View of The Influence Of Financial Ratio On Stock Prices In Manufacturing Companies Listed On The Indonesia Stock Exchange

Vol. 10, No. 1, June 2020, pp. 37-45

ISSN: 2720-9830, DOI:  37

The Influence Of Financial Ratio On Stock Prices In Manufacturing Companies Listed On The Indonesia Stock Exchange

Dahlia Gultom Universitas Darma Agung

Article Info ABSTRACT

Article history:

Received April 2020 Revised May 2020 Accepted June 2020

The object studied in this study is a manufacturing company in the cosmetic and household sub-sectors listed on the Indonesia Stock Exchange for the period 2018-2020. The purpose of this study was to determine the effect of ROA, ROE, EPS on stock prices either partially or simultaneously. The sample in the study were 7 companies. The sampling technique is purposive sampling. The regression equation obtained in this study is Y = 6.036 – 2.397X_1 + 2.375X_2 – 0.074X_3. The results show that ROA partially has no effect on stock prices where the significant value is 0.220 > 0.05, ROE partially has no effect on stock prices where the significant value is 0.169 >

0.05, and EPS partially has no effect on stock prices where the significant value is 0.395. > 0.05. Simultaneously, the ROA, ROE, EPS variables have no significant effect on the stock price 0.165 > 0.05, and the Adjusted R Square value is 0.089. This means that 8.9% of the dependent variable or stock price is influenced by the independent variable, while the remaining 91.1 % is influenced by other variables not examined in this study.

Keywords:

EPS ROA ROE Stock Price

This is an open access article under the CC BY-SA license.

Corresponding Author:

Dahlia Gultom

Universitas Darma Agung

Email: DahliaGultom@gmail.com

INTRODUCTION

The capital market is a means of investing, which allows investors to diversify, form a portfolio according to the risk they are willing to bear and the level of profit they get. The higher the stock price means the welfare of the shareholders is increasing.

If an investor invests in a number of stock portfolios, before that the investor must ensure that the investment made is the right one. This means that investors must assess various alternatives that will bring positive returns in the future. One of the investment appraisers is fundamental analysis. This means that investors can predict the future of their chosen portfolio based on the company's performance which is described from the company's secondary data, namely in the form of balance sheet data, profit and loss, changes in capital, cash flow and other supporting reports. Financial statements are the most important information for investors in making investment decisions.

Financial statements are basically the result of an accounting process that can be used as a communication tool between financial data or assets of a company and parties with an interest in the data or assets of the company.

The profitability ratios consist of: Return On Assets (ROA), Return On Equity (ROE), Earning Per Share (EPS), Net Profit Margin (NPM), Basic Earning Power (BEP), Gross Profit Margin (GPM). However, in this paper only focused on discussing three profitability ratios, namely, Return On Assets (ROA), Return On Equity (ROE), Earning Per Share (EPS).

Based on the description above, the authors are interested in taking the title of the study "The Effect of Financial Ratios on Stock Prices in Manufacturing Companies listed on the Indonesia Stock Exchange".

Jurnal Ilmiah Socio Secretum, Vol. 10, No. 1, Month 2020: 37-45 Scope of problem

Limitations of the problem in this study are:

The manufacturing companies referred to in this study are manufacturing companies in the cosmetic and household sub-sectors listed on the Indonesia Stock Exchange for the period 2018-2020.

Using the Financial Statements of Manufacturing Companies in the Cosmetics and Household Supplies sub- sector listed on the Indonesia Stock Exchange for the period 2018-2020.

The measuring instrument used in this ratio research is using ROA (Return On Assets), ROE (Return On Equity), EPS (Earning Per Share).

Formulation of the problem

The formulation of the problem in this study are:

Does Return On Assets (ROA) have a partial effect on stock prices in manufacturing companies in the cosmetics and household sub-sectors listed on the Indonesia Stock Exchange. household listed on the Indonesia Stock Exchange ?

Does Earning Per Share (EPS) have a partial effect on stock prices in manufacturing companies in the cosmetics and household sub-sectors listed on the Indonesia Stock Exchange?

Do ROA, ROE, and EPS have a simultaneous effect on stock prices in manufacturing companies in the cosmetic and household sub-sectors listed on the Indonesia Stock Exchange?

