Financial Distress and Earnings Management
An Empirical Study of Non-Financial Firms
Listed on the Indonesia Stock Exchange
Ayu Sheila
Soraya1, Dianwicaksih Arieftiara2
Universitas
Pembangunan Nasional, Indonesia
Email: [email protected]1,
[email protected]2
� Corresponding Author: Dianwicaksih
Arieftiara
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Abstract |
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Financial Distress, Income Management,
Indonesia Stock Exchange |
This study examines the relationship
between financial distress and earnings management among non-financial
companies listed on the Indonesia Stock Exchange during the period 2018�2022.
This study uses a quantitative approach using the modified Jones model to
measure discretionary accruals, with leverage, firm size, and profitability
included as control variables. Data from 342 companies are analyzed to
determine whether companies facing financial distress are more likely to
engage in earnings management as a strategy to improve their financial
performance. The findings reveal that profitability has
the strongest positive effect on earnings management, indicating that firms
with higher profitability are more likely to manipulate earnings to improve
financial results and meet market expectations. In contrast, leverage shows a
significant negative effect, indicating that firms with higher debt levels
are less likely to engage in earnings manipulation due to increased creditor
monitoring and financial discipline. Meanwhile, financial distress and firm
size have minimal impacts, with their coefficients showing no significant
effect on discretionary accruals. These results highlight the importance of
profitability and leverage as key drivers of earnings management while
suggesting that financial distress and firm size play a smaller role in this
context. This study acknowledges limitations, including its focus on
non-financial firms in Indonesia, the five-year observation period, and the
exclusion of additional factors such as governance and macroeconomic
conditions. Future research could address these limitations by expanding the
data set, incorporating more variables, and exploring other emerging markets.
under the terms and conditions of the Creative Commons Attribution (CC BY SA) license (https://creativecommons.org/licenses/by-sa/4.0/). |
1 Introduction
In recent decades, the issue of financial distress has become a major
concern in the global business world. This phenomenon is increasingly
significant along with the increasing dynamics of the global economy, changes
in economic policies, and crises that hit various industrial sectors
Specifically, in Indonesia, financial distress in non-financial companies
listed on the Indonesia Stock Exchange (IDX) is an important issue, especially
in the context of corporate governance which is still developing. Previous
studies have shown that the role of independent commissioners and revenue
management practices in Indonesia are closely related to the financial
condition of companies
The urgency of this research lies in the significant impact of financial
distress on business sustainability. Unethical earnings management practices
can damage investor confidence and hinder overall economic growth (
Literature review shows that the Altman Z-Score model is one of the tools
often used to predict corporate financial distress
This study aims to investigate the relationship between financial distress
and earnings management practices in non-financial companies listed on the IDX
during the period 2018�2022. This study also aims to provide insights that can
help companies, regulators, and other stakeholders in improving financial
transparency and accountability. By referring to the existing literature, this
study will contribute to the understanding of the dynamics of financial
distress and earnings management, as well as their implications for the
stability of the Indonesian financial market
2 Materials and Method
This study uses a quantitative
research method, which is well suited to achieving precision and objectivity in
data analysis. The quantitative approach allows for the systematic collection
and analysis of numerical data, allowing for reliable and accurate
evidence-based conclusions. By combining statistical calculations and
structured methodology, this study seeks to uncover the relationship between
financial distress and income management practices in a transparent,
replicable, and empirically based manner. Furthermore, the quantitative
approach is expected to produce results that are not only reliable but also
verifiable, ensuring that the findings can be generalized to a wider population
while maintaining statistical rigor.
The dataset used in this study
is derived from a comprehensive sample of 342 companies operating in the
non-financial sector, all of which are listed on the Indonesia Stock Exchange.
The selected data spans a five-year period, from 2018 to 2022, providing a
robust temporal framework for analyzing trends and patterns. This time frame
allows the study to capture variations in financial distress and earnings
management practices across different economic conditions, ensuring a more
nuanced understanding of the dynamics at play. The decision to focus on
non-financial companies was made to minimize the potential confounding effects
of financial sector-specific regulations and practices, which may differ
significantly from other industries.
