Earnings Quality Drivers: Do Firm Attributes and Ownership Structure Matter in Emerging Stock Markets?

Abstract

This research aims to examine the drivers of earnings quality (EQ) in emerging stock markets. By testing the impact of firm attributes and ownership structures on the level of earnings quality. The research followed a mixed-method approach (qualitative and quantitative) and was conducted based on a sample of 75 listed firms in Egypt as an emerging market from 2015 to 2022. The results of the research found that each firm attribute has a mixed impact on earning quality, such as firm size (positive on persistence and no impact on consistency) and ROA (U-shape on persistence and consistency). In addition, ownership structures uniquely and dynamically impact earnings, following positive, U-shape, and N-shape. This research sheds light on the drivers of the earnings quality (firm attributes and ownership structures) in the Egyptian-listed firms and helps policymakers implement appropriate corporate governance mechanisms. Our findings in Egypt can motivate future research to further investigate the most relevant factors that explain variations in earning persistence and consistency as a dimension of financial reporting quality in other emerging markets.

1. Introduction

Earnings quality (EQ) is a significant and major concept in the field of financial accounting and mainly indicates how accurately disclosed earnings volume reflects a firm’s actual performance in the financial reports. Where high-quality earnings provide stakeholders with valuable information and indicators to make well-informed decisions (Dechow et al. 2010). However, there is no generally accepted definition of earnings quality. However, a general theme is the usefulness of earnings numbers and their use as guides for decision-making. Franceschetti (2018) defined high-quality earnings as those that provide more information about the reality of a firm’s performance relevant to a specific decision made by a stakeholder, and Alqam et al. (2022) argued that earning quality is a major indicator of how fairly a firm’s value is being disclosed in its financial reports.

Going further, the concept of “Earnings Quality” in accounting thought refers to two main issues, earnings persistence, and earnings consistency (Perotti and Wagenhofer 2014; Beyer et al. 2019; Jim-Suleiman and Ibiamke 2022), where “Persistence” means the predictive power of previous earnings concerning future earnings. It mainly measures how well a firm’s current amount of earnings predicts what its future earnings might be. Moreover, persistence uses the term predict, which involves a forward-looking aspect. It suggests how likely past earnings are to be a reliable indicator of performance in the future. On the other hand, “Consistency” means the stability of a firm’s earnings over financial periods. It focuses on the degree of fluctuation in earnings figures year-to-year. Moreover, consistency uses the term stable, which involves a historical perspective. It highlights the level of similarity in earnings across different fiscal periods (Franceschetti 2018; Brazel et al. 2021; Lukman et al. 2024).

More deeply, EQ is essential for many reasons; the first is investor confidence, where trust in the transparency of financial statements and reports is vital for capital markets’ health, and the second is information reliability, where high-quality earnings provide stakeholders with a true view of a firm’s performance, enabling them to make well-informed decisions. (Al-Enzy et al. 2023; Shin and Kim 2019), the third is the capital markets, where efficient capital allocation requires reliable information, and high-quality earnings ensure that funds flow to firms. (Kustono et al. 2021; Rezaee and Safarzadeh 2023). The fourth is the cost of capital, where stakeholders perceive firms with consistent earnings as less risky; this point can lead to a lower cost of capital. (Huynh 2018; Noviari et al. 2021; Brazel et al. 2021).

So, understanding the drivers of EQ in emerging markets is crucial, where these markets are mainly characterized by heightened volatility and varying levels of market maturity, which can significantly affect financial reporting transparency. Moreover, EQ is a primary factor influencing investor confidence and market efficiency, where high-quality earnings reporting is essential for attracting both national and international investors in emerging markets. Thus, by studying how firm attributes and ownership structures impact EQ, this research provides insights into how these elements affect financial reporting accuracy and investor trust in emerging markets. Thus, this research aims to answer two main questions as follows:

Do firm attributes (e.g., size, age, leverage, asset growth, operating cash flow, tangibility, and profitability) impact earnings quality in emerging markets?

Do ownership structures (e.g., managerial, institutional, governmental, and concentration) impact earnings quality in emerging markets?

Based on the above, this research is driven by various motivations from the theoretical motivations side. The existing literature on EQ mainly focuses on developed stock markets (Xu et al. 2012; Masmoudi Ayadi and Boujelbène 2014; Ji et al. 2015; Tessema et al. 2018; Shin and Kim 2019; Dong et al. 2020), so examining emerging markets can discover unique drivers and challenges that may not apply to more mature markets. Moreover, scientific theories that developed in mature markets may need to be refined to account for the distinct drivers of emerging markets. Going to the practical motivation side, understanding the drivers of EQ can help investors evaluate the firm’s financial health in emerging markets, leading to more informed decisions. Moreover, by identifying factors that influence EQ, professional bodies, authorities, and regulators can implement measures to improve corporate governance practices in emerging markets. Thus, supporting national development, where high EQ can attract international capital and contribute to achieving the efficiency of emerging markets.

The rest of this research is organized as follows: part two reviews the literature and develops related hypotheses, part three presents the research statistics models, part four includes the results, part five is the discussion, and finally the conclusion in part six.

2. Literature Review and Hypotheses Development

Many academic studies have examined the association between earnings quality and firm attributes on one side and ownership structure on the other. Despite the considerable volume of research in this academic area, the results remain mixed, presenting two different perspectives of reality. Consequently, we have summarized this academic debate below to capture the knowledge gap and develop our hypotheses.

2.1. Earnings Quality and Firm Attributes

2.1.1. Theoretical Background

Earnings quality as a concept in accounting theories is considered a measure of the reliability and relevance of disclosed earnings. It shows how financial reports accurately reflect a firm’s financial performance. Many theories provided suggestions for the impact of firm attributes on earnings quality, such as agency theory, which assumes the conflict of interest between managers and stockholders. Thus, managers may engage in earnings management practices to maximize their compensation. Firm attributes like size, leverage, and profitability can exacerbate or mitigate this agency problem. Moreover, positive accounting theory suggests that managers’ decisions, including those related to accounting choices, are influenced by contractual incentives. Firms with higher growth opportunities or financial leverage may be more interested in managing earnings to meet their liabilities or attract more investors. So, these theoretical arguments were the starting point for our research in this area (Anam 2023; Dechow et al. 2010; Pujiati et al. 2022; Ramadan 2015).

2.1.2. Global Evidence from the Previous Literature

Firm attributes in accounting thought can influence managers’ decision-making or adoption of accounting practices. Many of these attributes are discussed in the accounting literature, and the researchers have outlined the most common of these attributes (firm size, firm age, financial leverage, assets growth rate, and operating cash flow) below:

In terms of firm size (FS), a stream of accounting research emphasized the positive impact of FS on EQ. Pujiati et al. (2022) and Anam (2023) found that the main reason is governance, where larger firms tend to have more internal controls and governance mechanisms. Moreover, Perotti and Wagenhofer (2014) and Valdiansyah and Murwaningsari (2022) argued that scrutiny is the reason why large firms are subject to more scrutiny from stakeholders. Conversely, many studies ensured the negative impact of FS on EQ. Kustono et al. (2021) and Song (2022) argued that large firms face more pressure to meet stakeholders’ expectations, which could lead them to manage earnings, resulting in lower quality. Furthermore, Bose and Yu (2023) and Sequeira et al. (2024) ensured that large firms have complex operations, making it difficult to measure earnings accurately.

The literature unequivocally presents a varied spectrum with mixed results regarding firm age (FA), where a part emphasized the positive impact of FA on EQ. Hashmi et al. (2018) and Rezaee et al. (2020) argued that over time, firms enhance their accounting practices and establish more streamlined processes, resulting in higher-quality earnings. Moreover, Kustono et al. (2021) and Rezaee and Safarzadeh (2023) noted that older firms mainly prioritize accurate financial reporting to maintain their reputation. Conversely, many studies ensured the negative impact of FA on EQ; Tarmidi et al. (2021) and Gaio et al. (2023) emphasized that well-established firms may become complacent, potentially leading to less rigorous accounting practices, which could result in lower earnings quality. Furthermore, Song (2022) and Bose and Yu (2023) ensured that as a firm ages, it can be difficult to maintain consistent earnings growth; this pressure might lead a firm to manage earnings to create strong performance with a low quality.

Regarding financial leverage (LEV), the literature has two conflicting waves of studies. Where a wave emphasized the positive impact of LEV on EQ. Perotti and Wagenhofer (2014) and Anam (2023) found that loan agreements often include debt covenants, which push firms to maintain healthy financial ratios, which lead to higher-quality earnings. Moreover, Kustono et al. (2021) and Alqam et al. (2022) noted that debt financing can be a motivator for firms to act efficiently, especially in terms of the quality of earnings. Conversely, another wave ensured the negative impact of LEV on EQ, Gaio et al. (2023) and Sequeira et al. (2024) argued that high debt levels can pressure firms to manipulate earnings to meet liabilities, resulting in lower quality. Furthermore, Pujiati et al. (2022) and Bose and Yu (2023) ensured that if the firm cannot meet its debt payments, it will face financial distress, which can lead firms to use more aggressive practices, resulting in lower quality.

Considering assets growth rate (AGR), a part of the literature emphasized the positive impact of AGR on EQ. Bose and Yu (2023) and Anam (2023) found that firms with high-growth rates focus more on building long-term value than short-term profits, which could result in higher quality. Moreover, Alqam et al. (2022) and Sequeira et al. (2024) noted that firms characterized by high-growth rates attract the attention of stakeholders, this can act as a wall against manipulation practices, resulting in higher quality. Conversely, another part of the literature ensured the negative impact of AG, Ramadan (2015) and Widianingsih and Setiawan (2022) argued that high-growth firms face huge pressure from shareholders to maintain their rates, which pushes firms to use earnings management practices to meet expectations. Furthermore, Obeng et al. (2020) and Al-Enzy et al. (2023) ensured that rapid growth may involve new and complex events, which can make it more difficult to measure earnings quality.

There are relatively few studies that investigated the impact of operating cash flow (OCF) in the accounting literature, and a part of it emphasized the positive impact of OCF on EQ. Rezaee et al. (2020) and Kustono et al. (2021) found that a strong operating cash flow indicates the firm is generating enough cash to cover its expenses, reducing the need for manipulative practices to increase earnings. Moreover, Tarmidi et al. (2021) and Rezaee and Safarzadeh (2023) noted that firms with consistently high OCF mainly have a stronger financial position, making them more interested in earnings quality. On the other hand, some studies ensured the negative impact of the OCF rate on EQ. Huynh (2018) and Shiah-Hou (2021) argued that net income can be influenced by a high volume of non-cash expenses, which reduce reported earnings and result in misleading information about earnings. Furthermore, Brahem et al. (2022) and Bose and Yu (2023) ensured that firms can manipulate operating cash flow by managing working capital, which results in lower earnings quality.

