THE USE OF FINANCIAL RATIOS AS PREDICTOR OF PROFIT GROWTH ON CONSUMER GOODS MANUFACTURING COMPANY LISTED IN INDONESIA STOCK EXCHANGE THESIS By Ruth Merlin Silitonga 008200900072 Presented to The Faculty of Economics, President University In partial fulfillment of the requirements for Bachelor Degree in Economics, Major in Accounting President University Cikarang Baru – Bekasi Indonesia 2013
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THE USE OF FINANCIAL RATIOS AS PREDICTOR OF PROFIT GROWTH ON CONSUMER GOODS MANUFACTURING COMPANY LISTED IN
INDONESIA STOCK EXCHANGE
THESIS
By
Ruth Merlin Silitonga 008200900072
Presented to
The Faculty of Economics, President University
In partial fulfillment of the requirements
for
Bachelor Degree in Economics, Major in Accounting
President University
Cikarang Baru – Bekasi
Indonesia
2013
i
THE USE OF FINANCIAL RATIOS AS PREDICTOR OF PROFIT GROWTH ON CONSUMER GOODS MANUFACTURING COMPANY LISTED IN
INDONESIA STOCK EXCHANGE
THESIS
By
Ruth Merlin Silitonga 008200900072
Presented to
The Faculty of Economics, President University
In partial fulfillment of the requirements
for
Bachelor Degree in Economics, Major in Accounting
President University
Cikarang Baru – Bekasi
Indonesia
2013
ii
PANEL OF EXAMINERS
APPROVAL SHEET
Herewith, the Panel of Examiners declares that the thesis entitled “The Use of
Financial Ratios as Predictor of Profit Growth on Consumer Goods
Manufacturing Company Listed in Indonesia Stock Exchange” submitted by Ruth
Merlin Silitonga majoring in Accounting, Faculty of Economics was assessed and
proved to have passed the Oral Examination on April 26th, 2013.
This thesis entitled “The Use of Financial Ratios as Predictor of Profit Growth on
Consumer Goods Manufacturing Company Listed in Indonesia Stock Exchange”
prepared and submitted by Ruth Merlin Silitonga in partial fulfillment of the
requirements for Bachelor Degree in Economics - Major in Accounting, has been
reviewed and found to have satisfied the requirements for a thesis fit to be examined.
We therefore recommend this thesis for Oral Defense on April 26th, 2013.
Cikarang, Indonesia,April 1st, 2013
Acknowledge, Thesis Advisor,
Dr. Sumarno Zain, SE, Ak., MBA Dr. Sumarno Zain, SE, Ak, MBA Head, Accounting Study Program
iv
DECLARATION OF ORIGINALITY
I declare that this thesis entitled “The Use of Financial Ratios as Predictor of Profit
Growth on Consumer Goods Manufacturing Company Listed in Indonesia Stock
Exchange” has been originally written by myself based on my own research and has
never been used for any other purpose before. I therefore request the thesis for oral
defense.
Cikarang, Indonesia, April 1st, 2013
Researcher,
Ruth Merlin Silitonga 008200900072
v
THE USE OF FINANCIAL RATIOS AS PREDICTOR OF PROFIT GROWTH ON CONSUMER GOODS MANUFACTURING COMPANY LISTED IN
INDONESIA STOCK EXCHANGE
ABSTRACT
Business is an activity which produces goods and/or services for consumer. One of the strategies to achieve business goal is by improving company performance. The evaluation of a company’s performance gives a wide-ranging impact on decision-making by the parties concerned. The ratio analysis is a widely used tool to examine company performance. The ratios can be classified into categories such as liquidity ratios, activity ratios, leverage ratios, profitability ratios, and market ratios. However, until now there is no standard form used to predict the growth in corporate profit. Thus, the author wanted to examine further the use of financial ratios as predictor of company’s profit growth. The author collected and analyzed data of 24 manufacturing companies specifically in consumer goods sector based on JASICA (Jakarta Stock Industrial Classification)index. The period of research was based on financial period of company which was ended as of December 31th, it started from year 2008 to 2011. Purposive sampling method was filtered by using some criterion such as: (1) the company published audited financial statement as of December 31th during research period, (2) the company is a go public manufacturing company listed in BEI, (3) the company is classified as consumer goods company. Dependent variable used in the research is profit growth (PG). Independent variables used are current ratio (CUR), inventory turnover ratio (ITO), debt to equity ratio (DER), return on assets ratio (ROA), and price to book value ratio (PBV). The method used to analyze the hypotheses was multiple regression analysis.
