Exploring the nexus between sectoral stock market fluctuations and macroeconomics changes before and during the COVID-19 pandemic

Suriani, Suriani et al. Article Exploring the nexus between sectoral stock market fluctuations and macroeconomics changes before and during the COVID-19 pandemic Cogent Business & Management Provided in Cooperation with: Taylor & Francis Group Suggested Citation: Suriani, Suriani et al. (2024) : Exploring the nexus between sectoral stock market fluctuations and macroeconomics changes before and during the COVID-19 pandemic, Cogent Business & Management, ISSN 2331-1975, Taylor & Francis, Abingdon, Vol. 11, Iss. 1, pp. 1-15, https://doi.org/10.1080/23311975.2024.2336681 This Version is available at: https://hdl.handle.net/10419/326219 Standard-Nutzungsbedingungen: Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden. Sie dürfen die Dokumente nicht für öffentliche oder kommerzielle Zwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglich machen, vertreiben oder anderweitig nutzen. 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If the documents have been made available under an Open Content Licence (especially Creative Commons Licences), you may exercise further usage rights as specified in the indicated licence. https://creativecommons.org/licenses/by/4.0/ Cogent Business & Management ISSN: 2331-1975 (Online) Journal homepage: www.tandfonline.com/journals/oabm20 Exploring the nexus between sectoral stock market fluctuations and macroeconomics changes before and during the COVID-19 pandemic Suriani Suriani, Anabela Batista Correia, Muhammad Nasir, João Xavier Rita, Jumadil Saputra & Mario Nuno Mata To cite this article: Suriani Suriani, Anabela Batista Correia, Muhammad Nasir, João Xavier Rita, Jumadil Saputra & Mario Nuno Mata (2024) Exploring the nexus between sectoral stock market fluctuations and macroeconomics changes before and during the COVID-19 pandemic, Cogent Business & Management, 11:1, 2336681, DOI: 10.1080/23311975.2024.2336681 To link to this article: https://doi.org/10.1080/23311975.2024.2336681 © 2024 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group. Published online: 09 Apr 2024. Submit your article to this journal Article views: 1157 View related articles View Crossmark data Citing articles: 2 View citing articles Full Terms & Conditions of access and use can be found at https://www.tandfonline.com/action/journalInformation?journalCode=oabm20 Banking & Finance | ReseaRch aRticle Cogent Business & ManageMent 2024, VoL. 11, no. 1, 2336681 Exploring the nexus between sectoral stock market fluctuations and macroeconomics changes before and during the COVID-19 pandemic suriani suriania, anabela Batista correiab, Muhammad nasira, João Xavier Ritab,c, Jumadil saputrad and Mario nuno Matab,c aDepartment of economics, Faculty of economics and Business, universitas syiah Kuala, Banda aceh, indonesia; bisCaL-instituto superior de Contabilidade e administração de Lisboa, instituto Politécnico de Lisboa, Lisboa, Portugal; cinsight: Piaget Research Center for ecological Human Development, Lisboa, Portugal; dDepartment of economics, Faculty of Business, economics and social Development, universiti Malaysia, Kuala nerus, terengganu, Malaysia ABSTRACT investors may find it challenging to invest due to economic fluctuations during cOViD-19. this study aims to examine the relationship between economic fluctuations and the indonesian sectoral stock market in the consumer goods sector (cgi), basic industrial and chemical sector (Bic), and miscellaneous industry (Msi), both before and during the cOViD-19 pandemic in indonesia. the monthly time-series data used in the empirical approach cover the period from January 2008 to December 2020. the analysis used forecast error variance decomposition, vector autoregression, impulse response function analysis, and causality investigation. the econometric results showed that previous period shocks in each industrial sector stock market had a disadvantageous effect on future stock market earnings. additionally, while the cgi stock market positively affects the Rupiah exchange rate, the Msi industrial sector is negatively impacted by inflationary pressures. also connected to the Msi stock market are fluctuations in inflation. conversely, the exchange rate affects Msi and cgi. Furthermore, for the cgi and Bic stock markets, a one-way causation relationship is observed. another notable result was that all three industrial sectors responded positively when inflation and exchange rates were disrupted. it implies that, for convenience, investors will seek out other areas of the stock market. therefore, a quick government response is needed to handle the economy during economic fluctuations accompanied by the cOViD-19 pandemic so that it does not have an impact on the future. 