







































Asian Themes in Social Sciences Research 
ISSN: 2578-5516 

Vol. 4, No. 1, pp. 1-8 
2020 

DOI: 10.33094/journal.139.2020.41.1.8 
© 2020 by the authors; licensee Online Academic Press, USA 

 
Accepted: 26 October 2020 | Published: 30 December 2020 

1 
© 2020 by the authors; licensee Online Academic Press, USA 

  

 
 
 
 

Macroeconomic Determinants of Stock Market Fluctuations: 
The Case of Indonesia 
 
 

Agustinus Susanto 
 
University of Eastern Finland, Finland. 

 

 
Abstract 

The study was aimed at assessing the macroeconomic determinants of stock market fluctuations in 
Indonesia. The quantitative data was collected through secondary sources such as World Bank. 
The data was collected from 1990 to 2019. The statistical testing was done through Descriptive 
statistics, Unit root testing and ARDL (Autoregressive Distributed Lag) model. The data was of 
mixed nature as it consisted of stationary and unit root. The findings revealed that the overall 
model was significant and in long run, there is a significant impact of macroeconomic indicators 
on stock prices in Indonesian market. Geographical limitation is the major issues that have 
restricted the collection and analysis of data. This study can also be improved by doing country 
comparison and industry comparison of same industries. 

Keywords: Interest rate, Exchange rate, Stock market, Inflation rate, FDI, Macroeconomic indicators. 

Licensed:  This work is licensed under a Creative Commons Attribution 4.0 License.  
Funding: This study received no specific financial support.    
Competing Interests: The author declares that there are no conflicts of interests regarding the publication of this 
paper. 

 
 
1. Introduction 

The stock markets are a vital part of country’s economy because these markets list all the public limited 
companies whose stocks are bought and sold by investors in this particular market. The stock markets tend to 
raise investment for companies benefitting the general public as well as large companies operating within the 
country (Pradhan, 2018). This market is most preferred by investors due to its high return and generally low 
to moderate risk associated with the stocks. However, the stock market is affected by the country’s external 
factors including political environment, macroeconomic variables and technological advancements (Babajide, 
Isola, & Somoye, 2016). All of them carry a significant place with regards to their impact on stock market. 
Nonetheless, macroeconomic indicators tend to define and signify the stock market to a great extent. There 
are various macroeconomic variables including country’s GDP, interest rate, inflation, exchange rate, 
unemployment rate, FDI etc. that help in determining stock market fluctuations. According to Demir (2019) 
the macroeconomic variables are dynamic and change rapidly with time which in turn, specify the changes in 
stock market returns which is of great interest for government, investors as well as companies. Indonesia is 
one of the emerging markets in the world primarily famous for its tourism industry having a stable stock 
market and dynamic macroeconomic indicators. Since the focus of scholars recently has been on emerging 
economies, most of the studies have depicted the macroeconomic determinants within BRICS economies 
(Asongu, Akpan, & Isihak, 2018; Syed & Tripathi, 2020; Umar & Sun, 2016). However, there is still little 
evidence related to the stock market fluctuations in Indonesia. Hence, this paper is aimed to fill the gap by 
focusing on the macroeconomic determinants and its impact on stock market fluctuations in Indonesia.  

The study is aimed at assessing the macroeconomic determinants of stock market fluctuations in 
Indonesia. For this purpose, the following objectives are made; 

• To examine the significance of macroeconomic dynamics on emerging economies. 

• To identify and explain the macroeconomic factors associated with stock market fluctuations. 

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© 2020 by the authors; licensee Online Academic Press, USA 

• To analyse the impact of macroeconomic determinants on stock market fluctuations with respect to 
Indonesian stock market. 

• To recommend investment strategies and techniques to investors for gaining higher returns with 
respect to macroeconomic fluctuations. 

The primary research question answered through the following paper is; 
What is the impact of macroeconomic determinants on stock market fluctuations in Indonesian stock market? 
Sub-questions are defined as under; 

• What is the significance of macroeconomic dynamics on emerging economies? 

