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Finance, Accounting and Business Analysis 
Volume 4 Issue 1, 2022 

http://faba.bg 

Effect of Earnings Quality Properties on The Performance of Companies: 

Empirical Evidence from South Africa 

Nyanine Chuele Fonou Dombeu1, Josue Mbonigaba1 , Bomi Nomlala1, Magret Odunayo2  

1School of Accounting, Economics and Finance, University of KwaZulu-Natal, South Africa 

2Departments of Accounting & informatics, Durban University of Technology, South Africa 

Info Articles  
 

Abstract 

Keywords:  
Earnings, Earnings quality, 

Performance, JSE listed companies 

 
Objective: This paper examines the effect of earnings quality properties on the 
performance of Johannesburg Stock Exchange (JSE) listed companies. The earnings 

quality properties considered include the accrual quality, conservatism, earnings 
persistence, earnings predictability and earnings smoothness. The five properties are 
examined individually as well as aggregately. Each property is further separated into 
its innate and discretionary components and the effect of each component on the 

company’s performance is examined. 

Methodology: The quantitative method and purposive sampling is used in this 
study. The sample consists of 800 observations obtained from 80 non-financial 
companies listed in the JSE during the period of 2009-2018. The performance is 
measured using the Return on Asset (ROA) and Tobin Q. The multilevel linear 
regression is used to test the formulated hypotheses. 

Results: It was found that, for both measures of performance, each earnings quality 

property as well the aggregate earnings quality property influence the performance 
of companies. Some exceptions included the accrual quality, predictability and 
smoothness which were found not to be significantly related to Tobin Q. 
Furthermore, it was found that both the innate and discretionary components of 

earnings quality properties influence the performance of companies. However, the 
innate component had a greater impact on the performance of companies than the 
discretionary component. 

Implication: The study will provide guidelines to investors and other capital market 
participants on which property of earnings could be used to assess the current 

performance of JSE listed companies and make prediction about their future 
performance. This in turn will assist in improving investors’ resource allocation 

decisions and allow policy makers to develop policies that will lead to more 
transparent accounting information in order to promote efficiency of the capital 
market. In addition, the study will inform capital market participants on how 

managers’ actions (discretionary component) and factors beyond the control of 
manager (innate component) affect the companies’ performance. 

 

  

   

*Address Correspondence:   
E-mail: mawempombo@gmail.com 
 
 

 

 



Finance, Accounting and Business Analysis 4 (1) 2022 

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INTRODUCTION 

 
This paper examines the impact of five earnings quality properties, namely, accrual quality, 

conservatism, earnings smoothness, persistence and predictability, on the performance of the companies 

listed in the Johannesburg Stock Exchange (JSE). In fact, previous studies (Chan et al., 2006; Salerno, 

2014) documented that reported earnings plays a vital role in the functioning of the capital market and 
earnings quality properties are factors that capture the capability of accounting data to represent faithfully 

a company’s operations (Domingues et al., 2016). Understanding the relation between the factors that 

affect reported earnings and the performance of a company is fundamental to investors and others capital 
market participants.  This may help to improve the way the capital market functions as well as the efficient 

allocation of resources and economic growth.  

Several studies (Francis et al., 2004; Dichev, 2006; Salerno, 2014; Sodan, 2015) have indicated 

that financial reports provide relevant information that help capital market participants to make decisions. 
Furthermore, it is argued that users of financial reports rely on reported earnings more than any other item 

in the financial statements to make decisions (Francis et al., 2004; Chan et al., 2006), since earnings is seen 

as the most important indicator of the firm’s performance.  
Because the users of accounting information depend largely on earnings to make economic 

decisions, managers of companies may be tempted to manipulate earnings numbers in order to mislead the 

capital market participants (Chan et., 2006; Domingues et al., 2016). Earnings manipulation is a deliberate 

alteration of financial reports with intend to distort accounting information. Earnings manipulation thus, 
lowers the quality of firm’s reported earnings. Manager’s motives for earnings manipulation include: a 

desire to hide firm’s cash flow problems, inability to meet loan contractual agreement, and desire to fulfil 

the expectations of capital market (Lisboa and Kacharava, 2018; Persakis and Iatridis, 2015; Dechow et 
al., 2003). Examples of big companies that misrepresented earnings to deceive the capital market 

participants include Parmalat, Enron, Worldcom, more specifically in South Africa, Leisurenet and 

Fidentia (Chan et al., 2006; Smit, 2015). These financial scandals have raised concerns about the quality of 

the companies’ reported earnings. The quality of reported earnings has thus become the focus of attention 
in accounting and finance research (Chan et al., 2006).  

Researchers have developed several earnings properties to assess the quality of accounting 

information. However, no agreement has been reached within the earnings quality research community on 

how to choose the earnings properties. For some authors (Francis et al., 2004; Dechow et al., 2010), the 
research questions being examined should guide which earnings properties to choose; while others like 

(Perotti and Wagenhofer, 2014; Lyimo, 2014) believe that research questions should be addressed with a 

variety of earnings properties in order to obtain consistent results. In this paper five earnings properties 
including the accrual quality, persistence, predictability, smoothness and conservatism are used to answer 

the question of how do various properties of earnings quality affect the performance of the companies 

listed in the JSE? The reason is that each of these properties is unique and irreplaceable by any other 

property.  In fact, it is argued that earnings properties are unrelated and that it would be useful to utilize 
several properties to evaluate a firm’s reported earnings (Dechow et al., 2010; Gutierrez and Rodriguez, 

2017).  

Each of the five properties of earnings considered is driven by both the firm’s business model and 
operating environment (innate component) and manager’s action (discretionary component) 

(Athanasakou and Olsson, 2016; Francis et al., 2004); therefore, the study focuses on two dimensions. 

Firstly, each property is considered separately and the combined effects of these properties on the 

performance of companies are investigated. In order words, each property is considered as a whole, that is, 
without any separation of its components and its effect on performance is investigated. Furthermore, an 

aggregate earnings quality property is formed (Sodan et al., 2015) based on the five properties being 

investigated, to alleviate the measurement errors associated with the individual properties, and its effect on 
companies’ performance is tested. The use of individual properties as well as the aggregate property allow 

for the reduction in the measurement errors and generalisation of the results in the South African (SA) 

context. Secondly, each property is separated into its innate and discretionary components and it is 

investigated how each component affect the performance of companies. Such partition is important 
because it provide insights on which factors (components) influence the most the performance of 

companies.  

