Rini Marcella1, Wiwik Utami2
Universitas Mercu Buana, Indonesia
Email: 55522120013@student.mercubuana.ac.id,
wiwik.utami@mercubuana.ac.id
Abstract
The property and real estate sector plays a crucial role in the Indonesian economy but faces
significant risks of financial distress. This study aims to identify the determinants
of financial distress within this sector, focusing on companies listed on the
Indonesia Stock Exchange between 2018 and 2022. The research examines the
effects of profitability, liquidity, leverage, interest rates, and inflation on
financial distress. Using a quantitative approach, this study applies
statistical techniques and regression analysis to secondary data from various
property and real estate companies. The findings reveal that: (i) profitability has a negative impact on financial distress,
indicating that lower profitability increases the likelihood of financial
difficulties; (ii) liquidity also shows a negative relationship with financial
distress, meaning reduced liquidity elevates financial risk; (iii) leverage
negatively influences financial distress, suggesting that higher debt levels
exacerbate financial instability; (iv) rising interest rates contribute to
financial distress by increasing borrowing costs, thereby straining company
resources; and (v) inflation worsens financial distress by eroding purchasing
power and dampening property demand. This study provides valuable insights for
stakeholders, including investors and policymakers, to enhance their
understanding of financial risk management in the property and real estate
sector. The novelty of this research lies in its specific focus on a critical
period and sector within the Indonesian economic landscape, addressing key
areas of concern for financial stability.
Keywords: Financial distress, Profitabilitas,
Likuiditas, Leverage, Interest rates, Inflation
Introduction
The property and real estate
sector is a crucial component of Indonesia's economy,
significantly contributing to employment opportunities and enhancing community
welfare. (i) This sector not only provides housing
and commercial spaces but also drives economic growth through investments and
infrastructure development. (ii) Furthermore, the real estate industry
generates substantial tax revenues that the government utilizes to fund
essential public services, including education, healthcare, and infrastructure
projects. (iii) In addition, the sector serves as a platform for innovation and
technological advancements, fostering sustainable urban development and
improving the quality of life for citizens. (iv) Given its importance, ensuring
the financial health and stability of businesses within this sector is
essential for maintaining national economic resilience. However, financial
distress poses a significant threat to the sustainability of property and real
estate companies in Indonesia. Recent trends indicate an alarming increase in
financial difficulties, as evidenced by several prominent companies seeking
debt restructuring or facing bankruptcy. For instance, well-known firms such as
PT Citatah Propertindo Tbk and PT Summarecon Agung Tbk have reported substantial losses, leading to
applications for suspension of debt payments. This situation highlights the
urgent need for stakeholders, including the government, investors, and industry
players, to collaborate in creating a supportive environment that mitigates financial
risks and promotes the long-term viability of the property sector. Addressing
financial distress not only safeguards investments but also ensures the
continued contribution of this sector to Indonesia's economic development.
Previous research has highlighted various factors that influence financial
distress in the property and real estate sector. Studies by Hertina
et al. (2022) and Sudaryo et al. (2022) indicate that
profitability and liquidity significantly affect financial stability, with
lower profitability and liquidity levels increasing the risk of financial
distress. Furthermore, research conducted by Vio Dawa
Faniko (2022) emphasizes the role of leverage in
exacerbating financial difficulties, suggesting that companies with higher debt
levels are more susceptible to financial distress. Additionally, Amin &
Hidayah (2023) found that rising interest rates and inflation negatively impact
financial performance, contributing to financial instability in the sector.
These findings underscore the need for a comprehensive analysis of the
determinants of financial distress, particularly in the context of Indonesia's
unique economic landscape. Furthermore, profitability, liquidity, and leverage
are critical factors influencing financial distress in the property and real
estate sector. The risk of financial distress increases when companies fail to
maintain adequate profitability and liquidity levels while managing high debt.
Research indicates that lower profitability and liquidity can significantly heighten
the likelihood of financial difficulties (Hertina et
al., 2022; Sudaryo et al., 2022). Consequently, the
problem formulation in this study encompasses the following elements:
1.