• What are the macroeconomic factors associated with stock market fluctuations? 

• What is the impact of macroeconomic determinants on stock market fluctuat ions with respect to 
Indonesian stock market? 

• What are some recommended investment strategies and techniques for investors in order to gain higher 
returns with respect to macroeconomic fluctuations? 

 

2. Literature Review 
There are numerous studies that have been conducted to analyse the important factors that tend to impact 

stock returns. For instance, in many textbooks and literature, one of the renowned factor models that have 
been discussed by many scholars is the single-factor model, which is known as the Capital Asset Pricing 
Model (CAPM).  The rationale behind this model has been derived from the important concept associated with 
diversification that states that well-diversified investors should only assess systematic risks associated with a 
given investment (Elbannan, 2015). 

In this regard, Kisman and Restiyanita (2015) have also argued that this systematic risk can be examined 
by looking at the sensitivity of every stock to a change within the overall market, which can be measured or 
assessed by looking at the Beta value. In other words, it can be stated that the market factor is the single most 
important factor that can be utilized to determine the stock return. Due to the simplicity of  the representation 
associated with this model, as well as its good theoretical background, CAPM is considered as a highly 
renowned model which is utilized in determining the return on stock under most financial textbooks and for 
this reason, the model has also been used by numerous practitioners under the stock markets (Brogaard & 
Detzel, 2015). 

Similarly, the topics associated with the stock return and macroeconomic variables have also gained 
tremendous attention from different scholars that have tried to determine their relationships, which also 
include their impact on emerging economies. Many of these scholars have also utilized both new and 
traditional techniques to verify the significance of macroeconomic factors. For instance, Boako, 

Omane‐Adjepong, and Frimpong (2016) have conducted their study about the exchange rate and stock return 
in which they have utilized the quantile regression to examine whether the exchange rate impact returns on 
stock and whether the returns on stock, in turn, influence the exchange rate. The results of their study 
indicated that in most of the quantiles, the exchange rate had a dramatic impact on the stock return. 
Nevertheless, the authors also found that the stock return had no impact on the fluctuation under  the 
exchange rate. 

Likewise, in their study, Kurov and Gu (2016) analysed the effect of monetary policy on the stock returns 
under both the sectorial and aggregate levels by utilizing panel and time-series regression models. For this 
purpose, they utilized the three-month sterling LIBOR futures contract for generating the monetary policy 
shock. The findings of their study indicated a substantial impact on the stock returns from both unexpected 
and expected changes within the interest rate. In addition to this, the authors were also able to detect and 
measure the structural break as the relationship between the interest rate and stock return becomes positive 
during the period of recession. 

On the other hand, to analyse the relationship between inflation and stock return, Kurov and Gu (2016) 
also utilized an asymmetric test within their study. In their study, the authors were able to observe a negative 
relationship between these variables during the low inflation environment. However, the relationship between 
the variables was found to be positive under the high inflation environment. Based on this observa tion, they 
concluded this finding as an inflation protection characteristic associated with stock return.  

Under the study of Rehman and Shah (2016) the lead-lagged relationship between macroeconomic 
variables, as well as the stock market was analysed by utilizing a vector auto-regression technique. In their 
study, the macroeconomic variables that were selected by the authors were mostly related to wholesales’ price 
index, interest rate, and industrial production index. The findings of their study indicated that these 
macroeconomic variables had a substantial impact on the stock market. However, the authors also discovered 
that future economic performance was not explained by the stock market fluctuations. 

These and other studies associated with macroeconomic impact on the stock markets have always 
remained an interesting topic for many scholars and researchers not only under the major stock mark ets in the 
developed economies but also in the stock markets of emerging economies. For European countries, the 
research of Antonios (2010) was based on examining the sensitivity of German stock returns to changes under 
the interest rates of the country. In this research, the evidence indicated that the term structure associated 



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© 2020 by the authors; licensee Online Academic Press, USA 

with interest rates was a highly important factor in explaining the stock returns’ sensitivity in both overall 
and industry level in Germany. Whereas, in the study of Siliverstovs and Kholodilin (2010) variance 
decomposition, and vector error correction model (VECM) was used by the authors to analyse the effect of the 
important macroeconomic factors on Switzerland’s stock market. 