Through the application of
systematic statistical analysis, this study aims to rigorously evaluate the
relationship between financial distress and earnings management, while
considering the influence of control variables such as leverage, firm size, and
profitability. These control variables are included to account for additional
factors that may influence the observed relationship, thereby increasing the
completeness and validity of the analysis. Overall, the methodological approach
adopted in this study is designed to provide clear and actionable insights into
how financial distress affects earnings management practices, especially in the
context of an emerging market such as Indonesia.
Revenue Management Measurement
In earnings management, discretionary accruals
are commonly used, assuming that non-discretionary accruals are determined by
the company's operational conditions, while discretionary accruals are
determined by managers exercising discretion over the accounting policies and
estimates prevailing in a company
To measure earnings management, this study uses
discretionary accruals derived from the calculation method introduced in
Dechow's study, specifically using the Modified Jones Model. The Modified Jones
Model is recognized for its robustness and wide application in academic and
practical research as a reliable method for isolating the discretionary
component from total accruals. This model adjusts for changes in earnings and
property, plant, and equipment to control for normal accrual activity, allowing
for proper identification of earnings manipulation.
The calculation of discretionary accruals using
the Modified Jones Model involves the following equation:
Total Accruals i,t = Net Income i,t
� Cash Flow From Operations i,t
Total accrual value is measured using the
following multiple regression equation:
Total Accrual i,t
/ A i,t-1
= α1(1/ A i,t-1 )
+ α2 (ΔREV i,t / A i,t-1 ) + α3 ( APD i,t / A i,t-1 ) + ε
Non-discretionary accruals are calculated using
the following formula:
NDA i, t =
α 1 (1/ A i,t -1 )
+ α2 (Δ REVi,t / A i,t-1
- ΔREC i,t / A i,t-1 ) + α3 ( APD i,t /A i,t-1 )
Next, discretionary accruals can be calculated
as follows:
DA i, t = (Total Accruals i,t / A i,t-1 ) - NDA i, t
With the following definition:
Total Accruals (TAC i, t ) ����������������� =
Total accruals of the company (i) in year (t)
Net Profit i,t
������������������������������������������������������������ = Net
profit of the company (i) in year (t)
Cash flow from operations���������� ������������� = Cash
from operating activities of the company (i) in
year (t)
A i,t
-1 ������������������������������������������������������� = Total assets of the company (i) in the previous year (t)
Δ
WARNING �������������������������������������������������������� =
Change in Company revenue (i) in year (t)
ΔREC ,t ��������������������������������� = Change in Company receivables (i)
in year (t)
PPE i,t
������������������������������������������������������ = Property, Plant and Equipment of the
Company (i) in year (t)
By applying this model, this study aims to
provide insights into the extent and patterns of earnings management among
financially distressed firms, as well as how these practices vary with factors
such as leverage, firm size, and profitability.
Financial Distress Measurement
In this study, financial difficulties will be
assessed using the Altman Z-Score method, which is recognized as a reliable
tool for evaluating financial health (Zainudin et al., 2023). The Altman
Z-Score is calculated using the following formula:
Z-score = 1.2 A+ 1.4B + 3.3C + 0.6D + 1.0E
Z-Score
= Financial
Distress
A ��������������������������� =
Working Capital / Total Assets
B ��������������������������� =
Retained Earnings / Total Assets
C ��������������������������� =
EBIT / Total Assets
D ��������������������������� =
Market Value of Equity / Total Liabilities
E ���������������������������� =
Sales / Total Assets
Control Variable Measurement
To ensure a comprehensive analysis, this study
incorporates several control variables known to influence earnings management: profitability,
leverage, and firm size. These variables are critical to capturing the broader
financial and operational context in which earnings management practices occur.
The methods used to measure these control variables are as follows:
Profitability
Profitability is an important indicator of a
company's financial performance and its ability to generate returns from its
assets. In this study, profitability is measured using the Return on Assets
(ROA) ratio, which is calculated as:
Return on Assets i,t = Net Income i,t / Total Assets i,t
This ratio reflects the efficiency with which a
company utilizes its total assets to generate net income. A higher ROA
indicates better financial performance, potentially reducing the need for earnings
management. Conversely, companies with lower profitability may be more likely
to manipulate earnings to improve their financial appearance.