This study addresses some shortcomings of the current earnings quality research.  In fact, 
although the literature on earnings quality research is abundant, studies that specifically focus on the 

association between earnings quality properties and the company’s performance are few. In addition, most 

of related studies have examined earnings properties as a whole and have mainly used single earnings 

property to measure the quality of earnings (Gutierrez and Rodriguez, 2017). Furthermore, existing related 
studies have been conducted in developed nations and some Asian countries, where the capital market is 



Finance, Accounting and Business Analysis 4 (1) 2022 

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well organised compared to developing nations such as South Africa. Beside the above, the results 
achieved by the existing related studies were mixed. For instance, the study by Ayu and Ahmar (2013) 

found a positive relationship between accrual quality and the company’s performance, whereas, Hejazi et 

al., (2014) found no relationship at all. Therefore, the results of these studies cannot be generalised. Apart 

from that, the quality of accounting information in a country is affected by a set of conventions such as the 
constitutional system, government regulations, ownership and capital structure, accounting standards and 

tax legislations. Each country has its own conventional setting; thus, the information on earnings changes 

according to different capital markets. Moreover, Dechow et al. (2010) and Dichev et al. (2013) reported 
that the earnings quality research had focused mainly on the earnings quality driven by reporting choice of 

managers (discretionary earnings quality) and has neglected earnings quality driven by the firm business 

models (innate earnings quality), although the quality of reported earnings is affected by both discretionary 

and innate earnings quality dimensions. 
South African studies on earnings quality in particularly (Ames, 2013; Smit, 2015; Sellami and 

Slimi , 2016) focus on determining whether the new accounting standards (IFRS) in the country has 

improved the quality of financial reports since its inception. Furthermore, some of these studies looked at 
the association between accounting quality and corporate governance, earnings management and firm’s 

value (Yeboach and Yeboach, 2015; Jordaan et al., 2018). Most of these studies used accrual quality as the 

measure of accounting quality although there are several others properties of earnings that can be used to 

measure the quality of financial reports. 
From the above, it is apparent that there is a lack of research that examines the effect of various 

measures of earnings quality on the performance of the companies in South Africa’s context. Therefore 

this paper aim at answering the following research questions: (1) how do individuals and aggregate 
measures of earnings quality influence the performance of the JSE listed companies? (2) How do the innate 

and discretionary components of each earnings quality property affect the performance of the JSE listed 

companies? The answer to these questions will provide guidelines to investors and other users in assessing 

whether the performance of a company, as represented in its financial statement truly reflect the 
company’s actual operations. This in turn will improve investors’ resource allocation decision and helps 

standard setters in South Africa to recommend or formulate policy that will improve the   transparency of 

financial reports. 
The remainder of the paper is organised as follows. Section 2 discusses the literature review and 

formulates the hypotheses of the study. The methodology of the study is explained in terms of the sample 

selection, data collection and variables measurements in Section 3. Section 4 reports and discusses the 

empirical results. Lastly, the conclusion is drawn in Section 5.   
 

LITERATURE REVIEW AND HYPOTHESES DEVELOPMENT 

 

This section discusses prior studies on  earnings quality and the company’s performance and the 

theoretical framework. Furthermore, it presents the hypotheses of the study after a discussion of the 
individuals and the aggregate earnings quality properties. 

 

Earnings Quality and the Performance of the Company 

The quality of the reported earnings impacts the results of the assessment of a company’s 

performance. Earnings that are of higher quality provide accurate information about the firm‘s current and 
future performance to the users (Dechow et al., 2010). Furthermore, high earnings quality also minimises 

information risk and prevents managers from exercising discretion at their own advantage. In contrast, 

low-quality earnings portray a false picture of the firm’s activities and increase information asymmetric 

between stakeholders (Ferrero, 2014). Therefore, high earnings quality illustrates the quality of the 
accounting system used by the firm. 

It has been empirically measured how related the earnings quality and some aspects of the 

company’s performance are (Barth et al., 2001; Francis et al., 2004; Bowen et al., 2008; Gray et al., 2009). 
Madhumathi and Ranganatham (2011) examined the association between earnings quality (measured by 

discretionary accruals), corporate governance and firm performance. The study revealed that transparent 

financial reports and good governance methods have a positive impact on company’s performance. 

Similarly, Mahmud et al. (2009) and Ayu and Ahmar (2013) investigated the relationship between 
earnings quality and firm’s performance, using the Malaysia and Indonesia data, respectively. Their study 

found that earnings quality is positively related to performance. In contrast, the study of Hejazi et al. 

(2014) found no relationship between earnings quality and performance, using Iran data. In general, these 
authors stressed the impact of a high earnings quality on the better performance of the company. This 

comes from the notion that accounting numbers provide accurate and useful information to the capital 

market.  



Finance, Accounting and Business Analysis 4 (1) 2022 

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However, some studies have demonstrated that accounting numbers do not always reflect value 
relevant information. For instance, the study of Negash (2008) revealed that the value relevance of 

accounting information did not ameliorate due to IFRS adoption. Francis et al. (2005) documented that 

accrual quality increases information risk and reflects earnings that do not represent the company’s 

operations. Some studies also examined the link between earnings quality and the cost of equity capital 
(Francis et al., 2004; Core et al., 2008; Gray et al., 2009; Dakhaoui et al., 2017). These studies found that 

earnings quality is negatively related to the cost of equity capital in the sense that, poor earnings quality 

leads to high cost of equity capital and hence weak company performance. On the contrary, Lambert et al. 
(2012) argued that, the companies with low cost of equity capital are less risky and display a higher 

earnings quality (more transparent financial report); as such, investors in their resource allocation 

decisions also consider the cost of equity, since the latter affects the performance of the company. 

Overall, mixed results are obtained in the literature with regard to the relationship between 
earnings quality and some aspect of the firm. Nevertheless, not many empirical studies have addressed the 

question of how related various earnings quality properties and some aspects of the companies such as the 

performance are. Furthermore, it is unknown how the innate and discretionary components of each 
earnings quality property affect the performance of the company. These questions are addressed in this 

study as contributions to the body of knowledge. 