1. Introduction investment activities are the backbone of developing economies worldwide. according to conventional thinking, investing now promotes future economic growth (sawulski etal., 2023). the number of investment activities reflects the state of the global economy between countries. the world’s macroeconomic fluctuations can be influenced by a global shock (Fitriana et al., 2023a). cOViD-19 is one such pandemic that has impacted global macroeconomics (Fitriana et al., 2023b; Mckibbin & Fernando, 2021). the sectoral economy is one of the concerns for investment activities (Defrizal et al., 2021). indonesia’s stock market is significant for investment as it operates with an open economic system (Muna & khaddafi, 2022). the market comprises nine sectoral stock indices, each with different industrial trade index value based on the stock market capitalisation. the main topic of this study is the performance of indonesia’s sectoral stock indices during the cOViD-19 pandemic and the crisis. there are 35 indices on the indonesian stock market, as per the BPs report of 2019. One of these is the sectoral index, which is divided into 9 important sectors in indonesia. the industrial trade value of © 2024 the author(s). Published by informa uK Limited, trading as taylor & Francis group. CONTACT suriani suriani [email protected] Department of economics, Faculty of economics and Business, universitas syiah Kuala, Darussalam, Banda aceh, indonesia. https://doi.org/10.1080/23311975.2024.2336681 this is an open access article distributed under the terms of the Creative Commons attribution License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. the terms on which this article has been published allow the posting of the accepted Manuscript in a repository by the author(s) or with their consent. ARTICLE HISTORY Received 28 august 2022 Revised 24 February 2024 accepted 26 March 2024 KEYWORDS Macroeconomic fluctuations; indonesia stock exchange; sectoral stock price; vector autoregressive model; cointegration approach REVIEWING EDITOR Mazhar abbas, cholistan University of Veterinary and animal sciences Bahawalpur, Pakistan SUBJECTS Monetary economics; Development economics; Financial economics 2 s. sURiani etal. each sector in the stock market is shown in Figure 1, which indicates that every sector has a different industrial trade value. the financial sector had the highest industrial trade value at 35%, with an industrial market capitalisation of 34%. On the other hand, the various industry sectors had the lowest industrial trade value at 4.2%, with an industrial market capitalisation of 4.4%. the infrastructure, utilities, and transportation sectors had an industrial trade value of 15.6%, the trade, service, and investment sector of 13.9%, the mining sector of 9.4%, the consumer goods industry of 8.3%, Basic and chemical industries by 6.4%, and the property, real estate, and construction sectors by 5.5%. the difference in trading value is related to the number of shares that investors are interested in the primary and secondary markets and the associated risks. During a certain period, the stock market in different sectors showed fluctuations. Figure 1 indicated that the agriculture sector index was dominating, which is very reasonable since indonesia is primarily an agricultural country. On the other hand, the property sector index had the lowest rank. it shows that as a developing country, indonesia is still prioritising investment in the real sector rather than the luxury sector. inflation is one of the indicators that measure the changes in prices of a particular group of commodities continuously over a certain period. the fluctuations in inflation can significantly impact the real sector. in cases of hyperinflation, community purchasing power is affected, which ultimately impacts the aggregate demand and supply, leading to market equilibrium. the speed at which inflation reaches its equilibrium level is called persistence. the empirical results show that the pressure for inflationary sources comes from food commodities and housing, water, electricity, gas, and fuel commodities. inflation in food consumption can significantly affect food security (suriani & sartiyah, 2020). Bilici and Çekin (2020) demonstrates a notable rise in inflation persistence, highlighting the efficacy of monetary policy in ensuring price stability. analysis of indonesia’s inflation rate indicates a consistent decline since early 2020. the events that transpired between July 2020 and October 2020 revealed real-life conditions that deviated from existing theories (suriani & Ridzqi, 2019). it was observed that a decrease in prices led to an increase in aggregate demand due to the higher purchasing power of the people, resulting in a rise in national output (gross domestic product). however, despite this observation, official data released by the indonesian central Bureau of statistics on november 5, 2020 confirmed that indonesia had entered a recession in the third quarter of 2020 with negative economic growth contracted at -3.49% (YoY). sembiring-kembaren (2020) also explained that the second contraction in the economic growth trend occurred after the second quarter of -5.32%. the data in Figures 2 and 3 shows that the growth trend in the inflation rate decreases until October 