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Abstract

The COVID-19 pandemic outbreak has caused disruptions in various aspects of life and the economy, including the movement of stock indexes. The objective of this study is to examine the influence of macroeconomic factors on the volatility of the Jakarta Composite Index both before and after the COVID-19 pandemic. Monthly secondary data from June 2007 to December 2019 were analyzed to identify pre-COVID trends, while data from June 2007 to December 2021 were used to identify post-COVID-19 patterns. The analysis incorporated the Granger causality test, Cointegration test, Vector Error Correction Model, and Augmented Dickey-Fuller. The results of the causality analysis revealed variations in the impact of interest rates, global oil prices, inflation, and exchange rates on the Jakarta Composite Index before and following the COVID-19 pandemic. These findings hold significance for investors as they enable the identification of potential risks associated with investments during global crises.

Plain Language Summary

This study aims to identify the impact of macroeconomic variables on Jakarta Composite Index volatility before and after the COVID-19 pandemic. This study analyzed monthly secondary data from June 2007 to December 2019 for pre-COVID identification and June 2007 to December 2021 for post-COVID-19 identification. Analysis was performed using the Granger causality test, Cointegration test, Vector Error Correction Model, and Augmented Dickey-Fuller (ADF). Finally, the results of the causality analysis identify differences in the influence of interest rates, inflation, exchange rates, and world oil prices on the Jakarta Composite Index before and after the COVID-19 pandemic. This research is useful for investors to detect possible risks in investing, in the event of a global disaster. This research focuses on investigating the relationship between the variables of interest rates, inflation, exchange rates, and world oil prices before and during the COVID-19 pandemic.

Keywords

cointegration test COVID-19 Granger causality stock index macroeconomics VECM

Introduction

The capital market functions as a mechanism for capital formation and long-term fundraising to encourage community involvement in raising funds to support national development financing. The economy of the nation and the lives of its citizens are both significantly impacted by the stock market (Al-Smadi et al., 2023). However, maximizing stock returns in the equity market is an expectation of high-risk investors (Muharam et al., 2021).

Many factors affect the movement of stock prices, including the supply and demand for shares, economic conditions, socio-cultural and political conditions, market perceptions of the company’s condition, information about the condition of the company that is owned by the public, and the company’s expected achievements in the future. These differences in market conditions require different investment strategies for investors. Apart from these factors, investor behavior and extraordinary events such as the COVID-19 pandemic can also have an impact on stock index movements.

The stock market has been greatly affected by the economic disruptions caused by the pandemic, making it more challenging to predict stock prices during this time. A significant empirical study conducted by Al-Awadhi et al. (2020) has explored the relationship between COVID-19 and market returns. At the onset of the COVID-19 crisis, there was a lack of information about stocks, leading to exaggerated initial reactions from investors due to the high level of uncertainty (Iyke & Ho, 2021). However, as time progresses, more information becomes available to investors, increasing confidence in the stock market and making investment decisions more manageable (Phan & Narayan, 2020).

The Jakarta Composite Index (JKSE) serves as an indicator that provides an overview of the overall fluctuations in stock prices within the Indonesia Stock Exchange (IDX). Investors commonly rely on the JKSE to gain insights into the movement of stock prices listed on the IDX, enabling them to determine whether the current state of the Indonesian capital market is bullish or bearish (Robiyanto et al., 2019). Beginning in early 2020, the COVID-19 pandemic had a significant impact on both the JKSE and the international stock markets. In March 2020, the JKSE experienced a significant decline driven by concerns surrounding the economic repercussions of the pandemic. The fall of the JKSE at that time could not be separated from the deteriorating performance of world stock exchanges due to the global COVID-19 disaster. The JKSE corrected quite deeply, closing at the level of 3,937.63 points at the end of the last trading day of March 2020 (Finance.yahoo.com). Capital market conditions like this will affect investors’ perceptions of investing and also affect stock price movements.

The movements of stock market prices in a country are influenced by macroeconomic dynamics, public perceptions, and international stock market indicators (Ramli et al., 2021). Therefore, investors can observe, examine, and analyze the patterns of stock market fluctuations, enabling them to make informed decisions regarding their investment choices.

The study of the correlation between macroeconomic factors and the dynamics of the Indonesian Stock Exchange remains a subject of interest (Robiyanto et al., 2019), as macroeconomics serves as a fundamental indicator of the overall economy (Gu et al., 2021). It is crucial to acknowledge that the nature of the relationship between various economic variables can differ across different time scales, ranging from the longest to the shortest periods, or vice versa (Das, 2021).

While numerous researchers worldwide have extensively studied the connection between stock price indices and macroeconomic variables, there is a limited number of studies examining the comparison of this relationship before and during the COVID-19 pandemic. To address this research gap, this study aims to investigate and explore this relationship, thereby contributing to the existing literature on the association between stock indices and macroeconomic variables in both pre-pandemic and pandemic contexts. This analysis is very important for investors because it allows them to identify potential risks associated with investing in the event of a global crisis, including natural disasters and pandemics.

It is important to note that the fluctuations and movements of the JKSE are influenced by various economic, political, and market sentiment factors at both the national and global levels (Handika et al., 2021). In addition, factors such as political events, government policies, changes in the global economy, and investor sentiment also contribute to changes in stock indices. This study centers around the examination of the impact of chosen macroeconomic variables, namely interest rates, inflation, the rate at which the US dollar is exchanged for the Indonesian rupiah (US$/IDR), and the global oil price represented by WTI (West Texas Intermediate). The objective is to analyze the extent of influence exerted by these macroeconomic factors.

Interest rates are annual percentages that represent the level of repayments on a loan or investment over the settlement agreement. This affects individual decisions to spend or save money. Interest rates are determined by the interaction between supply and demand, just like prices. In 2020, during the COVID-19 pandemic, Bank Indonesia undertook a series of interest rate cuts to boost liquidity, stimulate economic growth, and reduce the burden on the private sector. In 2021, Bank Indonesia will maintain low-interest rates to support the economic recovery that is still affected by the pandemic. However, in line with economic improvement and a surge in inflation, Bank Indonesia will begin gradually increasing interest rates from mid-2021 to 2022. Fluctuations in interest rates (BI Rate) are influenced by monetary policy, macroeconomics, and external factors such as global economic conditions. Adjustments in interest rates have implications for the interest rates applied to loans and deposits within the banking industry, consequently influencing individuals’ decisions regarding consumption, investment, and business operations in Indonesia (BI, 2020).

According to studies done by numerous academics, including Pernando et al. (2023), Fahlevi (2019), Utomo et al. (2019), and Wahyu Umaryadi et al. (2021), interest rates have a detrimental impact on stock market movements, both in the long-term and short-term. Interestingly, these findings contrast with the findings of Luwihono et al. (2021), who suggest that there is no relationship between interest rates and stock prices. Additionally, Elfiswandi et al. (2020) report a significant impact of interest rates on stock returns, while Moussa and Delhoumi (2021) confirm the presence of cointegration between the return index and interest rates. On the other hand, Chellaswamy et al. (2020) argue that Chinese and Indian interest rates do not have a significant effect on SSE and Nifty returns.

In summary, the relationship between interest rates and stock prices is intricate and diverse. While certain studies have demonstrated a negative association between the two, others have deemed it insignificant. The existence of cointegration between stock indexes and interest rates suggests a long-term connection between these variables. However, additional research is necessary to comprehensively comprehend the nature of this relationship and its implications for investors and policymakers.

During the pandemic from March 2020 to June 2021, inflation decreased (bi.go.id). Despite the gaps between previous studies, it is important to understand that there may be deviations or inconsistencies between theory and practice. For example, inflation experienced an up-and-down trend from 2012 to 2015, but the Jakarta Composite Index (JKSE) generally increased in that period (Vikaliana, 2018).

Studies conducted by Fahlevi (2019) and Utomo et al. (2019) indicate a positive relationship between short-term inflation and stock indexes. Kilci (2020) identifies a robust correlation between stock market indexes and inflation. In the context of South Africa, de Jesus et al. (2020) find that ALSI (All Share Index) significantly explains inflation. Conversely, research by Pernando et al. (2023) demonstrates that the stock index is not significantly impacted by inflation.

