Financial Sustainability and Corporate Credit Risk: Moderating Role of Earnings Management

Abstract

Many industries put on a show of sustainability to draw in investors even though they are not financially viable. This study examines how real-earnings management (REM) moderates the relationship between credit risk (CR) and financial sustainability (FS). Real earnings management (REM) uses three techniques to measure earnings management: cash flow, overproduction, and discretionary spending. The distance-to-default approach of the KMV model, as an inverse proxy, is used in the current study to enumerate CR. Panel data of non-financial listed companies from 2013 to 2021 is used in this study. This study used PROCESS macro to construct bootstrap confidence intervals to estimate the model and “simple slope analysis” to visualize the model. The findings demonstrate a significant negative relationship between credit risk and financial sustainability. Real earnings management as a moderator weakens the relationship between financial sustainability and credit risk. This study aids investors in integrating sustainability into their investment process and helps them make wise choices. In addition, the results of this study might assist managers in adjusting cash flow patterns, real earnings management practices, and financial sustainability to reduce credit risk.

1. Introduction

Globally, every financial and non-financial institution has an important role in the development of economic resources. For an economy, a strong, well-functioning financial sector is crucial, be it a developed or emerging market. It is very important for healthy sustained growth. It must be able to meet the federal government’s increasingly sophisticated demands. Sustainability is a three-dimensional economic, social, and environmental approach to an organization’s long-term survival. The potential for business continuity and long-term value creation for owners and investors is defined as a firm’s financial sustainability (FS) [1].

Financial sustainability (FS) metrics encompass a wide range of financial indicators, most of which are derived, such as return on assets, profitability, and sustainable ratios. Financial sustainability results from the concept of enhancing the benefit of investors by making the greatest possible investments with an appropriate level of risk [1]. Financial risk and financial distress are often correlated with the perception of financial sustainability but in the opposite direction. Elements that favor financial sustainability are indirect drivers of their opposite [2,3]. It is important to highlight the consequences of any decision that could affect the future sustainability of a company [4,5]. As a result, we established and strengthened a connection between risk and sustainability. The possibility of an unfavorable event is called risk. The chance of not achieving the expected results is also risk. Financial loss due to non-payment of debt owed to an individual or an organization is also a form of risk known as credit risk (CR) [6,7].

Financial sustainability explains the ability of a company to create and maintain a diverse resource pool over an extended period that would serve the interests of its customers, regardless of whether the company had access to financial resources or not. Financial sustainability is the financial management of organizations to ensure that present financial benefit does not adversely impact future financial achievements, particularly the well-being of future generations [8].

Financial management is necessary for businesses to achieve current and future financial success. It is vital to note the consequences of any decisions that may affect the future sustainability of the company [4,9].

It is so important to ask by what means this study measures FS or whether this measurement system has any impact on CR. These questions need to be addressed to find out if credit risk can deter opportunistic actions by business executives. It is important to understand what role real earnings management has in controlling managers and opportunistic behavior for the financial sustainability of firms. Thus, measuring real earnings management is essential for explaining the relationship between financial sustainability and credit risk. Without it, this study cannot be considered complete. This study attempts to measure REM comprehensively, resulting in better sustainability for credit risk in firms for profit-making and symmetric data roaming. Thus, the results may help managers in making sound decisions not only for firms but also for investors’ investment profitability.

The following sections structure this paper: Section 2 describes the literature review of prior research; Section 3 summarizes the data and methodology of REM-1, REM-2, and REM-3; Section 4 displays the empirical results and discussion; and Section 5 offers the conclusion.

2. Literature Review

2.1. Financial Sustainability

One of a company’s main objectives is “sustainability”. For decades, scholars have been studying the sustainability concept. This term specifies both a timeline dimension and a range dimension [8]. It is possible to distinguish between the ideas of intragenerational and intergenerational justice as regards the time dimension. This was presented by the Brundtland Commission (UN). According to the Brundtland Commission [10], “sustainable development” must ensure that the requirements of the present generation are met without sacrificing the ability of upcoming generations to achieve sustainability.

Economic activities within any generation must address the three main objectives of what is called the triple bottom line (TBL). This is a three-dimensional scope. Often referred to as “the three pillars” or the “3 Ps” (people, planet, and profit), these three sustainability objectives are interconnected [11]. Risk management may be impacted by financial sustainability measurements. Bezares et al. [12], using broad data of 65 companies meeting corporate sustainability criteria from FTSE 350 firms for 2006–2012, concluded that businesses that integrate sustainability concerns into their operations are better able to use their finances to generate stronger financial outcomes and robust shareholder returns than other businesses. The credit managers mostly need to analyze the complex debt situations in which multi-criteria approaches are useful in order to define a form of credit scoring to support the assessment process [13].

Sustainable growth rate (SGR) is an important indicator that can specify whether the business is successful or not [14,15]. Sustainable growth is the proportion of returns to shareholders who own part of the equity that is retained and reinvested into the business to fund its operations. It’s a free source of financing for the company (provided that you get the shareholders’ cooperation). SGR is defined as the long-term growth rate that a firm may anticipate is determined by increasing the return on equity by the profits retention rate of the firm [16].

Ahmed and Tirmizi [16] investigated the relationship between firms’ performance and financial sustainability with moderating behaviors of institutional ownership and managerial relationship in non-listed firms of Pakistan. Their findings conclude that institutional ownership has a negative and significant effect as a moderator on the relationship between financial performance and financial sustainability as compared to managerial ownership. The negative effect of institutional ownership on financial performance and financial sustainability highlights the shareholders’ value. Their study explained that the firms should gain profit and sustainability if the corporate sector reduces its institutional ownership. The main objective of firms should be to sustainably increase the shareholders’ wealth. The findings of Ahmed and Tirmizi [16] are also associated with the study done in Turkey by [17]. Both studies’ findings demonstrate that the focus of firms is to attain financial sustainability. However, their research is limited to financial sustainability and profit-making criteria, means through which firms get maximum profit and sustainability.

The assumptions of agency theory support management in fulfilling its credit risk management obligations for efficient financial sustainability, independently of all associated risks. Modern organizations stand out for their distinctions between ownership and control [18]. This means that the principal (owner) hires the manager (the agent) to perform the services of decision-making. Agency theory is a method of analyzing the relationship between the principal and the agent [19]. This theory analyzes contracts that are designed to encourage a rational agent (or principal) to act for the principal’s interests when they might conflict with their own. Separation of control and ownership has been identified as the root cause of corporate governance problems [20]. It can also be viewed as enabling earning management if you take the same perspective. Managers can manage earnings in the owner’s interests since ownership is not tied to control management to achieve financial sustainability [19].

Agency theory states that earnings management can occur when managers are motivated to maximize their self-interest while compromising the shareholder’s interest due to information asymmetry to sustain their financials [21]. Owners sought ways to regulate managers’ interests with the shareholders to create the agency problem in the corporate business. Management interest aligns with the firm’s most common goal maximization of shareholder wealth as measured by the firm’s value. This objective measurement of success has been the stock price.

The concept of earnings is founded on the managers’ capacity to operate efficiently. This involves the evasion of unnecessary inputs and the improvement of product characteristics, timely payment of bills and loan repayments, and obtaining low-cost and suitable sources of finance, all while meeting customer needs. This idea is predicated on the principle that if standard enterprises consistently attain standard returns or profits over an extended period, it leads to greater economic stability and efficiency. It is expected that companies will achieve higher returns and profitability over a long period of time. This approach concedes that certain managers apply methods that are more effective than others. These approaches are based on methods used to control operational efficiency, manage the capital structure, and fulfill customer requirements in order to maximize profitability. Managers who possess a typical level of financial competence receive a standard return on investment, whereas managers with superior financial ability are rewarded with above-average profits.

