Economic Performance of Indonesian Manuffacturing Ccompanies With Factors

. purpose of this research is to determine the effect of Enterprise Risk Management (ERM), Inventory Intensity, Corporate Image and Intangible Asset to Economic Performance in manufacturing companies listed on the Indonesia Stock Exchange (IDX) for the 4-year period 2015-2018. This study population includes all manufacturing companies listed on the Indonesia Stock Exchange (IDX) for the 2015-2018 period. The sampling technique was using purposive sampling technique. Based on the predetermined criteria, 17 companies were obtained. The type of data used is secondary data obtained from the Indonesia Stock Exchange website. The analysis method used is linear regression analysis of panel data.The results showed that Inventory Intensity and Corporate had a significant effect on Economic Performance. while Enterprise Risk Management and Intangible Asset has no effect on intellectual capital.


INTRODUCTION
In the current era of globalization, business competition is increasing very rapidly. Many business opportunities arise from various sectors so that business competition is getting fierce. Therefore, companies are required to improve their performance so that they can compete in the business world. Companies as business actors are not only required to be able to compete with other companies but also to be able to grow and develop from period to period. There are many things that cause the company to be unable to compete with other companies, one of which is in terms of economic performance. Economic performance can be characterized by annual stock returns or annual stock prices. Recently, several companies have found it difficult to maintain their share price to remain stable, as experienced by PT Bakrie & Brothers Tbk (BNBR), whose share price has decreased very sharply, namely from the price of Rp. 500 to Rp. 70 and finally stopped at the level of Rp. 50. Finally, the Indonesia Stock Exchange carried out a freeze (suspension) of BNBR shares. BNBR's share price has experienced a very sharp decline because in 2017 BNBR suffered a loss of up to Rp. 1.2 trillion and the company's debt burden of Rp. 12.57 trillion (m.detik.com, 2019). This caused the economic performance of BNBR to look bad in the eyes of market players, thus reducing investors' interest in investing. The same thing was experienced by PT Bumi Resources Tbk (BUMI) which experienced a decline in its share price. BUMI has problems in managing its debt , causing BUMI's shares to be at the highest level at a price of Rp. 3,650 decreased to the lowest level on April 16, 2014 amounting to Rp. 187 per share. In recent years, coal stock prices have decreased, adding to the pressure on BUMI's shares (liputan6.com, 2019). The condition of a company's economic performance can be seen through the company's annual stock return. Companies that have poor economic performance can be seen from the decreasing annual stock returns. Based on the observations, acquired several companies listed in Indonesia Stock Exchange that have inconsistent stock returns in 2014 until 2018.  negative average values are companies  with the stock code BATA, GJTL, INDF, MYOR, SMGR, TOTO, and UNVR. This condition will not be good for the company because investors or potential investors will rethink investing in the company, given the negative value of stock returns. Based on table 1.1 above, it can be seen as a whole that in 2015 was the year most companies had negative stock returns compared to other years, namely there were 6 companies out of 10 companies with stock codes BATA, GGRM, GJTL, INDF, ROTI and SMGR. This condition can make it difficult for the company to compete with other companies that have positive stock returns and reduce investor interest in investing. Of the 10 manufacturing companies above that experienced a sharp decline in stock returns, namely GGRM. GGRM experienced a sharp decline in 2017 amounting to 31.14%, experiencing a decrease in 2018 of -0.21%. This condition made GGRM's economic performance look bad to investors and discouraged investors from investing. The company's strategy that is not working properly causes a decrease in the company's share price which results in a decline in the company's economic performance so that the company finds it difficult to compete with other companies.
Several previous studies related to this research include research by Sunitha Devi (2017) which examined the relationship between ERM and firm value. In this study, it is known that the ERM value has a significant positive effect on firm value. In this study using the dependent variable, namely economic performance. Where both company value and economic performance are the final value as a result of the business of a company. Research by Citra Lestari Putri and Maya Febrianty Lautania (2016) examined the relationship between inventory intensity and effective tax rate. Where in general, inventory intensity research is often associated with taxes. The tax value will affect the return obtained. So that in these 3 studies try to explore the relationship between inventory intensity to economic performance. Then, in general, investment in intangible assets is more intensively carried out in manufacturing companies than in service companies (Trisnajuna & Sisdyani, 2015). This is supported by the results of research by Hidayati, Prasetyo, & Mardijuwono (2012) which found that the effect of using intangible assets is more significant in manufacturing companies than non-manufacturing companies.