The results of the variance decomposition indicated that every sector was sensitive to different types of 
innovations under the macroeconomic variables. In this regard, Siliverstovs and Kholodilin (2010) further 
argued that this information was highly useful for the allocation of strategic asset s in different sectors to 
control the risks associated with macroeconomic variables. Likewise, Artikis and Nifora (2012) in their 
research was found to study the interaction among the stock market and macroeconomic variables in Greece. 
The findings of their research indicated that the movement under the stock market was not the leading 
indicator associated with the fluctuations in macroeconomic variables while the macroeconomic dynamics were 
found to partially explain the fluctuations within the stock market. Korhonen and Peresetsky (2016) also used 
the EGARCH model to assess the impact of macroeconomic variables on the Russian stock exchange market 
under which they discovered that the stock market was heavily dependent on both the US Dollar exchan ge 
rate, as well as oil prices. 

While the study of Ozturk and Yilmaz (2015) was based on examining the impact of macroeconomic 
factors and dynamics on the stock return under the stock exchange market of Istanbul, Turkey. In this study, 
seven important macroeconomic factors were identified by the authors which were related to money market 
interest rate, consumer price index, industrial production index, gold price, oil price, money supply, as well as 
foreign exchange rate. The results of this study indicated that the money supply had a significant positive 
impact on the returns of stock, while the variables related to the exchange rate, oil price, production index, and  
interest rate had a significant negative impact on the stock returns. 

Besides, the above-stated countries, several studies describing the relationship between stock market 
fluctuations and macroeconomic determinants are also popular under the emerging Asian countries. For 
instance, the study of Yang, Kim, Kim, and Ryu (2018) under which the authors were found to utilize Fama’s 
proxy hypothesis framework can be considered as an example where the relationship between the real 
activities, inflation, and real stock return was assessed with respect to the Korean market. The findings of their 
research highlighted a negative relationship between the real stock return and inflation rate in the Korean 
stock exchange market. This negative relationship was described by the authors as driven from the 
inflationary pressure to the future potential earnings of the listed companies, as well as a higher nominal 
discount rate. 

Similarly, the relationship between the macroeconomic variables and the stock market in Singapore has 
also been analysed by Leong and Hui (2014) who were able to discover the presence of co-integration between 
every important macroeconomic variable under their study and market index. While examining the sectorial 
level, the authors were also able to find a co-integration between all macroeconomic variables and the property 
sector of the country. Nevertheless, while analysing the finance sectors, the authors were not able to found a 
significant relationship between real economic activities and money supply and for the hotel sector, an 
insignificant relationship was noted by the authors between the money supply and interest rates of the 
country. 
 

3. Methodology 
As stated earlier, the study is aimed at assessing the impact of macroeconomic determinants on stock 

market fluctuations which is based on numbers; hence, a positivist approach has been used to conduct the 
study. The reason why this research philosophy has been selected to conduct this study is that it adheres to 
the view that only factual or objective knowledge which is attained through observation (i.e., senses), as well 
as through measurement, can be considered as trustworthy. Due to this reason, in most positivism studies, the 
researcher’s role is restricted to collecting and interpreting the data in an objective way (Ryan, 2018). 
Therefore, by using this philosophy, objective inferences have been made by analysing the collected data in 
order to test the hypotheses and accomplish the objectives of this research. 

Similarly, as quantitative analysis has been made in this research, the quantitative research methodology 
has also been used to provide appropriate inferences related to the relationship between t he study's key 
variables (i.e., stock market fluctuations and macroeconomic determinants). This research methodology has 
further assisted to increase both the accuracy and objectivity of the inferences and conclusions that have been 
made to describe the relationship between stock market fluctuations and macroeconomic determinants of 
Indonesia (Bryman, 2017).  
 
