Keywords

Capital Structure, Audit Firm Size, Earnings Management, Long-term Debt, Short-term Debt, Palestine Stock Exchange.

Introduction

To finance their activities, companies may use either debt financing, including interest-bearing liabilities, or equity financing, where they acquire capital through share ownership (Czapi?ska, 2013). According to Song (2005), and Tiwari & Krishnankutty (2015), the capital structure reflects the debt-equity combination that the company employs to finance its assets.
The capital structure choice is of major importance given that it has a considerable effect on the cost of capital of the company, its level of risk, and the possibility of long-term performance (Danso et al., 2021). Hence, the optimal capital structure combination remains a crucial research topic in corporate finance (Jouida, 2018), and a question that has always attracted practitioners and academics (Parsons & Titman, 2009).

On the other hand, Companies’ stakeholders, who need fair and accurate information to make decisions (Bachner, 2015), use financial statements where the companies disclose their financial position and performance (Nugroho et al., 2018). However, Financial disclosure is effective only if it provides credible information relevant to decision-making (Wagner, 2019). The quality of financial statements builds upon their reliability and usefulness (Dimitropoulos & Asteriu, 2009). According to Zhai & Wang (2016), high-quality accounting information offers a comprehensive understanding of a firm’s fundamentals. However, managers often practice earnings management to manage financial statements outcomes (Mahjoub & Miloudi, 2015). They do so by utilizing flexible accounting rules and loopholes (Isiaka, 2019; Shu & Thomas, 2019). Although earnings management has no standardized definition, Schipper (1989), explains that managing earnings entails taking deliberate actions within the boundaries of accounting rules to manage the reported income (i.e., earnings). Riahi-Belkaoui (2004), however, defines earnings management as a way for directors to get personal gains while interfering with the external financial reporting process.

Although previous literature has addressed the association between financing decisions and financial reporting quality, it has not reached a clear consensus. For example, various studies explain that businesses with large amounts of debt are more inclined to use earnings management to prevent breaching their debt covenants (e.g. Alzoubi, 2018; Beatty & Weber, 2003; Dichev & Skinner, 2002; Lazzem & Jilani, 2018; Sweeney, 1994). Alternatively, Jensen (1986) indicates that debt financing imposes an effective monitoring technique that would lead to fewer earnings management practices. Also, higher leverage involves greater debt obligations, leaving directors with lower levels of free cash flows and thus limiting their capability to engage in earnings management practices (Jensen, 1986).

From another side, audit quality has been proven to be negatively related to earnings management (Aliyu et al., 2015; Houqe et al., 2017). That is, in addition, Titman & Trueman (1986) indicate that a high audit quality boosts the credibility of financial statements. According to DeAngelo (1981), audit quality is the degree to which the auditor is able to detect fraud and inaccuracies in financial statements, and disclose them as they are. Determinants used to identify audit quality vary. Among the major proxies identified in previous literature, this study focuses on audit firm size. DeAngelo (1981) explains that the size of the audit firm is a determinant of audit quality as larger firms are more exposed to risks if they fail to disclose substantial misstatements.

The Palestinian Context

Palestine stock exchange (PEX) was established in 1995 as a private company aimed at augmenting investment in Palestine and with the main mission of providing a “world-class” trading atmosphere that is characterized by transparency and efficiency (PEX, 2022). 48 Palestinian firms are listed on PEX, divided into the sectors of service, insurance, banking, industry, and investment sectors. The Palestinian Capital Market Authority (PCMA) regulates PEX (PEX, 2022). PEX with its listed companies, is considered the major means of attracting foreign investments and maintaining national savings, which significantly contributes to the Palestinian economy by pooling money into productive projects, decreasing unemployment rates, and thus improving the Palestinian standard of living (see for example Abuamsha, 2021; Badwan & Atta, 2020; Nasr et al., 2008). In addition, Safi (2018) identifies a significant causal relationship between PEX performance and Palestinian economic growth.