Does profitability affect financial distress?
2.
Does liquidity impact financial distress?
3.
Does leverage influence financial distress?
4.
How do interest rates affect financial distress?
5.
How does inflation contribute to financial distress?
In line with the formulation of
the problem, this study aims to determine the determinant variables of
financial distress based on the variables of profitability, liquidity,
leverage, interest rates, and inflation. The implication of this research is to
provide information and references for stakeholders, academics, and
practitioners interested in analyzing financial distress to support
decision-making and further research. The originality of this research relates
to the research period, namely 2018 to 2022, and the industrial sector,
specifically the property and real estate sector, which plays a vital role in
the countrys economy. Previous research by Oktavian & Handoyo
(2023) also indicates that the liquidity ratio significantly impacts financial
distress, supporting the importance of this variable in the analysis conducted
in this study.
LITERATURE REVIEW
The study's theoretical
framework is agency theory because company sustainability is pertinent to this
theory (Nugroho, Miglietta, et al., 2022; Sukarmi et
al., 2022; Yusufa et al., 2022). Additionally, agency
theory has implications for corporate sustainability due to the following
concerns:
1.
Agency Theory emphasizes the necessity of proper supervision and
incentives to motivate agents (managers) to act in the interests of the
principals (owners). An effective supervisory structure allows owners to
monitor managerial actions, promote accountability, and ensure that decisions
align with the long-term sustainability of the company (Cuevas-Rodrํguez et
al., 2012). This is particularly important in the property sector, where
financial mismanagement can lead to significant distress.
2.
Previous studies have indicated varying impacts of profitability,
liquidity, and leverage on financial distress. For example, Hertina et al.
(2022) found that profitability negatively affects financial distress,
suggesting that lower profitability increases the likelihood of financial
difficulties. Conversely, Sudaryo et al. (2022) indicated that high
profitability might lead to expectations from creditors, thereby increasing
financial distress risk when those expectations are unmet.
3.
In terms of liquidity, Sudaryo et al. (2022) demonstrated that low
liquidity ratios correlate with a higher likelihood of financial distress. This
finding aligns with Altman et al. (1994), who established that liquidity ratios
are significant predictors of financial distress. Additionally, research by
Oktavianthie (2022) supports this view, showing that companies with inadequate
liquidity face heightened risks of financial distress.
4.
Regarding leverage, Vio Dawa Faniko (2022) reported that high
leverage increases the risk of financial distress due to the burden of debt.
However, Oktavian & Handoyo (2023) found that leverage could also have a
negative effect on financial distress, indicating a complex relationship that
warrants further investigation. Hidayah (2023) also noted that companies with
high leverage ratios often struggle to maintain financial stability, further
complicating their operational capabilities.
5.
Fisher's Theory illustrates the relationship between nominal
interest rates, real interest rates, and inflation. According to Fisher, when
inflation rises, nominal interest rates must also increase to maintain stable
real interest rates. This dynamic is crucial for companies in the real estate
sector, where high borrowing costs can exacerbate financial difficulties
(Castellares & Toma, 2020). Understanding this relationship helps
stakeholders anticipate the impact of economic fluctuations on financial stability.
In summary, integrating agency
theory and Fisher's theory provides a comprehensive framework for analyzing the
determinants of financial distress in the property and real estate sector.
enables a better assessment of the risks and management practices necessary to
mitigate financial distress.
The application of Agency
Theory in the context of property and real estate companies can help reduce
conflicts of interest between owners (principals) and managers (agents). In
this regard, it is crucial for companies to ensure that managers act in the best
interests of the owners. Through stringent oversight, appropriate incentives,
and effective communication, companies can encourage decisions that support
long-term sustainability. Consequently, the risk of financial distress can be
minimized, as decisions made are more aligned with the long-term goals of the
company. On the other hand, Fisher's Theory, which focuses on the relationship
between interest rates and inflation expectations, also has significant
implications in the context of financial distress. This theory states that
nominal interest rates reflect inflation expectations. When inflation rises,
interest rates tend to increase as well, which can elevate borrowing costs for
companies. This increase can add to the debt burden, reduce liquidity, and
subsequently heighten the risk of financial distress. In the context of the
property sector, companies facing high inflation may have to contend with
rising operational costs, while revenue from property sales may not increase at
the same rate. This can lead to financial imbalances that potentially result in
difficulties in meeting financial obligations. By understanding and applying
both theories, companies can design better strategies for managing financial
risks and avoiding conditions of financial distress.