Asset tangibility (TANG) refers to the extent to which a firm’s assets are physical or tangible. Generally, the accounting literature emphasized the positive impact of TANG on EQ (Jim-Suleiman and Ibiamke 2022; Valdiansyah and Murwaningsari 2022), where firms with higher asset tangibility are often seen to have better earnings quality, and firms with higher TANG tend to have stronger financial foundations and a lower risk of earnings manipulation. However, a few studies (Pujiati et al. 2022; Anam 2023) argued that this impact is negative due to many risks such as obsolescence, cyclical sensitivity, and higher fixed costs.

Concerning profitability (ROA), many studies found that a firm with a high profitability profile is mainly characterized by high EQ due to investor confidence and sustainable growth (Pujiati et al. 2022; Anam 2023). Conversely, there are potential drawbacks—which may result in a negative impact—that should be considered, such as competitive pressure, investor expectations, and unethical practices (Alqam et al. 2022; Jim-Suleiman and Ibiamke 2022).

2.1.3. Knowledge Gap and Hypotheses Development

Based on these arguments in the previous related literature, the impact of firm attributes (size, age, financial leverage, asset growth, operating cash flow, tangibility, and profitability) on earnings quality in terms of persistence and consistency is a subject of ongoing research, and mixed results between positive, negative, and no relation were found in the literature in both developing and developed countries, thus leading to the development of the following hypotheses to find new evidence based on the Egyptian context as a developing and emerging country:

H1: 

Firm attributes positively impact earnings quality (persistence) in Egypt.

H2: 

Firm attributes positively impact earnings quality (consistency) in Egypt.

2.2. Earnings Quality and Ownership Structures

2.2.1. Theoretical Background

Ownership structure, as a concept in accounting theories, refers to the distribution of ownership rights (shares or stocks) among different stakeholders, significantly influencing a firm’s corporate governance and earnings quality. Many theories provided suggestions for the influence of ownership structures on earnings quality, such as agency theory, which assumes that a principal delegates decision-making authority to an agent, creating a conflict of interest, and argues that concentrated ownership can improve earnings quality. Going further, stakeholder theory assumes that firms should consider all stakeholders, not just shareholders. This theory suggests that institutional investors often have strong incentives to monitor managers, which can lead to improved earnings quality. Moreover, managerial ownership can align the interests of stockholders and managers, reducing agency costs and improving earnings quality. Furthermore, governmental ownership can provide financial stability and more quality in earnings. So, these theoretical arguments were the starting point for our research in this area (Ali et al. 2024; Katz 2009; Kristiawan 2024; Oyebamiji 2021; Xu et al. 2012).

2.2.2. Global Evidence from the Previous Literature

Ownership is one of the most popular factors in accounting thought that can shape and affect earnings in stock markets; many patterns of ownership structure are discussed in the accounting literature. The researcher has outlined the most common of these patterns (managerial, institutional, governmental, and concentration) below:

In the accounting literature, there is a significant focus on ownership concentration (OC) when investigating earnings quality, where Anwar and Buvanendra (2019) and Choi et al. (2020) argued that there is a positive impact based on the alignment of interests; when ownership is concentrated, particularly with large shareholders, their benefits become more closely aligned with those of minority shareholders, which encourages increased EQ. Moreover, Dong et al. (2020) and Kristiawan (2024) emphasized that the reason is oversight, where large shareholders have a greater interest in the firm’s financial sustainability, and this oversight can limit the practices of earnings management and increase earnings quality. From another perspective, Hashmi et al. (2018) and Oyebamiji (2021) argued that there is a negative impact because of information asymmetry, where in high OC, there might be low external pressure for transparency, and this makes managers more inclined to manage earnings. Going further, Piosik and Genge (2020) and Gultom and Wati (2022) ensured that the lack of accountability is the reason why high OC can lead to managers’ entrenchment, and this makes it difficult for minority shareholders to control firms’ earnings practices.

In terms of governmental ownership (GO), the impact of GO on EQ is a debated topic with arguments on both sides—positive and negative—based on the fact of differences between regulations in each country, Rezaee and Tuo (2019) and Sadaa et al. (2023) noted that GO can limit earnings management practices. Governments might be less interested in short-term earnings and more concerned with long-term, potentially leading to more quality. Going further, Le and Nguyen (2023) and Akter et al. (2024) argued that government-owned firms prioritize social performance over financial performance; this can lead to more control of accounting practices and increased earnings quality. On the other side, Anwar and Buvanendra (2019) and Attia et al. (2023) noted that there is a negative impact because of government interference, where GO can expose firms to engaging in new projects with lower earnings but with a higher social impact. Moreover, Kablan (2021) and Nguyen et al. (2021) showed that—in some cases—government-owned firms might have lower governance mechanisms compared to private firms, which can lead to earnings manipulation.

Regarding institutional ownership (IO), a stream of research emphasized the positive impact of IO on EQ. Kustono et al. (2021) and Bose and Yu (2023) noted that institutional bodies invest large volumes of money in share purchases, so they mainly monitor financial performance and pressure firms’ managers to maintain high-quality earnings. Moreover, Dong et al. (2020) and Istianingsih (2021) argued that IO decreases earnings management practices due to the fear of scrutiny from institutional investors. While the positive impact of IO on EQ is more notable in the literature, some perspectives support the negative effect. Ahmad et al. (2023) and Ali et al. (2024) noted that institutional investors might pressure firms to prioritize short-term earnings, which can lead firms to apply accounting practices that do not include earnings sustainability and quality. Going further, Nguyen et al. (2021) and Gultom and Wati (2022) found that firms might be under pressure to meet earnings expectations, which can lead to earnings manipulation and low quality.

Concerning Managerial ownership (MO), there are two perspectives on the incentives that managerial ownership offers for accounting choices, entrenchment, and incentive alignment (Dechow et al. 2010). A big part of the literature emphasized the positive impact of MO on EQ. Katz (2009) and Shiah-Hou (2021) found that when managers have a large volume of ownership shares, their interests become in the same line as those of other shareholders; this can motivate them to focus on the quality of earnings. Moreover, Alqirem et al. (2020) and Nugrahanti and Nugroho (2022) noted that managers who are also shareholders have a greater motivation to provide transparent reporting because their net wealth is directly related to earnings quality. Conversely, many studies ensured the negative impact of MO on EQ, Dong et al. (2020) and Piosik and Genge (2020) argued that managers with a high percentage of ownership might manage earnings to raise their stock options, resulting in reduced earnings quality. Furthermore, Nguyen et al. (2021) and Oyebamiji (2021) ensured that high financial performance could lead managers to take on more risks to achieve higher earnings, which can decrease the related quality.

2.2.3. Knowledge Gap and Hypotheses Development

Based on these arguments, the impact of ownership structure (concentration, institutional, governmental, and managerial) on earnings quality in terms of persistence and consistency is a subject of ongoing research, and mixed results between positive, negative, and no relation were found in the literature in both developing and developed countries, thus leading to the development of the following hypotheses to find new evidence based on the Egyptian context as a developing and emerging country:

H3: 

Ownership structures positively impact earnings quality (persistence) in Egypt.

H4: 

Ownership structures positively impact earnings quality (consistency) in Egypt.

We noticed that most studies in the previous literature did not disclose the difference between the effect of earnings persistence and earnings consistency when measuring earnings quality. The reliance was greater on “persistence” as a measure, and this is what will distinguish the current study, as it will take both persistence and consistency into consideration in testing the hypotheses, which will lead to discovering new evidence and contributions.

Overall, evidence in our research scope is mixed in terms of both the impact of firm attributes (Alqam et al. 2022; Huynh 2018; Sequeira et al. 2024; Song 2022) and the ownership structure (Anwar and Buvanendra 2019; Attia et al. 2023; Kustono et al. 2021; Sadaa et al. 2023) on earnings quality. This conflict in results also existed between developing and developed countries (Rezaee and Tuo 2019; Shin and Kim 2019; Tarmidi et al. 2021; Widianingsih and Setiawan 2022). So, testing our hypotheses may lead to new evidence in the literature based on an emerging business environment.

3. Method

3.1. This Study Sample and Limitations

Our scope in this study is emerging stock markets, and we selected Egypt as a sample country to be our empirical environment. Thus, our population is represented by the Egyptian firms listed in the stock market during the financial period from 2015 to 2022, and we chose this study sample according to three standards, including (a) the availability of financial statements. (b) the firm has not undergone any mergers or discontinuations during this study period. (c) excluding banking and financial sectors due to their unique characteristics and related governmental regulations in the Egyptian environment, which may affect the statistical results and make them unreliable. Thus, the adoption of our standards resulted in the selection of (75) firms to be this study sample, with (530) observations. (

Table 1. Tabulation of Global Industry Classification Standard (GICS) sector.

Sector	Firms	Freq.	Percent
Communication Services	3	20	3.77
Consumer Discretionary	10	77	14.53
Consumer Staples	15	103	19.43
Health Care	5	31	5.85
Industrials	7	54	10.19
Materials	18	131	24.72
Real Estate	17	114	21.51
Total	75	530	100.00

).

3.2. Variables Measurement

3.2.1. Earnings Quality (EQ)

We measured earnings quality through two facets, “Earnings Persistence and Earnings Consistency” (Franceschetti 2018), to capture more accurate results in our study and provide more evidence for our hypotheses in the Egyptian business environment.

Earnings Persistence (EQP)

Earnings persistence in accounting thought refers to the ability of current earnings to predict earnings in the future. So, it measures how long the effects of a firm’s earnings will persist into the future. Based on that, firms with more persistent earnings generate a more accurate valuation of earnings quality, and we used the following equation to measure earnings persistence based on the literature (Dechow et al. 2010; Islamiati 2023).

$${\mathrm{E}\mathrm{a}\mathrm{r}\mathrm{n}\mathrm{i}\mathrm{n}\mathrm{g}\mathrm{s}}_{\mathrm{i},\mathrm{t}+1}=\mathsf{\alpha }+\mathsf{\beta } {\mathrm{E}\mathrm{a}\mathrm{r}\mathrm{n}\mathrm{i}\mathrm{n}\mathrm{g}\mathrm{s}}_{\mathrm{i},\mathrm{t}}+{\mathsf{\epsilon }}_{\mathrm{i},\mathrm{t}}$$

(1)

where $\mathsf{\beta }$ measures earnings persistence, ${\mathrm{E}\mathrm{a}\mathrm{r}\mathrm{n}\mathrm{i}\mathrm{n}\mathrm{g}\mathrm{s}}_{\mathrm{i},\mathrm{t}}$ = net income after tax of firm (i) during financial year (t) and ${\mathrm{E}\mathrm{a}\mathrm{r}\mathrm{n}\mathrm{i}\mathrm{n}\mathrm{g}\mathrm{s}}_{\mathrm{i},\mathrm{t}+1}$ = net income after tax of firm (i) during financial year (t + 1).

Earnings Consistency (EQC)

Earnings consistency in accounting thought refers to the stability of a firm’s earnings over time. It implies that earnings are not subject to significant volatility. So, we used a method that calculates the standard deviation of a firm’s earnings relative to its average earnings over a specific period. A lower coefficient of variation (CV) indicates higher consistency, as the standard deviation represents the degree of variation around the mean, and we used the following equation to measure earnings consistency based on the literature (Chang et al. 2021; Cho 2022).