GleiBner et al. [22] investigated the financial sustainability of European companies, which covers the conditions of sustenance like growth rate, the strength of the firm to survive, desirable earnings risk traits, and assumed manageable earnings risk rate in excess of 0.39%, and the results showed that the risk portfolio of these firms is less as compared to the market investment risk. Creditworthiness plays a role in the relationship between environmental sustainability and credit concerns. According to Höck et al. [23], financially strong companies will do better in terms of their efforts toward protecting the sustainability of the environment. However, companies that are not very responsible with their money or have lower credit ratings will not be punished for trying to be sustainable. This study will look more closely at how being sustainable affects credit risk. It is important to highlight the consequences of any decision that could affect the company’s future sustainability.

2.2. Credit Risk

Credit risk (CR) is the possibility that a borrower may fail to make payments on any kind of debt. When borrowers of the banking sector default and cannot meet their debt obligation at a specific time, it is called credit risk [6]. The key risks associated with credit lenders are the potential loss of principal and interest, disruptions in cash flow, and greater collection charges. It can be total or partial, and it can happen in numerous circumstances [24]. Credit risk introduces the possibility that a borrower will fail to pay back a loan or other contractual obligations, which could result in the loss of principal or a financial reward. Credit risk occurs when a borrower expects to use future cash flow to pay current debt. The interest payments received from the borrower compensate investors for taking on credit risk. CR is directly linked to the return on investment. There is a strong correlation between the yields on bonds and their perceived credit risk [25].

Credit risk is defined by [26] as “the likelihood that if a counterparty defaults and goes out of business then it could make a legally bonded contract invalid”. According to Saunders and Allen [27], this risk refers to the potential non-payment of pledged cash flows from loans and securities held by financial institutions. Like other risks, credit risk has an impact on every financial contract. Additionally, it may discourage businesses from investing [28]. The structural credit risk model is based totally on the capital structure concept, which explains that a firm can default when the value of the asset is less than its debt value [29].

Inadequate credit risk management adversely impacts an institution’s operational capabilities, potentially leading to lack of confidence among customers [30]. Default rates can be decreased by carefully monitoring and managing credit risk, which is a major financial risk [31]. Considering the substantial scope of credit risk, stakeholders need to ensure that credit risk policies are implemented effectively and systematically monitor the process of implementing them across the whole loan cycle, from disbursement to recovery. Unsystematic risks include management, operational, financial, and industrial risks.

Financial institutions utilize credit scoring models to identify potential borrowers and assess CR, which in turn determines loan pricing and collateral requirements [32,33]. Income diversification helps financial institutions reduce credit risk and enhance financial sustainability [34].

2.3. Real Earnings Management

A company’s contribution to its value can be measured using earnings. Analysts and investors use firms’ earnings to evaluate their stock’s value. Under Generally Accepted Accounting Principles (GAAP), pure accounting statement choices are involved in accounting earnings management, while real earnings management does not involve making decisions about accounting statements; rather, it involves making changes to the timings or operations, investments, financing, or transactional structure of the company that would affect cash flow [35].

To understand the concept of REM, Fazeli and Rasouli [36] studied operating cash flows, production costs, and discretionary expenses of the companies listed in the Tehran Stock Exchange from 2002 to 2007 to avoid annual losses in earnings. Considering the predicted values of REM variables, abnormal values were obtained because of residuals of regression and therefore treated as an instrument to measure earnings manipulation. Using this construct, the sample list shows even those companies that had small positive earnings, having unexpected variables at higher levels. The Fazeli and Rasouli [36] research was based on Roychowdhury [37], who had made a very strong case for management’s REM practices.

Chen and Rothschild [21] observed another phenomenon: if managers try to meet quarterly financial reporting benchmarks and are involved in real earnings management, then their analysts use advertisement expenditures as a proxy to measure and find evidence of REM that is at an abnormal level or use the residual of the company’s monthly advertising expense using a time series regression method. Bens et al. [38] and Bens et al. [39] revealed that managers often repurchase stocks so that earnings per share (EPS) dilution is avoided, which arises from two things. The first one is exercising of employee stock options, and the second one is granting of employee stock options. Bens et al. [38] found that managers to some extent finance these repurchases by decreasing research and development expenditures. Baber et al. [40] and Bushee [41], in their studies, also established that research and development expenditures are reduced so that the earnings benchmarks are met. They subjectively proved that to manage earnings, managers are involved in many other activities besides the reduction of research and development expenditures.

There are various real earnings management techniques that have been investigated, including selling fixed assets [42,43,44,45] and decreasing the cost of goods sold by overproduction decisions [44,45,46], reduction in the expenditures for research and development [2,47], boosting sales by offering price discounts [36,37], and avoiding depreciation charges through the reduction in capital investments [48]. In the short term, these techniques might increase reported earnings, but they could damage the company’s interests later.

Most of the analysts explained another piece of evidence that executives manipulate earnings to beat analysts’ forecasts, and, as a result, they get some compensation as incentives [49]. The CEO of any company portrays its earnings just to build or keep his or her reputation by surviving during the time of retention. At retention time, the company’s executives move towards the manipulation of earnings [50]. As a result, all these methods of manipulating earnings have led to higher income in recent years, but there have also been some consequences. Companies would have lower operating cash flow if they are involved in REM to avoid less income reporting [51].

REM adoption through manipulations of operating cash flow, discretionary spending, and production cost will not lead to a decrease in performance over the following years, even if the underlying motivation of REM is meeting earnings benchmarks [52]. Tran [53] investigated the link between corporate governance (CG) and earnings management (EM). It was found that if corporate governance is strong and has more external directors, it will lead to lower earnings management. According to previous studies, it is important that accounting information is of better quality for socio-economic integration with other markets to take place smoothly [54]. The pressure to maintain the high market valuation of firms that are substantially overvalued is the primary motivation behind income-increasing management of earnings by overvalued firms [55].

In this study, we have listed three important variables to consider: distance-to-default (DTD), firm size, and risk. A measure of default risk called DTD is based on the transform-data maximum probable approach [56,57]. This indicator is derived from the National University of Singapore’s Risk Management Institute. DD is used as a proxy for measuring credit risk. DD has an inverse relation with the sustainability of firms as well as their own credit risk. The more the firms are distant from their defaulting point, the more the firm is financially sustained [58]. Alissa [59] examined whether the extent of a firm’s real activities management will influence the cost of equity capital of companies.

Literature on sustainability characteristics revealed that not as much evidence has been found to support the influence of financial sustainability characteristics on firms’ credit risk in both developed and underdeveloped countries. There is mixed evidence about the relationship between sustainable growth and credit risk in companies. However, there is less likelihood of sustainability when there is more credit risk. External environment sustainability should also act positively for firm performance. It is not clear how a firm can increase its financial performance by engaging in FS activities. This debate is important because it focuses on the larger question of how a company integrates FS into its overall strategic plan. Here is the issue of how a company’s financial sustainability gets affected by earnings management techniques and whether financial sustainability prohibits REM from enhancing accounting figures. Concerning managerial compensation, prior research has emphasized the impact of credit risk on the banking industry’s sustainability while ignoring other non-financial institutions [60].