By applying the principles of
agency theory, companies can reduce conflicts of interest between principals
and agents, fostering actions that support long-term sustainability. Effective
oversight, appropriate incentives, and strong communication between owners and
managers are essential elements in maintaining business value and
sustainability. Furthermore, Fisher's theory emphasizes the crucial
relationship between interest rates, inflation, and a company's financial
performance. When companies face financial pressures due to high inflation or
rising interest rates, their ability to meet financial obligations and maintain
operational stability can be significantly affected. In this context, companies
need to provide clear signals to stakeholders regarding their financial
condition. Such signals can include actions like mass layoffs, which may
indicate that the company is experiencing substantial financial difficulties or
is undergoing internal restructuring to cut costs. Additionally, reducing or
halting dividend payments may reflect financial distress or an effort to
allocate resources more efficiently, especially in light of the dynamics of
interest rates and inflation. A downgrade in credit ratings by rating agencies
can also signal that the company is facing a higher risk of bankruptcy. By
understanding the interplay between agency theory and Fisher's theory,
companies can better manage financial risks and enhance transparency with
stakeholders, thereby supporting sustainability in the face of economic challenges.
The Altman Z-Score model is an
important tool for measuring bankruptcy risk and provides valuable insights
into a company's financial condition. Research by Al-Manaseer and Al-Oshaibat (2018) demonstrates that the Z-Score can predict
potential insolvency with high accuracy, thereby helping investors and
management identify financial distress risks earlier. Furthermore, a study by
Kamaluddin (2019) indicates that companies with low Z-Scores tend to face
challenges in meeting financial obligations, which can lead to liquidity crises
and increased bankruptcy risk. Additionally, Mufidah and Handayani
(2024) found that Z-Score analysis can signal declining financial performance,
limited access to financing, and reputational damage that may be exacerbated by
difficult market conditions. Therefore, understanding the Z-Score and the
factors influencing it is crucial for companies in the property and real estate
sector to manage financial risks and prevent financial distress.
In this study, it is essential
to consider previous research relevant to the analysis of the factors
influencing financial distress in the property and real estate sector. One
study by Hertina et al. (2022) indicates that
profitability has a negative and significant impact on financial distress. This
research suggests that the lower a company's profitability, the higher the
likelihood that the company will face financial difficulties. Another study by Sudaryo et al. (2022) found differing results, showing that
profitability positively affects financial distress. This indicates that
companies with high profitability often have high expectations from their
creditors; thus, if they fail to meet these expectations, the risk of financial
distress increases. Additionally, research by Oktavian & Handoyo (2023) discovered that liquidity ratios positively
correlate with financial distress, meaning that higher liquidity ratios
increase the likelihood of a company facing financial difficulties. Conversely,
Sudaryo et al. (2022) demonstrated that companies
with low liquidity ratios are more likely to experience financial distress.
These studies reveal discrepancies in results, indicating that the relationship
between profitability, liquidity, and financial distress requires further
investigation to achieve consistent conclusions. This research aims to delve
deeper into the factors affecting financial distress in the property and real
estate sector, including the impacts of interest rates and inflation, as
discussed in the study by Fitriaty & Saputra
(2022).
CONCEPTUAL RESEARCH FRAMEWORK
The conceptual framework of this study is based on problem
formulation and a review of the literature. Additionally, the research's
conceptual paradigm can be illustrated as follows:
HYPOTHESIS DEVELOPMENT
As
shown in above, the formulation of hypotheses in this article includes the
following:
The Influence of Profitability on Financial Distress
Profitability affects financial
distress as it indicates the company's ability to generate profit. Hertina et al. (2022) found that profitability has a
negative and significant effect on financial distress, meaning that lower
profitability increases the likelihood of financial difficulties. Conversely, Sudaryo et al. (2022) indicated that high profitability can
also lead to financial distress due to heightened expectations from creditors.