The research result showed that the use of some financial ratios such as current ratio, inventory turnover ratio, debt to equity ratio, return on asset ratio and price to book value ratio explained limited indication toward profit growth. Further test showed simultaneously these ratios have no significant effect toward profit growth. Partially only current ratio and price to book value ratio have significant effect within certain condition.
To determine whether regression models really have shown a significant and
representative or BLUE (Best, Linear, Unbiased, Estimator), then the model must
satisfy the classical assumptions of regression by performing normality test,
autocorrelation test, multicollinearity test, and heterocedasticity test (Horngren, Datar,
Foster, Rajan, & Ittner, 2009).
1. Classical Assumption Test
a. Normality Test
Normality test aims to test whether the regression model, the residuals are
distributed normally around the regression line (Horngren et al, 2009). Good
regression models have good data distribution normal or near normal. To
detect normality can be done by looking at the graph of normal probability
plot (P-Plot) and the histogram of the results of the calculation of statistical
data. Basis for decision-making of normal probability plot analysis is when
the data is spread around the diagonal line and follow the direction of the
diagonal line of research data patterns, those data are considered to have a
normal distribution and regression models are considered to meet the
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assumptions of normality. Another way, data with bell shaped histogram is
considered have normal distribution (Neuman, 2011).
b. Autocorrelation Test
Good linear regression model is a regression that satisfies the assumption
that there is no autocorrelation. Autocorrelation is a correlation between the
variance errors of an observation to other observations. If the linear
regression equation has the autocorrelation, the regression estimator is still
unbiased, and consistent, it just not efficient anymore (Siagian & Sugiarto,
2006).
To detect the presence or absence of autocorrelation author can use the
method of Durbin Watson d-test. The following table can be used to
determine whether there is or there is not the presence of serial correlation in
the error.
Table III.2 Durbin Watson Rule
IF Decision ± D ≅ 0 There is positive autocorrelation between residuals D ≅ 2 There is no autocorrelation between residuals 2 < 𝐷 ≅ 4 There is negative correlation between residuals
Source:(Efferin, Darmadji, &Tan, 2008)
c. Multicollinearity Test
Good linear regression model can be viewed with no occurrence of
multicollinearity. It is a circumstances in which there is correlation between
the independent variables. To determine the presence of multicollinearity can
be done by looking at the value of the partial correlation score between the
independent variables. It showed by the value of condition index, the value
of tolerance and VIF values. Multicollinearity occurs if the condition index
exceeds 20, the value of tolerance is less than 0.1 and VIF values in excess
34
of 10. Tolerance value tells us how much variance in a variable that cannot
explained by other predictor variables. The range is from 0 to 1, where the
closer to 1, the more other predictor variables indicated cannot explain the
variance in variables calculated.
d. Heterocedasticity Test
Heterocedasticity test is used to see that the residual terms are unaffected by
the level of independent variables. (Horngren et al, 2009). Heterocedasticity
testing can be done by looking at scatter plot. A homocedastic data have no
pattern of distribution and the residual spread above and under 0 ordinat.
Another test can be used to see the heterocedasticity is using White test
methods. Basis for decision making is, if the value of Prob * Chi Square
over the significant value of the research, the data is considered
homocedastic or free from heterocedasticity.
2. Hypotheses Test
After the classical assumption test, then the hypothesis tested, start from
hypothesis 1 (H1) to hypothesis 5 (H5). Tests conducted with a significance test
consisting of F-test, R2, t test, and see beta coefficient to determine the significant
financial ratios and generate profit growth prediction models. The statistical calculation
called statistically significant if the value of the statistic test is in the critical areas (areas
where H0 is rejected). In the other hand, it is insignificant when the statistic test is in the
area where H0 is accepted.