Nevertheless, PEX exists in a high-risk environment (Awwad & Razia, 2021), due to the fact that it belongs to unique economic circumstances. The Palestinian economy operates under restrictions the Israeli government imposes, regulating Palestinian trade and people movement (Samhouri, 2018; Vaggi, 2011). Israeli security closures and movement restrictions (e.g. checkpoints) tighten the accessibility to markets and also dramatically increase transaction costs, not to mention that Israel collects most of the indirect tax revenues before transferring them back to the Palestinian government (Vaggi, 2011). In addition are high taxes imposed on products Palestinian companies import (Ahmad, 2011). Moreover, the Palestinian Monetary Authority (PMA), which was established after the 1994 Paris Economic Protocols, cannot produce and run its own Palestinian currency. Hence, it cannot establish independent monetary policies through which economic needs can be addressed (IMF, 2012). Besides, according to Paris Economic Protocols (1994), Palestinian companies are required to use the Israeli shekel for commercial trading and tax clearances, which leads to another important economic obstacle aroused as a result of the direct influence spilled over when the Israeli government adopts different monetary policies (Muhtaseb & Eleyan, 2021). These circumstances are reflected by the main economic indicators when compared between Palestine and Israel; for example, according to most recent figures published by WordData.info, the unemployment rate in Palestine is 24.9% versus 5.1% in Israel. In contrast, the average income in Palestine is $4,220 versus $49,560 in Israel (WorldData, 2022).

Given that the Palestine stock exchange sector forms a very important pillar in the Palestinian economic context and that the relationship between capital structure and earnings management has been overlooked in the Palestinian context, this research aims to examine this relationship while considering the use of Long-term and Short-term debts. It also aims to explore the moderating impact of the audit firm size on this relationship if any. More precisely, this research aims to answer the following questions:

1. How does long-term debt impact the level of earnings management in the context of Palestinian Listed Companies?
2. How does short-term debt impact the level of earnings management in the context of Palestinian Listed Companies?
3. Does audit firm size influence the relationship between long-term debt and earnings management in the context of Palestinian Listed Companies?
4. Does audit firm size influence the relationship between short-term debt and earnings management in the context of Palestinian Listed Companies?

Literature Review and Hypotheses Development

Capital Structure

To finance their activities, companies may use either debt financing, including interest-bearing liabilities, or equity financing, where they acquire capital through share ownership (Czapi?ska, 2013). The debt-equity decision remains the most extensive research area in corporate finance; therefore, the ideal capital structure arrangement is a major decision, given that it has a profound impact on the cost of capital, riskiness, and performance of the company (Danso et al., 2021). Early research was conducted by Modigliani & Miller (1958), who developed a theory valuing firms and shares in a world of uncertainty by explaining how it can be applied to answer questions regarding cost of capital. They brought the relevant specialists’ concerns together and provided an operational definition of the "cost of capital." Their research formed a building block for the development of a general equilibrium model through which stock prices are determined.

Krstevska et al. (2017) argue that Modigliani and Miller's theory forms a foundation of modern corporate finance. In their first argument, Modigliani & Miller (1958) assume that all bonds yield constant earnings per unit of time and that investors trade bonds and stocks in a perfect market. This parameter implies that bonds are not risky and that there are no arbitrage opportunities. Further, they ignore the tax factor. Through this, they develop a model which indicates that the capital structure of a firm does not influence its average cost of capital since individuals and firms can borrow at risk-free interest rates. However, in their second argument, they take the risk factor into account and thus add a risk premium to calculate the rate of return estimate. Under this argument, Modigliani & Miller (1958) suggest that debt increases both risks and returns to stockholders. When the tax influence is considered, part of the decrease in the firm's value is offset by the tax shields because leveraged firms have a lower tax liability than unleveraged ones. Still, Modigliani & Miller (1958) argue that the tax shield benefit balances out the increase in investors’ tax disadvantage, which would lead investors to ask for higher pretax returns. However, even under this model, financial distress is ignored. Nevertheless, considering that financial distress probabilities increase with the increase in leverage, in the extended model, the present value of the financial distress cost is subtracted to show how it lowers the firm's worth. Hence, under this scenario, the ideal debt-equity combination is a tradeoff between tax shield benefits and costs of bankruptcy.