Research purposes

In accordance with the background and the formulation of the problem, the objectives of this study are as follows:

To find out whether Return On Assets have an effect on stock prices in manufacturing companies in the cosmetics and household sub-sectors listed on the Indonesia Stock Exchange. To find out whether Return On Equity has an effect on stock prices in manufacturing companies in the cosmetics and household sub-sectors listed on the Indonesia Stock Exchange. To find out whether Earning Per Share has an effect on stock prices in manufacturing companies in the cosmetic and household sub-sectors listed on the Indonesia Stock Exchange.

To find out whether Return On Assets, Return On Equity, Earning Per Share have a simultaneous effect on stock prices in manufacturing companies in the cosmetic and household sub-sectors listed on the Indonesia Stock Exchange for the 2018-2020 period.

METHOD

Location and time of research

This research was conducted by taking data from the Indonesia Stock Exchange (IDX) website, the Indonesia Stock Exchange website: www.idx.co.id in the form of the company's financial statements to be studied. This research was conducted starting in March and is expected to be completed in August 2021.

Population and Research Sample

In this study, the population is cosmetic and household goods manufacturing sub-sector companies listed on the IDX during the 2018-2020 period, totaling 7 companies. The sampling technique used is purposive sampling. Purposive sampling is a sampling technique based on certain considerations and criteria.

The sample selection criteria used are as follows: Cosmetics and household goods manufacturing sub-sector companies listed on the IDX in 2018-2020. A manufacturing company in the cosmetics and household goods sub-sector listed on the IDX that publishes complete financial reports consecutively that have been audited in 2018-2020. Cosmetics and household goods manufacturing sub-sector companies that list stock prices during the period of observation on the Indonesia Stock Exchange.

In this study, there are 7 manufacturing companies that are used as the population.

Based on the population above, the 7 companies meet the criteria to be used as samples in this study.

Operational Definition and Measurement of Research Variables

in this study consists of the dependent variable and the independent variable as follows: Dependent variable is a variable that is influenced or becomes a result of the existence of an independent variable, namely stock prices (Y). The independent variable is the variable that is affected or the cause of the change or the emergence of the dependent variable. namely ROA, ROE, and EPS.

Types and Sources of Research Data

The type of data used in this study is quantitative data in the form of annual data with a research period starting from 2018-2020. The data source used in this study is in the form of annual financial report data from the Cosmetics and Household Goods Sub-Sector Company for the period 2018-2020 which was obtained from the IDX website, namely www.idx.co.id.

Method of collecting data

The data collection technique used by the researcher is documentation, namely by collecting secondary data in the form of notes, financial reports, photos, and other information related to this research.

Data analysis method

Multiple linear regression equation in this study, as follows:

Where : Y = a + 𝑏₁𝑋₁ + 𝑏₂𝑋₂ + 𝑏₃𝑋₃ + e Y = Share Price

a = Constant

b1,b2 ,b3 = Regression Coefficient X1 = Return On Assets /ROA X2 = Return On Equity /ROE X3 = Earning Per Share/EPS E = Error Coefficient Classic Assumption Test

The data analysis method used in this study is a statistical analysis method using SPSS version 22. The researcher tested the classical assumptions before testing the hypothesis. The classical assumption test consists of normality test, multicollinearity test, autocorrelation test, and heteroscedasticity test.

Hypothesis test

Partial Significance Test (t-test)

Partial test is used to determine how far the influence of the independent variable partially in explaining the variation of the dependent variable. This partial test is carried out in two ways. The first way is to compare t count with t table, the second way is based on the significance value. If the significance value <0.05, the independent variable partially has a significant effect on the dependent variable. On the other hand, if the significance value is > 0.05, the independent variable partially does not have a significant effect on the dependent variable.

Simultaneous Significant Test (F-test)

The F-test was conducted to show whether all the independent variables included in the multiple regression model have a joint effect on the dependent variable. The first way is to compare calculated F with F The second way is

based on the significance value. If the significance value is < 0.05 then the independent variables are simultaneously

effect on the dependent variable. On the other hand, if the significance value is > 0.05, the independent variable simultaneously has no effect on the dependent variable.

Coefficient of Determination (R²)

The coefficient of determination is a quantity that indicates the magnitude of the variation in the dependent variable that can be explained by the independent variable.