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a. Determination Coefficient (R2).
The coefficient of determination (R2) is a measure of how far the model's
ability to explain the dependent variable. Small value of R2 is indicate that
the ability of independent variables in explaining the dependent variable is
limited. In contrast, the value of R2 close to 1 indicates the independent
variables provide almost all the information needed by the dependent
variable. (Siagian & Sugiarto, 2006). Furthermore, the value used is the
adjusted R2 because the independent variables used for the research is more
than two.
b. F-test
F test is used to see the contribution of CUR, ITO, DER, ROA, and PBV to
explain the value of profit growth of manufacturing company in BEI. The
steps taken are (Efferin, Darmadji, & Tan, 2008):
• Formulate hypotheses (H0&Ha)
H0: µ = 0 (there is no significant value of independent variable toward
dependent variable simultaneously)
Ha: µ ≠ 0 (there is significant value of independent variable toward
dependent variable simultaneously)
• Determining the level of significance or α (can be 5%, 10%, or 1%)
• Comparing F-count to F-table
F-table can be calculated in Ms. Excel using formula as follow:
𝐹 − 𝑡𝑎𝑏𝑙𝑒 = 𝐹𝑖𝑛𝑣(𝑝𝑟𝑜𝑏𝑎𝑏𝑖𝑙𝑖𝑡𝑦,𝑑𝑓1,𝑑𝑓2)
F-count can be found directly using SPSS, for manual calculation the
formula as follow:
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𝐹 − 𝑐𝑜𝑢𝑛𝑡 =𝑀𝑆𝑅𝑀𝑆𝐸
𝐹 − 𝑐𝑜𝑢𝑛𝑡 = (𝑆𝑆𝑅𝑘 )
( 𝑆𝑆𝐸𝑛 − 𝑘 − 1)
Where:
MSR = Mean Square of Regression
MSE = Mean Square of Error or Residual
SSR = Sum Square of Regression
SSE = Sum Square of Error/Residual
Probability = α = 5%, 10%, or 1%
df1= k = number of independent variables
df2 =n-k-1
The decision taken based on as follow:
o If (-)F-table<F–count<F-table then accept H0
(There is no significant value of independent variable toward
dependent variable simultaneously)
o If (-)F-table>F–count>F-table then accept Ha
(There is significant value of independent variable toward dependent
variable simultaneously)
• Using F-significance
If F-significance > significance level of research, accept H0
(There is no significant value of independent variable toward dependent
variable simultaneously) and vice versa.
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c. T-Test
T-test is used to test the significance of the effect of CUR, ITO, DER, ROA,
and PBV partially for profit growth in manufacturing companies on the
Indonesia stock exchange.
The test is done is as follows (Efferin, Darmadji, & Tan, 2008):
• Formulate hypotheses (H0& Ha)
H0 : µ = 0 (there is no significant value of independent variable toward
dependent variable partially)
Ha : µ ≠ 0 (there is significant value of independent variable toward
dependent variable partially)
• Determining the level of significance or α (can be 5%, 10%, or 1%)
• Comparing t-count to t-table
T-table can be calculated in Ms. Excel using formula as follow:
𝑇 − 𝑡𝑎𝑏𝑙𝑒 = 𝑇𝑖𝑛𝑣(𝑝𝑟𝑜𝑏𝑎𝑏𝑖𝑙𝑖𝑡𝑦,𝑑𝑓)
Where:
Probability = α = 5%, 10%, or 1%
df = n-k-1
n = numbers of sample
k = numbers of independent variable
T-count can be found directly using SPSS, for manual calculation the
formula as follow:
𝑇 − 𝑐𝑜𝑢𝑛𝑡 =𝐵 − 𝐵(0)𝑆(𝐵)
38
Where:
B = coefficient of independent variables (B1, B2, B3, B4, B5)
B(0) = population slope with the value of 0
S(B) = Standard Error of independent variables
The decision taken based on as follow:
o If (-)T–table < t-count <T-table then accept H0
(There is no significant value of independent variable toward
dependent variable partially)
o If (-)T-table> T-count>T-table then accept Ha
(There is significant value of independent variable toward dependent
variable partially)
• Using T-significance
If T-significance > significance level of research, accept H0
(There is no significant value of independent variable toward dependent
variable simultaneously) and vice versa.
