. ESG sample group has a volatility mean of 0.031 while non ESG sample group has a mean voaltility of 0.042, indicating that the mean volatility is higher for volatility. While for liquidity, ESG sample group is slightly higher than non ESG sample group, where mean value of liquidity for ESG sample group is higher, indicating the ESG sample group has better liquidity than non ESG sample group. This is also supported by the fact that non ESG sample group has a minus liquidity, meaning that there is a company in non ESG sample group with high illiquidity.
4.2. Hypothesis One Regression Model
Using the SPSS program, the researcher will conduct a multiple regression model to test the first hypothesis with the priorly stated model. However, before conducting the multiple regression model, several assumptions of multiple linear regression must be first met.
Table 4.3: Model 1 Coefficient Table Coefficients
1 Unstandardized Coefficients Standardized Coefficients
B Std. Error Beta t Sig.
(Constant) .035 .004 9.402 <.001
SampleGroup -.011 .004 -.350 -2.900 .005
LTD .029 .013 .266 2.218 .031
GWTH .012 .009 .172 1.440 .155
The beta is stated as -0.332 with a significant value of 0.005, less than confidence level of 0.05.
This shows that the independent variable has a statistically significant relationship with the dependent variable. Table 4.3 shows a coefficient table with the added control variables. The beta of sample group decreases from -0.332 to -0.350. With a decreasing beta and still a significant relationship between independent and the dependent, the control variable could be concluded to have a partial confound towards the dependent variable or stock volatility.
Table 4.4: Model 1 Summary Model Summary
Model
R
R Square Adjusted R Square Std. Error of the Estimate Durbin-Watson
1 .459 .210 .168 .146910 2.315
The model summary shown in the table above could be concluded for the first regression model.
The R is 0.459 and the R squared is 0.210. With an R-squared of 0.210, there is a proportion of 21% of variance in stock volatility explained by the predictor and control variables.
The complete regression model for model 1 will be shown as the following:
𝑉𝑂𝐿 = 0.035 − 0.011(𝐸𝑆𝐺𝐿) + 0.029(𝐿𝑇𝐷) + 0.012(𝐺𝑊𝑇𝐻) + 0.147
Based on the regression model above, it could be interpreted as:
𝛽𝜊
= 0.035: When ESGL, LTD, GWTH and error term are zero, the volatility will account for 0.035𝛽1
= -0.011: When ESGL increases by one unit, the volatility will decrease by 0.011𝛽2
= 0.035: When LTD increases by 1 unit, volatility will increase by 0.029𝛽3
= 0.012: When GWTH increases by 1 unit, volatility will increase by 0.012 Based on the result of first regression model to answer the first hypothesis, it could be said the stock volatility within ESG leader companies are lower than those which are not listed in IDXESGL. The reasoning for this finding is because the coefficient of ESGL is found to be negative (-0.011). The researcher expected a negative sign for the coefficient of ESGL in this regression model, which could indicate that volatility is lower for companies listed in IDXESGL.Based on the result of the analysis, the first model could be concluded that companies that are listed in the ESGL index have a lower volatility, represented by the negative sign from its coeffieicient. Moreover, the control variables which are LTD and GWTH have a confounding impact, rather than a major one towards the regression model. Both control variables have a positive relationship with stock volatility.
4.3. Hypothesis Two Regression Model
Table 4.5: Model 2 Coefficient Coefficients
1 Unstandardized Coeeficients Standardized Coefficients
B Std. Error Beta t Sig.
(Constant) -12.807 2.220 -5.770 <.001
SampleGroup 1.008 .256 .374 3.935 <.001
FSIZE .493 .075 .600 6.575 <.001
ROA -4.936 3.600 -.123 -1.371 .176
PBR -.009 .015 -.051 -.596 .554
Table 4.5 shows a new coefficient table with the added control variables. The beta of sample group decreases from 0.605 to 0.374. With a decreasing beta and still a significant relationship between independent and the dependent, the control variable could be concluded to have a partial confound towards the dependent variable or stock liquidity. However, the ROA and PBR as control variables are not statistically significant when controlling stock liquidity. Only firm size is accounted as significant towards stock liquidity.
Table 4.6: Model 2 Summary Model Summary
Model
R
R Square Adjusted R Square Std. Error of the Estimate Durbin-Watson
1 .813 .661 .636 .8202722 2.633
The model summary shown in the table above could be concluded for the first regression model.