CV = Standard Deviation of Earnings/Average Earnings (across the period)

(2)

3.2.2. Firm Attributes

We measured firm attributes in our sample’s firms based on the previous literature (Ramadan 2015; Pujiati et al. 2022; Anam 2023) as follows: (

Table 2. The measurement of firm attributes.

Variable	Measurement Method
Firm size (FS)	The logarithm of total assets
Firm age (FA)	The logarithm of operations years after the first financial reporting date
Financial leverage (LEV)	Total liabilities over total shareholders’ equity
Assets growth rate (AGR)	(Current total assets − previous total assets) over previous total assets
Operating cash flow (OCF)	Net operating cash flow over total assets
Assets tangibility (TANG)	(Fixed asset + investment) over total asset
Profitability (ROA)	Net profit after interest and tax over total assets

)

3.2.3. Ownership Structure

There are many patterns of ownership structures in accounting thought, and based on the literature and available data in the Egyptian context, we chose four patterns—concentration, governmental, institutional, and managerial ownership—to be the scope of our study and used to test the impact of ownership structures that are mentioned in our hypotheses. Thus, we measured the structure of ownership in our sample’s firms (Katz 2009; Anwar and Buvanendra 2019; Oyebamiji 2021) as follows: (

Table 3. The measurement of ownership structure.

Variable	Meaning and Measurement Method	Implications and Significance
Ownership concentration (OC)	OC refers to the extent to which a few shareholders hold a significant portion of a firm’s shares. Measurement: The total percentage of a firm’s shares of block-holders (hold at least five percent of the outstanding shares)	Monitoring and control Reducing agency costs Risk-taking behavior
Governmental ownership (GO)	GO refers to the ownership of a firm’s shares by government units, bodies, or authorities. Measurement: Numbers of shares owned by government authorities over the outstanding shares during the financial year	Access to resources Strategic importance Political interference
Institutional ownership (IO)	IO refers to the ownership of a firm’s shares by institutional investors such as pension funds, mutual funds, and endowments. Measurement: Number of shares owned by institutional investors over the outstanding shares during the financial year	Long-term orientation Enhanced corporate governance Regulatory impact
Managerial ownership (MO)	MO refers to the ownership of a firm’s shares by its managers. Measurement: Numbers of shares owned by managers over the outstanding shares during the financial year	Alignment of interests Reduced short-termism capital allocation

)

3.3. Research Models

3.3.1. Model (1); Firm Attributes, Ownership Concentration, and Earnings Quality

We developed model (1) to test the impact of ownership concentration with firm attributes on earnings quality in terms of persistence (EQP) and consistency (EQC). In addition, we added Big4, industry effect, and year effect to smooth the model.

$$\begin{array}{cc}{\mathrm{E}\mathrm{Q}\mathrm{P}}_{\mathrm{i},\mathrm{t}},{\mathrm{E}\mathrm{Q}\mathrm{C}}_{\mathrm{i},\mathrm{t}}=& \mathsf{\alpha }0+\mathsf{\beta }1 {\mathrm{O}\mathrm{C}}_{\mathrm{i},\mathrm{t}}+\mathsf{\beta }2 {\mathrm{F}\mathrm{S}}_{\mathrm{i},\mathrm{t}}+\mathsf{\beta }3 {\mathrm{F}\mathrm{A}}_{\mathrm{i},\mathrm{t}}+\mathsf{\beta }4 {\mathrm{L}\mathrm{e}\mathrm{v}}_{\mathrm{i},\mathrm{t}}+\mathsf{\beta }5 {\mathrm{A}\mathrm{G}\mathrm{R}}_{\mathrm{i},\mathrm{t}}\hfill \\ & +\mathsf{\beta }6 {\mathrm{O}\mathrm{C}\mathrm{F}}_{\mathrm{i},\mathrm{t}}+\mathsf{\beta }7 {\mathrm{T}\mathrm{a}\mathrm{n}\mathrm{g}}_{\mathrm{i},\mathrm{t}}+\mathsf{\beta }8 {\mathrm{R}\mathrm{O}\mathrm{A}}_{\mathrm{i},\mathrm{t}}+\mathsf{\beta }9 {\mathrm{B}\mathrm{i}\mathrm{g}4}_{\mathrm{i},\mathrm{t}}\hfill \\ & +\mathrm{I}\mathrm{n}\mathrm{d}\mathrm{u}\mathrm{s}\mathrm{t}\mathrm{r}\mathrm{y} \mathrm{E}\mathrm{f}\mathrm{f}\mathrm{e}\mathrm{c}\mathrm{t}+\mathrm{Y}\mathrm{e}\mathrm{a}\mathrm{r} \mathrm{E}\mathrm{f}\mathrm{f}\mathrm{e}\mathrm{c}\mathrm{t} {\mathsf{\epsilon }}_{\mathrm{i},\mathrm{t}}\hfill \end{array}$$

(3)

3.3.2. Model (2); Firm Attributes, Governmental Ownership, and Earnings Quality

We developed model (2) to test the impact of governmental ownership with firm attributes on earnings quality in terms of persistence (EQP) and consistency (EQC). In addition, we added Big4, industry effect, and year effect to smooth the model.

$$\begin{array}{cc}{\mathrm{E}\mathrm{Q}\mathrm{P}}_{\mathrm{i},\mathrm{t}},{\mathrm{E}\mathrm{Q}\mathrm{C}}_{\mathrm{i},\mathrm{t}}=& \mathsf{\alpha }0+\mathsf{\beta }1 {\mathrm{G}\mathrm{O}}_{\mathrm{i},\mathrm{t}}+\mathsf{\beta }2 {\mathrm{F}\mathrm{S}}_{\mathrm{i},\mathrm{t}}+\mathsf{\beta }3 {\mathrm{F}\mathrm{A}}_{\mathrm{i},\mathrm{t}}+\mathsf{\beta }4 {\mathrm{L}\mathrm{e}\mathrm{v}}_{\mathrm{i},\mathrm{t}}+\mathsf{\beta }5 {\mathrm{A}\mathrm{G}\mathrm{R}}_{\mathrm{i},\mathrm{t}}\hfill \\ & +\mathsf{\beta }6 {\mathrm{O}\mathrm{C}\mathrm{F}}_{\mathrm{i},\mathrm{t}}+\mathsf{\beta }7 {\mathrm{T}\mathrm{a}\mathrm{n}\mathrm{g}}_{\mathrm{i},\mathrm{t}}+\mathsf{\beta }8 {\mathrm{R}\mathrm{O}\mathrm{A}}_{\mathrm{i},\mathrm{t}}+\mathsf{\beta }9 {\mathrm{B}\mathrm{i}\mathrm{g}4}_{\mathrm{i},\mathrm{t}}\hfill \\ & +\mathrm{I}\mathrm{n}\mathrm{d}\mathrm{u}\mathrm{s}\mathrm{t}\mathrm{r}\mathrm{y} \mathrm{E}\mathrm{f}\mathrm{f}\mathrm{e}\mathrm{c}\mathrm{t}+\mathrm{Y}\mathrm{e}\mathrm{a}\mathrm{r} \mathrm{E}\mathrm{f}\mathrm{f}\mathrm{e}\mathrm{c}\mathrm{t} {\mathsf{\epsilon }}_{\mathrm{i},\mathrm{t}}\hfill \end{array}$$

(4)

3.3.3. Model (3); Firm Attributes, Institutional Ownership, and Earnings Quality

We developed model (3) to test the impact of institutional ownership with firm attributes on earnings quality in terms of persistence (EQP) and consistency (EQC). In addition, we added Big4, industry effect, and year effect to smooth the model.

$$\begin{array}{cc}{\mathrm{E}\mathrm{Q}\mathrm{P}}_{\mathrm{i},\mathrm{t}},{\mathrm{E}\mathrm{Q}\mathrm{C}}_{\mathrm{i},\mathrm{t}}=& \mathsf{\alpha }0+\mathsf{\beta }1 {\mathrm{I}\mathrm{O}}_{\mathrm{i},\mathrm{t}}+\mathsf{\beta }2 {\mathrm{F}\mathrm{S}}_{\mathrm{i},\mathrm{t}}+\mathsf{\beta }3 {\mathrm{F}\mathrm{A}}_{\mathrm{i},\mathrm{t}}+\mathsf{\beta }4 {\mathrm{L}\mathrm{e}\mathrm{v}}_{\mathrm{i},\mathrm{t}}+\mathsf{\beta }5 {\mathrm{A}\mathrm{G}\mathrm{R}}_{\mathrm{i},\mathrm{t}}\hfill \\ & +\mathsf{\beta }6 {\mathrm{O}\mathrm{C}\mathrm{F}}_{\mathrm{i},\mathrm{t}}+\mathsf{\beta }7 {\mathrm{T}\mathrm{a}\mathrm{n}\mathrm{g}}_{\mathrm{i},\mathrm{t}}+\mathsf{\beta }8 {\mathrm{R}\mathrm{O}\mathrm{A}}_{\mathrm{i},\mathrm{t}}+\mathsf{\beta }9 {\mathrm{B}\mathrm{i}\mathrm{g}4}_{\mathrm{i},\mathrm{t}}\hfill \\ & +\mathrm{I}\mathrm{n}\mathrm{d}\mathrm{u}\mathrm{s}\mathrm{t}\mathrm{r}\mathrm{y} \mathrm{E}\mathrm{f}\mathrm{f}\mathrm{e}\mathrm{c}\mathrm{t}+\mathrm{Y}\mathrm{e}\mathrm{a}\mathrm{r} \mathrm{E}\mathrm{f}\mathrm{f}\mathrm{e}\mathrm{c}\mathrm{t} {\mathsf{\epsilon }}_{\mathrm{i},\mathrm{t}}\hfill \end{array}$$

(5)

3.3.4. Model (4); Firm Attributes, Managerial Ownership, and Earnings Quality

We developed model (4) to test the impact of managerial ownership with firm attributes on earnings quality in terms of persistence (EQP) and consistency (EQC). In addition, we added Big4, industry effect, and year effect to smooth the model.