Additionally, Oktavianthie (2023) emphasized that
companies with fluctuating profitability may face challenges in maintaining
stable cash flows, contributing to financial distress. Therefore, the
hypothesis is:
H01:
Profitability has no effect on financial distress.
Ha1:
Profitability affects financial distress.
The Influence of Liquidity on Financial Distress
Liquidity reflects a company's
ability to meet short-term obligations. Sudaryo et al.
(2022) showed that companies with low liquidity ratios are more likely to
experience financial distress. Conversely, Oktavian & Handoyo
(2023) found that high liquidity can help companies avoid financial
difficulties. Utami (2023) also supported this by stating that a strong
liquidity position enhances financial stability, allowing firms to navigate
economic downturns effectively. Thus, the hypothesis proposed is:
H02:
Liquidity has no effect on financial distress.
Ha2:
Liquidity affects financial distress.
The Influence of Leverage on Financial Distress
Leverage indicates a company's
dependence on debt. Vio Dawa Faniko
(2022) found that high leverage has a positive and significant effect on
financial distress, implying that higher leverage increases the likelihood of
financial challenges. However, Oktavian & Handoyo
(2023) presented contrasting findings, indicating that leverage may have a
negative effect on financial distress under certain conditions. Hidayah (2023)
suggested that companies with prudent leverage management can mitigate risks
associated with high debt levels. Therefore, the hypothesis is:
H03:
Leverage has no effect on financial distress.
Ha3:
Leverage affects financial distress.
The Influence of Interest Rates on Financial Distress
Interest rates can influence
borrowing costs, which in turn affect a company's financial condition. Research
indicates that rising interest rates are associated with increased financial
distress risk (Amin & Hidayah, 2023). Utami (2023) also noted that
fluctuations in interest rates can strain cash flows, leading to potential
financial difficulties. Thus, the hypothesis proposed is:
H04:
Interest rates have no effect on financial distress.
Ha4:
Interest rates affect financial distress.
The Influence of Inflation on Financial Distress
Inflation can harm purchasing
power and reduce product demand, potentially causing financial distress.
Mufidah & Handayani (2024) discussed that high
inflation can lead to poor financial performance. Hidayah (2023) further
emphasized that sustained inflation increases operational costs, thereby
exacerbating financial stress on companies. Therefore, the hypothesis is:
H05:
Inflation has no effect on financial distress.
Ha5:
Inflation affects financial distress.
This
research aims to analyze the impact of each of these variables on financial
distress within the property and real estate sector in Indonesia
Metode Penelitian
This form of research employs a
quantitative methodology. This study utilizes statistical methods to test
hypotheses derived from established theories (Darmawan, 2019). The sampling
mechanism in this study is illustrated in the following table:
|
No |
Description |
Number of Companies |
|
|
|
1 |
Total Companies in the
Property and Real Estate Sector on the Indonesia Stock Exchange |
92 |
|
|
|
2 |
The number of companies with
incomplete or inaccessible financial statements during the period from 2018
to 2022. |
52 |
|
|
|
3 |
Number of Companies that
published complete annual financial statements consistently during the period
2018-2022 |
40 |
||
|
|
Observation Years (2018 -
2022) |
5 |
||
|
Sample Size in the Research
Observation (40 companies x 5 years) |
200 |
Furthermore, the statistical method
used in this article to test the research hypothesis is logistic regression
analysis. Therefore, the equations in this study include:
|
Variable |
Dimension |
Indicator |
Measurement Scale |
|
Financial Distress |
Company's ability to meet its financial obligations |
Z-Score Altman |
Nominal |
|
Profitability |
Company's ability to generate profit |
ROA |
Ratio |
|
Liquidity |
Company's ability to meet short-term obligations |
CR |
Ratio |
|
Leverage |
Use of debt in the company's financial structure |
(DER) |
Ratio |
|
Interest Rate |
Applicable interest rate in the financial market |
BI Rate |
Ratio |
|
Inflation |
General increase in the prices of goods and services |
CPI (Consumer Price Index) |
Ratio |
RESULT AND DISCUSSIONS
RESULT
DESCRIPTIVE STATISTICAL
ANALYSIS
Descriptive statistical
analysis is employed to provide an overview of the data, focusing on the
maximum, minimum, mean, and standard deviation values. In this research, the
variables included in the descriptive statistical calculations are
Profitability (X1), Liquidity (X2), Leverage (X3), Interest Rate (X4),
Inflation (X5), and Financial Distress (Y). From this descriptive statistical
analysis, the following sample representation is obtained:
Table 3
|
|
N |
Minimum |
Maximum |
Mean |
Std.