To summarize, inflation plays a crucial role in shaping investors’ decisions and financial developments. The relationship between stock returns and inflation can vary across different markets and contexts. Some studies reveal a negative correlation, while others show positive or insignificant effects. Nonetheless, considering inflation is vital when making investment decisions.

Foreign exchange rates, as defined by Widagdo et al. (2020), represent the value of the relationship between foreign currency and domestic currency. It reflects the value of a country’s currency concerning other currencies used in international trade or the price of foreign currencies in terms of domestic currency. The exchange rate holds significance as a determining factor in the United States stock market (Beh & Yew, 2020). The US Dollar (USD) is widely utilized in international trade, and fluctuations in its exchange rate with the Indonesian Rupiah (IDR) can impact stock indices. The COVID-19 pandemic led to notable variations in the USD-IDR exchange rate. Initially, in March 2020, the IDR experienced significant depreciation, reaching levels above 16,000 rupiahs per dollar. However, it is worth noting that stock indices and exchange rates do not always exhibit an inverse relationship; in certain cases, they may move in the same direction. For instance, during the pandemic in April 2020, the exchange rate of the Rupiah against the US Dollar declined to Rp. 16,638/US$, yet stock market participants did not respond negatively.

A significant correlation exists between South Africa’s stock market and exchange rates (Hsueh et al., 2020). Short-term effects of exchange rates on stock market returns have been identified by Inegbedion et al. (2020). The impact of exchange rates on overall stock prices varies between bearish, bullish, and normal states in the short term, as estimated by the QARDL model (Chang et al., 2020). Multiple studies, including Endri et al. (2020), Fahlevi (2019), Hussainey and Khanh Ngoc (2009), Irfan et al. (2021), and Luwihono et al. (2021), indicate a positive and significant effect of exchange rates on the stock index. Conversely, studies by Areli Bermudez Delgado et al. (2018), Putri et al. (2019), and Utomo et al. (2019) suggest that exchange rates hurt the stock index. Saucedo and Gonzalez (2021) discover that while exchange rates are significant, they hold relatively less relevance in most portfolios. Additionally, Mohammed et al. (2021) establish that exchange rate volatility is influenced by the composite index of the Ghana Stock Exchange (GSE) in the long-term model.

Crude oil is a significant energy commodity that holds a crucial position in the economic development of a nation. In this research, the study examines the global oil price using West Texas Intermediate (WTI) standards. WTI refers to a premium grade of petroleum that is extracted in the state of Texas.

The decline in world oil prices in the last 2 years is a phenomenon that should be watched out for. From March 2010 to October 2014, world oil prices ranged from US$ 80 to US$ 110 per barrel. However, in January 2016, the price fell sharply to US$ 33.62 per barrel. Nonetheless, the JKSE increased during this period. When the COVID-19 pandemic first began, specifically between April and May 2020, global oil prices experienced a decline, reaching as low as US$18 per barrel (finance.yahoo.com). The economic disturbances resulting from the pandemic significantly affected stock markets, particularly in sectors like crude oil and gold, making the process of forecasting considerably more challenging during such times of crisis.

According to Inegbedion et al. (2020), the prices of crude oil have both long-term and short-term implications for stock market returns. Studies conducted by Das (2021) and Robiyanto et al. (2019) indicate a positive relationship between global oil prices and stock returns. On the other hand, fluctuations in the stock price index were found to have a statistically significant negative impact on the price of global crude oil (Ghazo et al., 2021). On a different note, Putri et al. (2019) argue that world oil prices do not affect the stock index.

International research on the subject of the correlation between stock prices and macroeconomic factors has contradictory results. Certain studies, including those by Madurapperuma (2022) and Ramli et al. (2021), argue in favor of a long-term stable relationship between macroeconomic variables and stock prices. Gupta and Kumar (2019) and Kuntamalla and Maguluri (2022) suggest that macroeconomic variables have an insignificant impact on stock prices in the long run.

These conflicting investigative results raise questions about the existence of this causal relationship. There is a lack of definitive criteria regarding which macroeconomic variables can impact the stock price index and whether the stock price index, in turn, affects these variables. Additionally, the implications of the COVID-19 pandemic on the global economy and financial crisis remain uncertain, making it unclear how these factors will influence the stock market (Madurapperuma, 2022). The purpose of this article is to clarify some of the important issues brought up, such as whether there is a long-term equilibrium link between the Jakarta Composite Index and factors like interest rates (BI Rate), inflation, exchange rates, and global oil prices. What are the short- and long-term effects of these chosen macroeconomic factors on the Jakarta Composite Index? Do macroeconomic factors and the Jakarta Composite Index have a causal relationship both before and after the COVID-19 pandemic?

The discoveries from this research hold practical significance for investors in the capital market, aiding them in making informed choices regarding the timing of buying and selling shares. Furthermore, comprehending the factors that determine the stock market is crucial for investors, regulators, policymakers, and academic researchers alike (Nkoro & Uko, 2014). For operations managers and investors, it is important to consider macroeconomic impacts when preparing budget plans (Luwihono et al., 2021). By considering these factors, operations managers and investors can make more informed decisions and develop strategies to mitigate the risks associated with macroeconomic changes.

Literature Review

Investment has become an important aspect of world economic development which aims to increase national income (Suhadak & Suciany, 2020). To accomplish this, it is crucial to forecast market behavior using one stock price indicator, the Jakarta Composite Index (Jeng et al., 2021). As a gauge of changes in stock prices listed on the Indonesian Stock Exchange (IDX), the Jakarta Composite Index was originally introduced on April 1, 1983. The basic day for calculating the index was August 10, 1982, with a value of 100. The number of listed issuers at that time was 13 issuers. Meanwhile, the number of listed issuers in 2023 will reach 869 issuers (Bursa Efek Indonesia, 2010).

Macroeconomics is concerned with the study of economic changes that affect entire societies, firms, and markets. Various macroeconomic factors can directly affect a company’s stock and performance. Basic Macroeconomic Assumptions consist of Economic Growth, Inflation Rate, Exchange Rate, Interest Rate, Oil Price, and Oil and Gas Lifting (https://fiskal.kemenkeu.go.id/).

The Arbitrage Pricing Theory is one theory examining the connection between stock returns and macroeconomic factors. Due to its potential to link stock returns to macroeconomic variables, APT is the methodology that has attracted the most interest (Ross, 1976).

The traditional approach and the portfolio approach are two theories about the dynamic interaction between macroeconomic variables and stock prices (Lee & Brahmasrene, 2020). According to conventional wisdom, local enterprises become more competitive as a result of the local currency’s depreciation, which raises their exports and share prices in turn. The portfolio approach contends that growing stock prices motivate investors to purchase more domestic assets, which leads to an increase in the value of the local currency.

Researchers, economists, policymakers, and practitioners have been studying the connection between macroeconomic factors and stock returns for many years (Bernanke et al., 1997; Endri et al., 2020; Fama, 1965; Hussainey & Khanh Ngoc, 2009; Lee & Brahmasrene, 2020; Mukherjee & Naka, 1995; Putri et al., 2019; Ramli et al., 2021).

Vector Autoregressive Model (VAR) analysis was used by Areli Bermudez Delgado et al. (2018) to examine the stock market components in Mexico, oil prices, and exchange rate. The findings demonstrate that the stock market index is negatively and statistically significantly impacted by the exchange rate. The consumer price index was also discovered to have a positive impact on the exchange rate and a negative impact on the stock market index. The findings also indicate that the relationship between oil prices and the exchange rate is statistically significant, indicating that an increase in world oil prices leads to an appreciation of the exchange rate.

Using GARCH analysis, Robiyanto et al. (2019) looked at the Dow Jones index, the exchange rate of the Rupiah against the US Dollar, and the global oil prices versus the JKSE. The findings indicate that while the exchange rate of the Rupiah against the US Dollar has a large negative impact on the JKSE, the Dow Jones Industrial Average and global crude oil prices have a significant positive association with the JKSE.