The R is 0.813 and the R squared is 0.661. With an R-squared of 0.661, there is a proportion of 66.1% of variance in stock liquidity explained by the predictor and control variables.
The complete regression model for model 2 will be shown as the following:
𝑳𝑰𝑸 = −𝟏𝟐. 𝟖𝟎𝟕 + 𝟏. 𝟎𝟎𝟖(𝑬𝑺𝑮𝑳) + 𝟎. 𝟒𝟗𝟑(𝑭𝑺𝑰𝒁𝑬) − 𝟒. 𝟗𝟑𝟔(𝑹𝑶𝑨) − 𝟎. 𝟎𝟎𝟗(𝑷𝑩𝑹) + 𝟎. 𝟖𝟐
Based on the regression model, it could be interpreted as :
𝛽4
= -12.807: When ESGL, FSIZE, ROA, PBR and error term are zero, the liquidity will account for -12.807𝛽5
= 1.008: When ESGL increases by one unit, the liquidity will increase by 1.008𝛽6
= 0.493: When FSIZE increases by 1 unit, liquidity will increase by 0.493𝛽7
= -4.936: When ROA increases by 1 unit, liquidity will decrease by 4.936𝛽8
= -0.009: When PBR increases by 1 unit, liquidity will decrease by 0.009Based on the result of the second regression model to answer the second hypothesis, the coefficient of ESGL is positive or 1.008. With this positive coefficient in the second regression model, it is indicated that IDXESGL company group has a higher liquidity. If the coefficient of ESGL is found negative in this regression model, the liquidity for ESGL companies will be stated as lower than non ESGL companies.
The second regression model has proven that stock liquidity within ESG companies is proven to be higher than those which are non ESG companies, proven by the positive sign of ESGL coefficient in the regression model. Control variables for this regression model include ESGL,
FSIZE, ROA and PBR. FSIZE has a positive relationship, while ROA and PBR have negative relationship with stock liquidity. Control variables also have a confounding impact, rather than a major one.
4.3. Hypothesis Three Regression Model
Table 4.7: Model 3 Coefficient Coefficients
1 Unstandardized Coeeficients Standardized Coefficients
B Std. Error Beta t Sig.
(Constant) -18.143 2.607 -6.958 <.001
SampleGroup 1.014 .236 .376 4.297 <.001
Volatility 25.469 7.727 .302 3.296 .002
FSIZE .638 .082 .775 7.795 <.001
ROA -2.461 3.399 -.062 -.724 .472
PBR -.019 .014 -.107 -1.320 .192
Table 4.6 shows a new coefficient table with the added control variables. The beta of sample group decreases from 0.586 to 0.376, while stock volatility as independent variable has an increase beta to 0.302. With both decrease and increase beta from independent variables, it could be said that the control variables have more than just confounding effects toward stock liquidity. Both independent variables are significant (Sample Group and Volatility) and only one control variable is significant which is firm size (with sig.value of <.001). The other two control variables which are ROA and PBR are not statistically significant towards stock liquidity. This means that to test the relationship between volatility and liquidity, control variables helped to avoid biases.
Table 4.8 Model 3 Summary Model Summary
Model
R R
Square Adjusted R Square Std. Error of the
Estimate Durbin-Watson
1 .847 .718 .692 .7553317 2.620
The model summary shown in the table above could be concluded for the first regression model.
The R is 0.847 and the R squared is 0.718. With an R-squared of 0.692, there is a proportion of 69.2% of variance in stock liquidity explained by the predictor and control variables. Based on Guildford’s criteria, the relation based on value of R could be defined as high correlation with strong relationship.