Researchers, However, considered Modigliani and Miller's assumptions unrealistic and restricted see for example (Breuer & Gürtler, 2008; Elliott & Santos, 2012). This triggered them to further address the issue of the debt-equity combination. Mile stones include (Modigliani & Miller, 1963; DeAngelo & Masulis, 1980; Modigliani, 1982; Myers, 1984; Mursalim & Kusuma, 2017). Further, some authors explain firms’ specific or macroeconomic factors could lead to capital structure changes (Gungoraydinoglu & Öztekin, 2011; Korajczyk & Levy, 2003). These factors include a firm's growth, asset tangibility, profitability, liquidity, size, age, non-debt tax shields, and risk (Shil et al., 2019).

Earnings Management

Firms’ stakeholders, who expect fair, accurate, and credible data for decision-making (Bachner, 2015), usually use financial statements where companies disclose their financial position and performance (Nugroho et al., 2018). The level of dependability and utility of financial statements' contents determines the quality of those statements (Dimitropoulos & Asteriou, 2009). Many researchers suggest that high-quality financial reporting decreases information asymmetry between managers and shareholders and thus enhances capital investment efficiency (Biddle et al., 2009; Bushman & Smith, 2001; Gomariz & Ballesta, 2014).

Managers often practice earnings management to manage financial statements results (Mahjoub & Miloudi, 2015). They do so by deploying various techniques to manage earnings, such as adjusting the timing of assets’ sales (Wang, Tung, Chen-Chang, Wang, & Ching-Hui, 2010), changing the aging of receivables and bad debt ratios (Lee & Choi, 2016), and accelerating the recognition of revenues or delaying the recognition of expenses (Franz, HassabElnaby, & Lobo, 2014). Regardless of the approach, earnings management is perceived to misrepresent information, lower its quality (Sun & Al Farooque, 2018), and lead to intense negative outcomes regarding stock returns and operating performance (Kothari et al., 2016). Additionally, previous literature identifies various motives behind earnings management such as to influence compensation, to avert reporting losses, to meet analysts’ forecasts, to affect credit ratings, and to avoid debt-covenant violations.

Capital Structure and Earnings Management

Several authors addressed the association between the financing decisions and earnings management. For instance, Al-Mohareb & Alkhalaileh (2019) identify a significant and positive relationship between financial leverage and managing earnings. They clarify that their results are in line with earlier studies which claim that businesses with greater levels of debt tend to practice more earnings management transactions so that they can dodge covenant violations e.g. (Alzoubi, 2018; Beatty & Weber, 2003; Dichev & Skinner, 2002; Lazzem & Jilani, 2018; Sweeney, 1994). Additionally, Haw et al. (2004) suggest that highly leveraged firms tend to manage earrings to overcome accounting constraints and thus enhance their debt contracting negotiation power. Therefore, the first hypothesis of the study is stated as:

H₁: There is a significant positive relationship between long-term debt and earnings management in the Palestinian listed companies' context.

Short-Term Debt vs. Long-Term Debt

Previous literature has not paid much attention to the difference between short-term and long-term debts when linked to earnings management. Vijayakumaran & Vijayakumaran (2019) argue that short-term debt increases liquidity concerns and thus lowers the firm’s creditworthiness because companies must make repayments sooner. Therefore, when firms choose short-maturity debt, they compromise between both the cost arising from underinvestment problems and the cost of liquidity risk. Moreover, Fung & Goodwin (2013) explain that the monitoring activities of creditors in the case of short-term debt may more frequently result in the withdrawal of funds, which would raise the costs of renegotiation. Consistently, Datta et al. (2005) indicate that managers expose themselves to extra monitoring activity from the creditors ’side by choosing short-term debt.

Very few studies address the connection between short-term debt and earnings management and their findings are inconsistent. For example, Afza & Rashid (2014) indicate that managing earnings is positively associated with short-term debt but inversely related to long-term and total debts, which is explained by the higher monitoring activity from the creditors. On the other hand, Fields et al. (2018) report that as short-term debt increases, debt refinancing pressures advance, and earnings management is higher. Alternatively, Trung et al. (2020) explain that earnings management is low at lower debt levels, in contrast, it increases at higher levels of debt. Since there is no clear consensus about the relationship between short-term debt and earnings management, and taking into consideration the paucity of research about this particular topic, this study hypothesizes the following.

H₂: There is a significant relationship between short-term debt and earnings management in the Palestinian listed companies' context.