$$\begin{array}{cc}{\mathrm{E}\mathrm{Q}\mathrm{P}}_{\mathrm{i},\mathrm{t}},{\mathrm{E}\mathrm{Q}\mathrm{C}}_{\mathrm{i},\mathrm{t}}=& \mathsf{\alpha }0+\mathsf{\beta }1 {\mathrm{M}\mathrm{O}}_{\mathrm{i},\mathrm{t}}+\mathsf{\beta }2 {\mathrm{F}\mathrm{S}}_{\mathrm{i},\mathrm{t}}+\mathsf{\beta }3 {\mathrm{F}\mathrm{A}}_{\mathrm{i},\mathrm{t}}+\mathsf{\beta }4 {\mathrm{L}\mathrm{e}\mathrm{v}}_{\mathrm{i},\mathrm{t}}+\mathsf{\beta }5 {\mathrm{A}\mathrm{G}\mathrm{R}}_{\mathrm{i},\mathrm{t}}\hfill \\ & +\mathsf{\beta }6 {\mathrm{O}\mathrm{C}\mathrm{F}}_{\mathrm{i},\mathrm{t}}+\mathsf{\beta }7 {\mathrm{T}\mathrm{a}\mathrm{n}\mathrm{g}}_{\mathrm{i},\mathrm{t}}+\mathsf{\beta }8 {\mathrm{R}\mathrm{O}\mathrm{A}}_{\mathrm{i},\mathrm{t}}+\mathsf{\beta }9 {\mathrm{B}\mathrm{i}\mathrm{g}4}_{\mathrm{i},\mathrm{t}}\hfill \\ & +\mathrm{I}\mathrm{n}\mathrm{d}\mathrm{u}\mathrm{s}\mathrm{t}\mathrm{r}\mathrm{y} \mathrm{E}\mathrm{f}\mathrm{f}\mathrm{e}\mathrm{c}\mathrm{t}+\mathrm{Y}\mathrm{e}\mathrm{a}\mathrm{r} \mathrm{E}\mathrm{f}\mathrm{f}\mathrm{e}\mathrm{c}\mathrm{t} {\mathsf{\epsilon }}_{\mathrm{i},\mathrm{t}}\hfill \end{array}$$

(6)

where

• EQP_i,t is the earnings persistence of firm (i) during financial year (t).
• EQC_i,t is the earnings consistency of firm (i) during financial year (t).
• FS_i,t is the size of firm (i) during financial year (t).
• FA_i,t is the age of firm (i) during financial year (t).
• LEV_i,t is the financial leverage of firm (i) during financial year (t).
• AGR_i,t is the asset growth rate of firm (i) during financial year (t).
• OCF_i,t is the operating cash flow of firm (i) during financial year (t).
• TANG_i,t is the level of assets tangibility of firm (i) during financial year (t).
• ROA_i,t is the return on assets ratio of firm (i) during financial year (t).
• Control variables (Big4, industry effect, and year effect).

4. Results

4.1. Descriptive Statistics

Table 4. Descriptive statistics.

Variable	Obs	Mean	Std. Dev.	Min	Max
EQP	530	0.319	0.393	−0.698	1.298
EQC	530	0.274	0.921	−1.526	1.837
OC	529	0.618	0.196	0.107	1
GO	530	0.139	0.238	0	0.8
IO	527	0.361	0.271	0	0.969
MO	530	0.035	0.056	0	0.145
FS	530	21.172	1.89	17.227	25.817
FA	530	18.581	6.387	4	30
Lev	525	0.454	0.23	0.005	1.027
AGR	529	0.096	0.212	−0.267	0.899
OCF	529	0.043	0.1	−0.198	0.302
Tang	526	0.319	0.232	0	0.964
ROA	530	0.045	0.082	−0.153	0.27

presents the statistical summary for earning persistence, earnings consistency, different types of ownership structure, and firm-specific attributes in the employed sample, covering the period from 2015 to 2022. The variables are subjected to Winsorization, where the extreme values at the top and bottom 3% are replaced with less extreme values to reduce the impact of outliers.

The two dimensions of the main dependent variable, “earnings quality,” reveal the diversity among the sampled Egyptian firms in terms of their earnings quality. For instance, earnings persistence (EQP) shows a high standard deviation (0.393) compared to the mean (0.319), along with a wide range between its minimum value (−0.698) and its maximum value (1.298). This suggests that some firms in the Egyptian capital market suffer from transitory earnings, while others can sustain a high level of earnings persistence. In a similar vein, earnings consistency (EQC) shows a high standard deviation (0.921) relative to the mean (0.274), with a wide range between its minimum value (−1.526) and its maximum value (1.837). This indicates that some firms in the Egyptian capital market suffer from low-consistent earnings, whereas others manage to maintain high-consistent earnings.

In terms of the summary statistics of ownership structure, three patterns of ownership show a relatively large difference among the sampled firms, as their standard deviations are relatively large. These patterns are governmental ownership (0.238), institutional ownership (0.271), and managerial ownership (0.056). It is noteworthy that the minimum values for these ownership patterns are all zero, indicating that some sampled firms have no governmental, institutional, or managerial ownership. On the contrary, the maximum values reach (0.8), (0.969), and (0.145) for governmental, institutional, and managerial ownership, respectively. Implying that the listed firms in the Egyptian Stock Exchange exhibit a wide variation in governmental, institutional, and managerial ownership.

Unlike other patterns of ownership structure, the minimum value of ownership concentration is non-zero (0.107), indicating that all the sampled Egyptian firms have a percentage of at least 10.7% of their outstanding stocks held by block-holders. Additionally, unlike other patterns of ownership structure, the ownership concentration pattern shows a relative homogeneity over the sampled firms as evidenced by the relatively modest standard deviation (0.196) compared to its mean (0.618).

Concerning the firm-specific attributes within the Egyptian application context, firm size (FS) shows a standard deviation of (1.89), which is very small relative to the mean (21.172) due to applying the natural logarithm on total assets, which caused smoothing in firm size among firms. Accordingly, firm size shows a small range between its minimum value (17.227) and its maximum value (25.817), which reflects the high concentration around the mean and the homogeneity in firm size among the sampled firms. The firm age (FA) of the sampled Egyptian firms ranges from 4 to 30 years. However, the average age of the sampled firms is around 19 years.

Regarding firm leverage (Lev), the minimum value (0.005) indicates the minor dependence of some Egyptian firms on debts. While the maximum value (1.027) reflects that other firms depend heavily on debt in their capital structure. The relatively high standard deviation (0.23) compared to the mean (0.454) supports the heterogeneity among Egyptian firms in terms of their preference for debt over equity for financing assets and operations. The summary statistics of the Assets Growth Ratio (AGR) suggest a sharp variation in asset growth within the sampled firms. As such, the standard deviation (0.212) represents around (221%) of the mean (0.096), in addition to a wide range between the minimum value (−0.267) and maximum value (0.899). Despite the large fluctuations in asset growth ratios, ranging from negative to positive values, the average Egyptian firm maintains a growth ratio of 0.096. The operating cash flow (OCF) has a mean of (0.043), indicating that, on average, Egyptian-listed firms achieve a positive cash flow from operations. However, the minimum value is negative (−0.198), which reflects the failure of some sampled firms to generate sufficient positive cash flows from their core activities to cover the cash outflows associated with operating processes, resulting in a negative net cash flow from operations. On the other hand, the maximum value (0.302) reveals the ability of other firms to generate a positive net cash flow from core operations. The large standard deviation (0.1) aligns with the large range, both suggesting the large variations among sampled firms concerning the operating cash flow ratio.

The average tangibility ratio (Tang) is (0.319), indicating that, on average, the fixed asset proportion in the sampled firms is approximately 31.9% of total assets. The maximum value of (0.964) reflects the potential for some firms in the Egyptian environment to benefit from tax-deductible depreciation on fixed assets. On the other hand, the minimum value (zero) indicates that some sampled firms waste their opportunity to reduce their taxes using depreciation. The standard deviation (0.232) is relatively high compared to the mean, reflecting the relatively large differences among the sampled firms regarding the tangibility ratio. In terms of profitability, the average return on assets (ROA) is 0.045, which means that on average the Egyptian sampled firms achieve returns on their assets of around 4.5%, indicating that every Egyptian pound the firms invest in their assets achieves on average 0.045 pounds. The minimum value is negative (−0.153), indicating that some firms in the Egyptian environment suffer from a negative rate of return on their assets due to their inefficient utilization of their assets and resources. Conversely, the maximum value (0.27) indicates that other firms achieve a positive rate of return on their assets, reaching up to 27%, as they optimally exploit their resources to maximize profitability. Consistent with the wide range, the standard deviation (0.082) shows a large dispersion around the mean.

In a nutshell, the descriptive statistics reveal a relative heterogeneity in the sample. As such, the Egyptian stock market includes firms with deteriorated earnings quality and firms with high earnings quality. Firms with zero governmental ownership and firms with 80% governmental ownership. Firms with zero institutional ownership and firms with 96.9% institutional ownership. Firms with zero managerial ownership and firms with 14.5% managerial ownership. Firms with low ownership concentration and firms with 100% ownership concentration. Both recently established and well-established firms. Heavily indebted firms and firms approaching self-financing. Both positive-growth and negative-growth firms. Both labor-intensive and capital-intensive firms. Finally, profitable firms and firms struggle to operate. However, it is worth noting that firm size (FS) stands out as the only highly homogenous variable in the sample. This homogeneity arises due to the utilization of the natural logarithm, which smoothens the data set.

Table 5. The frequencies of the audit quality.

Big4	Freq.	Percent	Cum.
0	356	67.42	67.42
1	172	32.58	100.00
Total	528	100.00

reports the frequencies of the audit quality of the sampled Egyptian firms; most of the sample (67.42%) is not audited by one of the Big 4 auditing firms, indicating potentially lower audit quality. In contrast, approximately one-third (32.58%) of the sample (n = 172) is audited by one of the Big 4 auditing firms, suggesting a higher level of audit quality.

4.2. Correlation

The correlation matrix offers a preliminary understanding of the linear relationships among the current study’s variables. Pearson’s correlation coefficients are employed to determine both the direction and strength of the linear relationship between any two variables included in this research. Furthermore, correlation coefficients are employed to detect any potential multicollinearity among regressors within the same regression model, which may result in imprecise estimations. (

Table 6. Correlation matrix.

Variables	EQP	EQC	OC	GO	IO	MO	FS	FA	Lev	AGR	OCF	Tang	ROA
EQP	1.000
EQC	−0.049	1.000
	(0.264)
OC	0.191 ***	0.156 ***	1.000
	(0.000)	(0.000)
GO	0.192 ***	0.147 ***	0.380 ***	1.000
	(0.000)	(0.001)	(0.000)
IO	−0.018	0.074 *	0.393 ***	−0.395 ***	1.000
	(0.684)	(0.089)	(0.000)	(0.000)
MO	−0.087 **	−0.115 ***	−0.193 ***	−0.233 ***	−0.418 ***	1.000
	(0.044)	(0.008)	(0.000)	(0.000)	(0.000)
FS	0.305 ***	0.039	0.344 ***	0.157 ***	0.102 **	−0.130 ***	1.000
	(0.000)	(0.368)	(0.000)	(0.000)	(0.019)	(0.003)
FA	0.017	0.034	0.144 ***	0.356 ***	−0.170 ***	−0.150 ***	0.023	1.000
	(0.704)	(0.429)	(0.001)	(0.000)	(0.000)	(0.001)	(0.602)
Lev	0.244 ***	−0.078 *	0.294 ***	−0.045	0.168 ***	−0.129 ***	0.558 ***	−0.028	1.000
	(0.000)	(0.073)	(0.000)	(0.306)	(0.000)	(0.003)	(0.000)	(0.519)
AGR	0.033	0.054	0.136 ***	0.051	0.066	−0.049	0.259 ***	0.035	0.192 ***	1.000
	(0.452)	(0.219)	(0.002)	(0.237)	(0.130)	(0.257)	(0.000)	(0.416)	(0.000)
OCF	0.084 *	0.150 ***	0.161 ***	0.293 ***	−0.082 *	−0.061	0.160 ***	0.122 ***	−0.096 **	0.046	1.000
	(0.055)	(0.001)	(0.000)	(0.000)	(0.060)	(0.158)	(0.000)	(0.005)	(0.029)	(0.296)
Tang	0.080 *	−0.118 ***	−0.085 *	−0.030	0.003	−0.006	−0.025	−0.101 **	−0.112 **	−0.159 ***	0.062	1.000
	(0.067)	(0.007)	(0.052)	(0.492)	(0.938)	(0.884)	(0.570)	(0.020)	(0.011)	(0.000)	(0.158)
ROA	−0.001	0.268 ***	0.219 ***	0.344 ***	−0.037	−0.114 ***	0.157 ***	0.131 ***	−0.163 ***	0.320 ***	0.447 ***	−0.273 ***	1.000
	(0.986)	(0.000)	(0.000)	(0.000)	(0.392)	(0.009)	(0.000)	(0.002)	(0.000)	(0.000)	(0.000)	(0.000)

*** p < 0.01, ** p < 0.05, * p < 0.1.

).