II. METHODS
scope of testing in this study includes testing the causal relationship or partially testing the effect of exogenous variables consisting of ERM Implementation, Inventory Intensity and Corporate image on Endogenous variables, namely Economic Performance. The population used in this study were manufacturing companies listed on the Indonesian stock exchange for the 2015-2018 period, with the selection of sample criteria using purposive sampling methods.
The object of research was carried out on 166 manufacturing companies listed on the Indonesia Stock Exchange. This study uses secondary data in the form of financial statements of manufacturing companies during the 2015-2018 period. Secondary data is obtained from the respective companies' websites and the Indonesia Stock Exchange website, namely IDX (www.idx.com). After selecting the data based on the specified criteria, there are 26 companies that are eligible to be used as data in this study. As for the explanations that are the reasons for the criteria and the number of companies that are not feasible are as follows, there are 8 manufacturing companies that suffered losses during 2015-2018. Next, there are 28 manufacturing companies that do not present their financial statements in rupiah (Rp). Then, there are 54 Manufacturing Companies that did not disclose their Enterprise Risk Management and Corporate Image in the Annual Report respectively from 2015-2018. And there are 38 companies that did not distribute dividends consecutively during the period from 2015-2018. So that the manufacturing companies that have met the criteria based on purposive sampling are 12 companies with 4 years of observation so that the number of observations in this study is 48 observations. The next step is to use the first data analysis test tool, namely descriptive analysis. Descriptive analysis in this study has a function in order to describe the correlation between exogenous variables and endogenous variables in this study by reading based on the acquisition value which consists of the average (mean), standard deviation, maximum, and minimum. This test must be carried out considering the importance of the test because so that we can find out whether the data used has been distributed normally or not, and whether the data to be used has multicollinearity and heteroscedasticity symptoms in its effect on the relationships between variables, later.
Furthermore, after the test is carried out, the test is used to determine the appropriate panel data regression model so that it can be used in analyzing panel data regression in this study. that is, by comparing the test results first, which will pass through 3 stages of testing, while the test stages in question include the Chow test, Hausman test and Lagrange Multiplier test. The three tests each have criteria limits in determining a feasible model. The following are the stages of the test: Chow test, is a test of data used to get a better model, between the common effect model or the fixed effect model. The criteria for this test result can be seen if the value of the company Cross-section F and Cross-section chi-square> α (0.05) then H0 is accepted, meaning that the model that can be used is the Common Effect Model (CEM) and if the value of the Cross-section Probability F and Cross-section chi-square <α (0.05), so the Fixed Effect Model (FEM) is more feasible to use. The Hausman test is a data test to get a better model, between the fixed effect model or the random effect model. The criteria for this test result can be seen. If the value of the F and Cross-section random probability> α (0.05) then H0 is accepted, meaning The model that can be used is the Random Effect Model (REM) compared to the Fixed Effect Model (FEM). Lagrange multiplier (LM) test, a data test to get a better model, between the common effect model or the random effects model. The criteria for this test result can be seen. If the value of the Breusch-pagan Cross-section Probability <α (0.05) then the Random Effect Model (REM) is more feasible to use than the Common Effect Model (CEM).