39
CHAPTER IV
ANALYSIS AND EVALUATION
IV.1 Classical Assumption Test Result
Multiple regression analysis is an analysis which studies the relationship
between the independent variable and dependent variable. In this research the dependent
variable is profit growth, while the independent variable consists of five financial ratios
which is the current ratio, inventory turnover, debt to equity ratio, return on assets, and
price to book value. For purposes of the research, those variables are represented by
CUR, ITO, DER, ROA, PBV, and PG.
As mentioned in the previous chapter, the research was conducted in the
Indonesia stock exchange by taking a sample of industrial manufacturing companies in
consumer goods sector. The total sample is 24 companies with year research period
from 2008 to 2011. To test the validity of the regression analysis has been done it is
necessary to test the classical assumptions. Classical assumption test was used to test
whether the data used are normally distributed, whether there is a presence or absence
of autocorrelation or correlation among the residuals of independent variables, whether
there is multicollinearity, and whether there is heterocedasticity or similarity among
residuals of independent variables.
1. Normality Test
Based on P-Plot charts and histograms generated by SPSS, it can be said that the
research data meets the assumption of normality. The residuals are spread along the
diagonal line in normal p-plot graph and histogram of the data is bell shaped.
40
Figure IV.1 Normal P-Plot
Figure IV.2 Histogram
41
Table IV.1 Descriptive Statistic Result
Based on the table above, it can be explained as follows:
a. Current ratio (CUR) data spread in a range of maximum value of 11.74 and
minimum value of 0.66. It means there is a company that can maintain its
current asset 11.74 times their current liability. It is good because the
company has a lot of fund to cover up the liabilities, but it also has
weakness, which are the increasing risk of holding that asset and less optimal
usage of current asset. The company with highest value is cosmetics &
household manufacturer company named as PT Mandom Indonesia Tbk.
The lowest current ratio value of 0.66 showed there is a company which has
less amount of current asset compared to its current liabilities. It means,
there is a possibility the company has difficulty to pay back its liabilities.
The lowest value of current ratio owned by food & beverages manufacturer
company, named PT Multi Bintang Indonesia Tbk. Overall, manufacturing
company specifically in consumer goods sector have ability to maintain the
current ratio at average value of 3.25.
b. Inventory turnover statistic showed that the least optimal production in a
company is 0.2 times which means there is a company that only able to
produce and sell the product only 0.2 number of cycle in a year. A less
42
number of production cycle means less active the company both in terms of
produce and selling the goods. As opposed to minimum ITO value, the
highest ITO of 11.53 showed that the most optimal production cycle in
consumer goods company is 11.53 times which means the company can
produce and sell the goods as amount as 11.53 times or it only took about 32
days to produce and sell the goods. In this research the lowest and highest
ITO value owned by PT PT Langgeng Makmur Industry Tbk (Houseware
manufacturer) and PT Mayora Indah Tbk (Food & Beverages
manufacturer)Overall, consumer goods companies can maintain ITO at
average ratio of 4.34
c. Statistic data showed the highest value of DER is 22.9. This value which
owned by PT Schering-Plough Indonesia Tbk, described that the company as
pharmaceutical company finance the operation mostly by using liabilities as
much as 22.9 times of its equity which is considered as high risk financing
option because the company has large portion of fixed charges that should be
paid in a certain period. The lowest value of DER is 0.1 owned by PT
Mandom Indonesia Tbk. It means the company confidently uses equity as
the main source to finance its operation. Overall, consumer goods companies
maintain its DER at value of 1.486 which means the companies prefer to
finance the operation through almost equal portion between debt and equity.
d. The highest of ROA is 0.57 owned by PT Taisho Pharmaceutical Indonesia
Tbk. It showed the most optimal usage of asset in terms of generating profit
is at ratio of 0.57 which means by using Rp 1 assets the company generate
Rp 0.57 profit. The lowest value of ROA showed that PT Schering Plough
Indonesia Tbk as a pharmaceutical company has difficulty to generate profit
using its asset because the statistic showed that the company suffer loss at
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ratio of 0.09. Overall, consumer goods companies generate profit by using
its asset at ratio of 0.15.