3.1. Unit Root Testing 

Under this research, the unit root testing method has been used to determine whether the analysed data 
associated with the key variables of this study is stationary or not.  After using this technique, the 
autoregressive distributed lag (ARDL) analysis has also been carry out in the research to evaluate the impact 
of macroeconomic determinants on the stock returns. Moreover, to conduct these time series analyses, the 
primary data that has been gathered for this research has focused on analysing the changes under the variables 
between 1990 and 2019. 



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The processing of a time series, by considering the findings of Beare (2018) typically comprise certain 
assumptions and limitations that are needed to be followed while predicting and analysing different time series 
under given research. Thus, it is highly essential to explain how certain criteria and assumptions are 
important to consider while conducting an econometric assessment that has also been carried out in this 
research. 

In addition to this, the quantitative data for this study has been collected by accessing the IDX Composite 
index of the Indonesian Stock Exchange (IDX) which can also be classified as secondary data. Therefore, it can 
be stated that for this study, the desk research method has been employed. As most stock markets in different 
countries are impacted by different macroeconomic determinants (which was also evident under the critical 
analysis of the literature review), this research has specifically focused on determining the impact of 
macroeconomic variables associated with the interest rate, inflation rate, and exchange rate of Indonesia on the 
stock market returns or returns on investment that are attained by the stockholders in the country. 

Similarly, it has also analysed the impact of foreign direct investments (FDI) that the local investors or 
stockholders in Indonesia can make in other countries to protect themselves from the risk associated with 
capital losses that they are compelled to face during high fluctuations under the stock exchange market in 
their home country (i.e., Indonesia). By considering these variables and the above literature review, the 
conceptual framework of this research has been provided below. 
 

 
Figure-1. Conceptual framework. 

 
The relationship between the explained and explanatory variables of this research (as highlighted by the 

above conceptual framework) can also be understood by using the following empirical equation: 

SPt = α + β1 (IRt) + β2 (I t) + β3 (XEt) + β4 (FDI t) + ε 
Where: 

• SP: Stock Price. 

• IR: Inflation Rate. 

• I: Interest Rate. 

• FDI: Foreign Direct Investment. 
In this research, Autoregressive Distributed Lag, as well as Augmented Dickey-Fuller have also been 

implemented. The detailed explanations of these methods have been provided below:  
 
3.2. Augmented Dickey-Fuller (ADF) 

As stated earlier, this research has analysed the impact of macroeconomic determinants on the stock 
returns (i.e., the return on investment) of the investors in Indonesia. Thus, to understand the current research 
phenomenon, the time series data associated with the macroeconomic determinants (i.e., inflation rate, interest 
rate, exchange rate, and FDI), as well as stock returns in Indonesia were collected from 1990 to 2019. 
Nevertheless, while using time-series data, researchers are often required to follow certain assumptions. Thus, 
the fundamental assumption criteria which have been used in this research have followed the stationarity of 
time-series. This stationarity of time-series data can be evaluated by using the ADF technique which has also 
been utilized in this research. This ADF technique typically assists in forming the basis of the assumption that 
is used to propose the null hypothesis of time-series data that also requires the use of unit-roots (Paparoditis & 
Politis, 2018). 

In this regard, it can be stated that the rejection or acceptance of the null hypothesis helps in determining 
whether time-series comprises unit-roots. Upon confirming the presence of unit roots under the time series, 
appropriate inferences are made to conclude that the data is stationary, and vice versa. The ADF approach can 
also be explained by using the following mathematical model: 



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As indicated by the above-provided equation,  can be described as the difference operator. On the other 

hand,  in the equation indicates the random error of stationary. Further,  highlights non-stationary time-

series data. 
 