Deviation |
|
Profitability |
200 |
-0.360 |
0.430 |
0.02015 |
0.072427 |
|
Liquidity |
200 |
0.080 |
24.880 |
2.79050 |
2.818211 |
|
Leverage |
200 |
-55.730 |
6.880 |
0.26230 |
4.600946 |
|
Interest
Rate |
200 |
0.040 |
0.060 |
0.05000 |
0.008967 |
|
Inflation |
200 |
0.020 |
0.060 |
0.03200 |
0.014734 |
|
Valid
N (listwise) |
200 |
|
|
|
|
Source data: processed data
Based on Table 3 above, the average
Profitability (X1) value during the study period was 0.02015, with the highest
value of 0.43 and the lowest value of -0.36, while the standard deviation was 0.072427.
The average Liquidity (X2) value during the study period was 2.79050, with the
highest value of 24.880 and the lowest value of 0.08, while the standard
deviation was 2.818211. The average Leverage (X3) value during the study period
was 0.26230, with the highest value of 6.880 and the lowest value of -55.730,
while the standard deviation was 4.600946. The average Interest Rate (X4) value
during the study period was 0.05000, with the highest value of 0.060 and the
lowest value of 0.040, while the standard deviation was 0.008967. The average
Inflation (X5) value during the study period was 0.03200, with the highest
value of 0.060 and the lowest value of 0.020, while the standard deviation was
0.014734.
CLASSICAL ASSUMPTION TEST
The Classical Assumption Test is a series
of tests conducted to evaluate whether the regression model meets the basic
assumptions necessary for drawing valid conclusions. The purpose of this test
is to ensure that the regression model satisfies the BLUE (Best Linear Unbiased
Estimator) criteria. Therefore, it is important to conduct classical assumption
testing that encompasses several aspects
RESULTS OF THE REGRESSION MODEL
FIT TEST
Hosmer and Lemeshow
Test
|
Chi-square |
df |
Sig. |
|
|
1 |
7.519 |
8 |
0.482 |
Based on the model fit results above, the
Chi-square value is 7.519 with a significance value of 0.482. Since the
significance value is greater than 0.05, it indicates that the model is capable
of predicting its observed values, meaning the model can be accepted as it fits
the observational data
RESULTS OF THE OVERALL MODEL
FIT TEST
Overall Model Fit Test
|
Iteration Historya,b,c,d |
|||||||||
|
Iteration |
-2 Log likelihood |
Coefficients |
|||||||
|
Constant |
Profitabilitas |
Likuiditas |
Leverage |
Suku Bunga |
Inflasi |
|
|||
|
Step 1 |
1 |
187.872 |
-0.623 |
10.855 |
0.219 |
0.043 |
10.688 |
-7.291 |
|
|
2 |
146.396 |
-1.116 |
23.529 |
0.501 |
0.175 |
0.176 |
-1.181 |
|
|
|
3 |
121.076 |
-2.018 |
38.409 |
0.865 |
0.489 |
-8.488 |
1.953 |
|
|
|
4 |
100.951 |
-3.903 |
56.220 |
1.341 |
1.357 |
-13.601 |
4.512 |
|
|
|
5 |
95.497 |
-5.565 |
72.469 |
1.763 |
2.032 |
-15.220 |
5.701 |
|
|
|
6 |
94.905 |
-6.357 |
80.299 |
1.974 |
2.325 |
-15.330 |
5.649 |
|
|
|
7 |
94.895 |
-6.481 |
81.489 |
2.007 |
2.368 |
-15.193 |
5.526 |
|
|
|
8 |
94.895 |
-6.484 |
81.511 |
2.008 |
2.369 |
-15.188 |
5.522 |
|
|
|
9 |
94.895 |
-6.484 |
81.511 |
2.008 |
2.369 |
