The impact of Indonesian oil price (CPI) and macroeconomics on investments in the manufacturing sector in Indonesia [version 1; peer review: awaiting peer review]

Background: Indonesia consumes oil as the main energy source in the production process and as a result of the development of the manufacturing industry. Thus, investment in manufacturing stocks will be affected by oil price fluctuations and macroeconomic conditions. Changes in oil prices will affect the performance of the manufacturing sector which in turn affects manufacturing stock prices. This paper aims to examine the impact of Indonesia's oil price shocks and macroeconomic factors on stock price movements in the manufacturing sector. Methods: This study uses monthly data for the 2009-2016 period in the manufacturing sector, and 67 stocks were selected on the basis consistently available in the period of the research. The cointegration and causality technique was used in this paper; firstly we applied a unit-panel root test, Secondly, we performed a residual test to indicate whether there was cointegration among variables in the long run equilibrium, and short the short run, we used a Granger causality test. Results: The panel unit root test (both Shin and Fisher) and the Pedroni cointegration residual test show that the data is stationary at 1%  level of significance, thus all variables simultaneously achieve long-run equilibrium, and in the short run, the Granger causality test shows that there is one way direction causality Conclusions: For long-term investment in manufacturing stocks, investors must consider the exchange rate, as it is also as a determining factor in influencing the movement of manufacturing stock prices, inflation, and the production index. Meanwhile, weakening of the rupiah in the short run will also determine investment conditions due to the dependency on raw materials for production from foreign sources. The price of oil as an energy source Open Peer Review Reviewer Status AWAITING PEER REVIEW Any reports and responses or comments on the article can be found at the end of the article. Page 1 of 9 F1000Research 2021, 10:338 Last updated: 26 JUL 2021


Introduction
Oil is probably the most important source of energy in the economy, and also an important component of the production cost of companies in the manufacturing sector. Fluctuations in oil prices will therefore affect the performance of the manufacturing sector. Disruption of the sector's performance will also cause fluctuations in the share price of the manufacturing sector. Furthermore, as far as we know, stock price movements are influenced by internal and external factors caused by macroeconomic conditions. However, investors can still invest even in unstable macroeconomic conditions, but need to be more prudent.
As a developing country, the manufacturing sector in Indonesia has an important role in producing goods to meet the needs of a growing market. To attract investors, macroeconomic conditions need to be the focus of companies and investors in developing investments. The macroeconomic conditions which were chosen as the focus of this study were inflation, the exchange rate of the rupiah against the dollar, the index of large and medium production and fluctuations in Indonesian oil prices (CPI).
Changes in oil prices are important because they have implications for stocks as a whole and in general are a portfolio strategy. This information is very important for investor strategies, so that investors are still able to get a return on investment by minimizing risk while oil prices are still uncertain.
The results of the study, which show the causality and cointegration relationship of oil price shock and macroeconomics to the dynamics of manufacturing sector stock price fluctuations, can be a long-and short-term consideration for companies and investors. It can also be a guideline for companies in developing production and a consideration for investors.
Theoretically, there are various reasons for which oil price can affect stock prices (Cai et al., 2017;Hamilton, 2008;Jones & Kaul, 1996;Rafailidis & Katrakilidis, 2014;Ramos & Veiga, 2013). One of the reasons relates to the impact of changes in oil price on the future expected discounted earnings of companies. An increase in oil price will increase the cost of production which in turn will reduce the profit and the future expected discounted earnings and, therefore, the stock prices. Another reason is related to the effect of changes in the stock prices of oil producing companies on the stock prices of other companies whose expected future earnings flows are adversely affected by the increase in oil prices. An increase in oil price will be beneficial for oil producing companies and will increase their stock prices. However, this puts pressure on the stock prices of other non-oil producing companies. Moreover, increased oil prices may create uncertainty for the economy and also for non-oil companies, which may result in delayed investment decisions which will adversely affect their stock prices.

Methods
Our dataset is monthly, beginning with January 2009 and ending with December 2016, a relatively rich sample sufficient for conducting the type of econometric modeling in this paper. Like most previous studies on the same topic, this study employs variables that include oil prices, stock prices, real output, inflation and exchange rates. As the proxy for the oil prices we use the CPI index, which is an average price of a basket of internationally traded Indonesian crude oils that is still used in some contracts today. The proxy for the manufacturing sector stock prices is the manufacturing sector closing price index obtained from the Indonesian Stock Exchange. For the output, because data on real output or real GDP are only available quarterly, then the industrial production index, which is available monthly, is used as a proxy. Inflation is calculated as the percentage change of the consumer price index.
To investigate the impact of changes in oil price on manufacturing sector returns, we perform cointegration and Granger causality analysis. Prior to both analysis it is necessary to investigate the stationarity properties of panel data by performing unit root tests. The software used in calculating the econometric model of the study is eviews10.
The first test of the data is carried out using the unit root test to determine whether the data is stationary at the level, this is the unit root test which is performed to avoid spurious regression results. The unit-panel root test method used in this study consisted of the Im, Pesaran and Shin methods together with the Fisher method. In the latest literature, the panel-based unit root test has higher strength than the time series-based unit root test.