The Augmented Dickey-Fuller (ADF) approach, discovered by Dickey and Fuller (1981), is used (Utomo et al., 2019) to test data stationarity, in studying macroeconomic variables including exchange rates, BI rate and inflation, and stock performance. The cointegration test developed by Johansen is used to determine the number of cointegrated variables (vectors), after which the Granger causality test is applied to assess how the variables relate to each other. Vector Error Correction Model tests are then used to estimate the long-run and short-run relationships between variables. According to the study’s conclusions, inflation, the BI rate, and the exchange rate are all detrimental to stock market performance in the long run. The US$/IDR exchange rate, and inflation, however, have a beneficial impact on the economy in the short term.

Endri et al. (2020) investigate how changes in global stock markets and macroeconomic factors including BI Rate, inflation, and exchange rates affect movements on the Jakarta Composite Index. Time series data for the period January 2012 to December 2018 were processed using the GARCH analysis technique. Based on the findings, the JCI was significantly influenced by the BI rate, inflation, and SSE with a negative relationship, significantly positively influenced by the STI and DJIA exchanges, and slightly positively by the FTSE 100, N225 was not much affected. Currency exchange rates and inflation, however, will have a beneficial impact on the performance of the Indonesian stock market in the near term. However, stock market performance and the BI rate are negatively correlated.

To determine the cause-and-effect relationship between the JKSE, Jakarta Islamic Index (JII), and the variables Industrial Production Index (IPI), Export Volume (EV), Unemployment Rate (UR), Exchange Rate (ER), and Interest Rate (IR), Ramli et al. (2021) used the Autoregressive Distributed Lag test with the Bound Test and the Vector Error Correction Model test. The findings show that the macroeconomic variables in the two stock markets have a long-term equilibrium relationship or cointegration. According to the results of the ARDL estimate test, macroeconomic factors have a long-term positive impact on the industrial production index and export values on the traditional stock market, while interest rates have a short-term negative impact on both. Only interest rates have no impact on traditional stocks in short-term relationships.

Irfan et al. (2021) use panel data regression analysis to evaluate the Jakarta Islamic Index (JII), the Bombay Stock Exchange of India (BSE) Sharia, foreign direct investment, imports, exports, gross domestic product, money supply, and exchange rate. Irfan reported the findings that both stock market indices have been positively and statistically significantly impacted by FDI, exports, GDP, and ER.

Das (2021) employs the wavelet analysis method to examine the relationship between exchange rates, stock returns, and crude oil prices in India during the years 1999 through 2021. The results show that the relationship between stock returns is in line with oil prices while the exchange rate is not in line with oil prices.

Regression analysis is used by Luwihono et al. (2021) to determine the connection between Indonesia’s inflation, the US$/IDR exchange rate, and interest rates. The findings indicate that, at a 5% share price, the rupiah exchange rate has a statistically significant positive impact, whereas interest rates and inflation have no bearing on stock prices.

The results of path analysis using multiple linear regression models of the variables analyzed such as stock returns of mass media companies, interest rates, inflation, and JKSE were published by Wahyu Umaryadi et al. (2021). They found that interest rates had a negative and significant effect, and JKSE respectively had a positive and significant effect on stock returns of mass media companies.

Rehman et al. (2021) noted the long- and short-term asymmetry of the development of the Saudi stock market in the share prices of the banking and financial services, energy and utilities, and petrochemical sectors. The study used the nonlinear auto-regressive distributive lag (NARDL) and linear autoregressive lag (ARDL) approaches to analyze the money supply, oil prices, and the Tadawul All Share Index on sectoral stock prices. Bin Amin and Rehman also found that the three industries of building and construction, energy, utilities, and petrochemicals do not experience long-term and short-term asymmetric effects of the money supply, except banks and the financial services sector, which only experience long-term effects.

Using the Autoregressive Distribution Lag (ARDL) model to analyze the NIFTY 200 variable, inflation, exchange rates, money supply growth, interest rates, and foreign institutional investment, Kuntamalla and Maguluri (2022) find that long-term macroeconomic variables have little impact on stock price.

The relationship between economic growth, inflation, exchange rates, interest rates, and unemployment rates and the KOMPAS 100 Index was examined by Pernando et al. (2023) using multiple linear analyses. According to the findings, the KOMPAS 100 Index is not significantly impacted by inflation. The KOMPAS 100 Index is positively and significantly impacted by economic growth. The KOMPAS 100 Index is negatively and significantly impacted by interest rates. The KOMPAS 100 Index is positively and significantly impacted by exchange rates. The KOMPAS 100 Index is negatively and negligibly impacted by unemployment.

According to several studies Masrizal et al. (2021) and Tronzano (2021), the COVID-19 epidemic and the Asian financial crisis both had a major impact on stock market performance. According to some researchers Asadi et al. (2020), stock market performance was only marginally impacted by changes in the international markets during the global financial crisis. It is therefore hard to ignore the phenomena of the decline in the Jakarta Composite Index at the beginning of the 2020 pandemic, which is believed to have been brought on by the worldwide COVID-19 pandemic related economic catastrophe. There haven’t been any empirical investigations done to compare changes in the Jakarta Composite Index before and after the COVID-19 pandemic. Of course, given how the economic crisis affected, this is crucial.

Empirical research is still insufficient from this perspective. So, as part of our investigation to determine how specific macroeconomic factors affect the Jakarta Composite Index before and after the COVID-19 outbreak, we consider this to be an intriguing development. Of course, there will be queries regarding its significance. This study generates the following hypothesis, which will be helpful and alert investors should a sudden worldwide catastrophe occur, based on what was discovered in the review of pertinent studies.

The following hypotheses were developed for this study based on findings from a review of pertinent studies:

H1. There was a long-term equilibrium link between the macroeconomic parameters and the JKSE both before and during the outbreak.

H2: There is a two-way causal relationship between macroeconomic conditions and the JKSE before and until the pandemic.

H3. Short-term interest rates are correlated with the JKSE.

H4. In the short run, JKSE and inflation are associated.

H5. The short-term relationship between the JKSE and the Rupiah’s exchange rate against the US dollar.

H6. The short-term relationship between the JKSE and WTI world oil prices.

Method and Materials

This study uses quantitative data, namely secondary data covering 151 months from June 2007 to December 2021 to explain the relationship between WTI crude oil prices, interest rates, US$/IDR exchange rate, inflation, and the Jakarta Composite Index before the COVID-19 pandemic, and 175 observations from June 2007 to December 2021 to explain the movement of the Jakarta Composite Index until the COVID-19 pandemic. Data extracted from Bloomberg, bps.go.id, and bi.go.id. The test was deliberately carried out in two versions, to be able to explain the comparison of JKSE fluctuations before and after the pandemic. Data analysis, using statistical software “Eviews 12” and Microsoft Excel.

The stationarity of the data was examined using econometric analysis, such as the Augmented Dickey-Fuller test (Dickey & Fuller, 1981). The data is not stationary if the t-statistic value is smaller than the crucial value (Mackinnon et al., 1999) at different degrees of confidence (1%, 5%, and 10%). The fact that the data is not stationary may also be noticed with a probability value greater than 0.05. In contrast, if the ADF value exceeds the critical value at any of the three levels of confidence (1%, 5%, and 10%), either the data is stationary or there isn’t a unit root.

Optimal lag test. The amount of latency employed has a significant impact on VAR estimation. The residual regression cannot display a white noise process if the lag in judicial stationarity is too tiny, which prevents the model from accurately estimating the genuine error. The ability to reject H0 as an extra parameter will be lessened if the input latency is too great, though.

To count the cointegrated variables (vectors) and ascertain whether there is a long-term link between the variables, the test proposed by Johansen (1988) is utilized.

The Granger causality test (Granger & Newbold, 1974) was used in both versions of the time series data range before and until the COVID-19 pandemic to see if there was a significant difference between the two time series data ranges.

To determine the long- and short-term relationships between the JKSE and macroeconomic factors, researchers employ the Vector Error Correction Model test (Engle & Granger, 1987; Johansen & Juselius, 1990). The creation of a VAR model for time series with non-stationary behavior and one or more cointegration relationships is known as the vector error correction model (VECM). Each dependent variable’s reaction to shocks on that variable and other dependent variables reveals the dynamic behavior of VECM. The properties of the VECM model can be seen in two different ways: through the impulse response function and variance decomposition.