 

Theoretical Framework 

 Two commonly used theories in earnings quality research are the capital need and decision 

usefulness theories.  The capital need theory has been used to assess the changes in the quality of the 
accounting information provided to the market. It explains the reasons for companies wanting to provide 

high quality accounting information to the market (Shehata, 2014; Choi, 1973). High quality accounting 

information reduces information asymmetry between stakeholders and lowers the company’s cost of 

equity capital (Yeh et al., 2014:239). Furthermore, it allows companies to easily obtain finances (both debt 
and equity), since the investors believe that these companies are less risky (Shehata, 2014). Shehata (2014) 

asserted that high quality financial reports also allow the capital market participants to predict accurately 

the future prospect of companies. Furthermore, the accurate determination of share prices depends on the 
quality of accounting information. Then, because investors are interested in companies with high share 

prices due to the high return they may get, the companies with high quality accounting information may 

easily raise capital.  

The decision usefulness theory is based on the IASB and FASB conceptual frameworks (Dunne et 
al., 2008). These frameworks state that the purpose of financial reports is the provision of useful 

information about the financial position, performance and changes in financial status of an entity, to 

investors, lenders and others users (IFRS, 2010). Accounting information is useful if it is relevant and 
reliable. IFRS (2010) emphasizes that such information should facilitate the decision making process of the 

users. In fact, accounting information is used by users for different purposes and is useful if it allows them 

to achieve their goals. Furthermore, information is useful if its communication to the market leads to the 

reactions of users (Chan et al., 2006). Such reactions can be observed through changes in security prices or 
trade volumes. The decision usefulness theory further stresses that useful accounting information supplies 

knowledge about the past performance of the company and allows for the accurate forecast of its future 

performance.  
This study lean on the decision usefulness and capital needs theories since they allow for the 

examination of accounting information provided to the capital market participants using factors that may 

influence that information. Furthermore, these theories have been successfully applied in related studies to 

answer research questions (Dunne et al., 2008; Tollerson, 2012; Eliwa, 2015).  
 

Hypotheses Development 

Earnings quality is a multidimensional concept, as such, it is measured with many properties 

(Perotti and Wagenhofer, 2014). The different earnings quality properties attempt to portray the quality of 
financial reports as well as their relevance and usefulness to the users. In this study, five properties of 

earnings quality, namely, accrual quality, conservatism, earnings persistence, earnings predictability and 

earnings smoothness are considered. These properties are examined individually as well as aggregately. 

Since each property of earnings is unique, unrelated to others properties and captures a specific aspect of 
the firm’s reported earnings, it is conjectured that each property differently affects the performance of the 

company as compared to the combined effect of the properties. Therefore the following hypothesis is 

formulated: 
 

H1: The individuals and aggregate measures of earnings quality influence the company’s 

performance.  



Finance, Accounting and Business Analysis 4 (1) 2022 

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To support H1, each of the properties considered is briefly discussed to illustration its effect on the 

company’s performance. Furthermore, specific hypotheses are formulated for each earnings quality 

property as well as for the aggregate earnings quality property, in support of the corresponding hypothesis.  

 

Accrual Quality 
Accrual quality is described by Dechow and Dichev (2002) as “the extent to which working 

capital accrual maps into cash flow realization”. This definition of accrual highlights one of the functions 

of accrual accounting, which is the adjustment in recognition of cash flow over time. The authors 
demonstrated that a “poor matching” means that the quality of earnings is lower. This function of accrual 

(adjustment in recognition of revenue over time) allowed some studies to argue that accrual is a desirable 

property of earnings quality, since it measures the earnings better as compared to cash flow (Dechow et al., 

1994; Kim et al., 2005; Barth et al., 2016). However, it is also argued that high accrual quality lowers the 
quality of reported earnings (Dechow and Dichev, 2002; Dechow and Shrang, 2004). This is because 

accrual is subjective in nature and is subject to the managers’ judgments and estimations. If these 

estimations are wrong, the true performance of the firm will be distorted. Furthermore, Chan et al. (2006) 
explained that earnings manipulation is done through the accrual process and Francis et al. (2005) claimed 

that accrual quality increases information risk. The authors demonstrated empirically that investors assign 

a price to accrual quality, since they perceive the accrual quality as providing information that cannot be 

diversified. Moreover, Dechow et al. (2010) explained that, even in the absence of intentional earnings 
manipulation, accrual could provide false information about the financial reports, since it is subject to 

unintentional errors that emanate from the improper application of the accounting system.  In light of the 

above, it can be concluded that accrual quality negatively affects the company’s performance. This results 
in the following hypothesis.  

 

H1a:  accrual quality has a negative effect on the performance of a company. 

 

Conservatism  
Conservatism is a desirable property of earnings, since it is a qualitative characteristic of high-

quality financial reports (Xu and Lu, 2008; Kan and Watts, 2009). It is a principle that allows accountants 

to be prudent in recognition of revenues and losses. In fact, under this principle, a loss should be 

recognized when there is a probability that the loss will occur in the future and that the loss can be 
measured reliably, whereas, the recognition of a gain is postponed until realization. Furthermore, Watts 

(2003) believed that market participants prefer underestimated earnings compared to overestimated 

earnings, since the former occur in rare circumstances. Therefore, conservatism is useful for decision-
making because it “captures the reliability of earnings” and reduces management incentives to manipulate 

reported earnings (Lafond and Watts, 2008). There are two types of conservatism, including conditional 

and unconditional conservatisms (Beaver and Ryan, 2005; Xu and LU, 2008); both types of conservatism 

lead to the understatement of earnings (Beaver and Ryan, 2005) and have different impact of financial 
reports (Ruch and Taylor, 2015). To examine the effect of conservatism on the firm’s performance, the 

following hypothesis is formulated.  

 
H1b: Conservatism has a negative effect on the performance of a company. 

 

Persistence and Predictability  

Persistence and predictability capture the ability of reported earnings to provide useful 

information to the users. Persistence refers to the stability of earnings (Dechow and Ge, 2006), whereas, 
predictability refers to past earnings’ ability to predict future earnings (Lipe, 1990). According to Francis 

(2004) and Dichev and Tang (2009), earnings that are more persistent and predictable reduce forecasting 

errors and allow financial analysts to determine the value of the firm more accurately. Assuming that more 
persistent and predictable earnings improve earnings quality, the following hypotheses can be formulated. 

 

H1c: Earnings persistence has a positive effect on the performance of a company. 