e. The value of PBV at 35.45 showed that the highest possible market valuation
for consumer goods stock is at 35.45 times of its current price while the
lowest market valuation is (33.24). The negative value arise because of the
negative profit report, thus make the nominal price negative. This negative
profit report owned by Schering-Plough Indonesia Tbk. Overall, the
consumer goods companies has average market valuation of 2.45.
f. Profit growth (PG) which is the ratio between the difference in income over
the previous 2 years showed the highest value at 9.02 and lowest value at
(2.71). This means consumer goods company in this research generates
profit growth at the highest point of 9.02 and suffer loss within the years at
the lowest point of (2.71). Overall, consumer goods companies have average
growth of profit of 48% within the year.
2. Autocorrelation Test
One of the test is testing the autocorrelation test. If there is a correlation in the
regression equation, the regression prediction remains unbiased, and consistent, but not
efficient anymore. This research uses Durbin Watson test for autocorrelation test
assumptions. After that, Durbin Watson value obtained through SPSS software and then
compared with Durbin Watson table. Based on the values obtained, calculated DW is
2.047, it is known that the data used in this research do not have autocorrelation.
Table IV.2 Durbin Watson Result
44
3. Multicollinearity Test
To determine whether there is any correlation between the independent variables
can be seen by calculating the tolerance and VIF, where tolerance values smaller than
0.1 indicate a correlation between the independent variables, as well as the VIF value
greater than 10. The presence of multicollinearity in the data allegedly did not have a
significant indication on the forecasting. Besides forecasting value can also be seen by
calculating the value of R2. The higher the value of R2 the better the forecasting value
indication.
Table IV.3 Tolerance and VIF Result
4. Heterocedasticity Test
Another classic test of this assumption is heterocedasticity testing, which is
testing to see if the residual variance is spread evenly (homocedastic) or not. If there is
heterocedasticity, the estimation of regression coefficients become inefficient. The
results can be estimated become less than it should be, it can be exceeded what it should
or become misleading.
45
In this research heterocedasticity test using the software Eviews White test.
Heterocedasticity happens if there is a significant correlation between the independent
variables with the residual value.
Here is a table of test results with assessments of White Prob. Chi-Square.
Seeing the value of Prob*. Chi-Square which is more than the significance of the
research (5%), then data in this research can be said to be free from symptoms of
heterocedasticity.
Table IV.4 Heterocedasticity Result
IV.2 Hypotheses Test Result
46
Regression Results of 5 financial ratios to profit growth after testing among
other classical assumption normality test, autocorrelation, multicollinearity test, and test
of heterocedasticity, the research data can be declared free of the symptoms of
autocorrelation, multicollinearity, and heterocedasticity, and is normally distributed.
Once the data is eligible as BLUE (Best, Linear, Unbiased, Estimator) by testing
the classical assumptions, then hypothesis testing can be performed from H1 to H5.
Hypothesis testing is done with a test of significance consisting of F test, R2, and t test.
Table IV.5 Hypotheses Result
1. Determination Coefficient (R2)
Based on statistical calculations have been made, it is obtained R2 value of
0.0789. This value indicates that the calculations using the independent variables only
provide information about profit growth of 7.8%. In other words, the independent
variables simultaneously (simultaneous) predicts only 7.8% variance of each sample on
dependent variable.
47
From the calculation of the standard error of the estimate obtained a smaller
value than the value of standard deviation. This means that the standard error of the
estimate become a good predictor in determining the dependent variable.
2. F test
In multiple regression analysis, the main thing that was about to be seen is
whether a series of independent variables simultaneously affect the dependent variable.
In the statistical calculations have been done, then be obtained F value is calculated as
follows:
Table IV.6 F-test Result
Basis for decision-making are as follows:
o If (-)F-table < F–count < F-table then accept H0
(There is no significant value of independent variable toward dependent
variable simultaneously)
o If (-)F-table > F–count > F-table then accept Ha
(There is significant value of independent variable toward dependent
variable simultaneously)
F-count = 1.543, where the F-table = 2.315 so that it is known that
simultaneously independent variables does not affect the variable y. Moreover, it can
take a decision based upon ANOVA significance values, with the basic hypothesis:
48
• If F-significance > significance level of research, accept H0
(There is no significant value of independent variable toward dependent
variable simultaneously) and vice versa.