The VECM model with a long lag (p−1) has the general shape as follows:

$Δ Yt = μ + Π Yt - 1 + Σ Γ i Δ Yt - i + α D + ε t$

Where: ΔYt is the vector of changes in the dependent variables in the model; μ is the vector of constants or intercepts; Π is the coefficient matrix representing the lagged dependent variables (Yt−1) that captures the long-run cointegration relationships; Γi is the coefficient matrix representing the lagged changes in the dependent variables (ΔYt−i); α is the vector of coefficients representing the deterministic variables D, which can include factors such as trends or dummy variables; εt is the vector of residuals or error terms at time t.

Results and Discussion

Summary of the Data

Table 1 presents data during the observation period which consisted of before the pandemic (2007M06–2019M12) and until the COVID-19 pandemic (2007M06–2021M12). The Proxy of the Jakarta Composite Index (JKSE) has an average fluctuation range of 4,329.848 points and a standard deviation of 1,439.860 points before the pandemic, and after the pandemic has an average range of 4,516.139 points with a standard deviation of 1,435.087 points. Interest rates proxied by IR have an average fluctuation range of 6.538079 points and a standard deviation before the pandemic of 1.245909 points, and after the pandemic has an average range of 6.174286 points with a standard deviation of 1.484710 points. Inflation proxied by INF has an average fluctuation range of 5.333245 points and a standard deviation before the pandemic of 2.301036 points, and after the pandemic has an average range of 4.848400 points with a standard deviation of 2.467155 points. The US exchange rate against the Rupiah is proxied by the exchange rate which has an average fluctuation range of 11,404.56 points and a standard deviation of 2,082,513 points before the pandemic and after the pandemic an average of 11,821.56 points with a standard deviation of 2,206,012 points. World oil prices proxied by WTI have an average fluctuation range of 74.56808 points and a standard deviation of 22.86812 points before the pandemic, and after the pandemic have an average range of 71.66629 points with a standard deviation of 23.35297 points (Table 1).

Table 1.

Descriptive Statistics.

Name	Before the COVID-19 pandemic					Until the COVID-19 pandemic
Name	N	Mean	Maximum	Minimal	SD	N	Mean	Maximum	Minimal	SD
JKSE	151	4,329.848	6,605.630	1,241.540	1,439.860	175	4,516.139	6,605.630	1,241.540	1,435.087
IR	151	6.538	9.500	4.250	1.245	175	6.174	9.500	3.500	1.485
INF	151	5.333	12.140	2.410	2.301	175	4.848	12.140	1.320	2.467
KURS	151	11,404.560	15,178.870	8,532.000	2,082.513	175	11,821.560	15,867.430	8,532.000	2,206.012
WTI	151	74.568	140.000	33.620	22.868	175	71.666	140.000	18.840	23.353

Source. Secondary data, processed.

Figure 1 on the interest rate movement chart, it can be explained how interest rate fluctuations arrived during the COVID-19 pandemic which began in early 2020. As a response to the economic impact caused by the COVID-19 pandemic, Bank Indonesia adopted an accommodative monetary policy. Starting from February 2020, the BI Rate was gradually lowered to encourage credit and investment to support economic recovery. The reduction in the BI Rate is aimed at reducing the burden on loan interest rates and boosting liquidity in the financial market. The reduction in the BI Rate had a positive impact on the stock market. Even though the reduction in the BI Rate was aimed at providing economic stimulus, the stock market continued to experience significant volatility during the pandemic. Unexpected developments in the infection and uncertainty about the COVID-19 pandemic’s long-term economic impact might have a negative influence on investor sentiment and cause significant share price volatility.

Figure 1.

LogJKSE, IR, INF, LogKURS, and LogWTI from June 2007 to December 2021.

The inflation movement graph can be explained, in 2008 there was a significant spike in inflation due to rising world oil prices and the global financial crisis. Inflation reached its peak in 2008 with high numbers. In 2009, inflation began to fall in line with the global economic recovery and the policy measures taken by the Indonesian government. In the 2010 to 2013 period, inflation in Indonesia was managed fairly well. Inflation is within a controlled and stable range. Tight monetary policy, controlling the price of goods, and increasing agricultural and energy production contributed to inflation stability. Bank Indonesia (BI) succeeded in keeping inflation within the set target, providing stability for the economy. In 2014 to 2016, inflation began to increase again, mainly due to the increase in fuel prices which removed subsidies. BI took steps to control inflation by raising the benchmark interest rate. During the 2017 to 2021 period, inflation in Indonesia was generally within a controlled range. BI succeeded in keeping inflation within the set target, by taking accommodative monetary policy measures. Despite fluctuations in commodity prices and temporary inflationary pressures, BI is trying to maintain economic stability and control inflation. The COVID-19 pandemic has significantly affected inflation in Indonesia, with reduced economic activity and slowing demand. However, Bank Indonesia and the government are taking policy steps to overcome the economic impact of the pandemic and maintain inflation stability.

On the chart of the movement of the US$/IDR exchange rate, it can be explained that in 2008, there was a significant weakening of the exchange rate of the Rupiah with respect to the US Dollar. External factors such as the global financial crisis and rising world oil prices affected the Rupiah exchange rate. This weakening occurred due to investor concerns and panic in global financial markets. In the 2009 to 2011 period, the Rupiah appreciated against the US Dollar. Factors such as post-crisis global economic recovery, tight monetary policy, and Indonesia’s economic stability contributed to the strengthening of the Rupiah. In addition, increasing demand for Indonesian commodities, such as coal and palm oil, has also provided support for the Rupiah. In the 2013 to 2015 period, the Rupiah experienced a significant weakening against the US Dollar. Factors such as the slowdown in global economic growth, especially in developing countries, as well as the uncertainty in US monetary policy, affected the Rupiah exchange rate. This weakness has been accompanied by pressure on the balance of payments and capital outflows from Indonesia’s financial markets. During the 2016 to 2021 period, the Rupiah experienced significant volatility against the US Dollar. Factors such as changes in global monetary policy, the trade war between the United States of America and China, and the impact of the COVID-19 pandemic have affected the Rupiah exchange rate. The COVID-19 pandemic in particular had a major impact on the Rupiah, with it falling sharply in early 2020, but then recovering in line with the government’s stimulus and economic recovery policies.

On the chart of world oil price movements. The COVID-19 pandemic that started in 2020 had a significant impact on oil prices. The decline in energy demand due to the decline in global economic activity and travel restrictions has caused a drastic decline in oil prices. The world price of WTI oil has a direct impact on companies and the energy industry in Indonesia and around the world. Energy companies listed on the IDX, especially those operating in the oil and gas sector, can be affected by changes in world oil prices. The performance of these companies can have an impact on the performance of the JKSE as a whole, especially if these companies have significant weight in the composition of the JKSE. Changes in world oil prices WTI can also affect investor sentiment. Volatility in world oil prices can create uncertainty and affect investors’ decisions to buy or sell shares. This investor sentiment can also affect the JKSE movement.

Stationarity Test

Stationary tests on time series data are necessary because time series data often have roots or units that are not stationary which causes the regression results to be dubious or commonly called false regression. Therefore, it is necessary to do a stationary test, one of which can be done with the Augmented Dickey-Fuller test (Dickey & Fuller, 1981). Table 2 shows the results of the Augmented Dickey-Fuller test of all variables simultaneously. As a result, it is known that all variables contain unit roots or are non-stationary at the level, so the test is continued to the first difference. After testing the variables at the first difference, JKSE, IR, INF, KURS, and WTI are stationary as can be seen in Table 2.

Table 2.

Group Unit Root Test.

Series:	JKSE, IR, INF, KURS, WTI
Sample:	2007M06–2019M12				2007M06–2021M12
Automatic lag length selection based on SIC: 0 to 1
	Before the COVID-19 pandemic				Until the COVID-19 pandemic
Method	Statistic	Prob.**	Cross-sections	Obs	Statistic	Prob.**	Cross-sections	Obs
Null: Unit root (assumes common unit root process)
Levin, Lin & Chu t*	−20.1150	.0000	5	744	−22.4797	.0000	5	863
Null: Unit root (assumes individual unit root process)
Im, Pesaran and Shin W-stat	−18.1068	.0000	5	744	−20.5347	.0000	5	863
ADF—Fisher Chi-square	263.115	.0000	5	744	314.321	.0000	5	863
PP—Fisher Chi-square	293.368	.0000	5	745	340.609	.0000	5	865

Source. Secondary data, processed. Notes. Trace test indicates 5 cointegrating eqn(s) at the 0.05 level. *Denotes rejection of the hypothesis at the 0.05 level. **MacKinnon-Haug-Michelis p-values. Probabilities for Fisher tests are computed using an asymptotic Chi-square distribution. All other tests assume asymptotic normality.