This study aims to determine how financial sustainability initiatives affect credit risks in the Pakistani industrial sector. Financial sustainability is practiced by the firms because of certain problems faced by the firms. The main concern of this study is to resolve problems of uncertainty in financial sustainability with credit risk in the presence of REM of companies. Hunjra et al. [61] have already suggested that the relationship of REM with credit risk is positive, since it improves investors’ knowledge about a company’s accounting data. Höck et al. [23] identified that sustainable companies with a strong credit rating tend to have less premiums of CR through the research of sustainability in environmental effects on the cost of credit risk for companies in Europe. According to Höck et al. [23], future research should expand by analyzing the impact of financial sustainability on the credit risk of companies that are from emerging market countries to generate yield for investors. Therefore, the current study is focused on moderating effect of REM on the relationship between financial sustainability and credit risk.

2.4. Hypotheses Development

The current study creates hypotheses using previous studies’ analysis and appropriate testing techniques. Hypotheses will be made and tested empirically by using suitable methods. The manufacturing sector deals with the financial performance and financial sustainability of firms to attract investors for investment. For this reason, the companies try to reduce their credit risk and unreliability. To study the impact of financial sustainability on credit risk, the retention ratio and KMV model are used to measure the financial sustainability and credit risk. Prior research focuses on the impact of credit risk on the banking industry’s sustainability [60] while ignoring the non-financial sector. Previous studies have explained that in the banking sector, the increase in credit risk reduces financial sustainability. Most of the preceding studies have been done on financial institutions regarding credit risk and financial sustainability. Höck et al. [23] conducted their study on non-financial institutions by investigating the impact of environmental sustainability on credit risk. Their findings suggested that credit risk is reduced due to the increase in the environmental sustainability of companies. In most of the literature regarding the previous studies, credit risk has an impact on financial sustainability and is affected by environmental sustainability. Hunjra et al. [61] examine in their article the impact of real earning management on credit risk, and their results reveal that real earnings management creates a positive and significant impact on credit risk. So, in this study, we analyze the impact of financial sustainability on credit risk with the moderating role of real earnings management in the time duration of 2013 to 2021 on a sample of 147 non-financial firms listed on the Pakistan Stock Exchange by testing the following hypotheses:

H1. 

Financial sustainability has a negative impact on corporate credit risk.

H2. 

Real earnings management moderates the relationship between financial sustainability and credit risk.

3. Research Design

3.1. Population, Sample, and Data Sources

The total number of listed firms on the Pakistan Stock Exchange as of 31 December 2021 was 532 [62]. Since non-financial firms differ greatly from financial firms in terms of their business operations, regulatory oversights, and the way they publish information in annual reports, the study began with sole focus on non-financial firms. To acquire the study sample, several filtration criteria were used for the remaining 369 non-financial companies [63]. During the period, companies that underwent delisting, merged, remained nonoperational, or were incapable of submitting comprehensive annual reports were not included. Additionally, companies for which comprehensive market value information was not available were not included. After 199 firms were eliminated by the use of filters, 170 firms remained in the initial sample of the study. After removing another 23 companies that had extreme values (outliers), our final sample consisted of 147 non-financial firms listed on the Pakistan Stock Exchange ( As shown in

Table 1. Population and Sample Selection.

Total firms listed on PSX (31 December 2021)	532
Less: Financial Companies	(163)
Less: Non-financial Companies with unavailable data for the study period	(199)
Initial Sample	170
Less: Outlier firms	(23)
Final Sample	147
No. of observations (of initial and final samples)
	Firms ∗ Years = No. of observations
Total number of initial sample observations	170 ∗ 9 = 1530
Less: Outlier firm-year observations	(23 ∗ 9 = 207)
Observations Net of Outliers	1323
Less: 2013 observations excluded (2013 observations used as lags for calculation of earnings management)	(147 ∗ 1 = 147)
Final sample observations	1176

). Data were gathered from the PSX website (PSX, 2021), State Bank of Pakistan, SBP reports [63], and firms’ annual reports. Data from 147 non-financial firms listed on Pakistan Stock Exchange (PSX) were collected from 2013 to 2021. The year 2013 was taken as a base year because in 2011, the Securities and Exchange Commission of Pakistan (SECP) made amendments to the regulations of 2008 about the establishment and maintenance of a risk management system [7].

3.2. Research Methods

Analytically speaking, the questions “When, how, and to what extent does X affect Y?” are typically addressed analytically via the following regression model, with moderation analyses [64].

$$\hat{\mathrm{Y}}={\mathsf{\beta }}_0+{\mathsf{\beta }}_1\mathrm{X}+{\mathsf{\beta }}_2\mathrm{W}+{\mathsf{\beta }}_3\mathrm{XW}$$

(1)

where $\hat{\mathrm{Y}}$ symbolizes the response variable, and ${\mathsf{\beta }}_0$ is the constant. ${\mathsf{\beta }}_1,{ \mathsf{\beta }}_2,$ and ${\mathsf{\beta }}_3$ are the regression coefficients of linear moderation. X is independent, and W is the moderating variable. To carry out a regression analysis to look into the moderation hypothesis, the impact of X on Y is contingent on W (in simple words, if the effect is being moderated by W). The regression coefficient (${\mathsf{\beta }}_3)$ of XW provides a statistical response to the question of the contingent influence of X on Y. If the confidence interval shows that ${\mathsf{\beta }}_3$ is not equal to zero, then W is linearly moderating the effect of X. The PROCESS macro is used to estimate the regression equations [64]. In recent years, academics have increasingly used the PROCESS macro to investigate mediation and moderation [65,66]. Confidence intervals are built using the bootstrapping approach, which resamples the entire number of observations at random with replacements to estimate the model. Using 5000 bootstrap samples, this study created upper and lower 95% confidence intervals.

3.3. Variables Computation

3.3.1. Financial Sustainability (FS)

A company’s financial sustainability is its capacity to maintain operations and generate value for its owners over the long term by combining investments and financing sources in the best possible way [1]. The idea of financial sustainability supports the maximization of value for stakeholders. There have been several research studies in this area, but some of them focus on the technique or measure of financial sustainability at the business level [1,67]. This study used the sustainable growth ratio (SGR) as a proxy for financial sustainability [16,68].

$$\mathrm{GR}=\mathrm{Retention} \mathrm{Ratio}\ast \mathrm{Return} \mathrm{on} \mathrm{Equity}$$

$$\mathrm{Retention} \mathrm{Ratio}=\frac{\mathrm{Retained} \mathrm{Earnings}}{\mathrm{Net} \mathrm{Income}}$$

(2)

$$\mathrm{Return} \mathrm{on} \mathrm{Equiy}=\frac{\mathrm{Net} \mathrm{Income}}{\mathrm{Shareholders} \mathrm{Equity}}$$

(3)

3.3.2. Corporate Credit Risk (CCR)

The current study measures credit risk using the KMV model, which was introduced by [69]. In the late 1980s, the Merton model was revisited by Kealhofer and Vasicek. They extended the Black and Scholes framework to produce a default forecasting model, first known as the Vasicek–Kealhofer (VK) model and later named Moody’s KMV by the three company founders, Kealhofer, McQuow, and Vasicek. (The KMV model was acquired in 2002 by Moody’s for $210 million) [70]. Höck [23] also used the KMV model, which can calculate the predicted default rate based on outcomes of different parameter calculations and then evaluate the credit risk of firms. Several researchers have employed the Merton KMV model in their studies, and it has shown strong predictive results of default (see, e.g., [71,72,73,74]). Therefore, the current study has also opted for this model.