3.3. Autoregressive Distributed Lag (ARDL) 

The statistical technique associated with ARDL is also commonly utilized to determine the long-term 
associations while conducting the econometric assessment under given research. By considering the findings 
of Nkoro and Uko (2016) this approach is commonly utilized to determine the long-term relationship or 
association between two quantitative variables. Moreover, this technique is also frequently used to form the 
basis of the analysis by using an iterative approach under which the time-series’ marginal log is maximized. 
The below-provided equation highlights the standard log-linear function that is generally used under this 
approach: 

 
In the above-provided equation, ROIt describes the log of return on investment, while the in the 

equation represents the error terms and α denotes the parameter estimate. 

 
Hence,  represents significant coefficient.  

 

4. Results 
For the purpose of analysing the collected data from the World Bank’s website, the results have been 

presented below. Along with the results, the discussion and conclusion is also given related to the research 
topic and the findings. This section has outlined descriptive statistics of the data, evaluation of unit root as the 
study has used time series data and assessment of relationship through ARDL approach.  
 
4.1. Descriptive Statistics  

This section determines the average of exchange rate, interest rate, inflation rate and FDI along the 
period of 1990 to 2019. The table of descriptive statistics below also show minimum and maximum values 
through which the range is computed.  On the other hand, standard deviation has been used to calculate the 
deviation as well.  

The results in the below table are showing that average exchange rate value in the period from 1990 to 
2019 has been $8,236.03, the average value of interest rate has been 548.3% in Indonesia and the average 
inflation rate has been 925.0%.  The average value of FDI had been $8,120,000,000. On the other hand, the 
deviation in exchange rate had been $4,078.24, the deviation in interest had been 74 4.7% and the deviation in 
inflation rate was 998.4%. The average values of stock price and stock returns are 179 and 6.18% respectively.  
 

Table-1. Descriptive Statistics. 

Variable Mean Std. Min Max 
Exchange Rate $8,236.03 $4,079.24 $1,842.81 $14,236.94 

Interest Rate 548.3% 744.7% -2460.0% 1560.7% 
Inflation Rate 925.0% 998.4% 303.1% 5845.1% 
FDI $8,120,000,000.00 $9,240,000,000.00 -$4,550,000,000.00 $25,100,000,000.00 

Stock Price Volatility 179.0 146.4 15.0 533.1 
Stock Return Volatility 6.18% 3.57% 0.00% 17.25% 

 
4.2. ADF Testing to Evaluate Unit Root  

As discussed in the initial section that ADF testing was carried out since the data is was time series in 
nature. It is assumed that non-stationary characteristics are present in null hypothesis of the study. The 
results have been presented below in Table 2 and are computed through E-Views.  As per the details of the 
results, the t-statistics of exchange rate is -1.59 with P value of 0.4886. This implies that P value is greater 
than all the standard values such as 5%, 1% or 10%. Hence it indicates that exchange rate has a unit root. 
Similarly, the t-statistics of interest rate is -5.448 with 0.0 as the P-value. This means that the data was 
stationary and there was no unit root in interest root as the P value was 0.000 (p-value <0.05) and the null 
hypothesis was negated in this case. The t-statistics of inflation rate was -4.285 with 0.0005 as p-value. Since 
the p-value is less than the threshold values hence, there is no unit root  in inflation rate too.  

Moreover, the t-statistics of FSDI was -8.347 with 0.00 as the p value hence it also means that due to 
stationarity in data, there is no unit root. In case of stock price and stock return, the t -statistics was -.183 and -



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3.124 respectively whereas, the p-values were 0.3658 and 0.0248. Overall, mixed nature of the data series can 
be observed from the results obtained  hence, the methodology adopted later is decided in accordance with the 
nature of the data.  
 

Table-2. UNIT root testing. 

Variable Test Statistics P-Value 

Exchange Rate -1.59 0.4886 
Interest Rate -5.448 0 
Inflation Rate -4.285 0.0005 

FDI -8.347 0 
Stock Price Volatility -1.83 0.3658 
Stock Return Volatility -3.124 0.0248 

 
4.3. Bounds Testing 

Bounds testing are another way or approach of co-integration where variables are bound together in the 
long run period. This method has several benefits as it is new and helps in determining whether there will be 
any association between the variables in future or in long run or not. This tests is used irrespective of the fact 
whether the series of data is mixed or not. The values shown below of t -statistics and f-statistics alongside the 
p-values indicate the results.  
 