Table 6 reports the Pearson’s correlation coefficients of the variables included in the current research models. It can be noted that there is no linear correlation between the two dimensions of the current study’s dependent variable. This suggests that earnings persistence and consistency are not alternatives to each other in reflecting the earnings quality. Indeed, they complement each other, as each indicator reflects a specific dimension of the earnings quality.

The absence of a linear correlation between the two dimensions of earnings quality can be explained by the fact that while earnings persistence and earnings consistency are 2 interconnected and mutually reinforcing dimensions of earnings quality, they do not substitute for each other. As such, high earning consistency does not guarantee that a firm’s earnings are persistent, and vice versa. Persistence pertains to the sustainability and recurrence of earnings, while consistency refers to the use of the same methods for the same items from period to period.

Concerning the earning persistence dimension of earnings quality, it has a significant positive association with four firm-specific attributes. These are firm size, leverage, operating cash flows, and tangibility. While other firm-specific attributes have no significant correlation with earning persistence. Three patterns of ownership structure have a significant relationship with earnings persistence. Specifically, ownership concentration and governmental ownership have a significant positive association with earnings persistence. Conversely, managerial ownership shows a significant negative association with earnings persistence. Nevertheless, institutional ownership has no significant association with earnings persistence.

Regarding the earning consistency dimension of earnings quality, it has a significant correlation with all patterns of ownership structure. Specifically, it has a significant positive association with ownership concentration, governmental ownership, and institutional ownership. Conversely, managerial ownership shows a significant negative association with earnings consistency. Earning consistency shows a significant positive association with two firm-specific attributes; these are operating cash flows and return on assets. On the other hand, earning consistency shows a significant negative association with the other two firm-specific attributes; these are leverage and tangibility.

According to the detection of multicollinearity among the regressors in each model, the results indicate that there is no potential for multicollinearity issues among all explanatory variables in the analysis. The highest observed correlation coefficient equals 0.558, which is found between leverage and firm size. It is worth noting that Pearson’s correlation coefficients do not account for non-linear relationships between variables. Therefore, it is essential to consider curvilinearity in the regression analysis.

4.3. Hypothesis Testing

The research models of the current study are estimated using the Ordinary Least Squares (OLS) method and the Generalized Least Squares (GLS) method, which consider any potential issues that the OLS method may encounter.

4.3.1. Hypotheses of Earnings Quality (Persistence)

Table 7. Earning persistence models.

Variable	EQP_M1	EQP_M2	EQP_M3	EQP_M4
OC	0.7295 **
OC2	−0.6180 **
GO		0.9797 ***
GO2		−1.2790 ***
IO			0.4477 **
IO2			−0.6454 ***
MO				−8.9499 **
MO2				179.4583 **
MO3				−817.647 *
FS	0.0605 ***	0.0632 ***	0.0612 ***	0.0642 ***
FA	0.002	−0.001	0.002	0.002
Lev	0.2882 ***	0.2879 ***	0.3408 ***	0.2840 ***
AGR	−0.071	−0.048	−0.056	−0.068
OCF	0.190	0.154	0.175	0.197
Tang	0.3093 ***	0.2889 ***	0.3026 ***	0.3569 ***
ROA	−0.8627 **	−0.9697 ***	−0.7883 **	−0.8203 **
ROA2	4.2864 **	3.5934 *	3.7444 *	4.2249 **
Big4	−0.1160 ***	−0.0812 *	−0.1119 **	−0.1044 **
_cons	−1.2176 ***	−1.1122 ***	−1.0893 ***	−1.1343 ***
Industry Fixed Effects	Yes	Yes	Yes	Yes
Year Fixed Effects	Yes	Yes	Yes	Yes
Number of obs	516	517	514	517
Prob > F	0.000	0.000	0.000	0.000
R-squared	0.251	0.267	0.259	0.253

*** p < 0.01, ** p < 0.05, * p < 0.1.

illustrates the regression results of all earning persistence models. The overall models are significant since Prob > F is less than 0.05. Additionally, the explanatory variables included in the earning persistence models have explained around 25% or 27% of earning persistence according to the value of R-squared of each model.

It is notable from Table 7 and

Table 8. The turning points of non-linear effects in the earning persistence models.

OC: Inverted-U-shaped

${\displaystyle \frac{\mathit{C}\mathit{o}\mathit{e}\mathit{f}\mathit{f}\mathit{i}\mathit{c}\mathit{i}\mathit{e}\mathit{n}\mathit{t} \mathit{o}\mathit{f} \mathit{O}\mathit{C}}{\mathbf{-}\left(\mathit{C}\mathit{o}\mathit{e}\mathit{f}\mathit{f}\mathit{i}\mathit{c}\mathit{i}\mathit{e}\mathit{n}\mathit{t} \mathit{o}\mathit{f} {\mathit{O}\mathit{C}}^{\mathbf{2}}\right)\mathbf{\ast }\mathbf{2}}}$ = ${\displaystyle \frac{\mathbf{0.7295}}{\mathbf{-}\left(\mathbf{-}\mathbf{0.6180}\right)\mathbf{\ast }\mathbf{2}}}$ = 0.590210356

GO: Inverted-U-shaped

${\displaystyle \frac{\mathit{C}\mathit{o}\mathit{e}\mathit{f}\mathit{f}\mathit{i}\mathit{c}\mathit{i}\mathit{e}\mathit{n}\mathit{t} \mathit{o}\mathit{f} \mathit{G}\mathit{O}}{\mathbf{-}\left(\mathit{C}\mathit{o}\mathit{e}\mathit{f}\mathit{f}\mathit{i}\mathit{c}\mathit{i}\mathit{e}\mathit{n}\mathit{t} \mathit{o}\mathit{f} {\mathit{G}\mathit{O}}^{\mathbf{2}}\right)\mathbf{\ast }\mathbf{2}}}$ = ${\displaystyle \frac{\mathbf{0.9797}}{\mathbf{-}\left(\mathbf{-}\mathbf{1.2790}\right)\mathbf{\ast }\mathbf{2}}}$ = 0.382994527

IO: Inverted-U-shaped

${\displaystyle \frac{\mathit{C}\mathit{o}\mathit{e}\mathit{f}\mathit{f}\mathit{i}\mathit{c}\mathit{i}\mathit{e}\mathit{n}\mathit{t} \mathit{o}\mathit{f} \mathit{I}\mathit{O}}{\mathbf{-}\left(\mathit{C}\mathit{o}\mathit{e}\mathit{f}\mathit{f}\mathit{i}\mathit{c}\mathit{i}\mathit{e}\mathit{n}\mathit{t} \mathit{o}\mathit{f} {\mathit{I}\mathit{O}}^{\mathbf{2}}\right)\mathbf{\ast }\mathbf{2}}}$ = ${\displaystyle \frac{\mathbf{0.4477}}{\mathbf{-}\left(\mathbf{-}\mathbf{0.6454}\right)\mathbf{\ast }\mathbf{2}}}$ = 0.346839170

MO: Negative-N-shaped

First turning point = ${\displaystyle \frac{\mathbf{-}\mathbf{2}\mathbf{\ast }\mathit{C}\mathit{o}\mathit{e}\mathit{f}\mathit{f}\mathit{i}\mathit{c}\mathit{i}\mathit{e}\mathit{n}\mathit{t} \mathit{o}\mathit{f} {\mathit{M}\mathit{O}}^{\mathbf{2}}\mathbf{+}\sqrt{\mathbf{4}\mathbf{\ast }{\mathbf{(}\mathit{C}\mathit{o}\mathit{e}\mathit{f}\mathit{f}\mathit{i}\mathit{c}\mathit{i}\mathit{e}\mathit{n}\mathit{t} \mathit{o}\mathit{f} {\mathit{M}\mathit{O}}^{\mathbf{2}}\mathbf{)}}^{\mathbf{2}}\mathbf{-}\mathbf{12}\mathbf{\ast }\mathit{C}\mathit{o}\mathit{e}\mathit{f}\mathit{f}\mathit{i}\mathit{c}\mathit{i}\mathit{e}\mathit{n}\mathit{t} \mathit{o}\mathit{f} {\mathit{M}\mathit{O}}^{\mathbf{3}}\mathbf{\ast }\mathit{C}\mathit{o}\mathit{e}\mathit{f}\mathit{f}\mathit{i}\mathit{c}\mathit{i}\mathit{e}\mathit{n}\mathit{t} \mathit{o}\mathit{f} \mathit{M}\mathit{O}}}{\mathbf{(}\mathbf{6}\mathbf{\ast }\mathit{C}\mathit{o}\mathit{e}\mathit{f}\mathit{f}\mathit{i}\mathit{c}\mathit{i}\mathit{e}\mathit{n}\mathit{t} \mathit{o}\mathit{f} {\mathit{M}\mathit{O}}^{\mathbf{3}}\mathbf{)}}}$ $={\displaystyle \frac{\mathbf{-}\mathbf{2}\mathbf{\ast }\mathbf{179.4583}\mathbf{+}\sqrt{\mathbf{4}\mathbf{\ast }{\mathbf{179.4583}}^{\mathbf{2}}\mathbf{-}\mathbf{12}\mathbf{\ast }\mathbf{-}\mathbf{817.647}\mathbf{\ast }\mathbf{-}\mathbf{8.9499}}}{\left(\mathbf{6}\mathbf{\ast }\mathbf{-}\mathbf{817.647}\right)}}$ = 0.03188319547