Optimal Lag Test

To get rid of data autocorrelation, the VECM approach is tested for the best lag length. The parameters LR: sequential modified LR test statistics (each test at 5% level), final prediction error, Akaike information criterion can be used to characterize the ideal length of the lag, the Schwarz information criterion and the Hannan-Quinn information criterion. The outcomes of this study’s optimal lag test are shown in Table 3. In this test, it was discovered that two (2) delays were the ideal quantity for data collected before and during the COVID-19 pandemic. The independent variable can impact the dependent variable, as seen by this. The best amount of lags can be determined by looking at the number of asterisks (*), which is the largest in lag two.

Table 3.

Optimal Lag Test Results.

Lag	LogL	LR	FPE	AIC	SC	HQ
Before the COVID-19 pandemic
0	603.6418	NA	2.00e−10	−8.144786	−8.043071	−8.103458
1	674.4771	135.8882	1.07e−10	−8.768396	−8.158104*	−8.520428
2	723.0153	89.81215*	7.78e−11*	−9.088644*	−7.969775	−8.634035*
3	738.5380	27.66620	8.88e−11	−8,959700	−7.332254	−8.298451
Until the COVID-19 Pandemic
0	679.9156	NA	2.57e−10	−7.893750	−7.801889*	−7.856477
1	744.1296	123.9216	1.62e−10	−8.352393	−7.801224	−8.128752
2	792.5944	90.69442*	1.23e−10*	−8.626835*	−7.616359	−8.216827*
3	808.6276	29.06602	1.37e−10	−8.521960	−7.052176	−7.925584

Granger Causality Test

The findings of the Granger causality test between factors before and after the COVID-19 outbreak are presented in Table 4. According to the calculations in Table 4, variables that have a causal relationship are indicated by variables with a probability value of not more than .05, at which point H0 will be rejected, indicating that this variable has an impact on other variables. In contrast, H0 is accepted if the probability is higher than .05, indicating that there is no correlation between the variables. The probability level (.05) of each variable makes the rejection of H0 clear.

Table 4.

Granger Causality Test Results.

Null hypothesis:	Before the COVID-19 pandemic			UNTIL THE COVID-19 PANDEMIC
Null hypothesis:	Obs	F-statistic	Prob.	Obs	F-statistic	Prob.
IR does not Granger Cause JKSE	150	3.52799	.0623	174	4.55169	.0343
JKSE does not Granger Cause IR		0.07912	.7789		0.37025	.5437
INF does not Granger Cause JKSE	150	5.74595	.0178	174	6.01633	.0152
JKSE does not Granger Cause INF		0.10058	.7516		0.04430	.8335
KURS does not Granger Cause JKSE	150	1.44943	.2306	174	2.30043	.1312
JKSE does not Granger Cause KURS		0.47035	.4939		1.03196	.3111
WTI does not Granger Cause JKSE	150	4.48232	.0359	174	3.71840	.0555
JKSE does not Granger Cause WTI		0.19056	.6631		0.34453	.5580
INF does not Granger Cause IR	150	17.3613	5.e−05	174	19.4215	2.e−05
IR does not Granger Cause INF		0.00081	.9773		0.10842	.7424
KURS does not Granger Cause IR	150	2.69575	.1028	174	4.17908	.0425
IR does not Granger Cause KURS		0.93327	.3356		0.39257	.5318
WTI does not Granger Cause IR	150	39.7857	3.e−09	174	45.6605	2.e−10
IR does not Granger Cause WTI		2.84470	.0938		2.06530	.1525
KURS does not Granger Cause INF	150	6.38908	.0125	174	8.58176	.0039
INF does not Granger Cause KURS		6.68251	.0107		4.49318	.0355
WTI does not Granger Cause INF	150	13.9254	.0003	174	17.1864	5.e−05
INF does not Granger Cause WTI		2.49976	.1160		1.90636	.1692
WTI does not Granger Cause KURS	150	2.04009	.1553	174	1.21324	.2722
KURS does not Granger Cause WTI		7.06631	.0087		5.18010	.0241

Source. Secondary data, processed.

From Table 4 it can be seen that there are quite prominent differences in the interrelationships between variables, both BI Rate variables, inflation, US$/IDR exchange rates, and the JKSE, in lag one (1) before and after the COVID-19 pandemic. Interest rates did not have a significant effect on JKSE in the pre-pandemic period, on the contrary, after the pandemic, interest rates had a significant effect on JKSE and had a one-way relationship. Inflation had a significant effect on JKSE before and after the pandemic with a unidirectional relationship. The world oil price had a significant effect on the JKSE before the pandemic, but after the pandemic, it had no effect. Exchange rates did not affect interest rates before the pandemic, but after the pandemic had a significant effect. Exchange rates and inflation had a significant two-way relationship before and after the pandemic. Likewise, exchange rates have had a significant effect on world oil prices WTI with a one-way relationship before and after the COVID-19 pandemic.

Cointegration Test

There are two methods for determining whether there is a long-term link between variables: the Johansen co-integration test and the Engle-Granger single equation test method (Engle & Granger, 1987). However, the Johansen cointegration test was used in this work. Each variable is examined using Johansen’s cointegration test about all other system variables that have changed over time.

The findings of the cointegration test before and after the COVID-19 epidemic are detailed in Table 5. The table explains that all factors, both before and after the pandemic, exhibit a long-term association with one another at the 5% test level. This is demonstrated by the trace statistic’s value, which is higher than the crucial number. Based on the p-value, it is possible to rule out the null hypothesis that there is no cointegration (Mackinnon et al., 1999). VECM estimate can therefore be continued for additional analysis.

Table 5.

Cointegration Test Results.

Hypo. no. of CE(s)	Eigenvalue	Trace statistic	Critical value 0.05	Prob.**
Before the COVID-19 pandemic
None*	0.358721	228.2604	69.81889	.0000
At most 1*	0.318400	162.9496	47.85613	.0000
At most 2*	0.265378	106.6027	29.79707	.0000
At most 3*	0.222899	61.26798	15.49471	.0000
At most 4*	0.151770	24.19675	3.841465	.0000
Until the COVID-19 pandemic
None*	0.394419	269.2509	69.81889	.0000
At most 1*	0.291737	183.4830	47.85613	.0000
At most 2*	0.273722	124.4984	29.79707	.0000
At most 3*	0.218689	69.80877	15.49471	.0000
At most 4*	0.149097	27.60909	3.841465	.0000

Source. Secondary data, processed. Notes. Trace test indicates 5 cointegrating eqn(s) at the 0.05 level. *Denotes rejection of the hypothesis at the 0.05 level. **MacKinnon-Haug-Michelis p-values.

VECM Estimation Test

If the t-statistic in the VECM estimate is higher than the t-table value, either a long-term or short-term link between the variables is present. More precisely, the connection between variables, whether positive or negative, can be observed as either a short-term or long-term correlation.

Table 6 describes the relationship between variable BI Rate and Inflation in the long term. In general, the BI rate had a significant negative effect on the Jakarta Composite Index before and after the COVID-19 pandemic. Meanwhile, inflation had a significant positive effect on the Jakarta Composite Index before and after the COVID-19 pandemic. In more detail, the VECM test results show that the t-statistics for the BI Rate were −2.81908 before the pandemic, and −3.30280 after the pandemic, exceeding the t-tables of 1.976346 and 1.974017. The inflation variable with t-statistics of 5.16320 before the pandemic, and 5.47796 after the pandemic, is larger than the t-tables of 1.976346 and 1.974017.

Table 6.

Long-Term VECM Test to JKSE.

Variables	Coefficient	t-statistic	t-table
Before the COVID-19 pandemic
IR(−1)	−0.681288	[−2.81908]	1.976346
INF(−1)	0.717559	[5.16320]	1.976346
Until the COVID-19 pandemic
IR(−1)	−0.789463	[−3.30280]	1.974017
INF(−1)	0.805851	[5.47796]	1.974017

Source. Secondary data, processed.