Table-3. Bound Values. 

 10% 5% 1% P-value 

 I(0) I(1) I(0) I(1) I(0) I(1) I(0) I(1) 

F 2.828 4.353 3.582 5.396 5.603 8.152 0.016 0.065 

t -0.2478 -3.622 -2.9 -4.133 -3.805 -5.235 0.004 0.042 
 
4.4. Autoregressive Distributed Lag Model  

From the results obtained in previous section (ADF testing), it  was assessed that the nature of the data is 
mixed where some variables have unit root whereas some are stationary hence, the model selected for analysis 
after considering the nature of data is ARDL Model. The results are presented below in Table 4 that shows 
the t-statistics and p-values obtained. Also, it has the optimal Lag order which was selected by E-Views 
automatically. To ensure that the errors of data are fixed so, HAC error was used to strengthen the 
assessment between the independent and dependent variables. For the purpose of selecting the optimal model, 
in the first lag, stock return volatility had significant result (B=-2.335 with P-value of 0.001). Furthermore, 
the interest rate had also significant result in first lag (B=-0.00511 with P value of 0.00). However, contrary to 
these results, inflation rate had insignificant results in first lag because of the values obtained less than the 
threshold value (B=0.00038 and P-value of 0.651).  The first lag of FDI shows it has significant results (B=-
0.00176 with P-value of 0.015). Similarly, all other results shown below are also of same nature and they 
indicate in second lag that significant results were not obtained.  
 

Table-4. ARDL model of the research short run. 

  Coef. Std. Err. t P>|t| [95% Conf. Interval] 
SR       

Stock Return Volatility     
LD. 0.96287 0.36002 2.67 0.019 0.18509 1.74066 

L2D. 0.31308 0.26306 1.19 0.255 -0.2552 0.88138 
Interest rate       
D1. -0.0064 0.00187 -3.4 0.005 -0.0104 -0.0023 

LD. 0.00494 0.00174 2.85 0.014 0.00119 0.00869 
L2D. 0.00487 0.00144 3.37 0.005 0.00175 0.00798 

L3D. 0.00358 0.00101 3.56 0.004 0.00141 0.00576 
Inflation rate      

D1. -0.0015 0.00134 -1.15 0.269 -0.0044 0.00135 

LFDI       
D1. -0.0041 0.00201 -2.04 0.062 -0.0085 0.00025 

_cons  0.80285 0.1985 4.04 0.001 0.37401 1.23168 
 

Overall it can be concluded that since the aim of the study was to determine the impact of macroeconomic 
determinants on stock market fluctuations with respect to Indonesian stock market therefore, it can be 
concluded that since the P-value is less than the threshold values such (p=0.019) therefore, statistical 
relationship to some extent can be observed. Based on this assertion, it can be found that macroeconomic 
indicators (inflation rate, exchange rate, FDI and can have impact over stock prices or stock returns 
considering the external factors remain the same. The Table 4 below was also divided into long run and short 



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run so that the association or the relationship between the macroeconomic indicators and stock price return 
can be assessed. It also depends on the lag values. In the long run period, FDI (p-value at 0.015) and stock 
return volatility (p-value at 0.001) have significant results. On the contrary, in the short run period, lag of 
interest rate (p-value at 0.005). Therefore, it depends on the lag values and the short run and the long run 
period.  

 
Table-5. ARDL model of the research long run. 