-15.188 |
5.522 |
|
|
|
a. Method: Enter |
|||||||||
|
b. Constant is included in the model. |
|||||||||
|
c. Initial -2 Log Likelihood: 263.582 |
|||||||||
|
d. Estimation terminated at iteration
number 9 because parameter estimates changed by less than .001. |
|||||||||
The initial -2 Log Likelihood (-2LL) value
was 263.582, while after iterations, it decreased to 94.895, indicating a
reduction of 168.687. The -2LL value reflects how well the model fits the data;
the lower the value, the better the model explains the data. This significant
decrease indicates that the new model is more effective than the initial model.
Thus, the reduction of -2LL from 263.582 to 94.895 shows that the regression
model has improved in its ability to explain the data
COEFFICIENT OF DETERMINATION
TEST
Result of the Coefficient of Determination
Test
|
Model Summary |
|||
|
Step |
-2 Log likelihood |
Cox & Snell R Square |
Nagelkerke R Square |
|
1 |
94.895a |
0.570 |
0.778 |
|
a. Estimation terminated at iteration
number 9 because parameter estimates changed by less than .001. |
|||
Based on the table above, the Coefficient
of Determination in the logistic regression is reflected by the Nagelkerke R
Square value. The Nagelkerke R Square is 0.778, which means that 77% of the
variability in the dependent variable can be explained by the variability in
the independent variables
MULTICOLLINEARITY TEST
|
Correlation Matrix |
|||||||
|
|
Constant |
Profitability |
Liquidity |
Leverage |
Interest
Rate |
Inflation |
|
|
Step
1 |
Constant |
1.000 |
-0.429 |
-0.543 |
-0.552 |
-0.722 |
0.360 |
|
Profitability |
-0.429 |
1.000 |
0.624 |
0.698 |
-0.053 |
-0.015 |
|
|
Liquidity |
-0.543 |
0.624 |
1.000 |
0.705 |
-0.064 |
0.061 |
|
|
Leverage |
-0.552 |
0.698 |
0.705 |
1.000 |
0.006 |
-0.007 |
|
|
Interest
rate |
-0.722 |
-0.053 |
-0.064 |
0.006 |
1.000 |
-0.753 |
|
|
Inflation |
0.360 |
-0.015 |
0.061 |
-0.007 |
-0.753 |
1.000 |
|
Result of Multicollinearity
Test
The correlation among independent
variables can indicate multicollinearity if the values are sufficiently high,
generally above 0.90. Table 4.5 shows that Profitability and Liquidity have a
correlation of 0.624, Profitability and Leverage 0.698, Liquidity and Leverage
0.705, and Interest Rate and Inflation -0.753. Overall, all correlations among
the independent variables are below 0.90, indicating no strong evidence of
multicollinearity.
LOGISTIC REGRESSION TEST
|
|
B |
S.E. |
Wald |
df |
Sig. |
Exp(B) |
95% C.I.for EXP(B) |
||
|
Lower |
Upper |
||||||||
|
Step 1a |
Profitability |
14.346 |
32.282 |
1 |
0.000 |
0.000 |
0.000 |
0.000 |
|
|
Liquidity |
-2.008 |
0.380 |
27.891 |
1 |
0.000 |
0.134 |
0.064 |
0.283 |
|
|
Leverage |
-2.369 |
0.495 |
22.914 |
1 |
0.000 |
0.094 |
0.035 |
0.247 |
|
|
Interest
Rate |
15.188 |
45.078 |
0.114 |
1 |
0.736 |
3943853.224 |
0.000 |
9.246 |
|
|
Inflation |
-5.522 |
29.235 |
0.036 |
1 |
0.850 |
0.004 |
0.000 |
3.069 |
|
|
Constant |
6.484 |
2.066 |
9.847 |
1 |
0.002 |
654.455 |
|
|
|
|
a.