The coefficient of determination is expressed in percentage (%) with the following formula:

KD = R² x 100%

Where :

KD = Coefficient of Determination R² = Square of Correlation Coefficient 100% = Contribution Percentage

RESULTS AND DISCUSSION

Descriptive Statistics of Research Variables

This descriptive analysis is used to explain and calculate financial ratios related to Return On Assets (ROA), Return On Equity (ROE), Earning Per Share (EPS) on Stock Prices in listed cosmetic and household goods manufacturing companies. on the Indonesia Stock Exchange. Descriptive analysis is used to determine the mean (mean), maximum and minimum values, and standard deviation.

Table 1 Descriptive Statistics of Research Variables

Jurnal Ilmiah Socio Secretum, Vol. 10, No. 1, Month 2020: 37-45 Based on table 4.1 above, it can be explained that:

The Return On Asset (ROA) variable has a minimum value of 0.18, a maximum value of 46.04, a mean value of 12.1571 with a standard deviation of 13.68052

The Return On Equity (ROE) variable has a minimum value of 0.18, a maximum value of 142.92, a mean value of 30.7433 with a standard deviation of 45.78489

The Earning Per Share (EPS) variable has a minimum value of 0.01, a maximum value of 97765.66, a mean value of 14734.8062 with a standard deviation of 25774,71899

The Stock Price variable has a minimum value of 65, a maximum value of 45400, a mean value of 6826.38 with a standard deviation of 13024,188.

Classic Assumption Test Results Normality test

Probability Plot (P-Plot)

Fig 1 : Normal ProbabilityPlot

Based on the normal probability plot graph, it shows that the regression model is not suitable for use in this study because the normal plot graph shows that the points do not spread around the diagonal line and the distribution does not follow the direction of the diagonal line and the data held is uneven and not good enough. the data is not normally distributed.

Kolmogorov-Smirnov . test (K-S)

The K-S test is carried out with the following conditions:

If the significant value > 0.05 means that the data is normally distributed If the significant value < 0.05 means that the data is not normally distributed

Table 2 Kolmogorov Smirnov . Test

Based on table 2 above, it can be seen that the amount of Kolmogorov-Smirnov on the ROA, ROE, EPS variables with a significant value of 0.000 has a significant value below 0.05, which means that the ROA, ROE, EPS variables are not normally distributed. For data variables that are not normally distributed, the residuals in manufacturing companies in the cosmetics and household sub-sectors may be caused by fluctuations in data listed on the BEI that do not meet the listing criteria.

From the abnormal data, the data transformation is carried out with Ln so that it becomes normal. For the Normal P-P Plot of Residual Regression graph. The following is the transformed data:

Data After Transformed

Source: SPSS output results(2021) Figure 2: Normal Probability Plot (P-P Plot)

Based on the normal probability plot graph, it shows that the regression model is feasible to use in this study because the normal plot graph shows that the points spread around the diagonal line and the distribution follows the direction of the diagonal line. Then the regression model meets the assumption of normality, which means that the data is normally distributed.

For the overall data, the results of the normality of the residual data based on the data transformation in detail are shown in the following table:

Table 3. Normality Test Results After Data Transformation

Based on the table above, it can be seen that the Kolmogorov-Smirnov value is 0.133> 0.05 and based on the significant value obtained 0.200> 0.05, thus the data is normally distributed.

Multicollinearity Test Before Test

Table 4 Multicollinearity Results After Testing

Based on table 4 above shows the results of multicollinearity testing. The test results show that there is no independent variable that has a tolerance value > 0.10 and a VIF value < 10. This is indicated by the tolerance value for ROA of 0.133 and VIF of 7.495, the tolerance value for ROE of 0.129 and VIF of 7.731, and the tolerance value for EPS is 0.899 and VIF is 1.113. Thus it can be concluded that from the above test there is no symptom of multicollinearity between independent variables in the regression method and has passed the multicollinearity test.