The results of this study indicate that the BI rate is negatively affecting the stock index in the long run. An increase in the BI Rate which hurts the stock price index in the long term can be caused by several factors, namely; 1. Higher borrowing costs: When the BI Rate rises, borrowing costs for companies and individuals increase. This can lead to a decrease in consumer demand, a decrease in economic activity, and hinder business growth. If the company faces higher borrowing costs, it results in reduced profits, and this can negatively affect the share price; 2. The outflow of investment: An increase in the BI Rate could cause foreign capital to flow out of the financial market. Foreign investors may choose to withdraw their investment from the local stock market and switch to financial instruments that offer higher yields. The withdrawal of foreign capital can negatively affect stock prices and depress the stock price index; 3. Impairment in asset values: An increase in interest rates can negatively affect the prices of assets such as property and stocks. The value of assets related to finance and property tends to decrease when interest rates rise. A decline in the value of these assets can reduce individual wealth and reduce investor confidence, which in turn can hurt stock prices and the composite stock price index; 4. Impact on economic activity: High-interest rates can affect overall economic activity. An increase in interest rates can reduce consumer purchasing power, discourage business investment, and reduce overall economic growth. This decrease in economic activity can affect the company’s performance and, as a result, the share price and the composite stock price index decrease.

This research is in line with previous research conducted by Pernando et al. (2023), Fahlevi (2019), Utomo et al. (2019), and Wahyu Umaryadi et al. (2021), indicating that the long-term interest rate hurts the stock price index movements.

Inflation had a positive impact on the composite stock price index before and after the COVID-19 pandemic. This can be caused by several factors such as; 1. Valuation of assets increases: When inflation occurs, the value of assets such as property, companies, and commodities tends to rise. An increase in the value of these assets can drive growth in company value and generate higher returns for investors. As a consequence, the stock price index may increase because companies listed on the stock exchange have a higher value; 2. Increased company revenue: Inflation can cause the selling price of products and services to increase, which in turn can increase company revenue. If the company’s income rises faster than its production costs are affected by inflation, then the company’s profit can increase. Increases in corporate profits tend to increase their share prices and affect the overall stock price index; 3. High demand: In some cases, inflation can indicate strong economic growth. If inflation is accompanied by high demand for products and services, the company can experience increased sales and profits. This will push the stock price up and have a positive impact on the stock price index; 4. Protective effect on debt: Inflation can benefit companies with fixed debts in a lower value for money. When inflation increases, the value of the currency decreases, and as a result, the debt held by the company effectively erodes in value. This reduces the company’s debt burden and improves its financial health. These improvements in financial health can affect a company’s stock price and, in turn, the stock price index.

This finding is also in line with research conducted by Fahlevi (2019) and Utomo et al. (2019) in that inflation is positively related to the stock index. This finding is not in line with research Pernando et al. (2023) which concluded that inflation has no significant effect on the stock index.

Information about the short-term relationships between variables may be found in Table 7. From the table, it can be seen that only the BI rate variable had a significant effect on the Jakarta Composite Index before and until the COVID-19 pandemic, with a negative relationship. This can be proven by the t-statistic value exceeding the t-table, namely before the pandemic −2.12824 and after the pandemic −2.02721 exceeding the t-table value of 1.976346. For other variables such as inflation, exchange rates, and world oil prices in the short term have no significant effect on the Jakarta Composite Index.

Table 7.

Short-Term VECM Test to JKSE.

Variables	Coefficient	t-statistic	t-table
Before the COVID-19 pandemic
D(IR(−1))	−0.057252	[−2.12824]	1.976346
Until the COVID-19 pandemic
D(IR(−1))	−0.052641	[−2.02721]	1.974017

Source. Secondary data, processed.

Comparing the BI Rate to other factors like exchange rates, inflation, and global oil prices, the BI Rate has a significantly negative short-term influence on the stock price index. This can be caused by several things, namely: 1. Direct effect on borrowing costs: The BI Rate is the interest rate used by the Indonesian bank to influence borrowing costs in the financial market. When the BI Rate rises, commercial banks tend to raise their lending rates to companies and individuals. This directly affects borrowing costs and can lead to a reduction in economic activity, including investment in the stock market. In the short term, this increase in borrowing costs could have a significant negative impact on share prices; 2. Investor sensitivity to interest rates: Investors tend to be very responsive to changes in interest rates, especially in the short term. Changes in interest rates can affect investors’ perceptions of the potential benefits that can be obtained from investing in stocks. If interest rates rise, some investors may prefer to shift their investments to financial instruments that offer higher yields or are safer, such as bonds. This can lead to the selling of shares and depressing the share price significantly.

In this study, the inflation variable in the short term has no significant effect on the composite stock price index. From several analyses, it can be concluded that the stock market tends to have a quick adjustment mechanism to changes in expected inflation. If inflation has been predicted by market participants and has been anticipated in stock prices, then the short-term impact of changes in inflation on stock prices may have been reflected beforehand. In this case, changes in inflation that occur in the short term may not have a significant impact on the stock price index.

The variable exchange rate of the dollar against the rupiah in the short term may not have a significant effect on the composite stock price index for several reasons such as: 1. Changes in the exchange rate can affect certain economic sectors more significantly than the stock market as a whole. For example, companies that are highly dependent on imports or exports can feel the direct impact of changes in exchange rates. However, its influence on the overall stock price index may be limited due to the involvement of different sectors and companies with different exposures to exchange rates; 2. Hedging securities: Some companies have risk management policies involving financial instruments such as futures contracts or options that serve to protect them from adverse changes in exchange rates. In this case, changes in exchange rates that occur in the short term may not have a significant impact on the company’s share price because they have taken steps to protect their exposure.

The world oil price variable in the short term may not have a significant effect on the Jakarta Composite Index. This could be due to the following reasons: 1. Sector diversification: The Jakarta Stock Exchange Composite (JKSE) generally includes companies from various sectors of the economy. While world oil prices can have a significant impact on companies operating in the energy sector or sectors closely related to oil, their impact on other sectors may be limited. Therefore, the direct impact of changes in world oil prices in the short term on the stock price index as a whole may not be too significant; 2. Influence of other fundamental factors: Stock prices are influenced by many other fundamental factors, such as income, profits, growth prospects, and overall economic conditions. While world oil prices may affect some sectors and companies, their influence may be offset by other, more significant factors in the short term. Changes in world oil prices may not immediately change the company’s fundamentals which can affect the value of their shares; 3. Adjustment of expectations: Stock market participants tend to have expectations of a rapid response to changes in world oil prices. If changes in world oil prices have been predicted and anticipated by the market, the short-term impact of these changes may have been reflected in stock prices. In this case, changes in world oil prices in the short term may not have a significant impact on the stock price index; 4. Effect of contrast between sectors and companies: In some cases, changes in world oil prices can have a contrasting effect between sectors or companies. For example, an increase in oil prices could benefit energy companies, while putting pressure on companies that are highly dependent on fuel costs. In such a situation, the impact of changes in world oil prices on the stock price index as a whole may become less significant or be seen in different sectoral movements.

Conclusion

This study aimed to examine the correlation between several key macroeconomic indicators before and into the COVID-19 period, including the BI Rate, inflation, the Rupiah exchange rate against the US dollar, and the international oil price West Texas Intermediate. There are significant differences in the interrelationships between the variables, which are determined by the results of the Granger causality test. Before the epidemic, there was no apparent relationship between BI Rate and the Jakarta Composite Index, but after the pandemic, there was a large and unidirectional relationship. With a unidirectional relationship, inflation had a sizeable impact on the Jakarta Composite Index both before and after the outbreak. Before the pandemic, West Texas Intermediate prices had a significant impact on the Jakarta Composite Index, but after the pandemic, the impact was small.

Companies, governments, and investors need to be aware of the causation relationship as well as the long-term and short-term relationships of macroeconomic variables on the movement of the Jakarta Composite Index, both before the epidemic and during the pandemic, based on these findings. It is important to consider macroeconomic impacts when preparing budget plans (Luwihono et al., 2021). By considering these factors, governments, companies, and investors can make more informed decisions and develop strategies to mitigate the risks associated with macroeconomic changes.