Second turning point = ${\displaystyle \frac{\mathbf{-}\mathbf{2}\mathbf{\ast }\mathit{C}\mathit{o}\mathit{e}\mathit{f}\mathit{f}\mathit{i}\mathit{c}\mathit{i}\mathit{e}\mathit{n}\mathit{t} \mathit{o}\mathit{f} {\mathit{M}\mathit{O}}^{\mathbf{2}}\mathbf{-}\sqrt{\mathbf{4}\mathbf{\ast }{\mathbf{(}\mathit{C}\mathit{o}\mathit{e}\mathit{f}\mathit{f}\mathit{i}\mathit{c}\mathit{i}\mathit{e}\mathit{n}\mathit{t} \mathit{o}\mathit{f} {\mathit{M}\mathit{O}}^{\mathbf{2}}\mathbf{)}}^{\mathbf{2}}\mathbf{-}\mathbf{12}\mathbf{\ast }\mathit{C}\mathit{o}\mathit{e}\mathit{f}\mathit{f}\mathit{i}\mathit{c}\mathit{i}\mathit{e}\mathit{n}\mathit{t} \mathit{o}\mathit{f} {\mathit{M}\mathit{O}}^{\mathbf{3}}\mathbf{\ast }\mathit{C}\mathit{o}\mathit{e}\mathit{f}\mathit{f}\mathit{i}\mathit{c}\mathit{i}\mathit{e}\mathit{n}\mathit{t} \mathit{o}\mathit{f} \mathit{M}\mathit{O}}}{\mathbf{(}\mathbf{6}\mathbf{\ast }\mathit{C}\mathit{o}\mathit{e}\mathit{f}\mathit{f}\mathit{i}\mathit{c}\mathit{i}\mathit{e}\mathit{n}\mathit{t} \mathit{o}\mathit{f} {\mathit{M}\mathit{O}}^{\mathbf{3}}\mathbf{)}}}$ $={\displaystyle \frac{\mathbf{-}\mathbf{2}\mathbf{\ast }\mathbf{179.4583}\mathbf{-}\sqrt{\mathbf{4}\mathbf{\ast }{\mathbf{179.4583}}^{\mathbf{2}}\mathbf{-}\mathbf{12}\mathbf{\ast }\mathbf{-}\mathbf{817.647}\mathbf{\ast }\mathbf{-}\mathbf{8.9499}}}{\left(\mathbf{6}\mathbf{\ast }\mathbf{-}\mathbf{817.647}\right)}}\mathbf{=}\mathbf{0.114437731}$

that the four patterns of ownership structure show non-linear effects on earnings persistence. Indeed, the curvilinear effects of all patterns of ownership structure take the form of an inverted U-shape, except managerial ownership takes the form of negative-N-shape. Earnings persistence initially improves with the increase in ownership concentration, governmental, and institutional ownership until the increase in these three ownership patterns reaches certain thresholds, which are considered as turning points beyond which earning persistence deteriorates with the increase in ownership concentration, governmental, and institutional ownership after 0.5902, 0.3830, and 0.3468, respectively. The non-linear effect of managerial ownership on earnings persistence includes two turning points, meaning that the direction of the effect changes twice during the sampled period. Initially, when managerial ownership increases from zero to 0.0319, earning persistence deteriorates. Subsequently, when managerial ownership increases from 0.0319 to 0.1144, earning persistence improves. Finally, when managerial ownership increases from 0.1144 to 0.145, earning persistence returns to deterioration. Table 8 illustrates the turning points of non-linear effects in the earning persistence models.

Concerning firm-specific attributes, firm size, leverage, and tangibility show a significant positive effect on earning persistence across all models of earning persistence. This implies that larger, more leveraged, and capital-intensive firms tend to have high-quality earnings in terms of their persistence. In contrast, three other firm-specific attributes—firm age, asset growth ratio, and operating cash flows—do not significantly influence earnings persistence. The insignificant effects from these firm-specific attributes remain consistent across all earnings persistence models.

It can be noted that the return on assets is the only firm-specific characteristic that has a curvilinear effect on earning persistence. As such, the results from all earnings persistence models indicate that the coefficient of ROA is significantly negative, while the coefficient of ROA squared is significantly positive. Accordingly, profitability, as measured by return on assets, has a curvilinear effect on earning persistence that can be represented by a U-shaped curve.

Continuing with the firm-specific attributes, audit quality shows a significant negative effect on earning persistence in all models of earning persistence. This suggests that firms audited by one of the big 4 auditors are exposed to transitory earnings.

Moreover, additional statistical analysis in terms of earnings quality “persistence” according to linear regression (Coef., St.Err., t-value, p-value) was added in

Table A1. Linear regression (EQP and OC).

EQP	Coef.	St.Err.	t-Value	p-Value	[95% Conf	Interval]	Sig
OC	0.73	0.334	2.19	0.029	0.074	1.385	**
OC2	−0.618	0.288	−2.15	0.032	−1.184	−0.053	**
FS	0.061	0.012	4.94	0	0.036	0.085	***
FA	0.002	0.003	0.62	0.536	−0.004	0.007
Lev	0.288	0.1	2.89	0.004	0.092	0.484	***
AGR	−0.071	0.078	−0.91	0.364	−0.224	0.082
OCF	0.19	0.192	0.99	0.323	−0.187	0.566
Tang	0.309	0.086	3.59	0	0.14	0.479	***
ROA	−0.863	0.362	−2.38	0.018	−1.575	−0.15	**
ROA2	4.286	1.949	2.20	0.028	0.456	8.116	**
Big4	−0.116	0.041	−2.80	0.005	−0.197	−0.035	***
Sec: base 1	0
2	−0.133	0.099	−1.34	0.181	−0.327	0.062
3	−0.224	0.098	−2.29	0.022	−0.415	−0.032	**
4	−0.025	0.12	−0.21	0.835	−0.26	0.21
5	0.283	0.115	2.45	0.015	0.056	0.509	**
6	−0.031	0.098	−0.31	0.754	−0.223	0.162
7	−0.083	0.094	−0.88	0.38	−0.267	0.102
8	0	0.099	−0.00	0.998	−0.194	0.194
2015b	0
2016	−0.013	0.052	−0.24	0.81	−0.115	0.09
2017	−0.041	0.054	−0.77	0.445	−0.146	0.064
2018	−0.056	0.06	−0.92	0.357	−0.174	0.063
2019	−0.077	0.055	−1.41	0.16	−0.185	0.031
2020	−0.165	0.06	−2.78	0.006	−0.282	−0.048	***
2021	−0.198	0.06	−3.33	0.001	−0.315	−0.081	***
2022	−0.175	0.066	−2.66	0.008	−0.305	−0.046	***
Constant	−1.218	0.287	−4.24	0	−1.782	−0.653	***
Mean dependent var		0.314		SD dependent var		0.392
R-squared		0.251		Number of obs		516
F-test		9.385		Prob > F		0.000
Akaike crit. (AIC)		399.174		Bayesian crit. (BIC)		509.573

*** p < 0.01, ** p < 0.05.

,

Table A2. Linear regression (EQP and GO).

EQP	Coef.	St.Err.	t-Value	p-Value	[95% Conf	Interval]	Sig
GO	0.98	0.28	3.50	0	0.43	1.529	***
GO2	−1.279	0.373	−3.43	0.001	−2.011	−0.547	***
FS	0.063	0.012	5.14	0	0.039	0.087	***
FA	−0.001	0.003	−0.26	0.794	−0.007	0.005
Lev	0.288	0.094	3.05	0.002	0.103	0.473	***
AGR	−0.048	0.076	−0.63	0.529	−0.197	0.101
OCF	0.154	0.196	0.79	0.432	−0.231	0.538
Tang	0.289	0.087	3.31	0.001	0.117	0.461	***
ROA	−0.97	0.356	−2.72	0.007	−1.67	−0.269	***
ROA2	3.593	1.971	1.82	0.069	−0.279	7.466	*
Big4	−0.081	0.044	−1.83	0.067	−0.168	0.006	*
Sec: base 1	0
2	−0.104	0.105	−0.99	0.322	−0.311	0.103
3	−0.193	0.095	−2.03	0.043	−0.379	−0.006	**
4	0.059	0.116	0.51	0.612	−0.169	0.286
5	0.318	0.116	2.73	0.006	0.09	0.547	***
6	0.012	0.099	0.12	0.903	−0.183	0.207
7	−0.068	0.092	−0.74	0.461	−0.25	0.113
8	0.039	0.097	0.40	0.687	−0.151	0.229
2015b	0
2016	−0.012	0.052	−0.24	0.809	−0.114	0.089
2017	−0.035	0.053	−0.67	0.506	−0.139	0.069
2018	−0.046	0.06	−0.77	0.441	−0.163	0.071
2019	−0.066	0.054	−1.22	0.222	−0.171	0.04
2020	−0.146	0.06	−2.46	0.014	−0.264	−0.029	**
2021	−0.161	0.061	−2.66	0.008	−0.28	−0.042	***
2022	−0.145	0.068	−2.14	0.033	−0.278	−0.012	**
Constant	−1.112	0.268	−4.16	0	−1.638	−0.586	***
Mean dependent var		0.316		SD dependent var		0.392
R-squared		0.267		Number of obs		517
F-test		9.556		Prob > F		0.000
Akaike crit. (AIC)		389.852		Bayesian crit. (BIC)		500.301

*** p < 0.01, ** p < 0.05, * p < 0.1.

,

Table A3. Linear regression (EQP and IO).

EQP	Coef.	St.Err.	t-value	p-Value	[95% Conf	Interval]	Sig
IO	0.448	0.201	2.23	0.026	0.053	0.842	**
IO2	−0.645	0.238	−2.72	0.007	−1.112	−0.178	***
FS	0.061	0.012	4.99	0	0.037	0.085	***
FA	0.002	0.003	0.58	0.56	−0.004	0.007
Lev	0.341	0.096	3.54	0	0.151	0.53	***
AGR	−0.056	0.077	−0.73	0.466	−0.208	0.095
OCF	0.175	0.192	0.91	0.363	−0.202	0.552
Tang	0.303	0.087	3.47	0.001	0.131	0.474	***
ROA	−0.788	0.361	−2.19	0.029	−1.497	−0.08	**
ROA2	3.744	1.961	1.91	0.057	−0.109	7.597	*
Big4	−0.112	0.045	−2.49	0.013	−0.2	−0.024	**
Sec: base 1	0
2	−0.123	0.1	−1.24	0.217	−0.318	0.073
3	−0.232	0.099	−2.36	0.019	−0.426	−0.039	**
4	−0.041	0.122	−0.33	0.739	−0.281	0.2
5	0.271	0.118	2.30	0.022	0.04	0.502	**
6	−0.047	0.1	−0.47	0.639	−0.243	0.149
7	−0.081	0.096	−0.85	0.395	−0.269	0.106
8	−0.007	0.099	−0.07	0.945	−0.202	0.188
2015b	0
2016	−0.018	0.052	−0.34	0.732	−0.119	0.084
2017	−0.043	0.053	−0.80	0.424	−0.147	0.062
2018	−0.06	0.06	−1.00	0.316	−0.178	0.058
2019	−0.076	0.055	−1.38	0.167	−0.184	0.032
2020	−0.163	0.059	−2.75	0.006	−0.28	−0.047	***
2021	−0.174	0.059	−2.96	0.003	−0.289	−0.058	***
2022	−0.183	0.066	−2.77	0.006	−0.313	−0.053	***
Constant	−1.089	0.276	−3.95	0	−1.632	−0.547	***
Mean dependent var		0.316		SD dependent var		0.391
R-squared		0.259		Number of obs		514
F-test		9.641		Prob > F		0.000
Akaike crit. (AIC)		390.509		Bayesian crit. (BIC)		500.807

*** p < 0.01, ** p < 0.05, * p < 0.1.

and

Table A4. Linear regression (EQP and MO).