$${\mathrm{V}}_{\mathrm{S}}={\mathrm{V}}_{\mathrm{A}} \mathrm{N}\left({\mathrm{d}}_1\right)-{\mathrm{Fe}}^{-\mathrm{rt}} \mathrm{N}\left({\mathrm{d}}_2\right)$$

$${\mathrm{d}}_1=\frac{\mathrm{Ln}\left(\frac{{\mathrm{V}}_{\mathrm{A}}}{\mathrm{F}}\right)+\left(\mathrm{r}+\frac{1}{2} {\mathsf{\sigma }}_{\mathrm{A}}^2\right)\mathrm{t}}{\sqrt{{\mathsf{\sigma }}_{\mathrm{A}}^2 \mathrm{t}}}$$

$${\mathrm{d}}_2={\mathrm{d}}_1-\sqrt{{\mathsf{\sigma }}_{\mathrm{A}}^2 \mathrm{t}}$$

$${\mathsf{\sigma }}_{\mathrm{E}}=\left(\frac{{\mathrm{V}}_{\mathrm{A}}}{\mathrm{F}}\right){\mathrm{Nd}}_1{\mathsf{\sigma }}_{\mathrm{A}}$$

$$\mathrm{DD}=\frac{\mathrm{E}\left({\mathrm{V}}_{\mathrm{A}}\right)-\mathrm{DPT}}{{\mathsf{\sigma }}_{\mathrm{A}}}$$

(4)

whereas, V_e = market value of firm’s equity, V_A = market value of firm’s asset, F = face value of firm’s debt, r = risk-free rat, N(.) = commutive standards normal distribution, T = horizon of the face value of the firm’s debt, σ_A = volatility of the firm’s underlying assets, σ_E = relationship between volatility and the equity of firms, DD = distance-to-default, E(V_A) = expected value of firm’s asset within one year, STD = short-term debt, LTD = long-term debt, and DPT = default point.

3.3.3. Earning Management (EM)

The third variable of interest in the present study is earnings management (EM). Discretionary accruals are frequently employed as a stand-in for earnings management inside a company. Prior research has employed the balance sheet technique and the cash flow approach to identify earnings management in a company through accruals (discretionary + non-discretionary) [75]. Consequently, following [76], the current study measures the total accruals (TA) as follows using the cash flow approach:

$${\mathrm{TA}}_{\mathrm{it}}={\mathrm{EAT}}_{\mathrm{it}}-{\mathrm{CFO}}_{\mathrm{it}}$$

(5)

whereas,

TA_it = total accruals of firm “i” for time period “t”;

EAT_it = earnings after tax of firm “i” for time period “t”; and

CFO_it = cash flow from operations of firm “i” for time period “t”.

Jones [77] suggested the following approach to estimate the non-discretionary percentage of total accruals:

$${\mathrm{TA}}_{\mathrm{it}}={\mathsf{\beta }}_0\left(\frac{1}{{\mathrm{A}}_{\mathrm{it}-1}}\right)+{\mathsf{\beta }}_1\left(\frac{{\Delta \mathrm{Rev}}_{\mathrm{it}}}{{\mathrm{A}}_{\mathrm{it}-1}}\right)+{\mathsf{\beta }}_2\left(\frac{{\mathrm{PPE}}_{\mathrm{it}}}{{\mathrm{A}}_{\mathrm{it}-1}}\right)+{\mathsf{\mu }}_{\mathrm{it}}$$

(6)

whereas,

TA_it = total accruals of firm “i” for time period “t”;

A_it−1 = total assets of firm “i” for time period “t − 1”;

ΔREV_it = change in revenues (REV_it − REV_it−1);

PPE_it = property, plant, and equipment of firm “i” for time period ”t”; and

μ_it = residual.

Dechow [78], however, contended that the present version of the simple cross-sectional Jones model (1991) is not very effective and suggested a modified Jones model as follows:

$${\mathrm{TA}}_{\mathrm{it}}={\beta }_0\left(\frac{1}{{\mathrm{A}}_{\mathrm{it}-1}}\right)+{\beta }_1\left(\frac{{\Delta \mathrm{Rev}}_{\mathrm{it}}-{\Delta \mathrm{Rec}}_{\mathrm{it}}}{{\mathrm{A}}_{\mathrm{it}-1}}\right)+{\beta }_2\left(\frac{{\mathrm{PPE}}_{\mathrm{it}}}{{\mathrm{A}}_{\mathrm{it}-1}}\right)+{\mathsf{\mu }}_{\mathrm{it}}$$

(7)

whereas

ΔREC_it = change in receivables (REC_it − REC_it−1)

The usual way of cash flow generated by operating activities, expenditure, and production costs is categorized under real earnings management (REM). These three ways of REM have been empirically employed by using the models proposed by Dechow et al. [47] and later on used by other various researchers in their studies [37,46,61,79,80,81]. Such a type of earnings management is known to be real earnings management (REM). These three models of REM have better predictive ability when examined in the context of the Pakistan manufacturing industry [82,83]. Therefore, the present study will also employ REM in the following three common ways:

Sales Manipulation Estimation Model (REM-1)

The numbers intentionally enhance sales revenue to exhibit income rise trends by giving greater sales reductions and adaptable credit terms [84], resulting in low cash flow from operations (CFO) for a given amount of sales.

$$\frac{{\mathrm{CFO}}_{\mathrm{it}}}{{\mathrm{Asset}}_{\mathrm{it}-1}}={\mathsf{\alpha }}_0+{\mathsf{\beta }}_1\frac{1}{{\mathrm{Asset}}_{\mathrm{it}-1}}+{\mathsf{\beta }}_2\frac{{\mathrm{Sales}}_{\mathrm{it}}}{{\mathrm{Asset}}_{\mathrm{it}-1}}+{\mathsf{\beta }}_3\frac{\Delta {\mathrm{Sales}}_{\mathrm{it}}}{{\mathrm{Asset}}_{\mathrm{it}-1}}+{\mathsf{\mu }}_{\mathrm{it}}$$

(8)

Overproduction Estimation Model (REM-2)

Overproduction is the second component of REM, and it is another popular strategy for corporate managers to influence current profitability. A greater level of output results in a lower per-unit fixed cost. The cost of goods has decreased, and in the current period, operational income is increased by a decrease in fixed costs per unit. As a result, excessively high manufacturing costs are a result of overproduction. The following model calculates normal production costs, and t unusual production expenses for the year is the term used to describe the model’s residuals., estimated as:

$$\frac{{\mathrm{Prod}}_{\mathrm{it}}}{{\mathrm{Asset}}_{\mathrm{it}-1}}={\mathsf{\alpha }}_0+{\mathsf{\beta }}_1\frac{1}{{\mathrm{Asset}}_{\mathrm{it}-1}}+{\mathsf{\beta }}_2\frac{{\mathrm{Sales}}_{\mathrm{it}}}{{\mathrm{Asset}}_{\mathrm{it}-1}}+{\mathsf{\beta }}_3\frac{\Delta {\mathrm{Sales}}_{\mathrm{it}}}{{\mathrm{Asset}}_{\mathrm{it}-1}}+{\mathsf{\beta }}_4\frac{\Delta {\mathrm{Sales}}_{\mathrm{it}-1}}{{\mathrm{Asset}}_{\mathrm{it}-1}}+{\mathsf{\mu }}_{\mathrm{it}}$$

(9)

Reduced Discretionary Expenses Estimation Model (REM-3)

The REM third measuring factor is DISEXP. To portray the company’s profitability as high for the current quarter, managers reduced discretionary spending. For a variety of reasons, including boosting CEO compensation or exhibiting an improvement in their short-term earnings [85], corporate executives usually modify R and D spending to avoid a decline in the firm’s profitability Additionally, some deceptive adjustments to advertising expenses may be made to enhance analyst expectations and attract investors [82,86].