2020, and gDP growth also has a downward trend to negative. this condition can occur because of the cOViD-19 pandemic at the end of 2019 (alam et al., 2021). its spread has spread globally in early 2020 Figure 1. the value of industrial trade in 9 sectors in 2019. cOgent BUsiness & ManageMent 3 and has an impact on sectoral stock market returns (alomari et al., 2022). if macroeconomic activity is disrupted, the economy will not be stable. it may have an effect on all macroeconomic variables, particularly the exchange rate, which is closely related to investment activities of stock prices (alam & Uddin, 2009). Research on the sectoral stock market focuses on the connections between the market and economic growth (hismendi et al., 2021), policy uncertainty and the market (si et al., 2021), the market and pandemics (adekoya etal., 2022; alomari etal., 2022), herding behaviour (Viona etal., 2023) and the market for stocks in the global health sector (Ye & geng, 2021). During the pandemic, there have been significant changes in the country’s economy. the industrial sector related to essential public needs is particularly noteworthy and presents an opportunity for entrepreneurs to generate profits, contributing to economic development (kritikos, 2014). surviving in crisis situations like the ongoing pandemic poses a challenge for companies operating in the consumption, basic, and miscellaneous industries. this study focuses on investment activities within these sectors’ stock markets and their ability to meet domestic community needs amid crises such as cOViD-19. though not dominating the market, this research explores whether these three sectors can withstand the challenges posed by crisis events and the current pandemic situation. in today’s global economy, the competition in the investment market is higher than ever. the value of the stock market and the capital market is of significant concern to investors. therefore, it is crucial to pay attention to government policies that support economic development in various sectors. Before investing, it is also essential to consider the macroeconomic conditions of a country. if the macroeconomy is stable and favourable, it means that the development activities in that country are suitable, which leads to economic growth. On the other hand, if macroeconomic conditions are unstable, a country’s economy gets disturbed. inflation is one of the macroeconomic variables that policymakers consider. according to a survey on inflation uncertainty by Breach et al. (2020), inflation risk is significant for nominal treasury yields. it was demonstrated that during the 1980s, the decomposition of goods was caused by varying inflation and accurate risk premiums, which were significant and positive. however, after 2008, they became small and negative. the inflation rate refers to the percentage change in prices over a given period, usually a month or a year. this percentage shows how fast prices have increased during that time. inflation measures the Figure 2. inflation rate trends in indonesia for november 2019 to october 2020. Figure 3. gDP growth rate trend from July 2017 to July 2020. 4 s. sURiani etal. price changes that happen repeatedly over time for a group of goods. inflationary changes can have a negative impact on the real economy. in case of hyperinflation, it can significantly affect the purchasing power of the community as a whole. it affects the aggregate demand and ultimately affects the aggregate supply to achieve market equilibrium. the speed at which inflation moves towards its equilibrium level is referred to as its persistence. the previous discussion about sectoral stock markets and stock market reaction has been carried out by several researchers, such as hismendi et al. (2021), Defrizal et al. (2021), nguyen and Pham (2018), and ng et al. (2017). also, discussion about stock market liberalisation for technological innovation (alavi et al., 2016) and the macroeconomic effect variables on the stock market (hsing, 2011) have been carried out. Previously, studies analysed the relationship between the stock market index and economic fluctuations. however, this study aims to examine the relationship between indonesia’s sectoral stock indexes and economic fluctuations during shocks. the study will focus on the response time and composition of shocks between the variables. specifically, this research will analyse three sectoral stock indexes - Basic industry & chemicals (Bic), Miscellaneous industry (Msi), and consumer goods industry (cgi), which have not been studied before. the expected contribution of this research is to determine the resilience of these three industry sectors during a shock. this information will help these industries continue to meet the needs of the indonesian people, even during frequent economic fluctuations, crises, and pandemics such as cOViD-19. this research will also help investors understand the resilience of the sectoral stock market and make more profitable investments. to support the research findings, the study will analyse the causality between the studied variables, focusing on the rapid response to shocks in inflation and the exchange rate. the causality test will help establish a causal relationship between the variables. the content stages of this study will begin by discussing the urgency of this research, followed by a literature review and analytical methods, the results of the study, and a concrete analysis. Finally, the study will conclude with a summary of the key findings. 