Vol. 5, No. 12, December 2024
E-ISSN: 2723 - 6692
P-ISSN: 2723 - 6595
http://jiss.publikasiindonesia.id/
Journal of Indonesian Social Sciences, Vol. 5, No. 12, December 2024 3270
KEYWORDS
ABSTRACT
Stock Return; DER, EPS, NPM,
PBV; Inflation, Exchange Rate,
SBI, and GDP
This research seeks to examine and offer insights into the factors
that impact stock returns of real estate and property companies
listed on the Indonesia Stock Exchange. The examined factors
include DER, EPS, NPM, PBV, inflation, exchange rate, SBI, and GDP.
The research focuses on real estate and property companies listed
on the Indonesia Stock Exchange during the 2010-2014 period,
involving a total of 38 companies. The study analyzes the impact of
these variables DER, EPS, NPM, PBV, inflation, exchange rate, SBI,
and GDP on the stock returns of these companies. Data were
collected from sources such as Bank Indonesia, the Central Bureau
of Statistics, and the Indonesia Stock Exchange. The findings reveal
that DER, EPS, NPM, and PBV significantly influence stock returns,
whereas inflation, exchange rate, SBI, and GDP do not have a
significant impact on the stock returns of real estate and property
companies.
Attribution-ShareAlike 4.0 International (CC BY-SA 4.0)
Introduction
The capital market plays a vital role in a country's economy, serving as a key indicator of
economic growth. Economic progress is often reflected in increased trading volume within the capital
market, making the state of the capital market a mirror of the nation's economic health (Benny, 2009).
One of its primary functions is to facilitate the mobilization of funds from the public, channeling them
into various investment sectors. For individuals with surplus funds, the capital market offers an
alternative avenue for investment, providing opportunities to grow their wealth while supporting
economic development (Aditya & Wirawati, 2013).
The primary motivation for investing is to generate profit. In the context of investment
management, this profit is commonly referred to as a return (Sawir, 2009). It is entirely natural for
investors to expect a specific level of return on the funds they have allocated to investments (Doughlas
et al., 2012).
The real estate and property sector is one of the key sectors in Indonesia that draws the
attention of investors, as it plays a crucial role in the country's economy. This sector is seen as an
important indicator of a nation's economic health.
According to processed data from the IDX, it is evident that the return on the real estate and
property sector has experienced growth from 2010 to 2014, as follows:
The Influence Factors on Stock Returns of Real Estate and
Property Companies Listed in Bei in 2010 - 2014
Putri Mayang
Politeknik Negeri Jakarta, Indonesia
Email: mayangputri8[email protected]
Correspondence: mayangputri85@gmail.com
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Figure 1.
Real Estate and Propety Stock Return 2010 - 2014
Source : IDX Statistics 2010-2014 / www.idx.co.id (processed)
The graph above illustrates real estate and property stock returns measured using capital gains
for real estate and property from 2010-2014. It can be seen that stock returns in 2014 decreased to
35% from 48% in 2013. Stock returns are influenced by microeconomic factors and macroeconomic
indicators, in this study the microeconomic variables used are Debt to Equity Ratio (DER), Earning
Per Share (EPS), Net Profit Margin (NPM), and Price to Book Value (PBV), while the macroeconomic
indicator variables are Economic Growth (GDP), Inflation, Interest Rates (SBI), and Exchange Rates.
To analyze stock returns, investors not only consider company performance factors but also
need to analyze factors outside the company (Bastian et al., 2009). Prastowo states that external
factors that can directly impact a company's performance include interest rates, exchange rates,
international economic conditions, a country's economic cycle, inflation rates, tax policies, and the
money supply.
According to Samsul, (2009) stock returns are influenced by both macro and micro factors.
Macro factors refer to external elements that are beyond the company's control, while micro factors
involve internal aspects within the company, such as net income, book value per share, debt-to-equity
ratio, and other financial ratios.
Real estate and property companies were chosen for this study because of the significant
increase in their stock returns during the research period. The study aims to analyze how various
factors, such as DER, NPM, PBV, EPS, inflation, exchange rates, SBI interest rates, and GDP growth,
influence the stock returns of companies in the real estate and property sector listed on the Indonesia
Stock Exchange between 2010 and 2014.
Stock returns are influenced by both microeconomic and macroeconomic factors.
Microeconomic factors include financial ratios such as the Debt to Equity Ratio (DER), Earnings Per
2010 2011 2012 2013 2014
Stock Return
33% 57% 39% 48% 35%
20%
25%
30%
35%
40%
45%
50%
55%
60%
%
Stock Return
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Share (EPS), Net Profit Margin (NPM), and Price to Book Value (PBV), which provide insights into a
company's performance and financial health. Macroeconomic factors, as defined by Prastowo and
Samsul, include variables like inflation, interest rates, exchange rates, and gross domestic product
(GDP). These external factors impact investor decisions and market trends by shaping the broader
economic environment.
Previous studies have explored the impact of these variables on stock performance. For
instance, research by Javed and Akhtar (2012) highlighted the significant role of macroeconomic
factors in influencing stock returns in emerging markets. Similarly, Anastasia et al., (2009) examined
fundamental and systematic risk factors affecting property sector stocks, revealing the interplay of
internal financial metrics and external economic conditions.