$$\frac{{\mathrm{DISEXP}}_{\mathrm{it}}}{{\mathrm{Asset}}_{\mathrm{it}-1}}={\mathsf{\alpha }}_0+{\mathsf{\beta }}_1\frac{1}{{\mathrm{Asset}}_{\mathrm{it}-1}}+{\mathsf{\beta }}_2\frac{{\mathrm{Sales}}_{\mathrm{it}-1}}{{\mathrm{Asset}}_{\mathrm{it}-1}}+{\mathsf{\mu }}_{\mathrm{it}}$$

(10)

whereas

CFO_it = cash flow from operations of firm ”i” for time period “t”;

Prod_it = CGS_it + ΔINV_it;

CGS_it = cost of goods sold of firm “i” for time period “t”;

ΔINV_it = change in inventory of firm ”i” for time period “t”;

DISEXP_it = RDE_it + SGAE_it + ADVE_it;

RDE_it = research and development expenses of firm “i” for time period “t”;

SGAE_it = selling, general, and administrative expenses of firm “i” for time period “t”;

ADVE_it = advertising expenses of firm “i” for time period “t”;

Sales_it = sales of firm “i” for time period ”t”;

Sales_it−1 = sales of firm “i” for time period “t − 1”;

ΔSales_it = change in sales of firm “i” for time period “t”;

Asset_it−1 = total assets of firm “i” for time period “t − 1”; and

µ_it = a residual term that describes the degree of abnormal cash flow of firm “i"” for the time “t”.

To measure each model of REM (Equations (8)–(10)), several lagged values were involved, so the panel dynamic models were employed. “Arellano-Bover/Blundell-Bond linear dynamic panel data estimation” is used in the current study to estimate the residuals because the residuals of each model are used as the three measures of EM. According to [87], this regression is an estimating process that uses a system “Generalized method of momentum GMM”. In the context of Pakistan, the same model has been utilized by [82].

Table 2. Parameters of REM.

Variables	REM-1	REM-2	REM-3
	CFOit/Ait−1	PRODit/Ait−1	DISEXPit/Ait−1
Constant	0.727	−0.246	−0.252
	(0.621) *	(0.754) *	(0.857) *
TA (lag1)	0	0.001	0
	(0.972) ***	(0.95) ***	(0.96) ***
1/Ait−1	−222	172	170
	−0.162	−0.284	−0.28
ΔSit−1/Ait−1		0.0001
		(0.969) ***
ΔSit/Ait−1		−0.0368
		(0.000) ***
Sit/Ait−1			0.012
			(0.989) ***
Sit−1/Ait−1	0.106
	(0.913) *
ΔSit/Ait−1			−0.049
			(0.957) *
Wald χ2	1.96	78.64	76.17
Adjusted R2	0.24	0.08	0.09
RMSE	16.441	15.661	15.661

Note: Z-statistics are reported in parentheses, *, **, *** shows the level of significance. As * represents p < 0.1, *** represents p < 0.01, RMSE: root mean squared error.

shows the results of the model’s parameters, i.e., REM-1, REM-2, and REM-3.

Researchers have identified that variables such as firm size, leverage, and liquidity (as shown in

Table 3. Control Variables.

Symbol	Variable	Definition	Computation
F-size	Firm size	A company’s total assets natural logarithm is referred to as the firm size.	F-size = Ln(Total Assets)
F-lev	Firm leverage	The amount of debt a company has on its balance sheet is indicated by the term “leverage,” which is the proportion of a company’s total liabilities to its total assets. It serves as a risk indicator because as leverage rises, the company will be less guaranteed to pay off all its debt.	F-lev = $\frac{\mathrm{Total} \mathrm{liabilities}}{\mathrm{Total} \mathrm{Assets}}$
F-liq	Firm liquidity	It is the ability to convert assets into cash without losing the worth or value of assets.	F-liq = $\frac{\mathrm{Liquid} \mathrm{Assets}}{\mathrm{Current} \mathrm{Liabilities}}$

) also affect the relationship between financial sustainability and credit risk. Hence, these have been taken as control variables in the present study following the available literature [51,61,88,89].

3.4. Empirical Models

For the empirical analysis, the following regression models are created. First, the following equation is used to validate the linear relationship between FS (proxied by SGR: sustainable growth rate) and CCR (inversely proxied by DD: distance-to-default).

$$\mathbf{Model} \mathbf{1} {\mathrm{DD}}_{\mathrm{it}}={\mathsf{\alpha }}_0+{\beta }_1{\left(\mathrm{SGR}\right)}_{\mathit{it}}+{\beta }_2{\left(\text{F-Size}\right)}_{\mathit{it}}+{\beta }_3{\left(\text{F-Lev}\right)}_{\mathit{it}}+{\beta }_4{\left(\text{F-Liq}\right)}_{\mathit{it}}+{\epsilon }_{i\mathrm{t}}$$

(11)

where DD_it = distance-to-default of firm “i” for time period “t”; (SGR)_it = sustainable growth ratio of firm “i” for time period “t”; (F-Size)_it = firm size of firm “i” for time period “t”; (F-Lev)_it = financial leverage of firm “i” for time period “t”; (F-Liq)_it = liquidity of firm ”i” for time period “t”.

Secondly, the study examined the moderating role of REM in the relationship between SGR and DD. According to [90], there may arise a multicollinearity issue between the main impacts of SGR and DD because of the interaction effect (SGR*REM). Accordingly, mean-centered SGR and REM (REM-1, REM-2, and REM-3 proxied by CFO_it, PROD_it, and DISEXP_it, respectively) are used in this study to create the interaction terms [91].

$$\mathbf{Model} \mathbf{2} {\mathrm{DD}}_{\mathrm{it}}={\mathsf{\alpha }}_0+{\beta }_1{\left(\mathrm{SGR}\right)}_{\mathit{it}}+{\beta }_2{\left(\text{REM-1}\right)}_{\mathit{it}}+{\beta }_3{(\text{SGR}\ast \text{REM-1})}_{\mathit{it}}+{\beta }_4{\left(\text{F-Size}\right)}_{\mathit{it}}+{\beta }_5{\left(\text{F-Lev}\right)}_{\mathit{it}}+{\beta }_5{\left(\text{F-Liq}\right)}_{\mathit{it}}+{\epsilon }_{i\mathrm{t}}$$

(12)

$$\mathbf{Model} \mathbf{3} {\mathrm{DD}}_{\mathrm{it}}={\mathsf{\alpha }}_0+{\beta }_1{\left(\mathrm{SGR}\right)}_{\mathit{it}}+{\beta }_2{\left(\text{REM-2}\right)}_{\mathit{it}}+{\beta }_3{(\text{SGR}\ast \text{REM-2})}_{\mathit{it}}+{\beta }_4{\left(\text{F-Size}\right)}_{\mathit{it}}+{\beta }_5{\left(\text{F-Lev}\right)}_{\mathit{it}}+{\beta }_5{\left(\text{F-Liq}\right)}_{\mathit{it}}+{\epsilon }_{i\mathrm{t}}$$

(13)

$$\mathbf{Model} \mathbf{4} {\mathrm{DD}}_{\mathrm{it}}={\mathsf{\alpha }}_0+{\beta }_1{\left(\mathrm{SGR}\right)}_{\mathit{it}}+{\beta }_2{\left(\text{REM-3}\right)}_{\mathit{it}}+{\beta }_3{(\text{SGR}\ast \text{REM-3})}_{\mathit{it}}+{\beta }_4{\left(\text{F-Size}\right)}_{\mathit{it}}+{\beta }_5{\left(\text{F-Lev}\right)}_{\mathit{it}}+{\beta }_5{\left(\text{F-Liq}\right)}_{\mathit{it}}+{\epsilon }_{i\mathrm{t}}$$

(14)

where (REM-1)_it, (REM-2)_it, and (REM-3)_it are the real earnings management of firm “i” for the time “t” and (SGR*REM-1)_it, (SGR*REM-2)_it, and (SGR*REM-3)_it are the interaction terms to account for the moderation effects of REM of firm “i” for the time “t”.