Leverage
Leverage represents the extent to which a
company relies on debt to finance its operations. This is measured using the
Debt to Asset Ratio, which is calculated as:
Debt to asset ratio i,t = Total liabilities i,t
/ Total assets i,t
This ratio shows the proportion of a company's
assets that are financed through liabilities. Companies with higher leverage
may face greater financial pressures, increasing the likelihood of engaging in
earnings management to meet debt covenants or reassure creditors.
Company Size
Firm size is another important control variable,
as larger firms often have greater resources and a more established reputation,
which can influence their financial reporting behavior. Firm size is measured
using the logarithm of total assets, calculated as:
Company
size i,t = Log
( Total Assets i,t
)
Larger firms may have greater regulatory
oversight and greater stakeholder scrutiny, potentially reducing their
propensity to engage in earnings management compared to smaller firms.
Data Analysis Techniques
This study uses Microsoft Excel and STATA
version 17 MP Parallel Edition for data analysis. Microsoft Excel will be used
for initial data preparation, cleaning, and basic descriptive statistics,
ensuring the data set is ready for further analysis. STATA, known for its
robust statistical capabilities, will handle the regression analysis, estimate
discretionary accruals using the Modified Jones Model, and examine
relationships between variables. The combination of these tools ensures
efficient, accurate, and comprehensive data analysis, supporting the study�s
goal of producing reliable, evidence-based conclusions.
Descriptive Statistics
This study uses descriptive analysis to
summarize the characteristics of the research sample, which is representative
of the population. Key statistical measures, including mean, standard
deviation, minimum, and maximum, are analyzed to provide insight into data
distribution, variability, and range. These measures help identify patterns,
trends, and anomalies, serve as a basis for further statistical analysis and
ensure the data set aligns with the research assumptions. Descriptive analysis
offers a clear overview of the data, facilitates transparency and prepares for
more advanced techniques.
Regression Model Feasibility Testing
Panel data analysis is a statistical method that
accounts for variation in data across two dimensions: cross-sections,
representing different entities, and time series, representing observations
over multiple time periods. This dual-dimensional approach allows for a more
nuanced understanding of the relationships among variables by capturing both
inter-entity and intra-entity variation. To determine the most appropriate
model to analyze panel data, several diagnostic tests will be performed. These
include the Chow test, which evaluates whether a fixed effects model is more
appropriate than a pooled ordinary least squares (OLS) model by testing for
significant differences in intercepts across entities. In addition, the Hausman
test will be applied to compare fixed effects and random effects models,
helping to identify the best model based on the exogeneity and consistency
assumptions. The Lagrange Multiplier (LM) test will also be performed to assess
whether a random effects model is preferable to a pooled OLS model. By
conducting these tests, this study ensures the selection of a statistically
robust and appropriate model to analyze the relationships between financial
distress, earnings management, and control variables, while accounting for the
complex structure of the panel data set.
Chow Test
The Chow Test is performed to determine whether
a common effects model or a fixed effects model is most appropriate to analyze
a data set. This test evaluates the F-probability value to assess whether the
fixed effects model provides a significantly better fit than the common effects
model by examining the difference in intercepts across entities. The hypothesis
for the Chow Test is as follows:
H 0 : common
effect model (prob. > 0.05)
H 1 : fixed
effects model (prob. < 0.05)
Hausman test
The Hausman test is used to choose between a
fixed effects model and a random effects model by examining the relationship
between the predictors and individual effects. This test determines whether the
individual-specific effects are correlated with the independent variable. The
hypothesis for the Hausman test is:
H 0 : random effects model (prob. > 0.05)
H 1 : fixed effect model (prob.0.05)
Lagrange
Multiplier Test
The
Lagrange Multiplier Test is performed to decide between a common effects model
and a random effects model based on the residual variance. It evaluates whether
the random effects contribute significantly to explaining the variability in
the data. The hypothesis for this test is:
H 0 : general effect model (prob. value > 0.05)
H 1 : random effects model (prob. value < 0.05)
Classical
Assumption Test
Multicollinearity
Test
Multicollinearity
is tested using the Variance Inflation Factor (VIF). If the VIF value is less
than 10, multicollinearity is absent. If the VIF value exceeds 10,
multicollinearity is present among the variables.