Audit Firm size and Earnings Management

DeAngelo (1981) explains that the size of the audit firm is a determinant of audit quality as larger firms are more exposed to risks if they fail to disclose substantial misstatements. Bauwhede et al. (2000) and Lawrence et al. (2011) indicate that larger audit firms execute better audits by adhering to training programs and standardized audit techniques. Further, Choi et al. (2010) suggest that larger audit firms are less likely to surrender to a client’s pressure to overlook financial misstatements. Geiger & Rama (2006) find that Type I and II error rates are lower for larger audit firms.

Most of the previous literature indicate that larger audit firms are associated with less earnings management (Chen et al., 2005; Chen et al., 2014; Lopes, 2018; Zhou & Elder, 2001; Zuo & Guan, 2014). Hence, the study hypothesizes the following third and fourth hypotheses:

H₃: Larger audit firms negatively influence the positive relationship between long-term debt and earnings management.

H₄: Larger audit firms negatively influence the significant relationship between short-term debt and earnings management.

Methodology

This research draws on data that is extracted from published financial information of the companies listed on PEX. There are 48 companies listed on PEX, which are divided into service, insurance, banking, industry, and investment sectors. The study excludes four of the listed companies since two of them has been listed only very recently (in 2020, and 2021), and therefore lacks the information needed for the study’s goal. The other two are classified as Islamic banks, where managers need to comply with the Islamic Shari’ah rules and regulations, which prohibit undergoing interest-bearing transactions. This implies a difference in the financing activities between Islamic banks and other modes (Archer et al., 1998; Sarker, 1999). Thus, the remaining 44 Palestinian listed companies make up the study's final sample.

Variables and Variables Measurement

Independent Variables: Long-Term Debt and Short-Term Debt

The study accounts for capital structure using both long-term debt and short-term debt. Based on a respective strand of previous literature (Ashraf et al., 2017; Chen, 2014; Danso et al., 2021; Daskalakis et al., 2017; Forte & Botelho, 2020; Li et al., 2018; Mboi et al., 2018; Shil et al., 2019), the ratio of long-term debt to total assets is used to assess long-term debt, and the ratio of short-term debt to total assets is used to measure short-term debt.

Dependent Variable: Earnings Management

A widely accepted determinant of managing earnings is “discretionary accruals”, which indicates the changes made to the accounting component of earnings (Fung & Goodwin, 2013). Previous literature has extensively used the original Jones model (Jones, 1991) and the modified model (Dechow et al., 1995) (Anagnostopoulou & Tsekrekos, 2017; Fields et al., 2018; Garcia?Blandon & Argiles?Bosch, 2018; Hoang & Phung, 2019; Kalbuana et al., 2021; Mendoza et al., 2020). However, this study determines discretionary accruals using a more recent model which Kothari et al. (2005) developed, and where they also consider the firm’s performance:

TAt/ At -1= α1 (1/ At -1) + α2 (Δ REVt- Δ RECt) / At-1 +α3 (PPTt/At-1) + α4 ROAt + εt

Where,
TAt: is total accruals for year t
α1, α2, α3: are the firm’s specific parameters
At-1: is total assets for year t-1
ΔREC: is the difference between net receivables in year t and net receivables in year t-1
PPEt: is gross property, plant, and equipment for year t
ROAt: is the return on assets for year t
εt: is the residual, representing the discretionary portion of the total

Since the purpose is to test earnings management quality rather than the direction, the absolute value is considered (Fung & Goodwin, 2013; Garcia?Blandon & Argiles?Bosch, 2018; Hickman et al., 2021; Mendoza et al., 2020; Mongrut & Winkelried, 2019; Pelucio-Grecco et al., 2014; Rezaee et al., 2020).

Moderating Variable

Audit firm size is used as a moderating variable. Prior literature focused on big audit firms versus non- big audit firms, as a proxy of the audit firm size, indicating that larger audit firms are more likely to discover and restrain earnings management (Becker et al., 1998; Chen et al., 2011; Iatridis, 2012; Jordan et al., 2010; Lin & Hwang, 2010; Van Tendeloo & Vanstraelen, 2008). Therefore, the study uses the “big- four” and “non-big four” characteristic to determine audit firm size.