Autocorrelation Test Before Test

Jurnal Ilmiah Socio Secretum, Vol. 10, No. 1, Month 2020: 37-45

Table 5 Autocorrelation Results After Testing

Table 6 Autocorrelation Test Results After Detected Runs Test

Unstandardiz ed Residual

Test Value^a -4255.60031

Cases < Test

Value 10

Cases >= Test

Value 11

Total Cases 21

Number of

Runs 8

Z -1.336

Asymp. Sig. (2-

tailed) .182

a. Median

Source: SPSS output results (2021)

Known asymp value. sig. (2-tailed) of 0.182 > 0.05, it can be concluded that there is no autocorrelation symptom, so that the classical assumption test can be continued.

Heteroscedasticity Test

Source: SPSS output results (2021) Figure 3: Heteroscedasticity Results After Testing

With the scatterplot graph above, it can be seen that the points spread randomly and are spread both above and below the number 0 on the Y axis and there is no clear pattern in the spread of the data. These results can be concluded that there is no heteroscedasticity in this regression model, so that the regression model is feasible to use to predict stock prices based on the variables that influence it, namely ROA, ROE, and EPS.

Multiple Linear Regression Analysis

Table 7 Results of Multiple Linear Regression Analysis After Testing

Based on the table above, the following multiple linear equations are obtained:

Y = 6.036 – 2.397X1 + 2.375X2 – 0.074X3

Information :

The constant value is 6.036, which means that if the ROA, ROE, and EPS are 0 or there is no stock price of 6.036.

The Return On Asset variable has a regression coefficient of -2,397, which means that ROA has a negative effect on stock prices. This illustrates that if every one unit increase in the ROA variable, assuming other variables remain, it will be followed by a decrease in stock prices of

-2,397.

The Return On Equiy variable has a regression coefficient of 2.375, which means that ROE has a positive effect on stock prices. This illustrates that if every one unit increase in the ROE variable, assuming other variables remain, it will increase the stock price by 2.375.

The Earning Per Share variable has a regression coefficient of -0.74, which means that EPS has a negative effect on stock prices. This illustrates that if every one unit increase in the EPS variable, assuming other variables remain, it will be followed by a decrease in stock prices of

-0.74.

Hypothesis Testing Results

Individual Parameter Significant Test (t Test)

Table 8 Significant Test Results Individual Parameters (t Test) After Testing

The number of observations (respondents) used in this study were 21 samples with 3 independent variables and hypothesis testing with = 5% obtained the value of t_table = 2,079, where the value of t_table was obtained from the table of T distribution values by looking at and equating the number of research samples with a significance value of 5% = 0.05 according to the number of respondents used in this study. Therefore, based on the results of statistical tests in the table above, it can be explained as follows:

The ROA test results obtained t_count of -1.274 < t_table 2.079 which means it can be concluded that ROA has a negative effect on stock prices and based on a significant value obtained by 0.220> 0.05 (greater than 0.05) meaning ROA has no significant effect on stock prices.

The ROE test results obtained t_count of 1.436 < t_table 2.079 which means it can be concluded that ROE has a positive effect on stock prices and based on a significant value obtained at 0.169 > 0.05 (greater than 0.05) meaning ROE has no significant effect on stock prices.

EPS test results obtained t_count is -0.872 < t_table 2.079 which means it can be concluded that EPS has a negative effect on stock prices and based on a significant value obtained by 0.395> 0.05 (greater than 0.05) which means EPS has no significant effect on stock prices .

Simultaneous Parameter Significant Test (F Test) Before Testing

Table 9 Simultaneous Parameter Significant Test After Testing

Based on the table above, it is obtained that F_count 1.920 < F_table 3.16, the value of F_table 3.16 is obtained from the percentage point distribution table for F for probability = 0.05. This means that where F_count is smaller than F_table and the sig value is 0.165 > 0.05, it shows that the ROA, ROE, and EPS variables together do not have a simultaneous effect on stock prices.

Coefficient of Determination (R〗^2)

The coefficient of determination test is used to determine the percentage of the influence of the dependent variable, namely by squaring the coefficients found. Here are the test results in the following table:

Table 10 Coefficient of Determination After Testing (Adjusted R)

Jurnal Ilmiah Socio Secretum, Vol. 10, No. 1, Month 2020: 37-45

From the table above, it can be explained that the Adjusted R Square value is 0.089. This means that 8.9% of the dependent variable or stock prices are influenced by the independent variables, namely Return On Assets, Return On Equity, and Earning Per Share, while the remaining 91.1% is influenced by other variables not examined in this study. .