The classic assumption test is a statistical requirement that must be met in processing regression analysis data using the Ordinary Least Square (OLS) approach in its estimation techniques.In panel data regression models based on Ordinary Least Square (OLS) such as Common Effect Models (CEM) and Fixed Effect Model (FEM), it is necessary to test the classical assumptions. Conversely, if the regression equation is more suitable to use the Random Effect Model (REM), it is not necessary to test the classical assumptions, because the Random Effect Model (REM) uses the General Least Squared (GLS) approach in its estimation technique. However, not all tests are carried out in panel data regression, only multicollinearity and heteroscedasticity tests are needed: Multicollinearity test, used to determine whether there is a relationship between independent variables. To find out whether the data in this study experienced multicollinearity symptoms, this can be seen based on the results of the correlation coefficient value. If the correlation coefficient value is> 0.8, it can be concluded that the OLS regression model experiences multicollinearity symptoms. Heteroscedasticity test is used to determine whether or not there is a difference in variance from the residuals of the panel data regression model. If the value for Prob. Breusch-Pagan LM> 0.05, then H0 is accepted which means that there are no symptoms of heteroscedasticity. However, if the value is Prob. Breusch-Pagan LM <0.05 (5%), then Ha is accepted, which means there is a symptom of heteroscedasticity. The purpose of the measurement stage of the hypothesis analysis with Panel Data Regression in this study is to confirm the answer about whether the factors in this study can be used to provide information to the public through Economic Performance 2015-2018 on manufacturing companies listed on the Indonesia Stock Exchange (IDX) during the 2015-2018 period.
F test, this test functions to find out whether all exogenous variables that are included in the model can have an effect simultaneously and have an influence on endogenous variables (model fit or not). With the test criteria If the F-Statistic < F Table, then H0 is accepted, which means that the exogenous variable (X) simultaneously has no influence on the endogenous variable (Y). On the contrary, if the value of the F-Statistic > F Table, then Ha is accepted which means that the exogenous variable (X) together has an influence on the endogenous variable (Y).
Determination Coefficient Test (R2), this test is to find out about how strong the regression model used in this study is to reveal exogenous variables to endogenous variables. The R-squared value explains how strong the contribution of the influence exogenous variables has to the interaction of endogenous variables. If the higher the Rsquared result is, the stronger the interaction level of the effect caused by exogenous variables on endogenous. In other words, the stronger the exogenous variable value, the better it will explain the endogenous variable. The t test explains how the significance level of each exogenous variable is used as partial observations of endogenous variables. Based on the table above, it can be described that Economic Performance as the dependent variable (Y) has the lowest value 11.21000 and the highest value is 24.86000 mean ( mean) of 19,16771 with a standard deviation of 3.050582. The standard deviation of 3.050582 indicates that the Economic Performance of the sample companies studied has a difference relatively low. The Variable Enterprise Risk Management (X1)has a minimum value of 0.060000 and a maximum value of 0.310000, a mean value of 0.142292 and a standard deviation of 0.055668, this indicates that 14.22% is the average Enterprise Risk Management. The standard deviation of 0.50% indicates that the total Enterprise Risk Management of the sample companies studied has a relatively low difference. The Inventory Intensity (X2) variable has a minimum value of 0.060000 and a maximum value of 0.270000, a mean value of 0.136250 and a standard deviation of 0.063098. This shows that 13.62% on average Inventory Intensity. The standard deviation of 0.60% indicates that the Inventory Intensity of the sample companies studied has a relatively low difference. The Corporate Image variable (X3) has a minimum value of 0.270000 and a maximum value of 1.000000, a mean value of 0.737708 and a standard deviation of 0.217042. This shows that 73.8% of the average Corporate Image. The standard deviation of 21.7% indicates that the firm size of the sample companies studied has a relatively large difference. The Variable Intangible Asset(X4) has a minimum value of 973239.0 and a maximum value of 1.74E + 08, a mean value of 20455600 and a standard deviation of 33647545. This shows that the average Intangible Asset is 20.45% of the companies used as the research sample. . The standard deviation of 33.64% indicates that the liquidity in the sample companies studied has a relatively large difference.