2. Literature review institutional investment activities frequently impact financial markets and actual economic activity (Bond et al., 2012; naik et al., 2021; nofsinger, 2005; Woolridge & snow, 1990). Debt instruments, commercial securities, shares, bonds, proof of debt, ciUs, sFcs, and other derivatives of securities are all considered to be securities. the stock market, however, still constitutes a portion of the capital market. it is possible to trade shares of publicly traded companies on a stock market. By offering shares to the public in an initial public offering, businesses can raise capital on the primary market (iPO). the primary market is the section of the capital market where issuers directly issue and offer equity-backed securities for sale to investors. securities that have never been traded are bought by investors. Only stock transactions and derivatives are conducted on the stock exchange. an efficient market is one in which information spreads quickly and is reflected in stock prices, also known as market rationality (Yalçın, 2010). the indonesia stock exchange currently has 35 stock indices. the stock index is a statistic that is evaluated on a regular basis and represents the overall price movement of a selection of stocks made using particular criteria and methodology. the goals and benefits of the stock index include measuring market sentiment, creating passive investment products like index Funds and index etFs as well as derivative products, acting as benchmarks for active portfolios, serving as proxies for asset classes in asset allocation, measuring and modelling returns on investment (return), systematic risk, and risk-adjusted-performance. Macroeconomic factors and stock market factors are closely related to each other. how much influence does the macroeconomy have on the stock market? in 2017, Milani conducted a study using a new-keynesian general equilibrium model to analyse the quantitative effects of interactions between the stock market, macroeconomic factors, and monetary policy. the model includes a wealth effect resulting from changes in asset prices that affect consumption. the research refutes the rational expectations hypothesis and suggests that economic agents develop nearly rational expectations over time based on their observed economic model. airaudo (2013) analysed a new-keynesian Dsge model that included limited asset market participation (laMP) to determine if monetary policy should respond to stock prices, in terms of the determinacy and learnability of the Rational expectations equilibrium (e-stability) (Ree). they found that when the degree of cOgent BUsiness & ManageMent 5 laMP is high enough to create an inverted aggregate demand channel for the transmission of monetary policy, interest rate regulation that allows a positive reaction to stock prices facilitates both the determinacy and the e-stability of the basic Ree. this implies that policy rules based on stock prices perform better than traditional rules based on output in terms of equilibrium determinacy and overall welfare. Bjørnland and leitemo (2009) used structural vector autoregressive (VaR) methods to measure the dependence on Us monetary policy. they resolved the simultaneity problem of detecting economic and stock price shocks by using a combination of short-run and long-run limits while maintaining the qualitative traits of a monetary policy shock as described in the literature. they found a significant correlation between interest rate policy and absolute stock values due to a change in monetary policy that accurately predicted the federal funds rate by 100 basis points. the interest rate rises by almost four basis points for every percentage point that actual stock prices rise. lawal et al. (2018) conducted a study to analyse the impact of interactions between monetary and fiscal policies on the behaviour of the nigerian stock market. the study also looked at the effect of policy interaction volatility on the stock market. the findings revealed that the interaction of monetary and fiscal policy has a significant impact on nigeria’s stock market returns, using the aRDl and egaRch models. the study identified five potential ways that monetary policy could influence stock market returns, which are the interest rate hypothesis, the credit hypothesis, the wealth effect hypothesis, the exchange rate hypothesis, and the monetary hypothesis. hsing (2011) carried out research to explore the impact of various economic factors on the south african stock market index. the study found that the money supply to gross domestic product ratio, the