Control Variables

The study controls for a set of variables (identified in Table 1 below), which previous literature has proved to influence the level of EM:

Table 1 Control Variables
Control Variable	Supporting Literature
Firm size	(Ali et al., 2015; Nalarreason et al., 2019; Türegün, 2018)
Growth opportunities	(AlNajjar & Riahi?Belkaoui, 2001; Madhogarhia et al., 2009)
Asset tangibility	(Iatridis & Kilirgiotis, 2012; Mendoza et al., 2020)
Board of directors size	(Asogwa et al., 2019; Ismail et al., 2010; Kao & Chen, 2004)
Cash flow intensity	(Burgstahler & Dichev, 1997; Hastuti et al., 2018; Roychowdhury, 2004)

Table 2 below demonstrates an overall summary of the variables that this study uses, with their

Table 2 Variables Of The Study
Variable	Role	Measurement
Long- term debt (LNGDBT)	Independent variable	Long- term debt/ Total assets
Short- term debt (SRTDEBT)	Independent variable	Short- term debt/ Total assets
Earnings Management (EM)	Dependent variable	Kothari et al. (2005) model
Audit firm size (AUDITSIZE)	Moderating variable	“big-four” and “non-big four” characteristic
Firm size (FIRMSIZE)	Control variable	Log of assets
Firm growth (FIRMGROWTH)	Control variable	Annual percentage change in total assets
Asset tangibility (ASTTANG)	Control variable	Long-term assets
Board size (BODSIZE)	Control variable	Number of directors on the board
Cash flow intensity	Control variable	Cash flow from operations/ Total assets

roles and measurements:

Analysis and Results

The descriptive statistics of the main variables deployed in the study are presented in Table 3 below. They provide an overview of the data used in the study:

Table 3 Descriptive Statistics
	N	Minimum	Maximum	Mean	Std. Deviation
LNG DBT	423	0.00	11.830213	0.095873	0.73865
SRT DBT	429	0.00	33.006	0.15089	12023305.510
DA	430	0.00	10	0.43	0.848
FIRM SIZE	430	13.14	22.48	17.857	1.701
FIRM GROWTH	430	-1-	993	2.53	47.930
ASSET TANG.	430	0	5	0.19	0.498
BOD SIZE	420	4	15	8.82	2.218
CSHFLWINTSTY	429	-0.26-	1.42	0.0438	0.10435
AUDIT SIZE	425	0	2	0.73	0.448

In terms of long-term and short-term debts, the results show that the mean values for the study sample are 0.095873 and 0.15089, respectively. The results also show that for some companies, the capital structure did not involve debt at all. As for discretionary accruals, the mean is 0.43, and the standard deviation of 0.848 reveals a big variation between the companies listed on PEX within the time period of the study.

Testing Hypotheses 1 and 2

The following multiple regression model is used to test the first two hypotheses of the study:

EMit = β0 + β1LNGDBTit + β2SRTDBTit + β3FIRMSIZEit + β4FIRMGROWTHit + β5ASTTANGit + β6BODSIZEit + β7CSHFLWINTSTY + εit

Where,
EMit: is earnings management for firm i at time period t
β0… β7: are the model parameters
LNGDBTit: is Long-term debt for firm i at time period t
SRTDBTit: is short-term debt for firm i at time period t
FIRMSIZEit: is the firm size for firm i at time period t
FIRMGROWTHit: is the firm growth for firm i at time period t
ASTTANGit: is the assets tangibility for firm i at time period t
BODSIZEit: is the board of directors’ size for firm i at time period t
CSHFLWINTSTY: is the cash flow intensity for firm i at time period t
εit: is the error term for company i at time period t.

Table 4 below displays the results the first multiple regression analysis.

Table 4 Model 1 Multiple Regression Analysis
Predictors	B	R2	ΔR2
Control Variables		0.317***
FIRMSIZEit	-0.227***
FIRMGROWTHit	0.01***
ASTTANGit	0.11
BODSIZEit	0.05**
CSHFLWINTSTY	0.52
Independent variables		0.365***	0.048***
LNGDBTit	0.07
SRTDBTit	-2.06***

Results reveal that the independent variables (long-term and short-term debts) explain 0.048 of the change in the dependent variable (discretionary accruals) while holding all other predictors constant and that this result is statistically significant at 1%. The results also depict a weak positive, but insignificant relationship (p-value > 0.05), between long-term debt and discretionary accruals, hence H1 is rejected.