EQP	Coef.	St.Err.	t-Value	p-Value	[95% Conf	Interval]	Sig
MO	−8.95	4.168	−2.15	0.032	−17.138	−0.761	**
MO2	179.458	90.56	1.98	0.048	1.525	357.392	**
MO3	−817.647	440.944	−1.85	0.064	−1684.021	48.727	*
FS	0.064	0.012	5.21	0	0.04	0.088	***
FA	0.002	0.003	0.53	0.598	−0.004	0.007
Lev	0.284	0.099	2.85	0.004	0.089	0.479	***
AGR	−0.068	0.077	−0.88	0.378	−0.22	0.084
OCF	0.197	0.193	1.02	0.308	−0.182	0.576
Tang	0.357	0.09	3.96	0	0.18	0.534	***
ROA	−0.82	0.365	−2.25	0.025	−1.537	−0.104	**
ROA2	4.225	1.947	2.17	0.03	0.4	8.05	**
Big4	−0.104	0.042	−2.50	0.013	−0.186	−0.022	**
Sec: base 1	0
2	−0.113	0.102	−1.10	0.272	−0.314	0.089
3	−0.213	0.099	−2.16	0.031	−0.407	−0.019	**
4	0.13	0.133	0.98	0.33	−0.132	0.392
5	0.32	0.116	2.75	0.006	0.091	0.548	***
6	0.019	0.102	0.19	0.853	−0.181	0.219
7	−0.061	0.097	−0.63	0.528	−0.252	0.129
8	0.044	0.101	0.43	0.666	−0.155	0.243
2015b	0
2016	−0.012	0.051	−0.23	0.816	−0.113	0.089
2017	−0.038	0.053	−0.72	0.475	−0.142	0.066
2018	−0.05	0.059	−0.84	0.399	−0.166	0.066
2019	−0.068	0.055	−1.25	0.213	−0.176	0.039
2020	−0.159	0.06	−2.67	0.008	−0.276	−0.042	***
2021	−0.193	0.06	−3.23	0.001	−0.31	−0.075	***
2022	−0.175	0.066	−2.65	0.008	−0.304	−0.045	***
Constant	−1.134	0.284	−3.99	0	−1.693	−0.576	***
Mean dependent var		0.316		SD dependent var		0.392
R-squared		0.253		Number of obs		517
F-test		8.819		Prob > F		0.000
Akaike crit. (AIC)		401.877		Bayesian crit. (BIC)		516.574

*** p < 0.01, ** p < 0.05, * p < 0.1.

.

4.3.2. Hypotheses of Earnings Quality (Consistency)

Table 9. Earning consistency models.

Variable	EQC_M1	EQC_M2	EQC_M3	EQC_M4
OC	0.6760 ***
GO		0.4477 **
IO			0.4243 **
MO				−8.4814 **
MO2				42.3107 *
FS	−0.015	−0.016	−0.003	−0.010
FA	−0.0668 *	−0.0778 *	−0.0667 *	−0.0781 *
FA2	0.0020 *	0.0023 *	0.0021 *	0.0023 *
Lev	−0.347	−0.201	−0.151	−0.268
AGR	−0.013	−0.003	−0.051	−0.024
OCF	0.8090 *	0.7728 *	1.0037 **	0.9480 **
Tang	−0.253	−0.317	−0.314	−0.324
ROA	4.1535 ***	4.2933 ***	4.3900 ***	4.4333 ***
ROA2	−18.8069 ***	−19.7572 ***	−18.8501 ***	−20.0318 ***
Big4	0.027	0.083	−0.073	−0.041
_cons	1.111	1.3804 *	1.224	1.6169 **
Industry Fixed Effects	yes	yes	yes	yes
Year Fixed Effects	yes	yes	yes	yes
Number of obs	516	517	514	517
Prob > F	0.000	0.000	0.000	0.000
R-squared	0.179	0.172	0.178	0.185

*** p < 0.01, ** p < 0.05, * p < 0.1.

illustrates the regression results of all earning consistency models. The overall models are significant since Prob > F is less than 0.05. Additionally, the explanatory variables included in the earning consistency models have explained around 17% or 19% of earning consistency according to the value of the R-squared of each model.

Notably, all patterns of ownership structure have a significant positive linear effect on earnings consistency, except the managerial ownership, which has the curvilinear effect that takes the form of a U-shape. Specifically, earning consistency initially deteriorates with the increase in managerial ownership until the increase in managerial ownership reaches a certain threshold, which is considered a turning point beyond which earning consistency improves with the increase in managerial ownership.

Table 10. The turning points of non-linear effects in the earning consistency models.

MO: U-Shaped

${\displaystyle \frac{\mathrm{C}\mathrm{o}\mathrm{e}\mathrm{f}\mathrm{f}\mathrm{i}\mathrm{c}\mathrm{i}\mathrm{e}\mathrm{n}\mathrm{t} \mathrm{o}\mathrm{f} \mathrm{M}\mathrm{O}}{-\left(\mathrm{C}\mathrm{o}\mathrm{e}\mathrm{f}\mathrm{f}\mathrm{i}\mathrm{c}\mathrm{i}\mathrm{e}\mathrm{n}\mathrm{t} \mathrm{o}\mathrm{f} {\mathrm{M}\mathrm{O}}^2\right)\mathrm{*}2}}$ = ${\displaystyle \frac{-8.4814}{-\left(42.3107\right)\mathrm{*}2}}$ = 0.100227602

illustrates the turning points of non-linear effects in the earning consistency models.

Concerning firm-specific attributes, it can be noted that firm age and profitability are the only two firm-specific characteristics that have curvilinear effects on earning consistency. As such, the results from all earnings consistency models indicate that the coefficient of ROA is significantly positive, while the coefficient of ROA squared is significantly negative. Accordingly, profitability, as measured by return on assets, has a curvilinear effect on earning consistency that can be represented by an inverted-U-shaped curve. On the other hand, the non-linear effect of firm age on earning consistency takes the form of a U-shaped curve. Because the coefficient of FA is significantly negative, while the coefficient of FA squared is significantly positive.

Based on the results of all earning consistency models, it is worth noting that most firm-specific attributes have insignificant effects on earning consistency. For instance, firm size, leverage, tangibility, audit quality, and asset growth ratio have no significant influence on earning consistency. Continuing with the firm-specific attributes, operating cash flows show a significant positive effect on earnings consistency across all earnings consistency models. This suggests that large positive cash flows support the consistency of earnings.

Moreover, additional statistical analysis in terms of earnings quality “consistency” according to linear regression (Coef., St.Err., t-value, p-value) was added in

Table A5. Linear regression (EQC and OC).

EQC	Coef.	St.Err.	t-Value	p-Value	[95% Conf	Interval]	Sig
OC	0.676	0.247	2.74	0.006	0.191	1.161	***
FS	−0.015	0.029	−0.53	0.599	−0.072	0.041
FA	−0.067	0.04	−1.66	0.097	−0.146	0.012	*
FA2	0.002	0.001	1.69	0.092	0	0.004	*
Lev	−0.347	0.229	−1.51	0.13	−0.798	0.103
AGR	−0.013	0.172	−0.07	0.941	−0.351	0.325
OCF	0.809	0.461	1.75	0.08	−0.097	1.715	*
Tang	−0.253	0.251	−1.01	0.314	−0.746	0.24
ROA	4.153	0.931	4.46	0	2.325	5.982	***
ROA2	−18.807	3.413	−5.51	0	−25.514	−12.1	***
Big4	0.027	0.109	0.25	0.804	−0.187	0.241
Sec: base 1	0
2	−0.35	0.25	−1.40	0.162	−0.841	0.141
3	−0.232	0.251	−0.93	0.355	−0.725	0.261
4	0.383	0.354	1.08	0.279	−0.312	1.078
5	−0.492	0.228	−2.16	0.032	−0.941	−0.044	**
6	−0.064	0.255	−0.25	0.801	−0.564	0.436
7	−0.092	0.219	−0.42	0.674	−0.524	0.339
8	−0.196	0.271	−0.72	0.47	−0.73	0.337
2015b	0
2016	−0.316	0.141	−2.24	0.026	−0.594	−0.039	**
2017	−0.196	0.143	−1.37	0.172	−0.477	0.085
2018	−0.061	0.142	−0.43	0.668	−0.34	0.218
2019	−0.118	0.151	−0.78	0.438	−0.415	0.18
2020	0.099	0.161	0.62	0.539	−0.218	0.416
2021	−0.061	0.164	−0.37	0.711	−0.383	0.261
2022	−0.204	0.164	−1.25	0.213	−0.527	0.118
Constant	1.111	0.811	1.37	0.171	−0.482	2.705
Mean dependent var		0.290		SD dependent var		0.916
R-squared		0.179		Number of obs		516
F-test		5.637		Prob > F		0.000
Akaike crit. (AIC)		1323.433		Bayesian crit. (BIC)		1433.832

*** p < 0.01, ** p < 0.05, * p < 0.1.

,

Table A6. Linear regression (EQC and GO).

EQC	Coef.	St.Err.	t-value	p-Value	[95% Conf	Interval]	Sig
GO	0.448	0.196	2.29	0.022	0.063	0.832	**
FS	−0.016	0.029	−0.54	0.589	−0.073	0.041
FA	−0.078	0.04	−1.92	0.055	−0.157	0.002	*
FA2	0.002	0.001	1.90	0.058	0	0.005	*
Lev	−0.201	0.224	−0.90	0.371	−0.641	0.24
AGR	−0.003	0.172	−0.02	0.988	−0.341	0.336
OCF	0.773	0.462	1.67	0.095	−0.134	1.68	*
Tang	−0.317	0.248	−1.28	0.202	−0.803	0.17
ROA	4.293	0.918	4.67	0	2.489	6.098	***
ROA2	−19.757	3.208	−6.16	0	−26.061	−13.453	***
Big4	0.083	0.116	0.71	0.477	−0.145	0.311
Sec: base 1	0
2	−0.215	0.29	−0.74	0.459	−0.784	0.355
3	−0.136	0.271	−0.50	0.615	−0.669	0.396
4	0.514	0.363	1.42	0.157	−0.199	1.227
5	−0.402	0.245	−1.64	0.102	−0.884	0.08
6	0.04	0.286	0.14	0.889	−0.522	0.602
7	0.069	0.249	0.28	0.782	−0.421	0.559
8	−0.116	0.29	−0.40	0.69	−0.686	0.454
2015b	0
2016	−0.306	0.142	−2.16	0.032	−0.586	−0.027	**
2017	−0.185	0.147	−1.26	0.208	−0.473	0.103
2018	−0.051	0.146	−0.35	0.728	−0.338	0.236
2019	−0.089	0.154	−0.58	0.565	−0.392	0.214
2020	0.136	0.163	0.83	0.405	−0.184	0.455
2021	−0.019	0.167	−0.11	0.909	−0.347	0.309
2022	−0.168	0.172	−0.97	0.331	−0.507	0.171
Constant	1.38	0.787	1.75	0.08	−0.167	2.928	*
Mean dependent var		0.290		SD dependent var		0.915
R-squared		0.172		Number of obs		517
F-test		5.995		Prob > F		0.000
Akaike crit. (AIC)		1329.404		Bayesian crit. (BIC)		1439.854

*** p < 0.01, ** p < 0.05, * p < 0.1.

,

Table A7. Linear regression (EQC and IO).