Heteroscedasticity
Test
The
heteroscedasticity test checks whether the error variance is constant across
observations. If the p-value > 0.05, there is no heteroscedasticity. If the
p-value < 0.05, heteroscedasticity is present.
Hypothesis Testing
F-Statistic Test
(Simultaneous Test)
This
test measures the collective effect of independent variables on the dependent
variable. If the calculated F-value > F-table or p-value < 0.05, the null
hypothesis (H0) is rejected and the alternative hypothesis (Ha) is accepted,
indicating that the independent variables collectively have a significant
effect on the dependent variable.
Coefficient of
Determination (R�)
This
measure indicates the ability of the independent variable to explain the
variance in the dependent variable. An R� value close to 1 indicates that the
independent variable provides almost all the information needed to predict the
variance in the dependent variable.
T Statistic Test
(Partial Test)
The
t-test assesses the individual effect of each independent variable on the
dependent variable. If the calculated t-value > t-table or p-value <
0.05, the null hypothesis (H0) is rejected, and the alternative hypothesis (Ha)
is accepted, indicating that the independent variables have significant
individual effects on the dependent variable. If the t-value < t-table or
p-value > 0.05, the null hypothesis is accepted, indicating that the
independent variables do not have significant individual effects on the
dependent variable.
Regression
Analysis
The
regression analysis technique used in this study is designed to test the
research hypothesis by evaluating the relationship between financial distress
and earnings management, while taking into account the influence of control
variables such as profitability, leverage, and firm size. The model is
represented by the following equation:
EMi,t
=α+β1FDi,t+β2LEVi,t+β3SIZEi,t+β4PROFi,t+ε
EM i,t ���������������������������������� = Earnings Management
FD i,t ������������������������������������ = Financial Distress
LEV i,t ���������������������������������� = Benefit
SIZE
i,t ��������������������������������� = Company size
PROF
i,t ������������������������������ = Profitability
α
������������������������������������������� =
Constant
β1,
β2, β3, β4, β5 ����������� =
Regression Coefficients
ε
�������������������������������������������� =
estimated error
3 Results and Discussion
���� Chow
Test
The Chow test result shows a probability
value of 0.9577, which is greater than the significance level of 0.05. This
indicates that there is no significant difference in the intercept across the
entities being analyzed. Consequently, the common effects model is determined
as the most appropriate model to analyze the panel data in this study. The
common effects model assumes that all entities share the same intercept,
simplifying the analysis by treating the data set as homogeneous without
entity-specific effects.
Hausman test
The Hausman test result shows a probability
value of 0.000, which is less than the significance threshold of 0.05. This
implies that there is a statistically significant difference between the fixed
effects and random effects models. Consequently, the fixed effects model is
considered the most appropriate model for the analysis. The fixed effects model
takes into account entity-specific characteristics that do not vary over time,
ensuring that unobservable factors unique to each entity are controlled, leading
to more reliable and robust results in the context of this study.
Lagrange
Multiplier Test
The Lagrange Multiplier (LM) test results show a
probability value of 1, which is significantly greater than the significance
threshold of 0.05. This indicates that the random effects model is not
appropriate, as there is no evidence to suggest that the random effects model
provides a better fit than the pooled ordinary least squares (OLS) model.
Therefore, the common effects model is chosen as the most appropriate model to
analyze panel data in this context. The common effects model assumes uniformity
across entities, treating all observations as homogeneous without taking into
account entity-specific effects.
Classical Assumption Testing
Multicollinearity
Test
The results
of the multicollinearity test revealed a Variance Inflation Factor (VIF) value
of 2.25, which is well below the threshold of 10. This indicates that there is
no significant multicollinearity among the independent variables in the
regression model. A low VIF value indicates that the predictor variables are
not highly correlated with each other, ensuring that the regression
coefficients are stable and reliable. This confirms that multicollinearity is
not a concern in this study, allowing for accurate interpretation of the
relationships between variables.