H1d: Earnings predictability has a positive effect on the performance of a company 
 

Earnings Smoothness 

Earnings smoothness is a technique used by managers’ to avoid the fluctuation of earnings. It is 

believed that smoothness reduces the earnings volatility, making it more stable (Beidleman, 1973; 

Subramanyam, 1996; Tucker and Zarowin, 2006). In turn, stable earnings facilitate the prediction of future 
earnings based on the past and current earnings. Goel & Thakor (2003) and Leuz et al. (2003) argued that 



Finance, Accounting and Business Analysis 4 (1) 2022 

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earnings smoothness decreases earnings quality, since managers take action to gain the capital market’s 
advantage.  Leuz et al. (2003) further asserted that smoothness is a form of earnings management, which 

indicates a poor earnings quality. McInnis (2010) and Erickson et al. (2017) argued that investors assess 

their investment risks based on the smoothing of operating cash flow instead of the smoothing of earnings. 

In fact, the smoothing of cash flow cannot be sustained over the long run, as is the case with earnings. It is 
further argued that opaque financial reports reduce the quality of reported earnings, thereby, increasing the 

information risk. Taking the above into consideration, it can be inferred that earnings smoothness 

negatively affects the company’s performance and that a company with a great level of earnings 
smoothness displays a performance that does not represent its true operations. Therefore, the following 

hypothesis can be formulated. 

 

H1e: Earnings smoothness has a negative effect on the performance of a company. 
 

Aggregate Earnings Quality Properties 

An aggregate earnings quality property (AEQP) is formed based on the five properties discussed in 

the previous subsections, namely, accrual quality, conservatism, earnings persistence, predictability and 
smoothness. A similar procedure was also adopted in Sodan (2015). Since each of the individual measure 

of earnings quality is expected to affect the performance of company, as demonstrated in the previous 

subsections, it is also expected the aggregate earnings quality properties to influence the company’s 

performance. However, no sign is assigned to the direction of such an effect, since each individual property 
affects differently the performance of company. Therefore, the following hypothesis is formulated: 

 

H1f: Aggregate earnings quality property has a significant effect on the performance of a company 
 

Innate and Discretionary Components of Earnings Quality Property 

The quality of earnings is affected by two distinct components, namely, the innate and 

discretionary components (Francis et al., 2005). The innate component of earnings quality properties refers 

to the aspects of the companies that are uncontrollable by managers and is linked to the companies’ 
characteristics and operating environments. On the contrary, the discretionary component is under the 

control of managers and is related to the firms’ accounting systems, corporate governance and managers’ 

decisions (Francis, 2005; Athanasakou, 2016). In fact, the classification of earnings quality properties into 

innate and discretionary components stems from the fact that each earnings quality property is affected by 
both managers’ actions and factors beyond the control of managers, such as the business models and 

operating environments. Since the innate component of earnings quality is related to the uncertainty in the 

firm’s economic environment, it is expected the innate component to impact the performance of a firm 
more than the discretionary component. Therefore, the following hypothesis can be formulated.  

 

H2: Innate component of earnings quality properties have a more significant impact on the 

company’s performance than the discretionary component. 
 

METHODS 

  

This section presents the methodology of the study in terms of the sample selection, data analysis 
techniques and measurement of the variables of the study. 

 

Sample and Data Analysis Techniques 

The sample for this study consists of all non-financial companies listed in the JSE Limited, for the 

period of 2009 to 2018. Financial companies were excluded from the sample in this study because they are 
well regulated industries with accounting rules that differ from that of other industries (Peasnell et al., 

2005; Persakis and Iatridis, 2015). The inclusion of a listed non-financial company in the sample was 

guided by the following conditions: (1) the financial statements  of the company must be available for the 

whole sample period, (2) the company must have all relevant information for the measurement of the 
dependent, independent and control variables, and (3) the company must have 5 past  consecutive years of 

data from the beginning of the sample period,  because the computation of accrual quality is based on the 

standard deviation of residual calculated over rolling 5 years period (Gray et al., 2009). After the 
applications of the above requirements, the final sample includes 800 observations, obtained from 80 

companies, drawn from an initial sample of 225 companies.  

The financial statements of the listed companies and the price data reports were extracted from 

the IRESS Research Domain database. The financial statements retrieved include the statements of 
financial position, the income, change in equity, cash flow and value added statements. These statements 



Finance, Accounting and Business Analysis 4 (1) 2022 

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were analysed to extract relevant information to calculate the variables of the study.  
All the variables of the study were winsorized to the 1st and 99th percentile to reduce the effect of 

outliers. The SPSS software version 27 was employed to obtain all the statistics. A number of tests 

including the collinearity, heteroskedasticity, normality and linearity were performed on the data before 

the analysis, to check the assumptions of linear regression.  
The correlation and multilevel regression analysis were applied to analyze the collected data.

 

Variables Measurements 

This subsection presents the models used to measure the dependent, independent and control 

variables of the study. 

 

Measurement of the Dependent Variable 
The dependent variable of the study is the company performance. This study uses both the return 

on asset (ROA) and the TOBIN Q to measure the firm’s performance. The ROA and TOBIN Q are 

calculated in Equations 1 and 2, respectively.  
 

assets Total

 taxandinterest  before Earnings
=ROA

     (1)  

assets of Book value

Debts)firm of lue(Market va
   Q TOBIN

+
=

      (2) 

 

Measurement of the Independent Variables 
The independent variables of the study are the earnings quality properties including the accrual 

quality, conservatism, earnings persistence, predictability and smoothness. The models for measuring these 

variables are presented next. 

 
 Accrual Quality 

 Accrual quality (AQ) is measured using the modified Dechow and Dichev (2002) model as in 

Francis et al. (2008) and Sodan (2015). The modified Dechow and Dichev (2002) model is given in 
Equation 3. 

 

ttitititititi PPESALESCFOCFOCFOWC  ++++++= +− ,,1,3,21,10,        (3) 

 

where,  
tWC

 is the change in the working capital in the year t minus the year t-1; tCFO
 the 

cash flow from the operation in the year t; tSALES
is the change in sales in year t ; tPPE

 is the property, 

plant and equipment in year t ; the prediction error, ti, the firm and year, respectively; and  is 

obtained from the regression model used. All variables are scaled by total assets at the beginning of the 

year t . The standard deviation of the residual, which is the proxy used for AQ, is computed over 5 years 

periods as 
titiAQ ,, )(=
.  