From the calculation, the significance value is 0.185 which is greater than the
significance of the research which is 0.05. It can be concluded that H0 is accepted which
means independent variables simultaneously does not affect the dependent variable.
3. T-test
Partial regression coefficients indicate whether the independent variables have
an effect partially or separately to the dependent variable. Level of influence was
calculated by t test.
Based on a statistical t-test value is obtained as follows:
Table IV.7 T-test Result
t-table = 1.9867
Decision was taken as follows:
• If (-)T–table < t-count <T-table then accept H0
(There is no significant value of independent variable toward dependent
variable partially)
49
• If (-)T-table > T-count >T-table then accept Ha
(There is significant value of independent variable toward dependent
variable partially)
T-count represented as follow:
• T-CUR > T-table, H0-1rejected, Ha-1accepted
• T-ITO < T-table, H0-2accepted, Ha-2rejected
• T-DER< T-table, H0-3accepted, Ha-3rejected
• T-ROA< T-table, H0-4accepted, Ha -4rejected
• T-PBV< T-table, H0-5accepted, Ha-5rejected
From the comparison of t-count and t table above it can be seen that the overall
t-count of independent variables is less than t-table so that four of the hypotheses 0 of
independent variables is accepted, which means partially independent variables not
affect the dependent variable except CUR in which Ha-1 accepted. The statistic also
show although t-count of PBV is less than t-table in significance of 5%, its Ha is
accepted when the significance of research is 10%.
IV.3 Result Interpretation
From the results of testing the hypotheses above can be briefly described as
follows:
Table IV.8 Summary of Hypotheses Testing Result
Independent Variables
Regression Coefficient
T-Count Sig.
Constant 0.813 2.011 0.047 CUR -0.146 -2.396 0.019 ITO 0.024 0.373 0.710 DER -0.001 -0.032 0.974 ROA 1.066 0.972 0.334 PBV -0.048 -1.799 0.075
50
R square = 0.0789 F – value = 1.543 Sig. = 0.185 DW = 2.047
Population regression model is as follow:
PG = 0.813 – 0.146 CUR + 0.024 ITO – 0.001 DER + 1.066 ROA – 0.048 PBV
R2 value of 0.0789 indicates that financial ratios CUR, ITO, DER, ROA, and
PBV affect profit growth of 7.8% in consumer goods manufacturing company. The
independent variables have effects that are not significant simultaneous views of the
significance of F which greater than 0.05.
However, when observed partially, financial ratios have different levels of
significance to the growth of profit. Current ratio (CUR) appeared to have a significant
effect on profit growth with a significance value of 0.019. CUR regression coefficient of
-0.146 indicates that if the value of CUR has increased by 1 unit, then the profit growth
declined by 14%. This is in contrast to the statement of Prasetyo Nurdin (2010) which
current ratio is partially has no significant effect on the company's profit growth in
manufacturing.
However, this research result supports previous research that has been done by
Eka Khairunnisa Zul (2009) and Ceky & Syamsudin (2009). One possible reason of
negative changes caused by current profit to growth ratio is a company's profit are used
to meet short-term obligations of the company by increasing the amount of a company's
current assets. Despite the decline in profit that may have an impact on a reduced
income of investors, but current ratio can give a guarantee or margin of safety against
the risk faced by the company, for example, when a sudden shock occurs then the
company is still able to meet its obligations to pay its debts.
51
Separately ITO has no significant effect on profit changes, further regression
coefficients showed little value for 0.024 This value indicates that the increase in the
value of ITO, increase profit by 2.4%. Profit growth shows that the optimization of
inventory, by which the company tried to increase the activity of enterprises ranging
from manufacturing to distribution to several times a year and / or increase sales of
inventory, they can increase profit growth, although not significantly. Test results on
ITO t-test supports the results of previous studies conducted by Suprihatmi SW (2006)
and opposite to the results of Angga Erwin Dianggoro (2011).
