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Article

Does Corporate Governance and Earning Quality Mitigate Idiosyncratic Risk? Evidence from an Emerging Economy

by
Habib Ur Rahman
1,
Asif Ali
2,
Adam Arian
3 and
John Sands
4,*
1
Faculty of Higher Education (Accounting and Finance), Holmes Institute, Gold Coast, QLD 4217, Australia
2
School of Economics and Management, Xidian University, Xi’an 710071, China
3
Faculty of Law and Business, Peter Faber Business School, Australian Catholic University, Brisbane, QLD 4014, Australia
4
School of Business, Faculty of Business, Education, Law and Arts, University of Southern Queensland, Darling Heights, QLD 4350, Australia
*
Author to whom correspondence should be addressed.
J. Risk Financial Manag. 2024, 17(8), 362; https://doi.org/10.3390/jrfm17080362
Submission received: 13 July 2024 / Revised: 8 August 2024 / Accepted: 12 August 2024 / Published: 15 August 2024
(This article belongs to the Special Issue Featured Papers in Corporate Finance and Governance)
Versions Notes

Abstract

:
This study investigates evolving corporate governance mechanisms within the context of an emerging economy. Addressing a literature gap, this study analyses the influence of corporate governance and earnings quality on idiosyncratic risk in an emerging economy. In particular, this research explores the impact of corporate governance practices and earnings quality on idiosyncratic risk. For this purpose, this research utilises a sample of 75 non-financial firms listed on the Pakistani equity market over nine years from 2010 to 2018. Employing the generalised method of moments, the findings of our empirical analysis reveal that firms with robust governance mechanisms and higher earnings quality experience minimal idiosyncratic risk. These outcomes provide valuable insights for standard setters, regulatory authorities, policymakers, and other stakeholders, emphasising the importance of governance mechanisms and earnings management in mitigating idiosyncratic return volatility.