Variable(s) entered on step 1: Profitabilitas, Likuiditas, Leverage, Suku Bunga, Inflasi. |
|||||||||
Result of Logistic Regression
Test
Based on the results of the logistic
regression analysis in the table above, the logistic regression model can be
formed by examining the estimated parameter values from the variables in the
equation. The regression model based on the estimated parameter values in the
variables in the equation is as follows:
FD = α + β₁Prof +
β₂Liq + β₃Lev + β₄IR
+ β₅Inf + ε
Thus,
FD = 6.484 - 5.522 Profitability - 2.008
Liquidity - 2.369 Leverage + 15.188 Interest Rate - 5.522 Inflation
Based on the logistic regression equation
above, it can be explained as follows:
1.
Profitability: Measured by ROA, the regression coefficient is
-81.511 and significant at 0.000 (less than 0.05). This indicates that
Profitability has a significant effect on Financial Distress. The Exp(B) value
of 0.000 suggests that higher Profitability decreases the likelihood of
Financial Distress.
2.
Liquidity: Measured by CR, the regression coefficient is -2.008
and significant at 0.000. This indicates that Liquidity also has a significant
effect on Financial Distress. The Exp(B) value of 0.134 shows that an increase
in Liquidity reduces the likelihood of Financial Distress.
3.
Leverage: Using DER, the regression coefficient is -2.369 with a
significance of 0.000, indicating a significant effect on Financial Distress.
The Exp(B) value of 0.094 indicates that higher leverage decreases the
likelihood of Financial Distress.
4.
Interest Rate: The regression coefficient is positive at 15.188
with a p-value of 0.736 (greater than 0.05), indicating that the interest rate
does not have a significant effect on Financial Distress.
5.
Inflation: The regression coefficient is -5.522 with a p-value of
0.850 (greater than 0.05), also indicating that inflation does not have a
significant effect on Financial Distress.
DISCUSSION
The Influence of Profitability
on Financial Distress
The first hypothesis (H1) states that
Profitability has a negative effect on Financial Distress. The regression
results confirm that profitability, measured by ROA, has a negative and
significant impact on Financial Distress. This finding aligns with agency
theory, where higher profitability aligns the interests of management and
shareholders, thereby reducing the risk of financial distress. Property and
real estate companies with high profitability tend to have healthy cash flows,
the ability to manage obligations, and a strong market position, all of which
reduce the risk of financial difficulties. High profitability also enhances the
company's credibility in accessing additional funding and investing in new
projects, which can strengthen competitiveness and market share. These findings
are consistent with the research by Dwiantari et al.
(2021), which shows a significant negative impact of profitability on financial
distress. However, this study contradicts the results of Oktavian & Handoyo (2023), which indicate that profitability may have
a positive effect on financial distress, reflecting potential high risks
despite high profitability. This suggests that profitability does not always
guarantee good financial conditions, especially if accompanied by aggressive
risk-taking behavior by management
The Influence of Liquidity on
Financial Distress
The second hypothesis (H2) states that
Liquidity has a negative effect on Financial Distress. The regression results
show that liquidity, measured by the Current Ratio (CR) proxy, has a negative
and significant impact on Financial Distress, thus supporting this hypothesis.
This finding aligns with agency theory, where high liquidity aligns the
interests of management and shareholders, reducing the likelihood of the
company experiencing financial distress. High liquidity indicates a company's
ability to meet short-term obligations, thereby reducing the risk of financial
difficulties. These results are consistent with the study by Dwiantari et al. (2021), which also found a significant
negative effect of liquidity on financial distress. Companies with high
liquidity have greater current assets compared to short-term liabilities,
making it easier to meet obligations. However, this study contradicts the
findings of Oktavian & Handoyo (2023), which
indicate that liquidity ratios have a positive effect on financial distress.