4. Results and Discussion

4.1. Descriptive Staistics

Descriptive statistics for DD, SGR, REM-1, REM-2, REM-3, and control variables for 147 firms in the final sample are exhibited in

Table 4. Descriptive Statistics.

	Min.	Max.	Mean	Std. Dev
SGR	−54.830	26.730	0.155	3.651
DD	1.320	6386.850	619.937	790.632
REM-1	0.000	120.155	0.620	4.468
REM-2	0.000	149.155	0.644	5.156
REM-3	0.000	30.813	0.435	1.624
F-Size	11.680	22.463	16.321	1.747
F-Lev	0.000	7.570	0.557	0.478
F-Liq	0.006	2483.414	17.144	173.906

Notes: SGR = sustainable growth rate [a proxy for financial sustainability (FS)]; DD = distance-to-default [inverse proxy for corporate credit risk (CCR)]; REM-1 = real earnings management—way 1 [sales manipulation estimation model]; REM-2 = real earnings management—way 2 [overproduction estimation model]; REM-3 = real earnings management—way 3 [reduced discretionary expenses estimation model]; F-Size = firm size [natural log of firm’s total assets]; F-Lev = firm leverage [a firm’s ratio of total liabilities to its total assets]; F-Liq = firm liquidity [a firm’s ratio of liquid assets to its current liabilities]; number of observations = 1176.

. The SGR of the sample firms has a mean of 0.155 with a range of minimum −54.83 to a maximum value of 26.73. This gives us a view that the firms having a minimum financial sustainability of −54.83 and a maximum financial sustainability of 26.73 are included in this sample. The average DD is 619.937 with the lowest range of 1.32 and maximum range of 6386.85. In the sample, the real earnings management shows a minimum value of 0.00 for all three measures, i.e., REM-1, REM-2, and REM-3. The REM-1 (sales manipulation) consists of a 0.620 mean value with a 0.00 minimum to 120.155 maximum range. The REM-2 represents an overproduction having a mean of 0.644 with a maximum value of 149.1556. This variable’s variance is 5.156, which shows that output levels are irregular but with relatively low degrees of fluctuation. REM-3 is the variable through which we get the least values. REM-3 denotes an anomalous amount of discretionary spending, which is achieved through discretionary expenses having a mean of 0.435 with a minimum value to a maximum value of 0.00 to 30.813 and a standard deviation of 1.624. This range explains that firms get the least earnings management by manipulating their discretionary expenses as compared to sales manipulation and overproduction.

4.2. Correlation Matrix and the Results of the Variance Inflation Factor (VIF)

In

Table 5. (a) Correlation Matrix, (b) VIF ¹.

(a)
	DD	SGR	REM-1	REM-2	REM-3	F-Size	F-Lev	F-Liq
DD	1
SGR	0.097 **	1
REM-1	0.153 **	−0.001	1
REM-2	0.161 **	−0.001	0.995 **	1
REM-3	0.082 **	−0.012	0.400 **	0.347 **	1
F-Size	0.691 **	0.043	0.129 **	0.126 **	0.139 **	1
F-Lev	0.079 **	0.018	−0.023	−0.022	−0.002	−0.078 **	1
F-Liq	0.041	0.007	−0.005	−0.005	−0.003	0.016	−0.064 *	1
(b)
	Model-1		Model-2		Model-3		Model-4
SGR	1.004		1.004		1.004		1.004
REM-1	-		1.018		-		-
REM-2	-		-		1.017		-
REM-3	-		-		-		1.020
F-Size	1.011		1.028		1.027		1.031
F-Lev	1.010		1.010		1.010		1.010
F-Liq	1.004		1.004		1.004		1.004

Notes: DD = distance-to-default [inverse proxy for corporate credit risk (CCR)]; REM-1 = real earnings management—way 1 [sales manipulation estimation model]; REM-2 = real earnings management—way 2 [overproduction estimation model]; REM-3 = real earnings management—way 3 [reduced discretionary expenses estimation model]; F-Size = firm size [natural log of firm’s total assets]; F-Lev = firm leverage [a firm’s ratio of total liabilities to its total assets]; F-Liq = firm liquidity [a firm’s ratio of liquid assets to its current liabilities]; * correlation is significant at 0.05 level or 5%; ** correlation is significant at 0.01 level or 1%. Notes: SGR = sustainable growth rate [a proxy for financial sustainability (FS)]; REM-1 = real earnings management—way 1 [sales manipulation estimation model]; REM-2 = real earnings management—way 2 [overproduction estimation model]; REM-3 = real earnings management—way 3 [reduced discretionary expenses estimation model]; F-Size = firm size [natural log of firm’s total assets]; F-Lev = firm leverage [a firm’s ratio of total liabilities to its total assets]; F-Liq = firm liquidity [a firm’s ratio of liquid assets to its current liabilities]. ¹ Centered variance inflation factor of models 1, 2, 3, and 4.

a,b, the correlation matrix and the results of the variance inflation factor (VIF) are displayed. The results of the correlation matrix (Table 5a) exhibit that no multicollinearity exists in the variables because no value of the correlation coefficient is greater than 0.9, which could cause multicollinearity between the variables [92]. The validity of the data is confirmed by the VIF (Table 5b), which adheres to the criterion outlined by [21], stating that multicollinearity is absent unless the VIF value exceeds 10. Therefore, Table 5b supports that there is no evidence of multicollinearity.

4.3. Relationship between Financial Sustainability and Credit Risk

Table 6. Impact of SGR on DD (Model 1) and moderation of REM on SGR-DD relationship (models 2, 3, and 4).

	Model 1		Model 2				Model 3				Model 4
Variables	Coeff	p-Value	Coeff	p-Value	Boot LLCI (95%)	Boot ULCI (95%)	Coeff	p-Value	Boot LLCI (95%)	Boot ULCI (95%)	Coeff	p-Value	Boot LLCI (95%)	Boot ULCI (95%)
Intercept	−46.071	0.000	−45.65	0.000	−49.847	−42.445	−45.967	0.000	−48.657	−42.276	−46.813	0.000	−49.724	−43.902
SGR	0.059	0.005	25.891	0.000	11.490	40.292	24.912	0.001	10.842	38.982	19.698	0.006	5.659	33.738
REM-1			16.379	0.000	8.164	24.593
Int-1			−34.569	0.026	−65.042	−4.096
REM-2							14.893	0.000	8.037	21.750
Int-2							−31.898	0.033	−61.256	−2.540
REM-3											−3.881	0.712	−24.464	16.702
Int-3											−24.801	0.218	−64.246	14.644
F-Size	0.697	0.000	0.841	0.000	2.131	3.551	0.639	0.000	2.966	3.312	3.527	0.000	2.689	3.365
F-Lev	0.135	0.000	0.186	0.000	1.421	2.951	2.395	0.000	1.714	2.077	2.637	0.000	1.472	2.802
F-Liq	0.038	0.073	0.172	0.067	−0.012	0.357	0.173	0.067	−0.012	0.357	0.170	0.071	−0.015	0.356
R2-adj.	0.497		0.506				0.507				0.500
F-stat.	285.430 ***		195.101 **				196.052 ***				190.617 ***
R2-change			0.002				0.002				0.0007
Obs.	1176		1176				1176				1176