Panel unit root tests
It is important to check the stationarity of data before proceeding with further analysis because often a method designed for stationary series is not suitable for non-stationary series, and if applied one may run the risk of obtaining completely misleading results (Nason, 2013). We check the stationarity of the data involved by employing the Im, Pesaran, and Shin (IPS) unit root test which is based on the following equation: Where ∆y (i,t) is the first difference of the dependent variable, β i is the coefficient of the lagged of the dependent variable. The null hypothesis of the test is all series follow a unit root process: β i = 0 for all i, against the alternative that some (but not all) have unit roots. Rejecting the null implies that series in the panel are stationary.
As an alternative approach to panel unit root tests, we also use Fisher's (1932) results to derive tests that combine the p-values from individual unit root tests. This idea has been proposed by Maddala & Wu (1999), Choi (2001) and Hurlin (2010). The null and alternative hypotheses are the same as IPS.

Panel cointegration analysis
To check whether a cointegrating, or long-run, relationship exists among variables, the methodology introduced by Pedroni (1999) is employed. This is a residual based test that includes several tests that allow heterogeneity of error variance and deterministic trend across panel members. Hence, the first step is to obtain the residuals from the following regression for each of the panel member: where M is the number of regressors; ε i,t are residuals that indicate deviatios from the long-run equilibrium. Variables y and x are assumed to be integrated of order one, or I(1). In the test equation, intercept α, coefficient of time trend and slope coefficients β are allowed to vary across panel individuals. The following step is to pool residuals from the previous stage and test for their order of integration by estimating following equation: The null hypothesis of the test is that ρ i = 0 for all i, i.e. there is no cointegration relationship. The alternative hypothesis is that ρ i < 0 for i = 1, … , N and ρ i = 0 for i = N + 1, … , N.

Panel Granger causality test
A variable is said to be Granger-caused by another variable if including the second variable in the information set will improve the forecast of the first variable. The validity of causal test is conditional upon testing the unit root and cointegration among the variables. To undertake the Granger causality test, we consider a k-variate panel vector Autoregression (pVAR) model as follows: matrices of parameters to be estimated, u i and e i,t are (1 × k) vectors of dependent variables-specific panel fixed-effects and idiosyncratic error terms, respectively. The order of the model can be determined according to Schwarz's Bayesian information criterion (BIC) which is one of the most successful criteria.
Pairwise Granger causality (Table 1) is a short-term variable relationship test conducted jointly, and the null hypothesis states that there is no joint causality relationship. The causality relationship is found in the second equation where y is not Granger to the variable x. If, statistically, the x and y coefficients are not significant, then there is another relationship between x and y. While if there is a one-way relationship from x to y, then the estimation coefficient is at lag y. Conversely, the one-way relationship from y to x is an indication of the estimated coefficient on lag x.  Having examined the descriptive statistics, we proceed to test for the stationarity properties of the data relying on the IPS and Fisher panel unit root test. The results are presented in Table 2 and as can be seen in the table, at the 1% significance level, both the IPS and Fisher unit root tests provide strong evidence that the variables under study are unit-root variables of order one. In other words, they are integrated of order one.

Based
Stationary data are an important factor for getting estimates in the long run to avoid false regression results. The unit root test is performed at the beginning before estimation is done to find out whether the data has been stationary at the level or not.
Based on panel unit root statistics we proceed to panel cointegration analysis in order to examine long-run relationship among the variables. The first stage is to determine that the relationships among variable are not spurious, the panel unit root result should be tested by the Fisher and Shin method in stationary at level. The result of the panel unit root test can be seen in Table 2 below.
The results of the Lm test, Shin and Fisher for all variables in the table above show that the data is stationary at the significance level of 1%.
The objective of the cointegration test is to test the long-term relationship between oil price shocks, macroeconomics, and stock prices in the manufacturing sector. The cointegration test uses the Pedroni method, where the dimensions are grouped into two types, namely dimensions based on "within dimensions " and dimensions "between dimensions ". Dimension testing "within dimensions " is carried out on the respective values of panel v stat, panel rho stat, panel PP stat and panel adf stat. Meanwhile, the "between-dimensions " dimension test was carried out on the group rho stat, group PP stat and group ADF stat.
The relationship of variables in the panel data is of concern at this time. With the cointegration of the Pedroni method, it is known that all variables in this study are co-integrated in the long run, meaning that all the variables used have linear dependence (cointegration), where all variables are cointegrated to achieve mutual balance together in the long run. All the results of the test for both "within dimensions" and "betweendimensions " are shown in Table 3 below.
In Table 3, the Pedroni cointegration residual test indicates cointegration with a significance level of 1%, so we reject the null hypothesis that there is no cointegration. This means that variable oil price shocks, macroeconomics, and stock prices in the manufacturing sector achieve long-run equilibrium.
For the short run we use pairwise granger causality to see the relationship in the short term, whether there is a two-way center, one-way causality, or no causality at all, the result of Pairwise granger causality test can be seen in Table 4 below. Table 4 shows that there is no bidirectional causality, and only one directional causality relation between variables. One-way direction relation is shown between the production index and inflation, and between inflation and both the stock price and exchange rate, It can be seen how much influence the cointegration of each variable has on other variables, with further testing the relationships between variables in this study are shown in Table 5.