  Coef. Std. Err. t P>|t| [95% Conf. Interval] 

LR       
Stock Return Volatility -2.3351 0.54827 -4.26 0.001 -3.5196 -1.1507 
LER -0.0274 0.00395 -6.94 0 -0.0359 -0.0189 

Interest rate -0.0051 0.00073 -7.03 0 -0.0067 -0.0035 
Inflation rate 0.00038 0.00082 0.46 0.651 -0.0014 0.00216 

LFDI -0.0018 0.00063 -2.81 0.015 -0.0031 -0.0004 
 

The results of the study can be validated from another study conducted in similar domain by Demir 
(2019) that has revealed that economic growth, portfolio investments and FDI help in raising the stock market 
whereas, interest rate and oil prices negatively impact it. ARDL approach was adopted in this study and it was 
suggested that stock exchange should increase capital inflow and their investments. The stationarity of the 
variables was also tested through ADF,  and unit root testing that were developed by Dickey and Fuller 
(1979) and Phillips and Perron (1988).  
 

4.5. Hypothesis Assessment Summary  
The primary assessment and overall evaluation has revealed that the overall model is significant. There is 

a more strong association during the long run as compared to short run. It was also found that stock prices is 
significantly dependent on its lagged values. Based on the results obtained, it can be stated that the proposed 
hypothesis has been accepted.  
 

5. Discussion  
Several studies are conducted in similar domain which has analysed the impact of macroeconomic 

indicators with stock prices. The following study used the case of Indonesian market so that in-depth data can 
be collected and evaluated. The data was obtained from 1990 to 2019 from World Bank. The data was also 
assumed to be mixed because some of the variables were stationary whereas, some had unit root. This was the 
major rationale behind opting for ARDL model testing as the data was of mixed nature. Furthermore, it had 
been found from the previous studies that risks is one of the essential element that is present and every stock 
market has to face this. Kisman and Restiyanita (2015) determined in their study that market factor can be 
used for finding the impact on stock returns though the stock prices fluctuate on daily basis due to various 
external factors. Similarly, CAPM model is found to be significant in case of assessing stock returns with other 
variables. The macroeconomic indicators such as inflation rate, exchange rate and FDI are affected by many 
changes in the local economy such as increase in prices, government international deals or any political shocks 
or inflow of more investments and dollar price fluctuations. Even oil has a significant part in changing the 
stock prices on daily basis.  

In addition to this, it was also found that there is a strong interrelationship between financial development 
and economic growth due to which the macroeconomic variables such as interest rate and exchange rate are 
influenced too. Despite of the studies conducted in this domain, Indonesian stock market was selected because 
the current studies are conducted on large economies such as China and USA. There is limited data available 
regarding Indonesian market and the fluctuations observed in stock prices. Thus, based on the results 
obtained above it is evident that in short and in long run, the macroeconomic variables behave differently and 
the results obtained are also different. In the short run, there might be no impact but in the long run, there is a 
significant impact.  
 

6. Conclusion  
In order to conclude the research article, it can be stated that the independent variables selected in this 

study were interest rate, inflation rate, exchange rate and FDI whereas, the dependent variable was stock 
return. Conclusively, the overall model was found to be significant and in the long run, there is impact of 
macroeconomic indicators on stock prices in the Indonesian market. The government needs to design policies 
for controlling inflation rate and exchange rate fluctuations. The government should encourage foreign 
investors so that inflow of FDI can increase and benefit the economy and growth. Precisely, it is advised that 
government should focus on creating a strong mechanism that helps in increasing investment and more 
exports should be exported through local production. This may lead towards significant impact on stock 
market in short run period.  



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7. Limitations of Study and Future Directions 
The following research article was based on Indonesian market hence, it entails that geographical 

limitation is the major issues that has restricted the collection and analysis of data. The results of the study 
cannot be generalized to other developing or developed countries. In order to improvise the results in future, 
the data can be collected from Malaysia and Thailand. In terms of the macroeconomic indicators, the future 
papers can include more indicators and assess them in short run and long run period. Moreover, this study can 
also be improved by doing country comparison and industry comparison of same industries. The researcher 
can use the data of different countries along with different indicators and find the impact on GDP and GNP 
too.  

 
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Asongu, S., Akpan, U. S., & Isihak, S. R. (2018). Determinants of foreign direct investment in fast-growing economies: 
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