The company leverage ratio which is Debt to Equity Ratio, partially also has no
significant effect on profit growth. It is seen from the value of significance of 0.974 and
the regression coefficients for -0.001. The implication of the value of -0.001 is an
increasing in the proportion of debt to equity only make 0.1% lower profit. Possible
causes for the decline in profit is likely that the increased interest expense led to a
decreased profit. Increasing interest expenses incurred when companies increase the
debt as a source of corporate financing. These results corroborate the results of previous
research results except the result from Eka (2009) which states that the DER has
significant effect on profit growth.
From the test t-test founded a value of significance of ROA for 0.334 and
regression coefficient amounted 1.066. This suggests that the effect of ROA on profit
growth is not significant. This contrasts with the results of Eka (2009) and in
accordance with the results of Angga (2011). However, aside from the significance
value, ROA is the most dominant ratio on the growth of profit, because ROA increases
by 1 unit causes an increase in profit of 106%. The reason for this is due to optimal use
of assets so that the number of assets owned by the company is able to create high sales,
it also possible that company can minimize expense so that profit generated becomes
greater.
52
PBV as the ratio between the market value and the actual value of the shares has
a significant effect on profit growth, with a record of significance of research value is
0.1. However, the interpretation of the regression coefficient value of -0.048 indicates
that the increase in PBV only affect profit increased by 4%. Price to Book Value can be
considered as an assessment of the market value of the intangible assets of the company.
High PBV values enabled the company to raise funds by selling less amounts of stock
in the amount (Wild, 2004). However at certain level value of the shares can become
stagnant and experiencing the opposite of the previous value which is become cheaper
so there will be a possible loss transaction. It is thought to explain the decline in profit
due to an increase in PBV. In addition, Bigham & Houston (2009) stated that profit
decline that occurred allegedly associated with the signaling theory in which a firm with
favorable prospects would want to finance with stock, which would mean bringing in
new investors to share the losses.
Overall, the financial ratios that significantly influence profit growth only
current ratio (CUR) with a negative regression coefficient value of 14.6%. Another
ratios affect profit growth is PBV with regression coefficient of 4.8%, with
consideration of the effect is significant at the research confidence interval of 10%.
Based on above explanation, the model of profit growth prediction can be formulated as
follow:
PG = 0.813 – 0.146 CUR + 0.024 ITO – 0.001 DER + 1.066 ROA – 0.048 PBV
53
CHAPTER V
CONCLUSION AND RECOMMENDATION
V.1 Conclusion
The purpose of research is to identify whether there is significant effect of
financial ratios such as current ratio (CUR), inventory turnover (ITO), debt to equity
ratio (DER), return on asset ratio (ROA), and price to book value ratio (PBV) to profit
growth simultaneously and partially. This research selected 24 from 41 manufacturing
companies in consumer goods sector. Sample of data used are financial ratio that are
published annually in the financial statements within the period of 2008 to 2011. In
order to prove the hypothesis regarding the purpose of research, the author used
multiple regression analysis. Multiple regression analysis used to measure the effect of
independent variable to dependent variable.
1. Answer for Statement of Problem
Based on the research, the answer for statement of problem could be concluded
as:
a. The determination of R2 showed that the independent variables (CUR, ITO,
DER, ROA, and PBV) can explain 7.8% of the variation in profit growth. It
means the independent variable only predict 7.8% of profit growth, the rest,
about 92.2% of profit growth affect by other factors.
b. Simultaneously, there is no significant effect of independent variables to
dependent variables. The f-test showed that together independent variables
have 0.185 significance, while the significance of the research is 0.05 which
means the result is in the area outside the significance of research. Therefore,
54
the null hypothesis (H0) is accepted which show there is no significant effect
of independent variable to dependent variable.
c. Based on t-test, there only two ratios which have significant effect to profit
growth partially. The ratio are current ratio (CUR) and price to book value
(PBV). CUR has significance value of 0.019 which is less than significance
value of research of 0.05. Therefore, alternative hypothesis is accepted
which show there is significant effect of CUR to PG. The regression
coefficient tells that if CUR is increased about 1 then PG will decreased for
14.6%.