real gDP growth rate, and the Us stock market index have a positive effect on the stock market index. however, the government deficit to gDP ratio, domestic real interest rates, and the effective exchange rate are considered negative factors in the exponential gaRch Model. therefore, the government is required to maintain economic expansion, fiscal responsibility, a high money supply ratio to gDP, low accurate interest rates, and low inflation rates to keep the stock market stable. Rahmayani and Oktavilia (2021) stated that the long-term lull in indonesia’s stock market is due to the higher cumulative total cOViD-19 cases. ahmad etal. (2021) found that stocks in consumer staples, healthcare, telecommunications, utilities, and financials attracted the most attention during the cOViD-19 pandemic, and different industries in the sample countries reacted to the outbreak differently. Regression analysis was used by Vithessonthi and techarongrojwong (2012) to look at how monetary policy impacts stock prices. the expected shift in the buyback rate has a negative effect on stock returns at the market level. contrarily, the findings reveal that the unanticipated change in the repurchase rate has no impact on stock returns. however, it is possible to observe at the firm level how the unexpected shift in the repurchase rate has impacted stock returns. the researchers also discovered that there was an asymmetry in the stock market’s reaction to a change in the repurchase rate. even though it is considered good news, an unexpected change in the buyback rate has a negative effect on stock returns. together, the evidence supports the notion that monetary policy announcements have a significant impact on stock prices and advances the question of whether the credibility of the monetary authority influences the stock market’s reaction to monetary policy actions. this study focuses on analysing the stock market response to economic fluctuations caused by inflation and changes in exchange rates, specifically for the consumption industry, basic industry, and miscellaneous industry during the pandemic. the study will test and analyse causality to explain the relationship between sectoral stock market conditions and monetary policy to control inflation and exchange rates. the findings of this research can contribute to the enrichment of knowledge in the field of investment when shocks occur. the consumption industry, basic industry, and miscellaneous industry are essential for meeting the basic needs of the community, such as food and clothing. this shows the strength and resilience of the sectoral stock market as an alternative to government policies to maintain the economy in times of global shocks. 3. Materials and methods the research methodology used in this study is quantitative in nature. the study is based on monthly data from January 2008 to December 2020 (158 data points) and focuses on sectoral stock variables, 6 s. sURiani etal. specifically the stock indices of the Basic, industry & chemicals, consumer goods, and Miscellaneous industries sectors. these variables are used to measure the real sector economy using index units. the study focuses on the three stock sectors mentioned above as they are crucial in analysing activities related to public consumption needs. consumption plays a significant role in determining the demand for goods and services in society (suriani etal., 2021). exchange rate and inflation variables are also used in the study to measure the change in the value of Rupiah currency relative to the dollar and the rate of price fluctuation, respectively. the quantitative research methodology used in this study employs vector autoregression (VaR) modelling. the VaR Model is advantageous as it is a simple model that does not require distinguishing between endogenous and exogenous variables. estimation is straightforward and can be applied to each equation individually using the Ols method. in most cases, the forecast results obtained through this method outperform those obtained through even the most complex simultaneous equation models. the study aims to explore the interrelationship (reciprocity) between economic variables and the formation of a structured economic model. holtz-eakin et al. (1988) formed the specific bivariate autoregression equation model (VaR) can be written as follows: y yx t i m it i m it t =++ + ∑∑ = − = − α α αδ ε 0 1 1 1 1 , (1) if equation (1) is transformed, the equation model for this study ss are devoted to three industrial sectors, namely Bic (Basic industry & chemicals), Msi (Miscellaneous industry), and cgi (consumer goods industry). the formula can be written as follows: SS SS INF ER t ti ti ti t =+∑ +∑ +∑ + − −− αγ γ γ ε 11 2 3 , (2) however, if the unit root test results show stationary data at the first difference and cointegrated, then the model chosen is the VecM model or called limited VaR. change is indicated by Δ (the first difference vector). the results of the unit root test indicate that it is stationary at the first difference and has not cointegrated, so