Finally, this study has limitations because it only observes stock index movements in Indonesia and focuses on the impact of certain macroeconomic variables such as West Texas Intermediate (WTI) prices, US$/IDR exchange rates, inflation, and interest rates. Therefore, for further research, it is suggested to expand the observation and includes cross-country analysis by including additional variables that can measure the causality relationship and macroeconomic effects and a more comprehensive stock index.

Footnotes

We would like to express our sincere gratitude to the Ministry of Education,Culture,Research,and Technology of the Republic of Indonesia,LPDP,PUSLAPDIK,and BPPT for their generous funding and support in making this research possible.

Declaration of Conflicting Interests

The author(s) declared no potential conflicts of interest with respect to the research,authorship,and/or publication of this article.

Funding

The author(s) disclosed receipt of the following financial support for the research,authorship,and/or publication of this article: Ministry of Education,Culture,Research,and Technology of the Republic of Indonesia,LPDP,BPPT,Identification Number BPI: 202101121213.

ORCID iD

Netti Natarida Marpaung

Data Availability Statement

The data supporting the findings of this study are available upon request from the corresponding author.

References

Al-Awadhi

A. M.

Alsaifi

Al-Awadhi

Alhammadi

(2020). Death and contagious infectious diseases: Impact of the COVID-19 virus on stock market returns. Journal of Behavioral and Experimental Finance, 27, 100326. https://doi.org/10.1016/j.jbef.2020.100326

Al-Smadi

A. W.

Al-Smadi

R. W.

Al-Afeef

M. A.

Al-Afeef

M. A. M.

Kalbouneh

N. Y.

(2023). Consequences to the stock market caused by currency fluctuations: Jordanian evidence. International Journal of Professional Business Review, 8(4), e01275. https://doi.org/10.26668/businessreview/2023.v8i4.1275

Areli Bermudez Delgado

Bermudez Delgado

Saucedo

(2018). The relationship between oil prices, the stock market and the exchange rate: Evidence from Mexico. North American Journal of Economics and Finance, 45, 266–275. https://doi.org/10.1016/j.najef.2018.03.006

Asadi

Bouvier

Wexler

A. S.

Ristenpart

W. D.

(2020). The coronavirus pandemic and aerosols: Does COVID-19 transmit via expiratory particles? Aerosol Science and Technology, 54(6), 635–638. https://doi.org/10.1080/02786826.2020.1749229

Beh

W. L.

Yew

W. K.

(2020). Macroeconomic factors and stock markets interdependencies: Evidence from United States and China. Journal of Critical Reviews, 7(5), 68–74. https://doi.org/10.31838/jcr.07.05.11

Bernanke

B. S.

Gertler

Watson

(1997). Systematic monetary policy and the effects of oil price shocks. Brookings Papers on Economic Activity, 1, 91–157. https://doi.org/10.2307/2534702

BI. (2020). Stimulus Kebijakan Bank Indonesia. Bank Indonesia.

Bursa Efek Indonesia. (2010). Buku Panduan Indeks Harga Saham Bursa Efek Indonesia (BEI). Indonesia Stock Exchange.

Chang

B. H.

Bhutto

N. A.

Turi

J. A.

Hashmi

S. M.

Gohar

(2020). Macroeconomic variables and stock indices: An asymmetric evidence from quantile ARDL model. South Asian Journal of Business Studies, 10(2), 242–264. https://doi.org/10.1108/SAJBS-09-2019-0161

10.

Chellaswamy

K. P.

Natchimuthu

Faniband

(2020). Stock market sensitivity to macroeconomic factors: Evidence from China and India. Asian Economic and Financial Review, 10(2), 146–159. https://doi.org/10.18488/journal.aefr.2020.102.146.159

11.

Das

(2021). The time–frequency relationship between oil price, stock returns and exchange rate. Journal of Business Cycle Research, 17(2), 129–149. https://doi.org/10.1007/s41549-021-00057-3

12.

de Jesus

Willows

G. D.

Olivier

A. M.

(2020). The influence of the market on inflation, not the other way around. Investment Analysts Journal, 49(2), 79–91. https://doi.org/10.1080/10293523.2020.1742999

13.

Dickey

D. A.

Fuller

W. A.

(1981). Time series with a unit root. Econometrica, 49(4), 1059.

14.

Elfiswandi Sanjaya

Pratiwi

Yulia

Ramadhan

M. F.

(2020). Macroeconomic factors, energy consumption and firms performance on stock return of mining and energy sector: Evidence from Indonesia. International Journal of Energy Economics and Policy, 10(6), 229–234. https://doi.org/10.32479/ijeep.10207

15.

Endri

Abidin

Simanjuntak

T. P.

Nurhayati

(2020). Indonesian stock market volatility: GARCH model. Montenegrin Journal of Economics, 16(2), 7–17. https://doi.org/10.14254/1800-5845/2020.16-2.1

16.

Engle

R. F.

Granger

C. W. J.

(1987). Co-integration and error correction: Representation, estimation, and testing. Econometrica, 55(2), 251–276. https://doi.org/10.2307/1913236

17.

Fahlevi

(2019). The influence of exchange rate, interest rate and inflation on stock price of LQ45 Index in Indonesia, 343, 157–163. https://doi.org/10.2991/icas-19.2019.34

18.

Fama

E. F.

(1965). The behavior of stock-market prices. The Journal of Business, 38(1), 34–105.

19.

Ghazo

Abu-Lila

Ajlouni

(2021). The macroeconomic determinants of stock price fluctuations in Amman Stock Exchange. Accounting, 7(1), 23–32. https://doi.org/10.5267/j.ac.2020.10.018

20.

Granger

C. W. J.

Newbold

(1974). Spurious regressions in econometrics. Journal of Econometrics, 2(2), 111–120. https://doi.org/https://doi.org/10.1016/0304-4076(74)90034-7

21.

Chen

Stan

(2021). Investor sentiment and the market reaction to macroeconomic news. Journal of Futures Markets, 41(9), 1412–1426. https://doi.org/10.1002/fut.22219

22.

Gupta

Kumar

(2019). Dynamic relationship between stock market sector indices and macroeconomic variables in India. International Journal of Applied Management Theory and Research, 1(2), 31–41. https://doi.org/10.4018/ijamtr.2019070103

23.

Handika

Damajanti

Rosyati

(2021). Faktor Penentu Fluktuasi Pergerakan Indeks Harga Saham Gabungan (IHSG) Di Bursa Efek Indonesia (BEI). Solusi, 19(3), 153. https://doi.org/10.26623/slsi.v19i3.3503

24.

Hsueh

H. P.

Liu

(2020). Analysis of the linkage between South African’s exchange rate and Stock Price Index. Journal of Physics: Conference Series, 1621(1). https://doi.org/10.1088/1742-6596/1621/1/012115

25.

Hussainey

Khanh Ngoc

(2009). The impact of macroeconomic indicators on Vietnamese stock prices. Journal of Risk Finance, 10(4), 321–332. https://doi.org/10.1108/15265940910980632

26.

Inegbedion

Obadiaru

Adeyemi

(2020). Stock prices reaction to oil price fluctuations: Empirical evidence from Nigeria. International Journal of Energy Economics and Policy, 10(5), 142–149. https://doi.org/10.32479/ijeep.8306

27.

Irfan

Kassim

Dhimmar

Zahid

Fuadi

N. F. Z.

(2021). Reaction of Islamic Stock Market to macroeconomic variables: A study of India and Indonesia. Jurnal Ekonomi Dan Bisnis Islam [Journal of Islamic Economics and Business], 7(1), 148. https://doi.org/10.20473/jebis.v7i1.25921

28.

Iyke

B. N.

S.-Y.

(2021). Investor attention on COVID-19 and African stock returns. MethodsX, 8, 101195. https://doi.org/10.1016/j.mex.2020.101195

29.

Jeng

Mohd Japar

Hamzah

S. R.

(2021). Performance and volatility modelling for Shariah compliant stocks in Malaysia using exponentially weighted moving average. Malaysian Journal of Science Health & Technology, 7(1), 1–13. https://doi.org/10.33102/mjosht.v7i.101

30.