PBV also has significance effect on profit growth partially in the
significance of research of 0.1. It has significance value of 0.075, then Ha is
accepted on significance of research of 10%. For significance of research of
5% then H0 is accepted which shows that there is no significant effect of
PBV to profit growth. Regression coefficient of PBV tells that for every
increased on PBV the PG will decreased as 4.8%. The other ratios, which are
inventory turnover (ITO), debt to equity (DER), and return on assets (ROA)
have significance of value more than 0.05 then H0 is accepted which is there
is no significant effect of ITO, DER, and ROA to PG.
d. Based on the t-test and regression coefficient of independent variables
towards dependent variables, so the model of predictor can be formulated as
follow:
PG = 0.813 – 0.146 CUR + 0.024 ITO – 0.001 DER + 1.066 ROA – 0.048 PBV
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V.2 Recommendation
Based on research some financial ratios did not have significant implications in
assessing the financial performance as measured by profit growth. However, when put
aside the significance of the research, the general financial ratios can still be used to
view the company's financial trends in this manufacturing company in the consumer
goods sector. It can be seen that the increase in current ratio turned out to reduce the
growth in corporate profit. The management can use this information as the basis for
optimizing the use of current assets in order to meet short-term obligations, but also still
produce a profit to the company.
Optimized inventory allows the company to produce the inventory in a shorter
period of time and high inventory cycle can be used to enhance profit growth. While it
can be seen that both the use of debt and equity to finance the company has less effect
on profit growth. Companies can use a debt in consideration of the trade-off theory
where companies get tax benefits in return on financial risk for the use of debt.
Companies can also use the equity as a source of corporate financing by risk advert
considerations. However, based on observations, 61 of 96 samples had a DER less than
1, which means the general manufacturing company in the consumer goods sector
choose equity as a source of corporate financing.
Although not have a significant effect on profit growth, ROA as a profitability
ratio indicates that a unit increase in ROA increase profit by 106%. This means setting
the expenses indirectly affect profit growth. This is because the least amount of expense
to the company, the greater the value of income earned before tax, which means the
company gets more profit. In addition it also illustrates the value of ROA effectiveness
in managing the company's assets so that profit earned greater than the expense used for
the utilization of assets.
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PBV has a significant effect on profit growth at 10% significance of the research
indicate that the use of equity as a financing company has its own weaknesses that
increase in the market valuation of the company's share price turned out to be inversely
impact to the growth in profit. Companies can use this information to maintain
proportion of stock and policies relating to company stock transactions. Profit decline
that occurred allegedly associated with the signaling theory in which a firm with
favorable prospects would want to finance with stock, which would mean bringing in
new investors to share the losses. This will make the prospective investor to make an
offer and buy shares at a price lower than that assessed by the market share.
There is a possibility of loss of stock transactions that impact on negative profit
growth. As known, the market price cannot be changed by the company as a result of
economic processes and the company does not have the authority to do that. However,
the company can exercise control over the decline in profit by maintaining other aspects
of the company such as sales levels, expense efficiencies, and others as an indicator that
the high PBV accompanied by a good performance from various aspects so that
prospective investors do not get the wrong picture with respect to high valued of PBV
and company still have the possibility to achieve gains from stock transactions.
The statistical research is one way to see the movement or trend of a
measurement. In other words, something judged by the pattern seen. Statistics can be
used to predict anything. But keep in mind, regardless of the significance of the
prediction model, there will always be a possibility that at some point the results of
calculations using the model predictions do not correspond with the reality of the
matter. Therefore, users of financial statements, in addition to referring to the scientific
testing, need to improve in-depth analysis skill of financial statements.
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Results from this research are expected to provide insight to the users of
financial ratios in measuring the performance of the company especially in the
manufacturing company sector consumer goods. Due to the limited research on
manufacturing companies, it is advisable for the next researcher to conduct the same but
with a different focus research which use a sample of companies operating in the other
sector of business other than consumer goods, where the results obtained can be used as
a comparison tool in measuring company performance.
58
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