 Conservatism  

As stated earlier, there are two types of conservatisms, namely, conditional and unconditional 
conservatism. The Basu (1997) model is used to measure the conditional conservatism (CONSER1) due to 

its popularity (Khan and Watts, 2009). The Basu (1997) model relies on the relationship between earnings 

and return to illustrate the timeliness recognition of losses and gains. The Basu (1997) model is provided in 

Equation 4. 
 

tititititi DRRDPEPS ,,1,0101,, /  ++++=−         (4) 

 

where, tiEPS , is the earnings per share of firm i in the period t; D the indicator variable which is 

equal to 1 if tiR , is negative ( tiR , <0) and 0 otherwise. tiR ,  is the stock return of firm i in the period t . 0  

reflects the incorporation of goods news into the current earnings period, 1  measures the difference of 

the sensitivity of earnings to positive and negative returns and 
ti ,
 is the error term of firm i in the year t.  



Finance, Accounting and Business Analysis 4 (1) 2022 

8 

 

 

From Equation (4), CONSER1 is estimated with the formula: 010 /)(  +
. If the value of this 

ratio is high, it means that the loss is recognised timely, that is, the company practices conservatism 

accounting. A low value indicates a less conservatism practice. The unconditional conservatism 

(CONSER2) is measured using the book to market ratio, computed as book value of company divided by 

its market value (Beaver and Ryan, 2000; Ahmed and Duellman, 2007; Persakis and Iatridis, 2015).  
 
 Earnings Persistence  

The earnings persistence is measured with the model described in Dechow et al. (2010). The 
model assumes that the present earnings are used to estimate the future earnings; the model also assumes 

that companies that are sustainable, display earnings that are more persistent. The model shows the 

correlation between the current and future earnings as in Equation 5. 
 

tititi EarningsEarnings ,,101,  ++=
+                                         (5) 

 

where, t is the period t and ti ,
the error term that incorporates the information that was not 

captured by the earnings in the period t, to explain the earnings in the period t+1. 1  measures the 

persistence of earnings. A high value of 1  indicates that the earnings is more persistent, a 1  close to or 

higher than one, indicates highly persistent earnings and 1  close to zero indicates a less or non-persistent 

earnings (Persakis & Iatridis, 2015).  

 
 Earnings Predictability 

The earnings predictability is commonly measured using the square root of the error variance 

from the earnings persistence model (Francis et al., 2004). Equation 6 defines the earnings predictability 
model. 

 

)(Pr ,

2

, titiedict =
                           (6) 

                                                     

where, tiedict ,Pr
is the firm’s i earnings predictability in the year t. The term 

)( ,

2

ti
is the 

estimated error variance of the firm i in the year t calculated from the earnings persistence (Equation 5). If 

the square root of the error variance is high, the predictability is low, and the earnings is of low quality and 
vice versa.  

 

 
 Earnings Smoothness 

Earnings smoothness is measured using the Leuz et al. (2003) model as in Perotti and 

Wagenhoffer (2014) and Sodan (2015). The model consists of dividing the standard deviation of operating 
income by the standard deviation of cash flow from operations as in Equation 7. The operating income 

and cash flow from operations are scaled by total assets at the beginning of year t . 

 

tititi CFOOISMOOTH ,,, /=
                                                       (7) 

 

where, SMOOTH is the earnings smoothness, OI  the standard deviation of operating income,

CFO  the standard deviation of cash flow from operation, and 
ti,

the firm and year, respectively. The 
standard deviation is calculated for each firm over rolling five-years windows. A high value of SMOOTH, 

indicates a less earnings smoothness and a low value implies smoother earnings.  
 

Measurement of Control Variables 

Firm’s characteristics such as the size, leverage and growth have been found in to affect the 

performance of a company. The logarithm of total assets is used to measure firm’s size as in Mahmud et 

al. (2009), Gaio and Raposo (2011) and Kuncova et al. (2016).  Leverage is measured using the ratio of 
debt to total assets (Bowen, 2008; Ahmad et al., 2015), whereas, growth is measured using growth rate in 

revenues (Mahmud et al., 2009; Ahmed and Duellman, 2011). 



Finance, Accounting and Business Analysis 4 (1) 2022 

9 

 

 

FINDINGS 

 

The empirical results of the study are presented and discussed in this section, to explain the 

correlation amongst the earnings quality properties, the relationships between the earnings quality 
properties and the firm’s performance as well as the relationships between the innate and discretionary 

components of earnings quality properties and the firm’s performance. 

 

Correlation analysis  

Table 1 reports the Pearson correlation matrix amongst the earnings quality properties.  

 
Table 1. Correlation Amongst Earnings Quality Properties 

 AQ CONSER1 CONSER2 PERSIST PREDICT SMOOTH 

AQ 1      

CONSER1 0.003 1     

CONSER2 0.043 0.087* 1    

PERSIST -0.045 0.144** -0.047 1   

PREDICT 0.075* 0.111** -0.118* 0.664** 1  

SMOOTH -0.199** 0.013 0.063 -0.118** -0.136** 1 

Notes: *. ** Correlation is significant at the 0.05 and 0.01 levels (2-tailed), respectively. See Appendix 1 for 
the description of variables 

 

The analysis of the correlation results in Table 1 reveals that, there is a low correlation amongst 
earnings quality properties, except for persistence and predictability which display a correlation of 0.66. A 

related study in Perroti and Wagenhofer (2014) also reported a high correlation between persistence and 

predictability. The low correlation illustrates that, each property is unique and distinct and that one 

property cannot be used as a substitute of others. Furthermore, the low correlation also means that 
multicollinearity is not a problem in the regression analysis. Moreover, the correlation amongst earnings 

quality properties is positive for most of the cases, except for few properties which display negative 

correlations; this finding is corroborated by Perroti and Wagenhofer (2014), who reported a negative 
correlation amongst some earnings quality properties.  

 

The Relation Between Earnings Quality Properties and Company’s Performance 

To determine, the association between earnings quality properties and performance, Equation 8 

was used to illustrate the effect of individual earnings quality properties on performance. Thereafter, the 

effect of the aggregate earnings quality properties on performance was measured with Equation 9. Each 
earnings quality property was added individually to Equation 8. 

 

titititititi GrowthLeverageSizeEQPePerformanc ,,4,3,2,10,  +++++=
  (8) 

 

titititititi GrowthLeverageSizeAEQPePerformanc ,,4,3,2,10,  +++++=
 (9) 

 
where, Performance is either ROA or Tobin Q; EQP represents the earnings quality properties and 

is either accrual quality, conservatism, persistence, predictability or earnings smoothness; i and t the firm i 

at period t, respectively; 


 are the regression coefficients; 


 is the error term; 
AEQP

 is the aggregate 

earnings quality property, computed by averaging each of the five individual measures of earnings quality 

(Gaio, 2010).  