1. Introduction

Stock return volatility in corporations is influenced by two primary types of risk: idiosyncratic and market risks, both of which are integral to investment decisions (Irvine and Pontiff 2009). Idiosyncratic return volatility, related to specific attributes of a corporation, can be mitigated through diversification (Fonou-Dombeu et al. 2022). While investors typically prioritise undiversifiable risk in their investment decisions, the market’s competitive nature also compels them to consider idiosyncratic risk (Domingues 2016). Further, the quality of accounting and the effectiveness of corporate governance are critical indicators of investors’ perceptions of a firm’s reputation, integrity, and trustworthiness (Silva 2019). However, there is a lack of studies examining the role of corporate governance mechanisms and earnings quality in risk management within emerging markets, such as Pakistan. Previous research in developed countries (Asker et al. 2015; Chang et al. 2015; Li et al. 2013; Nguyen 2011; Wamba et al. 2018) has established that robust corporate governance practices reduce idiosyncratic return volatility (also see Gholami et al. 2023).
Similarly, the existing literature (Morck et al. 2000; Aman 2011; Chan and Hameed 2006) has shown that poor earnings quality increases idiosyncratic volatility. Despite these findings, the literature on the relationship between corporate governance, earnings quality, and idiosyncratic risk in developing markets remains underdeveloped. This research aims to fill this literature gap in the empirical literature. In particular, this study aims to provide empirical evidence on how effective corporate governance and poor earnings quality influence firm-specific risk in an emerging market like Pakistan. Firms in emerging markets exhibit unique governance attributes and ownership structures, making idiosyncratic risk particularly significant. Emerging stock markets attract global investors due to their dynamic nature. For instance, the Pakistan equity market has outperformed regional markets, becoming the best-performing market in Asia. However, weak governance and poor investor protection for minority shareholders in these markets increase firm-specific risk, making investors more cautious about investing in such stocks (Pathaka and Ranajee 2020; Chauhan et al. 2018; Gul et al. 2010). Further, along these lines, insider trading is prevalent in emerging markets (Fareed et al. 2022), where insiders exploit private information for personal gain, further increasing idiosyncratic risk (Zhou et al. 2017). Regulatory authorities in emerging markets, such as the China Securities Regulatory Commission (CSRC) and the Securities and Exchange Commission of Pakistan (SECP), impose various restrictions, including price limits and short-selling regulations, which can impact firm-specific volatility (Luo et al. 2015).
This research addresses how corporate governance attributes and earnings quality impact idiosyncratic risk in Pakistani listed enterprises. In this context, this study analyses the impact of corporate governance attributes and earnings quality on idiosyncratic risk in Pakistani listed enterprises, using data from 75 non-financial firms from 2010 to 2018. Our contribution to the literature is twofold. First, this study examines the impact of corporate governance attributes by constructing a Board Quality Index (BQI) encompassing a comprehensive set of governance features. Then, this research investigates how individual characteristics and the BQI influence idiosyncratic risk, focusing on five internal corporate governance mechanisms: (1) board independence, (2) board size, (3) institutional ownership, (4) gender diversity, and (5) board characteristics. The novelty of our research lies in its dual contribution: (1) developing a comprehensive Board Quality Index, and (2) examining the specific context of corporate governance and earnings quality in an emerging market. Previous studies (Mathew et al. 2018; Pathaka and Ranajee 2020) suggest that robust earnings-quality reporting provides consistent information to stakeholders about a firm’s prospects, fostering trust in the reported earnings. Therefore, this study also examines the relationship between earnings quality and firm-specific return volatility.
This paper employs a panel unit root test to determine the stationarity properties of the variables, following Chang et al. (2011), who noted the importance of stationarity testing in dynamic panel data models (also see Ali et al. 2023; Rahman et al. 2023). Given that all variables are significant at the first difference, this study uses the Generalized Method of Moments (GMM) framework proposed by Arellano and Bond (1991) to test our hypotheses. Our findings reveal a significant correlation between earnings quality and idiosyncratic return volatility, indicating that poor earnings quality increases firm-specific return volatility. Furthermore, corporate governance mechanisms generally show a negative relationship with idiosyncratic risk, suggesting that Pakistani firms’ unique governance features and ownership characteristics help reduce firm-specific risk.
The structure of this study is as follows: Section 2 covers the literature review, Section 3 details the research methodology, Section 4 presents the findings, analysis, and discussion, followed by Section 5, which provides the conclusion and implications.

2. Theoretical Background and Hypothesis Development

In 1952, Markowitz introduced the portfolio theory, which suggests that investors can minimise risk by diversifying their portfolios. Building on this, Lintner (1965) and Sharpe (1964) developed the Capital Asset Pricing Model (CAPM), using Markowitz’s theory as its foundation. According to CAPM, investment risk is categorised into systematic and unsystematic. Systematic risk, associated with market factors, cannot be diversified. In contrast, unsystematic risk, specific to individual stocks, can be mitigated through diversification, exemplifying idiosyncratic risk (Nguyen et al. 2021). Jensen and Meckling (2019) proposed the agency theory, which explains the relationship between agents (executives) and principals (owners). This theory explains the connection between stock return volatility and ownership (Vo 2016). According to agency theory, information asymmetry can lead to adverse selection and moral hazard issues, resulting in poor financial reporting quality and encouraging dysfunctional executive behaviour. It might be relevant to note that the information asymmetry among stakeholders results in over- or under-investment, which directly impacts the firm’s earnings quality (see Jensen and Meckling 2019).