Discussion
From the results, it can be seen from the exchange rate, inflation and the joint production index, that the price of oil is Table 2. Test of the panel unit root.

Method Remark Stat
Lm pesaran, Shin Stationary (level) -24.25*** Fisher choi z stat Stationary (level) -18.58*** Information: significance 1%***, significance 5%**, significance 10%*.  co-integrated in the long run. Individually, it is known that manufacturing shares are integrated with oil prices and exchange rates. Furthermore, oil prices are significantly related to production and inflation indexes. This condition illustrates that, if the exchange rate cannot be maintained in stable conditions and is accompanied by high inflation, the shock of oil prices will affect the company's production activities in the long run.
The exchange rate of the rupiah will affect manufacturing production due to the large dependence on foreign raw materials to meet production goals in achieving long-term production balance. For long-term investment, it is very important to see changes in the exchange rate and the medium to large production index because these have a two-way causality and a significant positive effect in the short term. If the rupiah strengthens against the US dollar and this continues in the future, it will have an impact on rising production indices in the short term and this condition will also raise the production index in the long run, and vice versa.
While the increased purchasing power of consumers, in addition to reflecting the increased ability to invest in the community, also has a positive effect on the ability of the public to invest in manufacturing stocks, an increase in production will trigger an increase in oil prices.
Furthermore, the significant relationship of oil in the short term is based on the W-stat, but on the other hand, based on the Zbar-stat the price of oil is only related to the production index. It can be interpreted that the price movement of oil is divided due to the supply of oil producers and the demand of producers as consumers who use oil.

Conclusions
When people's purchasing power in the short term has a positive effect on investment, in the long run, this condition will strengthen long term investments, so that investment managers can hope to improve company performance to develop the company in the future.
For long-term investment in manufacturing stocks, investors must consider the exchange rate and the medium to large production index because these variables influence each other significantly in the movement of stock prices. The increase in the production index is an illustration of the improved performance of listed manufacturing companies, this condition is supported by increased output and sales of manufacturing companies, which will increase company profits. Rising corporate profits are a reflection of the company's improved performance. The improvement in performance shows an increase in demand for energy. Fluctuations in exchange rates and increases in the production index are central points in investment considerations.
The continuous weakening of the rupiah in the short run will determine investment conditions in the future. This factor is due to the dependency on raw materials for production from foreign sources, the amount of which will reduce the value of the rupiah.
Since the end of the cycle of Indonesia's mainstay commodities such as petroleum, the Indonesian economy can be strengthened through the manufacturing sector pathway. To do this, policies to improve infrastructure and transportation need special attention from the government, as improved transportation will strengthen investor confidence to re-invest.
Although the manufacturing sector in Indonesia is experiencing liberalization widely and is open to the world of investment, on the other hand, due to the problem of unequal access to electricity it is necessary to consider renewable energy as a companion in fulfilling Indonesia's energy needs and strengthening the Indonesian economy.

Data availability
Figshare: Data Panel Manufaktur.csv https://doi.org/10.6084/m9.figshare.13656005.v1 (Rachmawati et al., 2021) This project contains the following underlying data: • Data Panel Manufaktur.csv (Monthly data for January 2009 -December 2016, the operational variable determined in this study is the oil price index which is an index of oil prices (ICP), while ICP oil prices refer to Platts, Rim oil prices.) Data are available under the terms of the Creative Commons Zero "No rights reserved" data waiver (CC0 1.0 Public domain dedication).

Author contributions
Handri: Designed the research objectives and variables and analyzed the research findings using the tools Hendrati Dwi Mulyaningsih: Determined the research objectives, and analyzed the findings.
Achmad Kemal Hidayat: Interpreted the result and enhance in discussion.
Rudi Kurniawan: Interpreted the result and enhance in discussion.
Ani Wahyu Rachmawati: Analyzed the findings, prepare manuscript and submission