Heteroscedasticity
Test
The
heteroscedasticity test results show a Prob value > chi2 of 0.000, which is
less than the threshold of 0.05. This indicates the presence of
heteroscedasticity in the regression model, meaning that the residual variance
is not constant across observations. To address this issue, a corrective action
is applied using robust standard errors, which adjusts the standard errors of
the coefficients to remain consistent even in the presence of
heteroscedasticity. By applying this adjustment, the reliability of the
p-values and confidence intervals is maintained, ensuring accurate statistical
inference even when heteroscedasticity is detected.
Hypothesis Testing
F-Statistic Test
(Simultaneous Test)
F-Statistic Value:
The F-statistic is 481.13.
Prob > F: The
p-value associated with the F-statistic is 0.0000.
The
F-statistic test is used to determine whether all independent variables
included in the regression model collectively have a statistically significant
effect on the dependent variable. In this study, the results of the F-statistic
test show an F-statistic value of 481.13 with a related p-value (Prob > F)
of 0.0000. Since the p-value is significantly lower than the standard
significance threshold of 0.05, the null hypothesis (H0 )
is rejected. The rejection of the null hypothesis implies that the
independent variables�financial distress, leverage, firm size, and
profitability�have a significant simultaneous impact on the dependent variable,
earnings management.
These
results highlight the importance of these independent variables in influencing
earnings management practices. They suggest that variation in the level of
earnings management cannot be adequately explained by a single independent
variable alone but instead results from the combined effects of financial
distress, leverage, firm size, and profitability. The statistical significance
of the F-statistic further validates the overall fit of the regression model,
confirming that the included independent variables provide meaningful insights
into the determinants of earnings management.
By
demonstrating the simultaneous influence of these variables, these findings
underscore the importance of considering a multidimensional approach in
understanding earnings management practices. These conclusions support the
theoretical framework of the study and provide a strong basis for further
analysis of the individual contributions of each independent variable through
additional tests, such as t-tests for individual significance.
Coefficient of
Determination (R�)
R-squared: The
R-squared value is 0.5304.
Adjusted
R-squared: The Adjusted R-squared value is 0.5293.
The
determination coefficient R-squared of 0.5304 (53.04%) indicates that this
model explains most of the variability in the dependent variable. In other
words, 53.04% of the variability in the dependent variable, accrual earnings
management, can be explained by the independent variables: financial distress,
leverage, firm size, and profitability. This indicates that this model has
substantial explanatory power, as it captures more than half of the variability
in earnings management. The slightly lower adjusted R-squared of 52.93%
indicates that the results remain similar even after adjusting for the number
of predictor variables in the model.
Overall,
the model has moderate explanatory power as it captures about half of the
variability in the dependent variable. Although these results indicate a fairly
good fit, there is still some unexplained variability, suggesting that the
model could be further improved or that other factors may influence accrual
earnings management. The remaining 46.96% of unexplained variability suggests
that other factors, not included in the model, may also influence accrual
earnings management. This opens up the possibility for further refinement of
the model or exploration of additional variables that may improve its
predictive accuracy.
T Statistic Test
(Partial Test)
The
t-statistic test is used to assess the individual significance of each
independent variable in explaining the dependent variable, earnings management,
in the regression model. The results for each variable are as follows:
Altman Z Score
The
p-value for Altman Z Score is 0.832, which is greater than the significance
threshold of 0.05. Therefore, we fail to reject the null hypothesis (H0) indicating
that financial distress has no statistically significant effect on earnings
management. This suggests that financial distress, as measured by Altman Z
Score, is not a key factor influencing earnings management in this model.
Leverage
The
t-statistic test result for leverage shows a p-value of 0.000, which is
significantly below the threshold of 0.05. This leads to the rejection of the
null hypothesis (H0), which indicates that leverage has a statistically
significant negative effect on mod_jones_dac
(modified Jones discretionary accruals). This finding implies that as a firm�s
leverage (measured by the debt-to-asset ratio) increases, the level of earnings
management, as represented by discretionary accruals, tends to decrease. The
negative relationship can be attributed to the fact that higher leverage often
subjects firms to greater scrutiny from creditors and investors, thereby
limiting management�s ability to manipulate earnings. This increased scrutiny
can discourage opportunistic accounting practices, promoting more transparent
financial reporting.