On the other hand, the table shows that short-term debt and discretionary accruals are negatively associated (coefficient = -2.06) and that this relationship is significant at 1%, which suggests that for firms that choose more short-term debt financing, earnings management is less likely to occur; therefore, H2 is accepted.

Testing Hypotheses three and Four

To test the third and fourth hypotheses of the study, audit firm size in incorporated with the previous model:

EMit = β0 + β1LNGDBTit + β2SRTDBTit + β3AUDITSIZEit + β4LNGDBT*AUDITSIZEit + β5SRTDBT*AUDITSIZEit + β6FIRMSIZEit + β7FIRMGROWTHit + β8ASTTANGit + β9BODSIZEit + β10CSHFLWINTSTY + εit

Where,

AUDITSIZEit: is the audit firm size for firm i at time period t, and
The following table displays the results Table 5:

Table 5 Model 2 Multiple Regression With Moderating Variable Analysis
Predictors	B	R2	ΔR2
Control Variables		0.318***
FIRMSIZEit	-0.226***
FIRMGROWTHit	0.08***
ASTTANGit	0.09
BODSIZEit	0.05**
CSHFLWINTSTY	0.67
Independent variables		0.365***	0.047***
LNGDBTit	-1.2
SRTDBTit	-1.8***
Moderation		0.38***	0.04
INTERACTION1	0.44
INTERACTION2	1.29

The results indicate that although the audit firm size influences the level of associations between short-term and long-term debts and earnings management (coefficients = 0.44 and 1.86, respectively), this influence is insignificant (p-value > 0.05). Therefore, these results lead to the rejection of H3 and H4.

Sub-Periods Analysis

In order to check for the robustness of the results across time, the sample is split into two sub-periods; 2011- 2016, and 2016-2020. Then the model is tested again for each sub-period separately. Results are displayed in Table 6:

Table 6 Sub-Periods Analysis
Sub-period (2011-2015)
Model 1
Predictors	B	R2	ΔR2
Control Variables		0.339***
FIRMSIZEit	-0.33***
FIRMGROWTHit	0.08***
ASTTANGit	0.06
BODSIZEit	0.08**
CSHFLWINTSTY	0.38
Independent variables		0.381***	0.042***
LNGDBTit	4.18
SRTDBTit	-2.18***
Model 2
Predictors	B	R2	ΔR2
Control Variables		0.339***
FIRMSIZEit	-0.31***
FIRMGROWTHit	0.09***
ASTTANGit	0.03
BODSIZEit	0.11***
CSHFLWINTSTY	0.61
Independent variables		0.381***	0.042
LNGDBTit	-2.46
SRTDBTit	-2.17**
Moderation		0.413***	0.033**
Interaction 1	0.9
Interaction 2	2.83
Sub-period (2016-2020)
Model 1
Predictors	B	R2	ΔR2
Control Variables		0.657***
FIRMSIZEit	-.127***
ASTTANGit	0.07
BODSIZEit	-0.01
CSHFLWINTSTY	0.51**
Independent variables		0.71***	0.049***
LNGDBTit	0.159
SRTDBTit	-1.73***
Model 2
Predictors	B	R2	ΔR2
Control Variables		0.66***
FIRMSIZEit	-0.13***
FIRMGROWTHit	0.08***
ASTTANGit	0.08
BODSIZEit	-0.01**
CSHFLWINTSTY	0.52**
Independent variables		0.71***	0.046***
LNGDBTit	0.193
SRTDBTit	-1.35***
Moderation		0.72***	0.016**
Interaction 1	0.23
Interaction 2	0.09

As presented in Table 7, the results are valid for the two sub-periods; the association between long-term debt and earnings management is positive but insignificant for the two sub- periods. Also, the results indicate that the relationship between short-term debt and earnings management throughout the two periods is negative and significant at 1% (-2.18 for the time period 2011-2015, and -1.35 for the time period 2016-2020). In addition, consistent with previous findings, results depict a non-significant impact of audit firm size on the debt- earnings management associations across the two periods (p-value > 0.05).