Equation 8 was estimated using the multilevel linear regression model (MLM) with fixed effect. 

MLM is an appropriate estimating technique for the analysis of panel data as compared to traditional 
models such as ordinary least square. Furthermore, MLM does not require the assumption of 

independence of observations to be met as it is the case with the traditional models (Hox, 2010; Field, 

2013; Hair and Favero, 2019). The results of the estimations of Equation 8 and 9 are presented in Tables 2 
and 3, respectively. Starting with AQ, the second and third column of Table 2 shows that AQ has the 

coefficients (t-statistics) of 0.121 (6.275) and -0.350 (-0.574), for ROA and TOBINQ, respectively.  The 

results are statistically significant only when the performance is measured by ROA. This means that AQ 



Finance, Accounting and Business Analysis 4 (1) 2022 

10 

 

positively affects the performance of a company when the performance is measured using ROA. When the 
performance is measured by TOBINQ, there is no association between AQ and TOBINQ. The results 

indicate that an increase in AQ will lead to an increase in ROA. These findings imply that the companies 

with high AQ display a high profitability than the companies with low AQ. Since accrual is subject to 

judgement and estimates by managers, the results can further suggest that management estimates and 
judgements lead to increase in performance.  

For the conditional conservatism (CONSER1), Table 2 shows that the coefficients for CONSER1 

are -0.003 (t-statistic=-1.567) for ROA and -0.009 (t-statistic=-0.511) for Tobin Q, but the association is not 
statistically significant. This result implies that the conditional conservatism is not related to performance. 

With regard to the unconditional conservatism (CONSER2), Table 2 displays the coefficient values of -

0.005 (t-statistic=-10.539) and -0.039 (t-statistic=-9.336) for ROA and TOBINQ, respectively. These results 

are statistically significant. This indicates that there is a negative association between performance and 
CONSER2. The unconditional conservatism implies the understatement of net asset value through for 

example, the recognition of accelerated depreciation, immediate recognition of research and development 

costs as expenses (Ryan, 2006; Beaver and Ryan, 2005). Unlike the conditional conservatism, the 
unconditional conservatism is not subject to the occurrence of an event (Beaver and Ryan, 2005). 

Therefore, the unconditional conservatism practice impacts the performance of company. The hypothesis 

H1b is confirmed only for unconditional conservatism.  

For the earnings persistence (PESIST), Table 2 indicates a positive significant relation between 
PERSIST and ROA, with a coefficient of 0.024 (t-statistic=3.962) and a negative insignificant relation 

between PERSIST and TOBINQ, with a coefficient of -0.044 (t-statistic=-0.829). These results suggest that 

earnings persistence directly influence the performance of companies when it is measured by ROA and 
that companies with higher persistent earnings display a high performance as compared to those with less 

persistent earnings. 

Concerning the earnings predictability (PREDICT), Table 2 displays that predictability has 

coefficients of 0.070 (t-statistic=6.380) and 0.162 (t-statistic=1.552) for ROA and TOBIN Q, respectively. 
This means that the earnings predictability significantly influences the performance of a company when 

such performance is measured by ROA. However, the association is not significant when the performance 

is measured by TOBINQ. These results imply that, the earnings predictability is positively related to ROA 
and companies with higher earnings predictability (low value of PREDICT) do not display a high 

performance as compared to those with low earnings predictability (high value of PREDICT). This may be 

due to the fact that earnings predictability is also affected by accounting factors such as management’s 

involvement (Dichev & Tang, 2009); this makes it difficult to accurately predict current/future earnings 
based on past/current earnings. This conclusion was also drawn by Holt (2013) who found no noticeable 

pattern with regard to the ability of current earnings per share to accurately predict future earnings per 

share. 
With regard to the earnings smoothness, Table 2, portrays the estimated coefficients of -0.013 (t-

statistic=-4.588) and 0.061 (t-statistic=2.361) for ROA and TOBINQ, respectively. This result indicates 

that, the earnings smoothness is negatively related to ROA and positively related to TOBINQ. It can be 

concluded that, the earnings smoothness influences the performance of a company and the positive or 
negative effect, depends on the indicators used in the measurement of performance.  

The results of estimation of the aggregate earnings quality property (AEQP) are presented in Table 

3.  
 



Finance, Accounting and Business Analysis 4 (1) 2022 

11 
 

Table 2. Results of the regression of performance on each earnings quality property and control variables 
Notes: *** and ** denote significance at 1% and 5% level, respectively. T-statistics are in parentheses.  

The descriptions of the variables are provided in Appendix 1. 

 ROA TOBINQ ROA TOBINQ ROA TOBINQ ROA TOBINQ ROA TOBINQ ROA TOBINQ 

Intercept 0.127*** -1.208*** 0.247*** -1.300*** 0.347*** -0.453 0.249*** -1.316*** 0.229*** -1.340*** 0.252*** -1.334*** 

 (-3.761) (-3.922) (-8.516) (-4.997) (-11.993) (-1.72) -8.668 (-5.061) (-8.08) (-5.146) (-8.759) (-5.135) 
Size -0.000678 0.115*** -0.007*** 0.119*** -0.011*** 0.081*** -0.058*** 0.122*** -0.007** 0.120*** -0.005*** 0.115*** 

 (-0.326) (-6.079) (-3.622) (-7.029) (-6.208) -4.816 (-3.999) (-7.126) (-3.65) (-7.074) (-2.992) (-6.724) 

Leverage -0.00794*** -0.691*** -0.065*** 0.684*** -0.057*** 0.772*** -0.077*** 0.699*** -0.090*** 0.634*** -0.079*** 0.742*** 

 (-4.4) -4.19 (-3.527) (-4.165) (-3.305) -4.93 (-4.189) (-4.22) (-4.797) (-3.823) (-4.338) (-4.486) 
Growth 0.045*** -0.111 0.049*** -0.111 0.030** -0.294** 0.047*** -0.106 0.036** -0.145 0.043*** -0.095 

 -2.936 (-0.777) 3.092 -1.776 -2.008 (-2.132) (-2.98) (-0.741) (-2.324) (-1.008) -2.758 (-0.661) 

AQ 0.421*** -0.35           
 (-6.275) (-0.574)           