Interpretation of Research Results

Table 10 The Effect of Return On Assets (ROA) on Stock Prices

Based on the statistical test results in the t_count table, it can be seen that the Stock Price variable as measured by ROA shows the t_count value of -1.274 with a significant value of 0.220 > 0.05, meaning that it can be said that stock prices have no partial effect on ROA. The results of this study are the same as the research conducted by Martina Rut Utami and Arif Darmawan (2017) where the results show that partial ROA testing on stock prices has no effect and this study contradicts the results of research conducted by Suryani Ekawati (2018) where the results show that ROA has a significant and negative effect on stock prices because if the value of the ROA ratio increases, the stock price will also increase.

4.6.2 Effect of Return On Equity (ROE) on Stock Prices

Based on the statistical test results in the t_count table, it can be seen that the Stock Price variable as measured by ROE shows the t_count value of 1.436 with a significant value of 0.169 > 0.05, meaning that it can be said that stock prices have no significant effect on ROE. The results of this study are the same as those conducted by Martina Rut Utami and

Arif Darmawan (2017) where the results show that the partial ROE test on stock prices has no effect and this study contradicts the results of research conducted by Suryani Ekawati (2018) where the results show that ROE has a significant and positive effect on stock prices, because the higher the ratio , meaning that the more efficient the use of own capital by the company's management and the share price will increase.

The Effect of Earning Per Share (EPS) on Stock Prices

Based on the statistical test results in the t_count table, it can be seen that the Stock Price variable as measured by ROA shows the t_count value of 0.872 with a significant value of 0.395 > 0.05, which means that it can be said that stock prices have no significant effect on EPS. The results of this study are the same as those conducted by Pande Widya Rahmadewi, Nyoman Abundanti (2017) where the results show that the partial test of the EPS variable shows no effect on stock prices and this study contradicts the results of research conducted by Martina Rut Utami and Arif Darmawan ( 2017) where the results show that the partial test of EPS on stock prices has a positive effect, because if the profit generated by the company increases, EPS will increase, so earnings per share increase and this also increases stock prices.

Effect of Return On Assets, Return On Equity, Earning Per Share on Stock Prices

The results of statistical testing in the t_count table can be seen that the stock price as measured by Return On Assets, Return On Equity, Earning Per Share shows the t_count value of 5.230 with a significant value of 0.165 where the significant level is greater than the significant rate = 0.05. This shows that "there is no significant simultaneous (simultaneous) effect between ROA, ROE, EPS on Stock Price".

CONCLUSION

Based on the results of the analysis and discussion that has been carried out on financial ratios in manufacturing companies in the cosmetics and household sub-sectors listed on the IDX for the period 2018-2020, there are several conclusions including the following:

Based on the results of multiple linear regression analysis that has been carried out in this study, the regression equation is obtained as follows:

Y = 6.036 – 2.397X1 + 2.375X2 – 0.074X3

The Return On Asset (ROA) variable partially has no effect on stock prices in cosmetic and household goods companies listed on the IDX for the 2018-2020 period, which has a t_count value of -1.274 with a significant value of 0.220 > 0.05.

The Return On Equity (ROE) variable partially has no effect on stock prices in cosmetic and household companies listed on the IDX for the 2018-2020 period, which has a t_count value of 1.436 with a significant value of 0.169 > 0.05.

The Earning Per Share (EPS) variable partially has no effect on stock prices in cosmetic and household companies listed on the IDX for the 2018-2020 period, which has a t_count value of 0.872 with a significant value of 0.395 > 0.05.

Simultaneously, the variables ROA, ROE, EPS have no significant effect on stock prices in cosmetics and household goods companies listed on the Indonesia Stock Exchange for the period 2018-2020, which have a t_count value of 5.230 with a significant value of 0.165 > 0.05.

The variables ROA, ROE, and EPS have a low significant relationship to stock prices. This is shown from the results of testing the coefficient of determination obtained the Adjusted R Square value of 0.089. This means that 8.9% of the dependent variable or stock prices are influenced by the independent variables, namely Return On Assets, Return On Equity, and Earning Per Share, while the remaining 91.1% is influenced by other variables not examined in this study. .

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