The complete regression model for model 2 will be shown as the following:
𝑳𝑰𝑸 = −𝟏𝟖. 𝟏𝟒𝟑 + 𝟐𝟓. 𝟒𝟔𝟗(𝑽𝑶𝑳) + 𝟏. 𝟎𝟏𝟒(𝑬𝑺𝑮𝑳) + 𝟎. 𝟔𝟑𝟖(𝑭𝑺𝑰𝒁𝑬) − 𝟐. 𝟒𝟔𝟏(𝑹𝑶𝑨) − 𝟎. 𝟎𝟏𝟗(𝑷𝑩𝑹) + 𝟎. 𝟕𝟔
Based on the regression model, it could be interpreted as :
𝛽9
= -12.807: When VOL, ESGL, FSIZE, ROA and PBR and error term are zero, the liquidity will account for -18.143𝛽10
= 1.008: When VOL increases by one unit, the liquidity will increase by 25.469𝛽11
= 0.493: When ESGL increases by 1 unit, liquidity will increase by 1.014𝛽12
= -4.936: When FSIZE increases by 1 unit, liquidity will increase by 0.638𝛽13
= -0.009: When ROA increases by 1 unit, liquidity will decrease by 2.461𝛽14
= -0.009: When PBR increases by 1 unit, liquidity will decrease by 0.019The last regression model is to test the third hypothesis and find out how is the relationship between stock volatility and stock liquidity in the Indonesia Stock Market. All 60 samples are used because it could showcase the overall condition between companies listed in IDXESGL and non ESG companies. If it is done separately or utilize two regression models to explain relationship of volatility and liquidity within each sample group, it may not capture a wider range of samples. Hence, a combined sample between IDXESGL companies and non ESG companies were utilized.
From the complete regression model, the coefficient for volatility (VOL) is found positive or 25.469. This indicates that every increase in volatility, will also increase liquidity by 25.469.
Indicating that there is rather a positive relationship between stock volatility and stock liquidity.
The third regression model to test the third hypothesis has a coefficient for volatility of 25.469.
It shows a positive relationship, which resulted in a rejection of H3. The expected sign relationship between liquidity and volatility was negative since both have bidirectional movement. This result contradicts with findings from Sójka and Kliber (2019), where they found that both volatility and liquidity are inextricably linked and share features.
5. Conclusion and Recommendations
This research was conducted in order to investigate the relationship between stock liquidity and stock volatility and to extend that intention by comparing the stock volatility and stock liquidity between Environmental, Social and Governance companies (listed in the IDXESGL index) against those which are not. The total sample is 60 companies, with each sample group having 30 companies within group. In order to test the priorly stated hypothesis, the researcher utilized descriptive statistics analysis, t-test and simple linear regression. There are two main variables, one is the independent variable which is stock volatility and the other one is the dependent variable which is stock liquidity. Each of the variable has their own way of calculation, where the predictor or independent variable, which is stock volatility will be calculated using Garman- Klass Volatility. The other two models of volatility calculation were conducted, namely Close- to-Close Volatility and Parkinson Volatility. Garman-Klass Volatility becomes the primary measurement of volatility due to its extension from Parkinson Volatility by adding the opening and closing price, thus showing a more generalized and objective way of calculating volatility.
The outcome variable, or dependent variable, which is stock liquidity has been calculated firstly using Amihud Illiquidity Measure and then the annual liquidity will be achieved by using 1/ILLIQUIDITY formula. The dataset for this study will utilize a period ranging from December 2020 until December 2021.
Based on the several tests and research findings, the answer to this research question is the mean of volatility within ESG companies is significantly lower than those which are not ESG companies and the mean of liquidity of ESG companies is higher than those which are not ESG companies. The t-test showed a significant in mean difference between the two sample groups in terms of volatility and liquidity. Several previous studies found that the movement between volatility and liquidity are usually diametrically opposed, indicating that there could be a negative relationship between stock volatility and stock liquidity. After linear regression analysis was conducted, it could be found that within the ESG companies sample group,
research findings suggest that there is indeed a negative relationship between stock volatility and stock liquidity.
Since this study is focused mainly upon investment area and the stock market, this study could be useful, especially for investors when investing their money in stocks. Investors need to realize that investing in stock market produces a high risk and high return investment. Hence, a reduced herding behavior upon making investment decision would be wise considering the risk that may come upon it. Investing money in companies that focuses its operational on ESG aspects may benefit investors in the long – run since the stock liquidity is proven to be higher than those which do not implement ESG in their business operational. When liquidity is high, stock volatility becomes lower, indicating a lower risk of investment in the stock market. It is advised that investors should thoroughly conduct their own research before buying company’s shares to avoid great loss. This paper could also support the increase of sustainable investment by investors in the Indonesia Stock Market.
For practical recommendations, there are several parties that this researcher would like to address. Firstly, for BEI (Bursa Efek Indonesia) as policy maker in Indonesia Stock Exchange.
The researcher recommends BEI to create a sustainable investment awareness program. This could be done through a weekly or monthly seminar, competition of sustainable investment which gives rewards to investors who gain highest return. Moreover, BEI should also implement an online company visit or introduction, especially for companies who conduct their business operational based on ESG aspects.