CONSER1   -0.003 -0.009         

   (-1.567) (-0.511)         
CONSER2     -0.005*** -0.039***       

     (-10.539) (-9.336)       
PERSIST       0.024*** -0.044     

       -3.962 (-0.829)     
PREDICT         0.072*** 0.162   

         (-6.38) (-1.532)   
SMOOTH           -0.013*** 0.061** 

           (-4.588) (-2.361) 



Finance, Accounting and Business Analysis 4 (1) 2022 

12 

 

Table 3. Results of the Regression of Performance on AEQP and Control variables 

  ROA TOBINQ 

 Coefficient t-statistic Coefficient t-statistic 

Intercept 0.346*** 11.898 -0.543** -2.045 

AEQP -0.0264*** -10.314 -0.197*** -8.361 

Size -0.011*** -5.924 0.089*** 5.3 

Leverage -0.056*** -3.222 0.764*** 4.841 

Growth 0.031** 2.055 -0.256* -1.843 

     

N 800   800   

Notes:  ***, ** and * denote significance at 1%, 5% and 10%, respectively.  

The description of the variables is provided in Appendix 1 

 

Table 3 displays that the estimated coefficients of AEQP are -0.0264 (t-statistic=-10.314) and -
0.197 (t=-8.361) for ROA and TOBINQ, respectively. This indicates that AEQP is significantly related to 

the performance of company. This result is consistent with H1f. 

The above findings can be summarized as follows: (1) Each individual earnings quality property is 

related to performance, except for the conditional conservatism; furthermore, the interpretations of the 
results are different from one property to another, (2) AEQP is related to performance, with the estimated 

coefficients different from those obtained in individual regression of earnings quality measures and (3) 

There is a low correlation between earnings quality properties, which indicates that each property is 
distinct and captures different economic concepts, as explained in subsection 4.1.  

In light of the above, it can be concluded that the individuals and aggregate measures of earnings 

quality influence the performance of company, which supports the hypothesis H1. Therefore, investors can 

use various properties of earnings to assess the company’s performance and make prediction about future 
performance. 

With regard to control variables, in Tables 2 and 3, the majority of the regression indicates that 

the size and leverage have an inverse relation with performance, whereas, growth has a direct effect on 
performance. Similar findings were reported in (Dogan, 2013; Quand and Xim, 2014; Kuncova et al., 

2016; Karuma et al., 2018). In particular, it was reported a negative association between leverage and 

performance in (Quan and Xin, 2014), a positive association between size and performance in (Dogan, 

2013; Kuncova et al., 2016) and a positive relation between leverage and performance in ( Karuma et al., 
2018).  

To check the robustness of the aforementioned results, the study uses the bootstrapping estimation 

technique and found the results consistent with that of the multilevel regression discussed above. 
 

The Relation Between Innate and Discretionary Components of Earnings Quality Properties and 

Performance 

To partition each earnings quality property into innate and discretionary components, a procedure 

or model used by Francis et al. (2005) was utilized. The model regresses each property of earnings quality 

into innate factors, as in Equation 10.  
 

titii

tiitiitiitiitiiiti

CI

NegEarnopercyclesalesCFOsizeEQP

,,,6

,,5,,4,,3,,2,,1,0, )()(





+

++++++=

 (10) 

 

Where tiEQP , represents the earnings quality properties including accrual quality, conservatism, 

persistence, predictability or earnings smoothness; ti
CFO

,
)(

the standard deviation of the cash flow from 

the companies’ operations calculated over rolling five year period; tiSales ,)(
 the standard deviation of 

sales calculated over rolling five years period; tiOpercycle .  the operating cycle, computed as the log of 

sum of account receivables days and inventory days; CI  is the capital intensity; tiNegEarn , the negative 

earnings; ti,
the residual, which measures the discretionary component of earnings quality property  and 



Finance, Accounting and Business Analysis 4 (1) 2022 

13 

 

ti,  the firm and year, respectively. The predicted or estimated value, obtained from Equation 10, 

represents the innate component of earnings quality property.  

Equation 10 is used to compute the innate and discretionary components of EQP.  In order to test 

the effect of innate and discretionary components of each EQP on the performance, Equation 11 is used, 
where performance is regressed on innate and discretional EQP.  Control variables (size, leverage and 

growth) that have been found to influence the firm performance are included in Equation 11. 

 

tititititititi GrowthLevSizearyEQPDiscretionInnateEQPePerformanc ,,5,4,3,2,10,  ++++++=
 (11) 

 

where, Performance is either ROA of TOBINQ. Equation 11 is used to test the hypothesis H2, 

where each earnings property is added individually to the model. The results of estimating Equation 11, 
using multilevel regression are reported in Table 4. It is shown in Table 4 that, for accrual quality (AQ), 

unconditional conservatism (CONSER2) and earnings predictability (PREDICT), both the innate and 

discretionary portions of these properties affect the performance of company, measured by ROA. 
However, no association was found between these properties and TOBINQ. The results indicate that the 

discretionary accrual and predictability positively affect the performance (ROA); this is in line with Bowen 

et al. (2008)’s view that the discretionary action of managers in the application of accounting rules, 

 

Table 4.  Results of the regression of performance on innate and discretionary components of each 
property of earnings quality  

  AQ  CONSER1  CONSER2  PERSIST  PREDICT  SMOOTH  

  ROA TOBINQ ROA TOBINQ ROA TOBINQ ROA TOBINQ ROA TOBINQ ROA TOBINQ 

Intercep
t 

-
0.3975*** 

-
1.5317*** 0.3124*** 

-
0.9449*** 0.7956*** 

-
3.7125*** 0.2646*** 

-
1.4411*** 

0.139729**
* -0.5865** 0.3057*** 

-
1.3517*** 

  (-4.758) (-1.938) (-10.114) (-3.35) (-14.967) (-7.607) (-9.521) (-5.763) (-4.795) (-2.184) (-10.967) (-5.073) 

Innate 2.2983*** 0.8148 
-

0.0823*** 
-

0.4243*** 
-

0.0277*** 0.1274*** 0.1639*** 
-

1.1587*** 0.4262*** 
-

2.8274*** 
-

0.0843*** 0.0819 

  (-8.145) (-0.305) (-5.624) (-3.172) (-11.62) (-5.814) (-8.904) (-6.953) (-10.084) (-7.278) (-10.333) (-1.044) 