the best model is VaR at this point (see test results in tables 1 and 3). so the best model in this research is VaR at i(1) model and can be written in the new equation formed as follows: ∆ ∆ ∆ ∆ ∆∆ BIC BIC MSI CGI INF E t ti ti ti ti = +∑ +∑ +∑ +∑ +∑ − − −− αγ γ γ γ γ 11 2 3 4 5 RR ti t − + ε , (3) ∆ ∆ ∆ ∆ ∆∆ MSI MSI BIC CGI INF ER t ti ti ti ti t = + ∑ + ∑ + ∑ + ∑ +∑ −− −− αγ γ γ γ 11 2 3 4 −− + it ε , (4) ∆ ∆ ∆ ∆ ∆∆ CGI CGI BIC MSI INF E t ti ti ti ti = +∑ +∑ +∑ +∑ +∑ − − −− αγ γ γ γ γ 11 2 3 4 5 RR ti t − + ε , (5) ∆ ∆∆∆ ∆∆ INF INF BCI MSI CGI E t ti ti ti ti =+∑ +∑ +∑ +∑ +∑ −− −− αγ γ γ γ γ 11 2 3 4 5 RR ti t − + ε , (6) ∆ ∆∆ ∆ ∆ ∆ ER ER BCI MSI MSI INF t ti ti ti ti =+∑ +∑ +∑ +∑ +∑ −− − − αγ γ γ γ γ 11 2 3 4 5 tt i t− + ε , (7) Table 1. the unit root tests. Results. Variables aDF i(0) aDF i(1) PP i(0) PP i (1) BiC −0.9132 −11.5121 −0.9132 −15.5106 (0.7820) 0.0000)*** (0.3626) (0.0000)*** Cgi −1.7733 −11.5121 −1.7718 −11.4822 (0.3926) (0.0000)*** (0.3933) (0.0000)*** Msi −1.8676 −10.9795 −1.8631 −10.8928 (0.3469) (0.0000)*** (0.3490) (0.0000)*** inF −2.4150 −7.7846 −2.0957 −8.0441 (0.1393) (0.0000)*** (0.2468) (0.0000)*** eR −1.0476 −12.2690 −0.9892 −12.2979 (0.7353) (0.0000)*** (0.7564) (0.0000)*** note: (.) is significant value and *** is level of significance (1%). cOgent BUsiness & ManageMent 7 For equation (2), the variable ss is the dependent variable (sectoral shares). this research focuses on three equation models for dependent variables (Bic, Msi, cgi) for this sectoral stock. in addition, the error term is ε , and the variables t is period, and α are constants. the letters eR, iR, and inF stand for the exchange rate, interest rate, and inflation, respectively. the time-series analysis test stages for the VaR modelling will involve the data stationarity test, model stability test, best lag length test, cointegration test, and causality test. the upcoming tests are the Forecast error Variance Decomposition Function and the impulse Response Function. the Unit-Roots test for data stationarity employs the enhanced Dickey-Fuller and Phillips Perron techniques: YY t tt = + −≤ ≤ − ρε 1 11 ρ , (8) the εt variable in equation (6) represents the stochastic error term in the classical assumption, with the difference (variant) set to zero and y being the time-series. additionally, if the value of at the first difference level is equal to 1, the data is considered non-stationary. Once the data is stationary, the stationary test is completed in the second difference test, which also serves as the continuation of the unit root test. then the model stability test is required in using time-series data. this test proves that the model is good, obtaining unbiased results. after testing the model stability condition (once the data is proven to be stationary), the data is tested again in the VaR system. We can see the stability test results in 2 (two) forms, namely in a modulus value table and a graph of the inverse roots of aR characteristic polynomial. the characteristics considered in the modulus value have a modulus value below 1. the model test is said to be stable, and if the modulus value is below 1, and if the modulus value is above 1, the model is to be unstable. the characteristics of the inverse roots of the aR characteristic polynomial graph concerning the polynomial elements are shown from the circle’s position. if the points are still in the circle, then the model is said to be stable. With the achievement of these characteristics, the model in the VaR system is categorised as stable so that the next test stage can be carried out, namely the determination of the optimal lag. since the lags of these variables are used as independent variables in the VaR Model, it is necessary to determine the ideal lag length. the optimal length of time is known as the optimal lag length, and it is used in time-series analysis to quantify how long a variable’s influence lasts on other variables. in order to keep the estimation system free of autocorrelation issues, the lag length Optimal test is also required. since the optimum lag is employed in this simulation, it is assumed that the autocorrelation problem won’t come up. in this study, the schwarz information criterion, the sequentially modified lR test statistic, the Final prediction error, the akaike information criterion, the hannan-Quinn information criterion, and the akaike information criterion are just a few of the existing criteria that are taken into account when determining the Optimal lag length. in the case of two or more non-stationary time-series variables, cointegration is feasible. the long-term stability of the relationship depends on whether the time-series variables are cointegrated (Moosa & Vaz, 2016). the cointegration test developed by Johansen was employed in this study. at a confidence level of α = 5%, the trace statistical analysis used by the Johansen test results in a significance greater than the critical value. the probability value, which is less than 5%, denotes cointegration. 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