Researcher also suggests that public policymaker involves financial influencer and content creator to increase awareness of sustainable investment and teaches fundamental of investing to the public. The program should be adjusted for new investors who are coming into the capital and stock market to understand better about investment. This program could consist of monthly or weekly comprehensive programs which allow investors to be involved in real practical investment activity. A program such as demo investment could also be implemented to give investors brief description regarding investing activity.
References
Amihud, Y. (2002) ‘Illiquidity and stock returns: Cross-section and time-series effects’, Journal of Financial Markets, 5(1), pp. 31–56. Available at:
https://doi.org/10.1016/S1386-4181(01)00024-6.
Będowska-Sójka, B. and Kliber, A. (2019) ‘The causality between liquidity and volatility in the Polish stock market’, Finance Research Letters, 30(January), pp. 110–115. Available at:
https://doi.org/10.1016/j.frl.2019.04.008.
Boffo, R., and R.P. (2020) ‘ESG Investing : Practices, Progress an d C hallenges’, OECD Paris, [Preprint]. Available at: https://www.oecd.org/finance/ESG-Investing-Practices- Progress-Challenges.pdf.
Fajrihan, J. (2013) ‘Dampak Kebijakan Dividen Terhadap Volatilitas Harga Saham’, Journal of Chemical Information and Modeling, 01(01), pp. 1689–1699.
Gopalan, R., Kadan, O. and Pevzner, M. (2012) ‘Asset liquidity and stock liquidity’, Journal of Financial and Quantitative Analysis, 47(2), pp. 333–364. Available at:
https://doi.org/10.1017/S0022109012000130.
Hendrawaty, J. (2010) ‘Analisis Likuiditas Saham Sebelum Dan Sesudah Pengumuman Reverse Stock Split Pada Perusahaan Yang Terdaftar Di Bursa Efek Indonesia’, Thesis (S1) UAJY, pp. 12–33.
Hashemijoo, M., Mahdavi Ardekani, A. and Younesi, N. (2012) ‘The Impact of Dividend Policy on Share Price Volatility in the Malaysian Stock Market.’, Journal of Business Studies Quarterly, 4(38), pp. 111–129. Available at:
http://ezproxy.lib.monash.edu.au/login?url=http://search.ebscohost.com/login.aspx?dire ct=true&db=bth&AN=91711854&site=ehost-live&scope=site.
Mike, S. and Farmer, J.D. (2008) ‘An empirical behavioral model of liquidity and volatility’, Journal of Economic Dynamics and Control, 32(1), pp. 200–234. Available at:
https://doi.org/10.1016/j.jedc.2007.01.025.
Romli, H., Febrianti Wulandari, M. and Sartika Pratiwi, T. (2017) ‘Faktor-Faktor Yang Mempengaruhi Volatilitas Harga Saham Pada Pt Waskita Karya Tbk’, Jurnal Ilmiah Ekonomi Global Masa Kini, 8(1), pp. 1–5. Available at:
http://ejournal.uigm.ac.id/index.php/EGMK/article/view/281.
Sutrisno, B. (2017) ‘Hubungan Volatilitas dan Volume Perdagangan di Bursa Efek Indonesia’, Esensi, 7(1), pp. 15–26. Available at: https://doi.org/10.15408/ess.v7i1.3894.
Svanes, K. and Øyaas, C.B. (no date) ‘Stock Market Liquidity and Sustainability’.
Torre, M. La et al. (2020) ‘Does the ESG index affect stock return? Evidence from the Eurostoxx50’, Sustainability (Switzerland), 12(16). Available at:
https://doi.org/10.3390/SU12166387.
Wulandari, R. (2021) ‘Comparison of volatility and performance of shares in Indonesia, Malaysia, China and America (Study on the content, FBMS, DJICHKU and DJIMI)’, Asian Management and Business Review, 1(1), pp. 46–56. Available at:
https://doi.org/10.20885/ambr.vol1.iss1.art5.
Zheng, H. (2019) ‘Using Sampling Matching Methods to Remove Selectivity in Survey Analysis with Categorical Data’.\
Zhou, D. and Zhou, R. (2022) ‘Esg performance and stock price volatility in public health crisis:
Evidence from covid-19 pandemic’, International Journal of Environmental Research and Public Health, 19(1). Available at: https://doi.org/10.3390/ijerph19010202.