2.1. Earnings Quality (EQ) and Idiosyncratic Return Volatility (IDR)

Earnings quality has been popular in the finance literature over the last two decades, and several studies have attempted to explore the association between earnings quality and idiosyncratic volatility. Along these lines, one strand of literature (Campbell et al. 2001; Fonou-Dombeu et al. 2022; Persakis and Iatridis 2015) attempted to explore the theoretical linkages of the association between the earnings quality and idiosyncratic volatility (also see Arian 2024). Healy et al. (1999), along with Diamond and Verrecchia (1991), argued that enhancing the quality of financial reporting and disclosures reduces information asymmetries, thereby mitigating stock price volatility. Similarly, Leuz and Verrecchia (2000) assert that information asymmetry is directly related to stock return volatility. Scholars such as Easley and O’hara (2004) have demonstrated that a corporation’s disclosure policy, earnings accounting treatment, and financial reporting quality significantly impact its information environment, influencing the idiosyncratic volatility of the firm. Another stream of research utilised proxies like Dechow and Dichev’s (2002) accrual quality and Jones’ (1991) absolute abnormal accruals to measure earnings quality, yielding mixed findings. For instance, Aboody et al. (2005) and Francis et al. (2005) analysed EQ as a measure of information risk, emphasising that EQ affects the expected return. On a similar note, Nguyen et al. (2024) provided strong evidence from the hand-collected data of 800 Vietnamese non-financial firms that corporate governance quality restrains earnings management.
Further studies by Hutton et al. (2009), Jin and Myers (2006), Piotroski and Roulstone (2004), Durnev et al. (2003), Ferreira and Laux (2007), Rajgopal and Venkatachalam (2011), and Mitra (2016) documented a significant positive correlation between firm-specific return volatility (FSRV) and EQ, concluding that poor EQ is associated with low idiosyncratic volatility. Conversely, researchers such as Li et al. (2014), Bartram et al. (2012), and Zhou et al. (2017) found an inverse correlation between EQ and IDR, suggesting that higher earnings quality mitigates FSRV. Moreover, scholars argue that stock price behaviour varies between emerging and developed markets. They conclude that poor earnings quality in emerging markets enhances idiosyncratic volatility (Morck et al. 2000; Aman 2011; Chan and Hameed 2006). On these theoretical grounds, this study proposes the following empirical conjecture.
H1. 
Earnings Quality (EQ) positively influences the idiosyncratic return volatility.

2.2. Corporate Governance (Hereafter, CG) and Idiosyncratic Return Volatility (IDR)

The existing literature presents mixed empirical findings regarding investment in local equity markets (Bekaert and Harvey 1997; Vo 2016; Bai et al. 2004; Fareed et al. 2022). Cronqvist and Fahlenbrach (2008) and Umutlu et al. (2010) state that a higher investment tendency in emerging markets enhances the quality of information CG practices, thereby reducing information and transaction costs. Drawing on agency theory and CAPM, studies have found that good CG mechanisms help enterprises mitigate idiosyncratic risk (Ferreira and Laux 2007; Chang et al. 2015; Asghar et al. 2020; Muhammad et al. 2022).
Most previous studies have documented that return volatility is linked to macroeconomic factors such as spillover effects, political affairs, and economic recessions. However, recent studies suggest that CG practices also influence idiosyncratic return volatility (Asker et al. 2015; Rogers and Securato 2009). Brennan and Xia (2001) and Alti (2003) state that excess volatility arises from incomplete information, reducing monitoring effectiveness. Research has shown that CG practices, such as board composition and ownership structure, significantly determine a corporation’s value (Wang 2016). Effective CG can prevent minority shareholder wealth expropriation, particularly in emerging markets lacking robust CG mechanisms (Gompers et al. 2003; Chen et al. 2012; Gul et al. 2010; Abu-Ghunmi et al. 2015). Better CG practices are associated with lower exposure to idiosyncratic return volatility (Ghafoor et al. 2019).
Key CG characteristics include board composition, board independence (BDI), board size (BSZ), ownership structure (ISO), and gender diversity (GDD). The literature suggests that board independence is crucial for good CG (Black and Kim 2012; Bebchuk and Weisbach 2010). Board independence creates both opportunities and risks for a business (Peng 2004). Minimal levels of BDI are linked with minimal levels of risk, whereas higher levels of information asymmetry pose challenges for independent directors overseeing risky firms (Brick and Chidambaran 2008). A higher ratio of independent directors effectively monitors firms’ operations (Tricker 2010). Furthermore, board size influences decision-making mechanisms and affects the board’s effectiveness, although larger board sizes are linked with higher total risk (Ho et al. 2013). Gender diversity plays a significant role in effective decision-making and improving board effectiveness (Benkraiem et al. 2017; Loukil et al. 2019; Guizani and Abdalkrim 2023).
A parallel strand of research documents mixed findings concerning board composition’s impact on idiosyncratic return volatility (Chong et al. 2018; Zhang et al. 2018; Chakraborty et al. 2019). Chichernea et al. (2015) assert that institutional ownership’s influence on idiosyncratic volatility varies by type, and Vo (2016) states that institutional ownership improves CG. Scholars present mixed results regarding the link between ISO and IDR (Xu and Malkiel 2003; Zhang 2010). Board attributes such as gender diversity, CEO duality, board size, ownership structure, and independent directors are negatively linked with idiosyncratic risk (Wu et al. 2020; Fareed et al. 2022; Haider and Fang 2016, 2018; Ali et al. 2024). Based on the above discussion, this study proposes the following hypotheses:
H2. 
Board independence has an inverse relationship with idiosyncratic return volatility.
H3. 
Board size has an inverse relationship with idiosyncratic return volatility.
H4. 
Gender diversity has an inverse impact on idiosyncratic return volatility.
H5. 
Institutional ownership negatively affects idiosyncratic return volatility.