EQC	Coef.	St.Err.	t-Value	p-Value	[95% Conf	Interval]	Sig
IO	0.424	0.167	2.54	0.011	0.096	0.752	**
FS	−0.003	0.029	−0.11	0.913	−0.061	0.055
FA	−0.067	0.04	−1.68	0.093	−0.145	0.011	*
FA2	0.002	0.001	1.80	0.073	0	0.004	*
Lev	−0.151	0.215	−0.70	0.482	−0.573	0.271
AGR	−0.051	0.175	−0.29	0.771	−0.395	0.293
OCF	1.004	0.472	2.13	0.034	0.077	1.931	**
Tang	−0.314	0.248	−1.27	0.206	−0.801	0.173
ROA	4.39	0.956	4.59	0	2.512	6.268	***
ROA2	−18.85	3.566	−5.29	0	−25.856	−11.844	***
Big4	−0.073	0.118	−0.62	0.538	−0.305	0.159
Sec: base 1	0
2	−0.532	0.238	−2.23	0.026	−1	−0.064	**
3	−0.42	0.241	−1.74	0.082	−0.894	0.053	*
4	0.27	0.333	0.81	0.418	−0.385	0.925
5	−0.631	0.225	−2.80	0.005	−1.073	−0.188	***
6	−0.245	0.247	−0.99	0.323	−0.731	0.241
7	−0.235	0.221	−1.07	0.287	−0.669	0.199
8	−0.4	0.258	−1.55	0.122	−0.906	0.107
2015b	0
2016	−0.298	0.14	−2.12	0.034	−0.574	−0.022	**
2017	−0.219	0.142	−1.54	0.123	−0.498	0.06
2018	−0.086	0.142	−0.61	0.543	−0.365	0.192
2019	−0.142	0.153	−0.93	0.352	−0.442	0.158
2020	0.072	0.162	0.45	0.656	−0.246	0.39
2021	−0.141	0.166	−0.85	0.395	−0.467	0.185
2022	−0.271	0.171	−1.58	0.114	−0.606	0.065
Constant	1.224	0.8	1.53	0.127	−0.348	2.795
Mean dependent var		0.290		SD dependent var		0.912
R-squared		0.178		Number of obs		514
F-test		5.007		Prob > F		0.000
Akaike crit. (AIC)		1314.265		Bayesian crit. (BIC)		1424.563

*** p < 0.01, ** p < 0.05, * p < 0.1.

and

Table A8. Linear regression (EQC and MO).

EQC	Coef.	St.Err.	t-Value	p-Value	[95% Conf	Interval]	Sig
MO	−8.481	3.543	−2.39	0.017	−15.443	−1.52	**
MO2	42.311	24.815	1.71	0.089	−6.446	91.067	*
FS	−0.01	0.028	−0.35	0.723	−0.066	0.046
FA	−0.078	0.04	−1.94	0.053	−0.157	0.001	*
FA2	0.002	0.001	1.90	0.059	0	0.005	*
Lev	−0.268	0.213	−1.26	0.21	−0.686	0.151
AGR	−0.024	0.173	−0.14	0.891	−0.364	0.317
OCF	0.948	0.469	2.02	0.044	0.027	1.869	**
Tang	−0.324	0.253	−1.28	0.202	−0.822	0.174
ROA	4.433	0.924	4.80	0	2.619	6.248	***
ROA2	−20.032	3.398	−5.90	0	−26.708	−13.356	***
Big4	−0.041	0.108	−0.38	0.703	−0.252	0.17
Sec: base 1	0
2	−0.244	0.242	−1.01	0.314	−0.719	0.231
3	−0.273	0.24	−1.14	0.256	−0.745	0.199
4	0.419	0.363	1.15	0.249	−0.295	1.133
5	−0.51	0.221	−2.31	0.021	−0.944	−0.076	**
6	−0.009	0.249	−0.03	0.972	−0.497	0.48
7	−0.004	0.217	−0.02	0.987	−0.43	0.423
8	−0.223	0.257	−0.87	0.387	−0.729	0.283
2015b	0
2016	−0.31	0.142	−2.18	0.03	−0.589	−0.03	**
2017	−0.196	0.143	−1.37	0.172	−0.477	0.085
2018	−0.064	0.144	−0.44	0.657	−0.347	0.219
2019	−0.12	0.151	−0.79	0.427	−0.418	0.177
2020	0.115	0.159	0.73	0.468	−0.197	0.427
2021	−0.046	0.162	−0.29	0.775	−0.365	0.273
2022	−0.206	0.163	−1.26	0.209	−0.527	0.115
Constant	1.617	0.785	2.06	0.04	0.075	3.159	**
Mean dependent var		0.290		SD dependent var		0.915
R-squared		0.185		Number of obs		517
F-test		5.591		Prob > F		0.000
Akaike crit. (AIC)		1322.931		Bayesian crit. (BIC)		1437.628

*** p < 0.01, ** p < 0.05, * p < 0.1.

.

5. Discussion

Our research was divided into two main parts to answer 2 questions as follows: Do firm attributes (e.g., size, age, leverage, asset growth, operating cash flow, tangibility, and profitability) impact earnings quality in emerging markets? Do ownership structures (e.g., managerial, institutional, governmental, and concentration) impact earnings quality in emerging markets? To answer these questions, we conducted an empirical study on 75 Egyptian-listed firms during the period from 2015 to 2022.

In terms of the first part, we divided it into two sub-questions as follows: Do firm attributes impact earnings persistence? and Do firm attributes impact earnings consistency? Then we selected seven attributes based on the literature (Ramadan 2015; Alqam et al. 2022; Jim-Suleiman and Ibiamke 2022; Anam 2023) to serve as the drivers of earnings quality (firm size, firm age, financial leverage, assets growth rate, operation cash flow, tangibility, and profitability). In our study, we fill this gap with new evidence from the Egyptian stock market. First, from the “persistence” side, which most of the literature depends on measuring earnings quality, we found that FS, LEV., and TANG have a positive impact; this result was in the same line with (Anam 2023; Jim-Suleiman and Ibiamke 2022; Pujiati et al. 2022) and against the results of (Bose and Yu 2023; Song 2022). Moreover, FA, AGR, and OCF do not have a significant impact; this result was against the results of (Alqam et al. 2022; Rezaee et al. 2020; Tarmidi et al. 2021). In addition, there is a U-shape impact of ROA, which is a unique result in the literature. Second, from the “consistency” side, we found that OCF has a positive impact. Moreover, FS, LEV., AGR, and TANG have not had a significant impact. In addition, there is a U-shape impact of FA and ROA. (

Table 11. The summary of firm attributes impact on earning quality.

Variable		The Impact on Persistence			The Impact on Consistency
		Pos.	Neg.	Other	Pos.	Neg.	Other
Firm attributes	FS	√					not significant
	FA			not significant			U-Shape
	LEV	√					not significant
	AGR			not significant			not significant
	OCF			not significant	√
	TANG	√					not significant
	ROA			U-Shape			U-Shape

).

In terms of the second part, we divided it into two sub-questions as follows: Do ownership structures impact earnings persistence? and Do ownership structures impact earnings consistency? Then we selected four main structures based on the literature (Piosik and Genge 2020; Tran and Dang 2021; Gultom and Wati 2022; Aldoseri and Hussein 2024) to serve as the drivers of earnings quality (ownership concentration, institutional ownership, governmental ownership, and managerial ownership). In our study, we fill this gap with evidence from Egypt. First, from the “persistence” side, we found that IO, GO, and OC have a U-shape impact. Moreover, MO has an N-shape impact; these results are unique in the literature. Second, from the “consistency” side, we found that IO, GO, and OC have a positive impact; this result was in the same line with (Dong et al. 2020; Istianingsih 2021; Kristiawan 2024; Sadaa et al. 2023). In addition, MO has a U-shape impact. Thus, the ownership structures uniquely and dynamically impact earnings quality (

Table 12. The summary of ownership structure impacts on earning quality.

Variable		The Impact on Persistence			The Impact on Consistency
		Pos.	Neg.	Other	Pos.	Neg.	Other
Ownership structures	OC			U-Shape	√
	IO			U-Shape	√
	GO			U-Shape	√
	MO			N-Shape			U-Shape

).

Based on the overall results, it appears that the earning persistence models have higher R-squared values compared to the earning consistency models. This suggests that the different patterns of ownership structure and firm-specific attributes are more effective at explaining the variation in earning persistence as a dimension of earnings quality. The reason why the explanatory variables related to ownership structure and firm-specific attributes may better explain earning persistence compared to earning consistency could be due to the different factors influencing these two dimensions of earnings quality. Earnings consistency may be more influenced by factors like changes in accounting policies, one-time events, and economic fluctuations that can introduce more variability into earnings over time. These factors might be more difficult to capture accurately with the explanatory variables used in the earning consistency models, resulting in lower R-squared values. Therefore, the difference in the explanatory power of the models for earning persistence and earning consistency could stem from the unique factors influencing each dimension of earnings quality and how effectively the chosen explanatory variables account for these factors in the respective models. The relatively small R-squared values of earning consistency models can motivate future research to further investigate the most relevant factors that explain variations in earning consistency as a dimension of financial reporting quality.

6. Conclusions

To sum up, this research fills a current knowledge gap by examining the drivers of earnings quality (firm attributes and ownership structure) in an emerging stock market such as Egypt. So, by providing new evidence on the mixed impact of earnings drivers (positive, negative, U-shape, and N-shape), we offer practical suggestions to regulators for developing the financial reporting process and recommend vital reforms in determining the limits and percentage of each ownership pattern as well as reforms in the Egyptian corporate governance code in accordance with our findings regarding earnings quality. Based on the above, the findings can contribute to the academic literature, inform policymaking, and ultimately enhance financial transparency and investor protection in emerging markets. Additionally, the research’s insights on earnings quality in emerging markets can inspire further academic research into the key factors that affect variations in earnings quality between developed and developing countries as part of financial reporting quality and sustainable development.

The use of only the Egyptian market as a sample of emerging markets, while practical due to the volume of data firms provide (our sample consisted of 75 listed firms during the period from 2015 to 2022), may limit this study’s applicability to broader emerging markets. In future academic research in the accounting field, it would be valuable to conduct this research comparatively between many countries with different factors. Additionally, explores how earnings management practices affect earnings quality. Additionally, it would be beneficial to examine the role of corporate governance mechanisms—especially the board of directors—in shaping earnings quality and determining its level. Going further, it would also be worthwhile to investigate potential differences in the drivers of earnings quality between developing and developed countries. Furthermore, studying the impact of earnings quality on capital allocation and market efficiency would provide important insights into the wider economic implications of this phenomenon.