Table 7 Anova: Policy Change
Model		Sum of Squares	df	Mean Square	F	Sig.
1	Regression	7.400	1	7.400	9.261	0.002b
	Residual	341.986	428	0.799
	Total	349.386	429
Dependent Variable: DA

Table 8 Model 1 Multiple Regression Analysis (Group 1)
Predictors	B	R2	ΔR2
Control Variables		0.537***
FIRMSIZEit	-0.21***
FIRMGROWTHit	-0.05***
ASTTANGit	0.2**
BODSIZEit	0.04***
CSHFLWINTSTY	1.5***
Independent variables		0.573***	0.035***
LNGDBTit	0.5
SRTDBTit	-1.79***

** p < 5%, *** p< 1%

Table 9 Model 2 Multiple Regression With Moderating Variable Analysis (Group 1)
Predictors	B	R2	ΔR2
Control Variables		0.542***
FIRMSIZEit	-0.214***
FIRMGROWTHit	0.08***
ASTTANGit	-0.22**
BODSIZEit	0.03
CSHFLWINTSTY	1.48***
Independent variables		0.575***	0.033***
LNGDBTit	1.63
SRTDBTit	-1.29***
Moderation		0.608***	0.033***
INTERACTION1	0.08
INTERACTION2	0.26

Note: INTERACTION1 is LNGDBTit*AUDITSIZEit, INTERACTION2 is SRTDBTit*AUDITSIZEit.

** p < 5%, *** p<1 %

Table 10 Model 1 Multiple Regression Analysis (Group 2)
Predictors	B	R2	ΔR2
Control Variables		0.854***
FIRMSIZEit	-0.35***
FIRMGROWTHit	0.16***
ASTTANGit	0.25
BODSIZEit	-0.04**
CSHFLWINTSTY	0.24
Independent variables		0.863***	0.01
LNGDBTit	-0.342
SRTDBTit	-0.552

Sub-Groups Analysis

To further validate the results, and provide more depth to the analysis, a dummy variable is added to demonstrate whether the companies changed their accounting policies from one year to another on an annual basis. The logic behind this variable is that firms usually manage earnings by utilizing flexibility in accounting policies and methods (Isiaka, 2019; Shu & Thomas, 2019). Table 7 below shows that the mean square of the firms that changed their accounting policies differs significantly from those that did not:

Table 11 Model 2 Multiple Regression With Moderating Variable Analysis (Group 2)
Predictors	B	R2	ΔR2
Control Variables		0.868***
FIRMSIZEit	-0.1**
FIRMGROWTHit	0.11
ASTTANGit	0.13
BODSIZEit	-0.04
CSHFLWINTSTY	0.54
Independent variables		0.878***	0.01
LNGDBTit	-4.72
SRTDBTit	0.376
Moderation		0.893***	0.01
INTERACTION1	0.973
INTERACTION2	-1.62

The previous regression models are then repeated for the two groups; where group 1 reflects the firms which changed their accounting policies, and group 2 reflects the firms which did not.

Repeated Analysis for Group 1

The results of the first multiple regression analysis model, tested for group 1 firms, are displayed in Table 8 below:

The results show that for firms that changed their accounting policies, the association between long-term debt and earnings management, is insignificant (p-value <0.05). Whereas short-term debt is negatively related to earnings management, and this result is significant at 1%.

Similarly, the second model is repeated for this particular group, and the results are displayed below Table 9:

The results show that the audit firm size is found to have an insignificant influence on the link between the independent variables and the dependent variable (p-value > 0.05).

Repeated Analysis for Group 2

Table 10 shows the results of model 1, tested for group 2 (i.e. firms that did not change their accounting policies):

According to the results, the negative association between short-term debt and earnings management (coefficient = -0.552) is insignificant.

Consistently, the second model is tested for the same group, where results are presented in Table 11:

The results suggest that the interactions between the audit firm size and the independent variables (long-term and short-term debts) have insignificant relations with the dependent variable (earnings management).