They argue that high liquidity does not always reflect good financial
conditions; idle funds or inefficient use of funds can increase the potential
for financial distress. In certain situations, companies may maintain high
liquidity as a precautionary measure, but this can hinder the efficient use of
funds.
The Influence of Leverage on
Financial Distress
Hypothesis three (H3) states that leverage
has a negative effect on financial distress. The regression results indicate
that leverage, measured by the Debt Equity Ratio (DER), has a negative and
significant impact on financial distress, thus this hypothesis is accepted.
Agency theory explains that as leverage increases, managers have greater
responsibility to ensure that the company meets its obligations. Close
monitoring by creditors encourages managers to manage the company more
carefully and efficiently, which can reduce the risk of financial distress.
This study is consistent with the findings of Oktavian & Handoyo (2023), which show that increased leverage can
raise the likelihood of financial difficulties, aligning with agency theory
that suggests higher risks may arise from conflicts of interest between
management and shareholders. However, this study's results contrast with those
of Carmenita et al. (2023), who found that leverage positively influences
financial distress. They argue that high leverage can provide fresh funding for
operations and investments, enhancing the company's ability to cope with
financial difficulties. Furthermore, stringent oversight from creditors may
encourage management to handle finances better, thereby reducing the risk of
financial distress.
The Influence of Interest Rates
on Financial Distress
Hypothesis four (H4) states that interest
rates have a positive effect on financial distress. The regression results show
that the significance value of the interest rate variable is 0.736, indicating
that it is not statistically significant. Therefore, this hypothesis is
accepted, suggesting that changes in interest rates do not have a significant
impact on the financial distress condition of the company. According to
Fisher's Theory, the nominal interest rate consists of the real interest rate
and the expected inflation rate. In the context of property and real estate
companies, the characteristics of long-term assets such as land and buildings
may explain why changes in interest rates do not significantly affect them. If
the real interest rate remains stable, changes in the nominal interest rate
will not have a substantial impact on the interest burden incurred, allowing
companies to better plan their finances and manage debt. Companies in this
sector can also adjust the selling or rental prices of assets in line with
inflation, thereby covering the interest burden. The characteristics of stable
or appreciating assets help companies maintain a healthy financial condition
even with changes in interest rates. Thus, Fisher's theory explains that
companies can anticipate changes in interest rates and inflation, contributing
to their financial stability.
The Influence of Inflation on
Financial Distress
Hypothesis five (H5) posits that inflation
has a positive effect on financial distress. However, the regression results
indicate that inflation has a negative impact on financial distress, leading to
the rejection of this hypothesis. The significance value for the inflation
variable is 0.850, which means it is not statistically significant, suggesting
that inflation does not influence the financial distress of the company.
Fisher's Theory states that the nominal interest rate consists of the real
interest rate and expected inflation. In the context of property and real
estate companies, the characteristics of long-term assets such as land and
buildings, which tend to be stable or appreciate in value, can explain the
ineffectiveness of inflation on financial distress. Companies in this sector
can adjust the selling or rental prices of their assets in line with inflation,
ensuring that revenues remain aligned with inflation changes and that interest
burdens can be managed. As a result, inflation does not have a significant
impact on the financial condition of companies, especially in the context of
relatively low inflation rates. This suggests that the characteristics of
assets and the companies' ability to adjust prices can mitigate the risk of
financial distress despite changes in inflation.
CONCLUSION
Based on the problem formulation, research findings,
and discussion, the following conclusions can be drawn:
1.
Profitability (ROA) has a negative and significant
effect on financial distress.
2.
Liquidity (CR) has a negative and
significant effect on financial distress.
3.
Leverage (DER) has a negative and
significant effect on financial distress.
4.
Interest rates do not have an effect on
financial distress.
5.
Inflation does not have an effect on
financial distress
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Copyright holder: Rini
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