Despite the existing literature, there remains a lack of comprehensive studies integrating both
microeconomic and macroeconomic determinants in the context of the Indonesian real estate and
property sector. This industry, being a vital economic indicator, requires an in-depth analysis of these
factors to understand their collective impact on stock returns during various economic cycles
(Iskandar, 2009).
Given the fluctuations in real estate and property stock returns observed between 2010 and
2014, particularly the significant decline in 2014, there is an urgent need to investigate the underlying
causes. Understanding these dynamics can provide valuable insights for investors, policymakers, and
stakeholders to make informed decisions and strategies (Desy & Astohar, 2012).
This study aims to examine the impact of DER, EPS, NPM, PBV, inflation, exchange rates, SBI
interest rates, and GDP growth on the stock returns of real estate and property companies listed on
the Indonesia Stock Exchange from 2010 to 2014. By addressing the identified research gap, the study
seeks to enhance both the theoretical and practical understanding of the factors influencing stock
performance in emerging markets.
Research Methods
This study employs an explanatory research design to analyze the relationships between
variables and examine how one variable influences another. The independent variables include DER,
EPS, NPM, PBV, inflation, exchange rate, interest rate (SBI), and GDP growth, while the dependent
variable is stock return.
The analysis method used is multiple linear regression, with hypothesis testing conducted
through t-tests and independent sample t-tests (two mean difference tests). Prior to the analysis,
classical assumption tests are performed, including normality, multicollinearity, and
heteroscedasticity tests, as the study involves panel data (Sugiyono, 2009).
The regression equation is:
T-test
The t-test, also known as the test statistic, is used to perform a hypothesis test on the regression
coefficient individually. It helps assess the significance of the regression coefficient. According to
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Gujarati, significance testing is a process where sample results are used to assess the validity of the
null hypothesis. The decision to accept or reject the null hypothesis is based on the value of the test
statistic. When testing the regression coefficient, there are two possible outcomes: if the population
regression coefficient equals zero, it suggests that the independent variable has no effect on the
dependent variable; if it is not equal to zero, it indicates that the independent variable does have an
influence on the dependent variable (Thobarry & Achmad, 2009).
Results and Discussion
Regression Test Results
This test is performed by regressing all independent variables DER, EPS, NPM, PBV, inflation,
exchange rate, SBI, and GDP on the dependent variable, which is stock returns. In determining the
appropriate panel data regression model, it was concluded that the Pooled Least Squares (PLS) model
is the most suitable for this study. A good regression model is indicated by R² and Adjusted R² values
that are close to one. Based on the regression results, the R² value for the pooled least square model
is 0.302099, while the Adjusted R² for the pooled least square model is 0.271253.
The F-Stat test results of the pooled least square model show that the probability F-Stat is very
small and below the alpha value of 0.05 so that the pooled least square model has a regression
coefficient that is able to explain the dependent variable together. The regression test results can be
seen in table 1:
Table 1. Real Estate and Property Panel Data Regression Results
Dependent Variable: SHARE RETURN
Total panel (balanced) observations: 190
Variable
Coefficient
Std. Error
t-Statistic
Prob.
C
-1.207242
4.235870
-0.285004
0.7760
DER
-0.314975
0.129327
-2.435496
0.0158
EPS
0.002361
0.000393
6.014880
0.0000
NPM
0.270480
0.111205
2.432269
0.0160
PBV
0.233760
0.062449
3.743202
0.0002
INFLATION
-0.278630
0.245756
-1.133766
0.2584
COURSE
-1.49E-05
0.000180
-0.082718
0.9342
SBI
47.36756
43.93705
1.078078
0.2824
GDP
0.049120
0.503687
0.097521
0.9224
R-squared
0.302099
Mean dependent var
0.442269
Adjusted R-squared
0.271253
S.D. dependent var
1.088443
S.E. of regression
0.929168
Akaike info criterion
2.737154
Sum squared resid
156.2668
Schwarz criterion
2.890961
Log likelihood
-251.0297
Hannan-Quinn criter.
2.799459
F-statistic
9.793652
Durbin-Watson stat
2.103842
Prob(F-statistic)
0.000000
Source: Eviews 8 output
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Based on Table 1 above, the regression equation results are as follows:
Stock Return = -1.2072 - 0.3149DER + 0.0023EPS + 0.2704NPM + 0.2337 PBV
Hypothesis Testing
H1: DER has a negative effect on stock returns
H2: EPS has a positive effect on stock returns
H3: NPM has a positive effect on stock returns
H4: PBV has a positive effect on stock returns
H5: Inflation has a negative effect on stock returns
H6: Exchange rate has a positive effect on stock returns
H7: SBI has a positive effect on stock returns
H8: GDP has a positive effect on stock returns
Regression Test Results
This study investigates the impact of DER, EPS, NPM, PBV, inflation, exchange rate, SBI, and GDP
on stock returns in the real estate and property sector by employing panel data regression analysis.