Comparison between Groups 1 and 2

In the preceding section, the analysis revealed that H2, which suggests that short-term debt and earnings management are significantly associated, is accepted. The results in the table above reveal that for firms that change their accounting policies and thus are more likely to manage earnings, the short-term debt-earnings management coefficient is negative and statistically significant at 1%. Compared to an insignificant negative coefficient of 0.552 revealing the association between short-term debt and earnings management for group 2, which are less likely to practice earnings management. This finding validates the conclusion that short-term debt negatively and significantly impacts earnings management.

Discussion

Choosing the optimal capital structure has been a research question that has always attracted practitioners and academics (Parsons & Titman, 2009).

On the other hand, Companies’ stakeholders, who need fair and accurate information to make future decisions (Bachner, 2015), use financial statements, where entities disclose their financial positions and performance (Nugroho et al., 2018). However, the quality of financial statements builds upon the degree of their contents’ credibility and usefulness (Dimitropoulos & Asteriu, 2009). Therefore, the quality of financial statements might be questionable if managers practice earnings management. They do so by taking advantage of the limitations in the accounting techniques used in developing financial statements (Isiaka, 2019).

Despite the vast literature exploring the various influence capital structure plays in firms, researchers have largely overlooked this topic in the Palestinian context. Taking into consideration the significant role that PEX plays in the Palestinian economy and the importance of sustaining this sector, in addition to the uniqueness of the economic circumstances under which this sector operates, this research examines the relationship between capital structure and earnings management in the context of Palestinian Listed companies.

Building on data extracted from reported accounting information of 44 firms listed on the Palestine Stock Exchange (PEX) for the period 2011 – 2020, the findings reveal that short-term debt is significantly and negatively related to earnings management. This result does not support Afza & Rashid (2014) and Fields et al. (2018) conclusions that short-term debt and earnings management are positively correlated; however, the negative association can be explained by Datta et al. (2005)’s argument that firms that use short-term debt expose themselves to extra monitoring from creditors. On the other hand, the findings also indicate that there is no significant association between long-term debt and earnings management. This result is not in line with prior research which either identified a significant and positive connection between long-term debt and earnings management, such as Al-Mohareb & Alkhalaileh (2019), Alzoubi (2018), Beatty & Weber (2003), Dichev & Skinner (2002), Lazzem & Jilani (2018), and Sweeney (1994), or which identified a significant but negative connection between long-term debt and earnings management such as (Alsharairi, 2012; Ghosh & Moon, 2010; Jelinek, 2007; Jensen, 1986).

By addressing the association between debt funding and earnings management in the context of Palestinian listed companies, this study bridges a significant research gap. And taking into consideration that the topic of the study is interdisciplinary, it draws attention to very important associations that have been overlooked in the context of Palestinian listed companies; therefore, it contributes significantly to previous literature. Hence, the results of this research offer a major knowledge foundation that is of major concern to the accounting and auditing professionals and which may be strongly considered to sustain and evolve the PEX sector. Additionally, the findings form a starting point for more profound future research in capital structure and financial reporting in the Palestinian context and in similar contexts of other developing countries, especially those that undergo risky and unsettled economic circumstances.

Conclusion & Recommendations

The topic of capital structure and financial reporting has almost been ignored in the context of Palestinian companies; therefore, more in-depth research is recommended to be conducted in this subject area. Moreover, the outcomes of this research indicate that short-term debt and earnings management are significantly and inversely related. Therefore, it is important to explore the reasons behind this relationship. Additionally, this study does not support a respectful strand of previous research which identifies a significant positive, or negative association between long-term debt and earnings management; therefore, it is essential to re-investigate this relationship in the Palestinian context, and similar contexts, but using different indicators or measures.

Besides future suggested research, taking into consideration the that short-term debt is inversely related to earnings management, it is important for regulatory bodies, such as the Palestinian Capital Market Authority (PCMA), to consider this finding for the future establishment of related regulations, as it might be essential to impose some type of restrictions that are based on the different tools of financing.

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Received: 19-Jan-2023, Manuscript No. AAFSJ-23-13152; Editor assigned: 21-Jan-2023, PreQC No. AAFSJ-23-13152(PQ); Reviewed: 04-Feb-2023, QC No. AAFSJ-23-13152; Revised: 23-Mar-2023, Manuscript No. AAFSJ-23-13152(R); Published: 30-Mar-2023