Notes: SGR = sustainable growth rate [a proxy for financial sustainability (FS)]; REM-1 = real earnings management—way 1 [sales manipulation estimation model]; Int-1 = SGR*REM-1; REM-2 = real earnings management—way 2 [overproduction estimation model]; Int-2 = SGR*REM-2; REM-3 = real earnings management—way 3 [reduced discretionary expenses estimation model]; Int-3 = SGR*REM-3; F-Size = firm size [natural log of firm’s total assets]; F-Lev = firm leverage [a firm’s ratio of total liabilities to its total assets]; F-Liq = firm liquidity [a firm’s ratio of liquid assets to its current liabilities]; * p < 0.10, ** p < 0.05, *** p < 0.01.

presents the results for the relationship between SGR and DD. SGR has a positive and statistically significant relationship with DD, indicating that the more financially sustained a firm is, the more distant it becomes from the default (i.e., less credit risk). Thus, on the basis of econometric results, H1 is accepted (i.e., financial sustainability has a negative impact on corporate credit risk) and is supported by previous literature [3]. It is worthwhile to note here that a positive relationship has been reported between SGR and DD because DD has been used as the inverse proxy of credit risk in the present study. It is also logical in the financial sense that if a company is growing sustainably, it means that its profits, retained earnings, and assets are increasing continuously in the longer run. Its interest coverage ratio will also improve. It will be in a better position to pay its due interest on loans on time. Its liquidity will also improve. It will also be able to pay back principal loan amounts on agreed timelines. Therefore, the chances of default (corporate credit risk CCR) of a financially sustained firm are very low. In addition, the corporate credit risk will further reduce as the financial sustainability will improve with passage of time.

Weber [93] also concluded that the sustainability criteria of a firm influences its creditworthiness (CR) as part of its financial performance. BELÁS [94] also established a link between credit risk and financial sustainability. Höck [23] also got similar results for the impact of environmental sustainability on credit risk (CR). Keeping in view these references, it can be safely said that results regarding the relationship between financial sustainability (FS) and corporate credit risk (CCR) are sufficiently supported by existing literature. Therefore, we can say that the above results obtained are empirically and logically correct and supported by findings of previous researchers in the field. The results of the current study are a valuable contribution to the literature concerning the nonfinancial firms listed on the stock exchange of a developing country.

4.4. Moderating Role of REM in the Relationship between SGR and DD

Using a bootstrap analysis, models 2, 3, and 4 (Table 6) find the impact of REM-1, REM-2, and REM-3 on DD, respectively. The results show a positive and significant impact of REM-1 and REM-2 on DD, while REM-3 has an insignificant relationship with DD. The positive impact indicates that REM has a favorable and large influence on DD. If a firm is involved in the manipulation of financial statements through managerial activities (specifically sales manipulation and overproduction), it improves investors’ comprehension of corporations’ financial data. As a result, the firm will ultimately make itself distant from the default point. In other words, the distance-to-default of the firms will be increased due to the REM activities. Additionally, the interaction terms’ coefficients (i.e., Int-1, Int-2, and Int-3) are positive. However, only the Int-1 and Int-2 remained statistically negative and significant, suggesting that if the firms practice REM-1 (sales manipulation) and REM-2 (overproduction) in financially sustained firms, respectively, they cannot distance themselves from the default. In other words, such firms’ distance-to-default will be decreased (i.e., the corporate credit risk is increased). On the contrary, Int-3 (REM-3 related to discretionary expenses) makes a statistically insignificant impact in the financially sustained firms. The control variables, F-Size and F-Lev, show statistically significant results: however, F-Liq stayed insignificant in models 2, 3, and 4.

SGR attenuated DD with varying levels (i.e., low, medium, and high) of REM-1 and REM-2 (

Table 7. Conditional effects of SGR at levels of REM.

	REM Levels	Conditional Effect	p-Value	BootLLCI (95%)	BootULCI (95%)
REM-1	Low	15.075 ***	0.001	5.942	24.207
	Medium	14.623 ***	0.001	5.558	23.688
	High	13.553 ***	0.003	4.579	22.529
REM-2	Low	14.936 ***	0.001	5.826	24.046
	Medium	14.514 ***	0.001	5.468	23.561
	High	13.533 ***	0.003	4.570	22.497
REM-3	Low	-	Insig.	-	-
	Medium	-	Insig.	-	-
	High	-	Insig.	-	-

Notes: REM-1 = real earnings management—way 1 [sales manipulation estimation model]; REM-2 = real earnings management—way 2 [overproduction estimation model]; REM-3 = real earnings management—way 3 [reduced discretionary expenses estimation model]; *** p < 0.01. Insig. = insignificant.

). The results indicate significant results for all the levels of REM at a 1% level of significance. However, the results have shown that the conditional effect has been statistically significantly decreased with each increase in the levels of REM-1 and REM-2 (i.e., from low to high level) at a 1% level of significance. Put simply, the more financially sustained a firm is, the more difficult it will be for the firm to make itself distant from the default, hence the distance-to-default is being decreased with each level increase of the REM-1 and REM-2. The conditional effect of REM-3, on the other hand, stayed insignificant.

The visual depiction of probing the interactions is shown in Figure 1 and Figure 2 for REM-1 and REM-2, respectively. The figures demonstrate the varying levels, i.e., l (as low), m (as medium), and h (as high) levels of REM with three different slopes. These visual depictions are also in line with the results discussed above in Table 7 (for REM-1 and REM-2), ultimately supporting hypothesis 2. The emphasis is that as the level of REM is increased in a financially sustained firm, the distance-to-default of such firms is reduced. The figure for the REM-3 has not been presented because the conditional effect of REM-3 remained insignificant in Table 7.

4.5. Robustness Analysis

The present study further uses the Altman Z-score model (AZS–developed by Edward Altman) in [95] as an additional measure of corporate credit risk (CCR) to ensure the robustness of the study. The accuracy of the said model has been reported in several studies, see, e.g., [96,97]. The AZS model is calculated as

Z = 1.21X1 + 1.4X2 + 3.3X3 + 0.6X4 + 0.99X5

where X1 = working capital to total assets, X2 = retained earnings to total assets, X3 = earnings before interest and taxes to total assets, X4 = market capitalization to total liabilities, and X5 = sales to total assets. The evaluation criteria for the said model is if AZS is less than or equal to 1.8, there is a high level of credit risk in the firm; if AZS is more than 1.8 and less than 2.99, there is a medium level of credit risk in the firm; and if the AZS is more than or equal to 2.99, there is a lower level of credit risk in the firm.

Table 8. Robustness check with AZS as a proxy of corporate credit risk [impact of SGR on AZS (Model 1) and moderation of REM on SGR-AZS relationship (models 2, 3, and 4)].