The findings reveal varying levels of significance for these variables, as summarized below:
1. Debt to Equity Ratio (DER):
The regression results show that DER has a negative effect on stock returns (coefficient = -
0.3149, p-value = 0.0158). This finding aligns with the financial theory that higher leverage increases
financial risk, deterring investors and negatively impacting stock performance. Previous research by
Lulukiyah, (2010) similarly highlights the adverse effects of high DER on stock returns in Indonesian
markets.
2. Earning Per Share (EPS):
EPS has a positive and significant effect on stock returns (coefficient = 0.0023, p-value =
0.0000), corroborating the signaling theory, which suggests that higher earnings per share signal
better company performance and attract investors. These results are consistent with studies by
Aristas & Astobar, (2012) , which confirm the positive impact of EPS on stock returns in emerging
markets.
3. Net Profit Margin (NPM):
NPM positively influences stock returns (coefficient = 0.2704, p-value = 0.0160). This is in line
with profitability theory, where higher profit margins indicate better operational efficiency,
enhancing investor confidence. The findings align with similar studies by Dubravka, (2010), who
identified profitability ratios as key determinants of stock performance.
4. Price to Book Value (PBV):
PBV also exhibits a positive and significant relationship with stock returns (coefficient = 0.2337,
p-value = 0.0002). This supports the valuation theory, which posits that companies with higher PBV
ratios are perceived as having strong growth potential, thus attracting investment. The results echo
those of Mulia et al., (2012), who emphasize PBV's role in stock valuation.
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Insignificant Variables and Their Implications
1. Inflation:
Inflation does not significantly affect stock returns (p-value = 0.2584). This contrasts with the
findings of Kurniadi, (2013), who observed inflation's impact on other sectors. A possible explanation
is that real estate and property stocks may already price in inflation risks due to their sensitivity to
long-term investment trends.
2. Exchange Rate:
The exchange rate's impact on stock returns is insignificant (p-value = 0.9342), diverging from
studies like (Javed et al., 2012), which reported exchange rates as critical to stock market
performance. This could be due to the sector's reliance on domestic demand, reducing exposure to
exchange rate fluctuations.
3. SBI Interest Rates and GDP:
Neither SBI interest rates (p-value = 0.2824) nor GDP (p-value = 0.9224) significantly influence
stock returns. While these findings diverge from economic growth theories that often emphasize the
macroeconomic environment's impact, they suggest that microeconomic factors dominate investor
decision-making in this sector. Similar patterns were noted in the study by Thobarry, (2009) which
found mixed macroeconomic impacts on stock returns.
This study confirms the dual influence of microeconomic and macroeconomic factors on stock
performance. While microeconomic indicators such as DER, EPS, NPM, and PBV significantly affect
stock returns, macroeconomic variables like inflation, exchange rate, interest rates, and GDP show
limited influence in this sector. This divergence highlights the need for sector-specific analyses to fully
understand stock return dynamics.
The findings underscore the importance of corporate financial health in determining stock
returns, as reflected in DER, EPS, NPM, and PBV. Investors and stakeholders in the real estate and
property sector should prioritize these metrics when evaluating investment opportunities.
Additionally, the muted impact of macroeconomic variables suggests that sector-specific resilience
mechanisms may buffer against broader economic fluctuations.
Conclusion
The conclusions drawn from this study are as follows: The Debt-to-Equity Ratio (DER)
negatively impacts the stock returns of real estate and property companies listed on the Indonesia
Stock Exchange for the period 2010-2014. Earnings per Share (EPS) positively affects stock returns
in the real estate and property sector during the same period. Net Profit Margin (NPM) also has a
positive effect on stock returns in this sector from 2010 to 2014. Price-to-Book Value (PBV) positively
influences stock returns in real estate and property companies listed on the Indonesia Stock Exchange
for the 2010-2014 period. Inflation, exchange rates, SBI, and GDP, however, do not have any
significant effect on the stock returns of real estate and property companies listed on the Indonesia
Stock Exchange during the same period.
These results provide evidence that investors should prioritize microeconomic indicators when
evaluating stock investments in the real estate and property sector. The findings underscore the
importance of firm-specific financial metrics such as DER, EPS, NPM, and PBV, which play a more
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significant role than macroeconomic factors in influencing stock returns. Furthermore, this study
contributes to a deeper understanding of the unique dynamics within the real estate and property
sector, which may differ substantially from those in other industries, emphasizing the need for sector-
specific investment strategies.
For future researchers, several suggestions are proposed to build on this study. First, extending
the analysis period beyond 2010–2014 could provide insights into whether the observed patterns
hold across different economic cycles. Second, incorporating additional variables such as corporate
governance, market sentiment, and regional economic indicators may offer a more comprehensive
view of stock return determinants. Third, conducting comparative sector analyses could help identify
industry-specific dynamics and provide broader market insights. Fourth, given the limited impact of
macroeconomic variables found in this study, future research could explore why these factors appear
to have muted effects in the real estate and property sector compared to others. Lastly, employing
advanced econometric techniques, such as structural equation modeling or machine learning
methods, could enhance the robustness and predictive accuracy of future studies, offering deeper and
more nuanced insights into stock performance determinants.
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