DISCR
E 0.2966*** -0.4211 -0.0009 -0.0106 -0.005*** 

-
0.0542*** 0.0084 0.0633 0.4262*** 

-
2.8274*** -0.0043 0.0599** 

  (-4.434) (-0.671) (-0.308) (-0.397) (-10.139) (-11.892) (-1.39) (-1.191) (-10.084) (-7.278) (-1.484) (-2.139) 

Size  0.0245*** 0.1305*** 
-

0.0102*** 0.1007*** 
-

0.0292*** 0.2128*** -0.014*** 0.1718*** -0.0091*** 0.1417*** 
-

0.0053*** 0.1149*** 

  (-5.822) (-3.285) (-5.194) (-5.623) (-11.664) (-9.264) (-6.904) (-9.763) (-5.077) (-8.701) (-2.975) (-6.723) 

Leverag
e 

-
0.0688*** 0.6997*** 

-
0.0767*** 0.6399*** 

-
0.0858*** 0.8624*** 

-
0.0796*** 0.6735*** -0.0937*** 0.6378*** 

-
0.0651*** 0.7371*** 

  (-3.94) (-4.228) (-4.25) (-3.88) (-5.293) (-5.772) (-4.514) (-4.228) (-5.413) (-4.028) (-3.764) (-4.437) 

Growth 0.0479*** -0.1092 0.0428*** -0.1457 0.0053 -0.1358 0.0414*** -0.0427 0.0298*** -0.0768 0.0410*** -0.0943 

  (-3.172) (-0.76) (-2.742) (-1.018) (-0.373) (-1.034) (-2.731) (-0.311) (-1.993) (-0.56) (-2.758) (-0.658) 

                          

N 800 800 800 800 800 800 800 800 800 800 800 800 

Notes: *** and ** denote significance at 1% and 5% level, respectively. T-statistics are in parentheses.  
 

The descriptions of the variables are provided in Appendix 1. provides an advantage to 

shareholders, due to the reporting of an increase in performance 
For the conditional conservatism (CONSER1), earnings persistence (PERSIST) and smoothness 

(SMOOTH), only the innate component influences the performance, when measured by ROA. But when 

the performance is measured with TOBINQ, insignificant association was found. The exception was meet 

with the earnings smoothness; it was found that both the innate and discretionary smoothness are 
associated with performance, when measured by TOBIN Q. 

These results illustrate that each component of earnings quality property affect differently the 

performance of companies, emphasizing the need to partition each earnings quality property into its parts. 
In addition, the direction of effect depends on the measurement used to evaluate the performance; this 

indicates that each measure of performance reflects a specific business outcome or aspect of the company. 

This is in line with Hamann et al. (2013) who refer to performance as a multidimensional concept. 

These results further suggest that the performance of companies is affected by either (1) the innate 
component of earnings quality property, (2) the discretionary component of earnings quality property 

and/or (3) both the innate and discretionary components of earnings quality properties, depending on the 

property used. However, in most of the cases, the innate components have a greater impact on 
performance than the discretionary component. Therefore, the results support H2. Therefore, it can be 



Finance, Accounting and Business Analysis 3 (1) 2021 

14 

 

concluded that the quality of reported earnings is mostly affected by companies’ characteristics and 
operational environment; which emphasizes the need to separate each property into its innate and 

discretionary components. Furthermore, the accounting standards applied in South Africa (IFRS) have 

improved the accounting quality by reducing managers’ opportunistic reporting decisions. Therefore, 

investors need to pay more attention to the companies’ business models and the operating environments, 
when evaluating earnings quality. Nevertheless, because the accounting discretion of manager may change 

from one reporting period to the next, investors must also pay attention to the accounting discretion.  

 

CONCLUSION 
 

This paper investigated the association between various properties of earnings including accrual 

quality, conservatism, earnings persistence, predictability and smoothness and the performance of JSE 

listed companies. The findings indicate a statistically significant association between the earnings quality 
properties and the performance of these companies. An exception was noted for the conditional 

conservatism, which was found to be unrelated with performance.  

The paper further separated each earnings property into its innate and discretionary parts and 
examined the effect of each part on the companies’ performance. The results revealed that both 

components influence the performance of the companies, in most of the cases. However the innate 

component displays a higher impact on performance than the discretionary component. This result 

suggests that, in South Africa, the operational environments of the companies had a greater impact on 
performance compared to the accounting discretions excised by the managers of these companies.  

Furthermore, for some properties such as the conditional conservatisms, their separation into the 

innate and discretionary components has shift the statistical insignificance to the statistically significance; 
this emphasize the advantage of partitioning the properties into their innate and discretionary parts. 

Overall, the results achieved in this paper emphasize the importance of earnings quality in the evaluation 

and prediction of companies’ performance. The results show that the choice of the measurement for the 

evaluation of performance matter, as different results may be obtained with different measurements of 
performance 

 

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Appendix 1 - Description of variables 

Variable  

symbol 

Variable Definition 

AQ Accrual quality  The standard deviation of the residual from a regression of working 

capital, on prior, current and future cash flow from operations, sales 

and properties plants and equipment; all variables are scaled by asset 
at beginning of the year 

CONSER1 Conditional 
conservatism 

The ratio of coefficient of bad news to the coefficient of good news, 
obtained from a regression of earnings per share deflated by prior 

price per share on return 

CONSER2 Unconditional 

conservatism 

 the book to market ratio 

PERSIST Earnings 

persistence 

The slope coefficient from a regression of profit before extraordinary 

item, scaled by asset beginning of the year, on previous profit before 

extraordinary item scaled by asset beginning of the year 

PREDICT Earnings 

predictability 

The square root of the error variance from earnings persistence 

model 

SMOOTH Earnings 

smoothness 

The ratio of operating income deflated by asset beginning of the year 

to the standard deviation of cash flow, scaled by asset beginning of 
the year; the standard deviation is computed over 5-years period 

ROA Return on assets The ratio of earnings before interest and tax and total assets 

TOBINQ TOBINQ The market value of firm plus debts divided by total assets.  

Size Size The natural logarithm of total assets 

Leverage Leverage The ratio of debts over total asset 

Growth Growth The growth rate in revenue. 

AEQP Aggregate earnings 
quality property 

Average of AQ, CONSER, PERSIST, PREDICT and SMOOTH. 

Innate Innate Innate component of earnings quality property 

DISCRE Discretionary Discretionary component of earnings quality property 

 

 
 