III. RESULT AND DISCUSSION
Panel data regression estimates consist of 3 regression models, namely the Common Effect Model (CEM), Fixed Effect Model (FEM), and Random Effect Model (BRAKE). The panel data regression model was selected to determine which model is appropriate for use in research. To choose the right model it is necessary to carry out certain tests, namely the Chowtest, Hausman and testLagrange Multiplier test. In detail, these tests are described as follows:  Basuki and Prawoto (2016) there are formally three procedures for testing panel data estimates, namely the F statistical test which is used to choose between: 1) Model Common effects or fixed effects 2) The test Lagrange Multiplier (LM) is used to choose between the common effects model or themodel random effects 3) The Hausman test is used to choose between the fixed effects model or themodel. random effects With this concept, if the OLS data has been tested with the Chow test, the results will be tested again to determine the OLS or GLS data used.
Based on the results of the tests that have been carried out, it is known that in thetest, chow the FEM model is selected with a cross-section f value of 0.000 less than 0.05. Then, in thetest Hausman , the selected model is REM model estimation with avalue random cross section of 0.9045, greater than 0.05. And in the LM test the selected model is the REM model estimation with avalue random cross section of 0.000 less than 0.05. So the model used is the Random Effect Model (REM) model.

Enteprise Risk Management (ERM) and Economic Performance
This can be explained that the ERM Implementation variable in this study is measured by the dimensions of ERM disclosure according to COSO, at least the research results of manufacturing companies that reveal the ERM dimension according to COSO because the dimensions of ERM disclosure according to COSO are not something that becomes the company's obligations in managing the company's performance. The results of this study do not support the positive theory of accounting, namely between theory and reality is not appropriate. Management's predictions in choosing policies for the state of a company in the future are not in line with the reality. The results of this study mean that the higher the level of ERM disclosure of a company does not make a measure of management's success to increase the high rate of return on shares, and vice versa, a low level of ERM disclosure in a company does not guarantee a low stock return as well. From the analysis conducted by the author, it can be concluded that ERM implementation has no effect because of the lack of company awareness of the importance of managing and risk management. This means that in this case investors and shareholders consider the disclosure or application of ERM not something that needs to be observed and considered in making their decision to invest. Results of research conducted in accordance with Koeswara and Harjito (2016) which states that the Enterprise Risk Management no effect on the value of the Company. But it is not in accordance with the research conducted by Hoyt and Liebenberg (2008) which states that the implementation of Enterprise Risk Management has a positive effect on Firm Value.

Inventory Intensity and Economic Performance
This supports the theory of stewardship, namely the actions of managers in accordance with the wishes of the principal. Managers will do their best to achieve organizational goals. Every company wants profit and gets high profit, high profit can be obtained from the manager's actions in managing the company's inventory. the more the intensity of the inventory managed by the manager, it can increase company profits. Increased company profits can improve the company's economic performance. The results of this study indicate that inventory intensity affects the market in general and will affect the company's economic condition, meaning that if the intensity of the company increases, the company's stock returns are in good condition. Because the more intense the company is in managing its inventory, it will increase company profits and generate profits. This can provide high returns for investors. Then the purchasing power of the community and shareholders in company shares will also increase. And the analysis conducted by the author, it can be concluded that inventory intensity has an effect on economic performance because the higher the level of company stock returns, it will provide a positive value in the eyes of potential investors to invest. The better the company's assessment, the better the company's economic performance. The results of the study are in accordance with Fahrani, Nurlaela and Chomsatu (2017) which states that Inventory Intensity has a significant effect on corporate tax aggressiveness. But it is not in accordance with the research conducted by Andhari and Sukartha (2017) which states that Inventory Intensity has no effect on Tax Aggressiveness.

Corporate Image and Economic Performance
It can be explained that the corporate image variable in this study is measured by the awards the company receives. The results of the study show that many manufacturing companies have consistently awarded each year. The results of this study are in line with the stewardship theory which states that the actions of managers in improving relationships with other parties so that the company's reputation can survive and maintain the survival of the company. The results of this study mean that the more awards the company receives can be used as a benchmark for management's success to increase the high stock return rate, and vice versa, the few awards that the receives companydo not guarantee a low stock return rate.From the analysis conducted by the authors concluded that the Corporate Image has effect in accordance with the result of research conducted Ferryanto and Hatane (2015) which states that the Corporate Image positive effect on the Financial Performance. But it is not in accordance with research conducted by Glinkowska (2015) which states that Corporate Image has no effect on Financial Performance.

Intangible Asset and Economic Performance
The results of this study fail to prove the effect of Intangible Asset on Economic Performance. This is not in accordance with the Stewardship theory which states that the rights, privileges, and benefits of ownership or control. In improving company performance. Because these characteristics explain the high uncertainty of useful life and the absence of a physical form. so that it cannot be confirmed by the company in improving performance. The results of this study are in line with the results of Daulay's (2017) study which states that Intangible Assets have no effect on Economic Performance. While the results of this study contradict the results of Purwanti and Mu'ah's research. 2019, that Intangible Asset has an influence on Economic Performance.

IV. CONCLUSION
Based on the results of the analysis carried out in this study, the results show thatvariables Enterprise Risk Management and Intangible Assethave no effect on Economic Performance, and Inventory Intensity and Corporate Image have an influence on Economic Performance.