Johansen

(1988). Statistical analysis of cointegration vectors. Journal of Economic Dynamics and Control, 12(2), 231–254. https://doi.org/https://doi.org/10.1016/0165-1889(88)90041-3

31.

Johansen

Juselius

(1990). Maximum likelihood estimation and inference on cointegration—With applications to the demand for money. Oxford Bulletin of Economics and Statistics, 52(2), 169–210. https://doi.org/10.1111/j.1468-0084.1990.mp52002003.x

32.

Kilci

E. N.

(2020). Do confidence indicators have an impact on macro-financial indicators? An analysis of the financial service and real sector confidence indexes: Evidence from Turkey. European Journal of Government and Economics, 9(1), 74–94. https://doi.org/10.17979/ejge.2020.9.1.5948

33.

Kuntamalla

V. R.

Maguluri

K. J.

(2022). Causal analysis of stock prices and macroeconomic variables: Evidence from Indian stock market. Asian Economic and Financial Review, 12(7), 459–472. https://doi.org/10.55493/5002.v12i7.4530

34.

Lee

J. W.

Brahmasrene

(2020). An exploration of dynamic relationships between macroeconomic variables and stock prices in Korea revisited. Journal of Asian Finance, Economics and Business, 7(10), 23–34. https://doi.org/10.13106/jafeb.2020.vol7.no10.023

35.

Luwihono

Suherman

Sembiring

Rasyid

Kalbuana

Saputro

Prasetyo

Taryana

Suprihartini

Asih

Mahfud

(2021). Macroeconomic effect on stock price: Evidence from Indonesia. Accounting, 7(5), 1189–1202. https://doi.org/10.5267/j.ac.2021.2.019

36.

Mackinnon

Jorm

A. F.

Christensen

Korten

A. E.

Jacomb

P. A.

Rodgers

(1999). A short form of the positive and negative affect schedule: Evaluation of factorial validity and invariance across demographic variables in a community sample. Personality and Individual Differences, 27(3), 405–416. https://doi.org/10.1016/S0191-8869(98)00251-7

37.

Madurapperuma

(2022). The dynamic relationship between economic crisis, macroeconomic variables and stock prices in Sri Lanka. Journal of Money and Business, 3(1), 25–42. https://doi.org/10.1108/jmb-06-2022-0033

38.

Masrizal Sukmana

Al Mustofa

M. U.

Herianingrum

(2021). Can country risks predict Islamic stock index? Evidence from Indonesia. Journal of Islamic Accounting and Business Research, 12(7), 1000–1014. https://doi.org/10.1108/JIABR-04-2020-0127

39.

Mohammed

Nketiah-Amponsah

(2021). Relationship between exchange rate volatility and interest rates evidence from Ghana. Cogent Economics and Finance, 9(1), 1–19. https://doi.org/10.1080/23322039.2021.1893258

40.

Moussa

Delhoumi

(2021). The asymmetric impact of interest and exchange rate on the stock market index: Evidence from MENA region. International Journal of Emerging Markets, 17(10), 2510–2528. https://doi.org/10.1108/IJOEM-01-2020-0089

41.

Muharam

Najmudin

Mawardi

Arfinto

E. D.

(2021). Do instabilities in national macroeconomic factors contribute to channeling volatility spillover from the global to the Islamic equity market? Comparative Economic Research, 24(1), 103–121. https://doi.org/10.18778/1508-2008.24.06

42.

Mukherjee

T. K.

Naka

(1995). Dynamic relations between macroeconomic variables and the Japanese stock market: An application of a vector error correction model. Journal of Financial Research, 18(2), 223–237. https://doi.org/10.1111/j.1475-6803.1995.tb00563.x

43.

Nkoro

Uko

A. K.

(2014). Impact of the volatility of macroeconomic variables on the volatility of stock market returns: Nigeria’s experience. In Dinçer

Hacioğlu

Ü.

(Eds.), Global strategies in banking and finance (pp. 218–230). IGI Global. https://doi.org/10.4018/978-1-4666-4635-3.ch014

44.

Pernando

Irawati

Muluk

(2023). The effect of macroeconomic variables on Kompas 100 Indeks on the Indonesia stock exchange. In Proceedings of the 19th International Symposium on Management (INSYMA 2022). Atlantis Press International BV. https://doi.org/10.2991/978-94-6463-008-4

45.

Phan

D. H. B.

Narayan

P. K.

(2020). Country responses and the reaction of the stock market to COVID-19—A preliminary exposition. Emerging Markets Finance and Trade, 56(10), 2138–2150. https://doi.org/10.1080/1540496X.2020.1784719

46.

Putri

P. Y.

Achsani

N. A.

Pranowo

(2019). The effects of macroeconomic variables and corporate financial performance on stock prices of palm oil companies in Indonesia. Jurnal Manajemen Dan Agribisnis, 16(1), 12–22. https://doi.org/10.17358/jma.16.1.12

47.

Ramli

M. R.

Masbar

Majid

M. S. A.

Djalil

M. A.

(2021). The analysis effect of macroeconomic variables on Shariah and conventional market share at Indonesia. Palarch’s Journal of Archeology of Egypt/Egyptology, 18(1), 3450–3466. https://www.archives.palarch.nl/index.php/jae/article/view/5366

48.

Rehman

Ullah

Afridi

F. e. A.

Ullah

Zeeshan

Hussain

Rahman

H. U.

(2021). Adoption of green banking practices and environmental performance in Pakistan: A demonstration of structural equation modelling. Environment, Development and Sustainability, 23(9), 13200–13220. https://doi.org/10.1007/s10668-020-01206-x

49.

Robiyanto

Santoso

M. A.

Atahau

A. D. R.

Harijono

(2019). The Indonesia stock exchange and its dynamics: An analysis of the effect of macroeconomic variables. Montenegrin Journal of Economics, 15(4), 59–73. https://doi.org/10.14254/1800-5845/2019.15-4.5

50.

Ross

S. A.

(1976). The arbitrage theory of capital asset pricing. Journal of Economic Theory, 13(3), 341–360. https://doi.org/10.1016/0022-0531(76)90046-6

51.

Saucedo

Gonzalez

(2021). The effect of macroeconomic variables on the robustness of the traditional Fama–French model. A study for Mexico using different portfolios. Journal of Economics, Finance and Administrative Science, 26(52), 252–267. https://doi.org/10.1108/JEFAS-03-2021-0010

52.

Suhadak

Suciany

(2020). Brief technical note: The influence of exchange rates on inflation, interest rates and the composite stock price index: Indonesia 2015–2018. Australasian Accounting, Business and Finance Journal, 14(1), 105–120. https://doi.org/10.14453/aabfj.v14i1.11

53.

Tronzano

(2021). Financial crises, macroeconomic variables, and long-run risk: An econometric analysis of stock returns correlations (2000 to 2019). Journal of Risk and Financial Management, 14(3), 127. https://doi.org/10.3390/jrfm14030127

54.

Utomo

S. H.

Wulandari

Narmaditya

B. S.

Handayati

Ishak

(2019). Macroeconomic factors and LQ45 stock price index: Evidence from Indonesia. Investment Management and Financial Innovations, 16(3), 251–259. https://doi.org/10.21511/imfi.16(3).2019.23

55.

Vikaliana

(2018). Effect of inflation, interest rate/bi rate, and rupiah exchange rate on Indonesian Composite Index (IDX) at Indonesian Stock Exchange (ISE). The Management Journal of Binaniaga, 2(1), 41. https://doi.org/10.33062/mjb.v2i01.54

56.

Wahyu Umaryadi

M. E.

Hamdana Saragih

D. A.

Burhan

(2021). Interest rate, inflation and Jakarta composite index on digital media based companies stock return in Indonesia. Journal of Physics: Conference Series, 1807(1), 012025. https://doi.org/10.1088/1742-6596/1807/1/012025

57.

Widagdo

Jihadi

Bachitar

Safitri

O. E.

Singh

S. K.

(2020). Financial ratio, macro economy, and investment risk on Sharia stock return. Journal of Asian Finance, Economics and Business, 7(12), 919–926. https://doi.org/10.13106/JAFEB.2020.VOL7.NO12.919