	Model 1		Model 2				Model 3				Model 4
Variables	Coeff	p-Value	Coeff	p-Value	Boot LLCI (95%)	Boot ULCI (95%)	Coeff	p-Value	Boot LLCI (95%)	Boot ULCI (95%)	Coeff	p-Value	Boot LLCI (95%)	Boot ULCI (95%)
Intercept	5.607	0.263	8.769	0.078	−0.986	18.524	8.589	0.084	−1.163	18.341	6.654	0.179	−3.060	16.368
SGR	0.609	0.000	1.155	0.000	0.988	1.321	1.146	0.000	0.980	1.311	0.632	0.000	0.599	0.664
REM-1			0.201	0.090	−0.031	0.433
Int-1			−1.458	0.000	−1.896	−1.021
REM-2							0.150	0.141	−0.050	0.349
Int-2							−1.434	0.000	−1.868	−1.000
REM-3											0.182	0.564	−0.438	0.803
Int-3											−1.055	0.000	−1.372	−0.738
F-Size	−0.147	0.625	−0.332	0.267	−0.918	0.254	−0.319	0.285	−0.905	0.266	−0.207	0.488	−0.791	0.378
F-Lev	−1.680	0.125	−1.972	0.067	−4.085	0.141	−1.971	0.068	−4.085	0.142	−1.776	0.099	−3.887	0.335
F-Liq	0.009	0.002	0.009	0.001	0.004	0.015	0.009	0.001	0.004	0.015	0.009	0.001	0.004	0.015
R2-adj.	0.552		0.570				0.569				0.570
F-stat.	355.165 ***		252.327 ***				252.053 ***				252.277 ***
R2-change			0.0161				0.0158				0.0160
Obs.	1176		1176				1176				1176

Notes: SGR = sustainable growth rate [a proxy for financial sustainability (FS)]; REM-1 = real earnings management—way 1 [sales manipulation estimation model]; Int-1 = SGR*REM-1; REM-2 = real earnings management—way 2 [overproduction estimation model]; Int-2 = SGR*REM-2; REM-3 = real earnings management—way 3 [reduced discretionary expenses estimation model]; Int-3 = SGR*REM-3; F-Size = firm size [natural log of firm’s total assets]; F-Lev = firm leverage [a firm’s total liabilities to its total assets]; F-Liq = firm liquidity [a firm’s liquid assets to its current liabilities]; *** p < 0.01. Int-1 = SGR*REM-1; Int-2 = SGR*REM-2; Int-3 = SGR*REM-3.

and

Table 9. Robustness check with AZS as a proxy of corporate credit risk [conditional effects of SGR at levels of REM].

	REM Levels	Conditional Effect	p-Value	BootLLCI (95%)	BootULCI (95%)
REM-1	Low	0.698 ***	0.000	0.657	0.740
	Medium	0.679 ***	0.000	0.641	0.717
	High	0.634 ***	0.000	0.602	0.666
REM-2	Low	0.697 ***	0.000	0.656	0.738
	Medium	0.678 ***	0.000	0.640	0.716
	High	0.634 ***	0.000	0.602	0.666
REM-3	Low	0.313 ***	0.000	0.218	0.407
	Medium	0.291 ***	0.000	0.190	0.392
	High	0.243 ***	0.000	0.128	0.357

Notes: REM-1 = real earnings management—way 1 [sales manipulation estimation model]; REM-2 = real earnings management—way 2 [overproduction estimation model]; REM-3 = real earnings management—way 3 [reduced discretionary expenses estimation model], *** p < 0.01.

display the results of the study while taking the AZS (Altman Z-score) as a proxy of corporate credit risk. From the results, it can be seen that REM-1, REM-2, and REM-3 are significantly moderating the relationship between SGR and AZS. Table 9 shows that the conditional effect was statistically significantly decreased with each increase in the levels of REM-1, REM-2, and REM-3 (i.e., from low to high level) at a 1% level of significance. Therefore, the additional analysis has also shown that the study is robust across all levels of REM-1, REM-2, and REM-3 when credit risk is measured through the AZS (Altamn Z-score).

5. Conclusions

5.1. Conclusions

In conclusion, this study has contributed significantly to the intricate relationship between financial sustainability (FS) and corporate credit risk (CCR) with the moderating role of real earnings management (REM). Strengthening the sustainability of firms helps managers develop a significant image in public and excellent relationships with shareholders. Risk management is a strategy that works not only in the short run but also in the long run. According to the empirical findings, there is an inverse relationship between financial sustainability and credit risk because the increase in distance-to-default (DD) causes an increase in the financial sustainability of firms. The DD stands for the distance between the default point and the projected market value. If the value is lower, the possibility of default or credit risk is higher. A positive relationship is found between sustainable growth rate (SGR) and DD. As DD increases, the CR reduces. It is important to note here that a positive relationship has been reported between SGR and DD because DD has been used as the inverse proxy of credit risk in the present study. Therefore, the chances of default (corporate credit risk CCR) of a financially sustained firm are very low. In addition, the corporate credit risk will further reduce as the financial sustainability improves with the passage of time. Financially sustained firms also exhibit higher growth rates over extended periods, which improves their capacity to deal with debt and other risk factors

Secondly, the relationship between financial sustainability and credit risk with the moderating role of REM-1 (REM*OCF), REM-2 (REM*DISEXP), and REM-3 (SGR*A-Prod) is investigated. The results for REM 1 and REM2 were significant, suggesting that REM moderates the relationship between financial sustainability FS and credit risk CR.

As a robustness analysis, to further strengthen the above findings, all three measures of REM proved significant moderators when credit risk was measured by the Altman Z-score.

The research area explored in this study is unique and least explored by any other researchers in the past. Therefore, the results obtained in the current study are significant contributions to the existing literature on financial sustainability, credit risk, and real earnings management in context of developing countries. Therefore, we can say that the results obtained in this study are empirically proven and supported by findings of previous researchers in the field.

The current study’s findings have significant implications for various stakeholders, including regulators and legislators, those who develop corporate strategies, investors, and academics. The present study is beneficial for investors, as it allows them to evaluate the likelihood of default (through DD) in their portfolios and identify any significant degree of default risk. This knowledge enables them to minimize potential losses in terms of returns. The study’s findings have significant consequences for many different stakeholders, including regulators and legislators, those who develop corporate strategies, financiers, and academics. The current study provides an accurate approach to assessing the CR of firms in a developing economy. To effectively assess CR, this should help policymakers put in place an efficient FS reporting system. At the same time, regulatory entities such as the SECP should implement policies of risk management systems. When developing policies, policymakers should anticipate the likelihood of such opportunism and increase their monitoring mechanisms to ensure compliance. In this respect, the SECP should concentrate on strengthening the efficiency of the earnings management of firms by developing regulations about the size, leverage, or liquidity to encourage more participation in financial sustainability. This study presents empirical data on the link between REM and CR. Managers and senior executives should avoid using REM as an entrenchment tactic when dealing with shareholders. Although strengthening the sustainability of firms helps managers to develop a significant public image and excellent relationships with shareholders, reducing the chances of risk being analyzed is a plan of action that works not only in the short run but also in the long term. Most of the managers engage in REM projects to build beneficial connections with stakeholders and to increase the value of the firms. To limit managers’ potential for personal advantages, a robust monitoring system is required by SECP. Additionally, people in business should prevent managers from utilizing REM to conceal CR actions by enhancing external oversight by institutional shareholders and external block holders. This evidence suggests that before making financial and credit decisions, investors should closely examine a firm’s earnings management strategy and activities, since there is a genuine chance that a firm’s management may have substantial motivations to alter the data they show by ignoring GAAP regulations to maximize profits and earn personal gains. Simultaneously, before making investment decisions, investors should analyze a business’s credit risk system, since it is an essential indicator of how ethical the company is in its credit risk management behavior. The distance of firms from their defaulting point and their level of sustainable growth must be evaluated by the investors for investments. These factors enhance the financial sustainability of firms over a longer duration.

5.2. Limitations and Future Recommendations

This study, like every research, has some limitations, providing fresh insights for future research possibilities. This study is based on the manufacturing sector. Further research can be conducted by using data from various financial firms and comparing the results. This study can also be conducted in different countries in the future. REM is used as an earnings management technique in this study. The comparison between different countries can be made concerning real earnings management and accrual earnings management practices and their impact on the firm’s sustainability and credit risk.