The
results of this study underline the role of leverage as an important factor
influencing managerial behavior in financial reporting, especially in companies
where debt obligations play an important role in their capital structure.
Company Size
The
t-statistic test result for firm size shows a p-value of 0.710, which is
greater than the significance threshold of 0.05. Consequently, we fail to
reject the null hypothesis (H0), concluding that firm size does not have a
statistically significant effect on earnings management. This finding suggests
that firm size, as measured by the logarithm of total assets, does not play a
significant role in influencing the extent of earnings management practices in
this study. Larger firms are typically subject to higher levels of regulatory
oversight and stakeholder scrutiny, which may deter earnings manipulation,
while smaller firms may have less oversight but potentially lower capacity for
complex earnings management techniques. However, this result suggests that in
this context, firm size alone is not a determining factor in explaining
variations in discretionary accruals.
This
insignificant relationship may also imply that other factors, such as industry-specific
characteristics, market conditions, or internal governance practices, may have
a more direct influence on earnings management than firm size. Further
investigation into these variables may provide additional insights into the
drivers of discretionary accruals.
Profitability
The
t-statistic test result for profitability reveals a p-value of 0.000, which is
significantly less than the threshold of 0.05. Therefore, we reject the null
hypothesis (H0), concluding that profitability has a statistically significant
positive effect on earnings management.
These
findings suggest that as profitability, as measured by profitability,
increases, so does the level of earnings management through discretionary
accruals. This positive relationship suggests that managers of more profitable
firms may have stronger incentives to engage in earnings manipulation to
further improve reported financial performance. High profitability may create
pressure to maintain or exceed market expectations, leading to the use of
discretionary accruals to smooth earnings or present a more favorable financial
position.
These
results underscore the role of profitability as an important determinant of
earnings management. It highlights the importance of closely monitoring
accounting practices in highly profitable firms to ensure that financial
statements accurately reflect their true economic performance, reducing the
risk of misleading stakeholders.
Regression
Analysis
EMi,t
=0.0205058 +0.0000816 FDi,t � 0.0316403
LEVi,t+0.0003914 SIZEi,t+0.1503039 PROFi,t
Financial
Difficulties:
Coefficient:
0.0000816
Interpretation:
A one-unit increase in financial distress results in a minimal increase of
0.0000816 in earnings management, assuming all other variables are held
constant. This very small positive effect suggests that financial distress, as
measured by the Altman Z Score, has a very small impact on earnings management.
Leverage:
Coefficient:
-0.0316403
Interpretation:
A one-unit increase in leverage is associated with a 0.0316403 decrease in
earnings management, holding other factors constant. This negative relationship
suggests that higher leverage reduces earnings management activity, potentially
due to increased creditor scrutiny or tighter financial discipline.
Company Size:
Coefficient:
0.0003914
Interpretation:
A one-unit increase in firm size causes a very small increase of 0.0003914 in
earnings management, holding other variables constant. This indicates a
negligible positive relationship between firm size and earnings management,
indicating that firm size has little or no practical effect on earnings
management in this model.
Profitability:
Coefficient:
0.150339
Interpretation:
A one-unit increase in ROA is associated with a 0.1503039 increase in earnings management,
assuming other variables are held constant. This strong positive coefficient
suggests that higher profitability significantly increases earnings management
activity, likely reflecting managerial incentives to improve reported financial
performance.
4 Conclusion
This study provides an in-depth exploration of the factors influencing
earnings management, with a particular focus on discretionary accruals as
measured using the modified Jones model. The findings highlight important
insights into the role of profitability, leverage, financial distress, and firm
size in shaping earnings management practices. Among these variables,
profitability and leverage stand out as the most significant drivers, while
financial distress and firm size exhibit minimal impact.
Based on these findings, the study recommends that companies facing
financial distress focus on improving transparency and accountability in their
financial reporting. Implementing a stronger oversight system, including the
active role of independent auditors and strengthening corporate governance, can
help reduce incentives for earnings management practices. In addition,
companies are advised to optimize their capital structure to reduce the
pressure from financial distress, while maintaining the trust of investors and
other stakeholders. In doing so, companies can achieve better financial
stability while maintaining the integrity of their financial statements.
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