3. Research Design

3.1. Sample Selection

The initial sample consisted of non-financial firms listed on the Pakistan equity market from 2009 to 2018. The idiosyncratic risk was calculated based on the returns of individual firms’ stocks over the current and previous years, covering 24 months. Therefore, data for other variables span 2009–2018, while the research period itself is 2010–2018. Firms in service industries, insurance companies, and financial institutions were excluded since the empirical models for earnings quality used in this study do not adequately reflect their activities.
Initially, data were collected from 2003 onwards to estimate the parameters for Dechow and Dichev’s (2002) model, since these parameters require lag and lead values of cash flows from operations and five annual residuals for earnings quality indicators. After restricting our sample to firms with complete data for all response, explanatory, and control variables and excluding firm years with missing information on any of these indicators, the final sample comprised 75 non-financial firms. This research excluded financial firms due to their (1) unique regulatory environment and (2) accounting practices. Including financial firms could introduce variability that might affect our empirical results. All empirical analyses in this study are based on this final sample. The sample selection criteria are detailed in Table 1.

3.2. Empirical Design

This study examined the effect of corporate governance and earnings quality on idiosyncratic risk. To verify hypotheses, this research estimates the following regressions:
I D R i t = β 0 + β 1 I D R t − 1 + β 2 A B S i t + β 3 B D I i t + β 4 B S Z i t + β 5 I S O i t + β 6 G D D i t + ∑ n β n X i t n + μ i t
I D R i t = β 0 + β 1 I D R t − 1 + β 2 A C F i t + β 3 B D I i t + β 4 B S Z i t + β 5 I S O i t + β 6 G D D i t + ∑ n β n X i t n + μ i t
I D R i t = β 0 + β 1 I D R t − 1 + β 2 A B S i t + β 3 B D I i t + ∑ n β n X i t n + μ i t
I D R i t = β 0 + β 1 I D R t − 1 + β 2 A C F i t + β 3 B D I i t + ∑ n β n X i t n + μ i t
In the abovementioned models, the dependent variable is an idiosyncratic risk, which IDR denotes. Following Xu and Malkiel (2003), the measurement of idiosyncratic return volatility is as follows:
γ ∗ i , j , t = φ 1 + β i γ j , t   + μ i , j , t
where, γ * ,   γ , β i ,   a n d   μ i , j , t   represent the company’s return, the industry return, the firm i exposure to its industry return, and the natural interpretation of idiosyncratic risk. This study defines idiosyncratic risk as VAR ( μ i , j , t ) and industry return volatility as V A R ( μ i , j , t ) for each period. The key independent variables are corporate governance and earnings quality. Corporate governance is measured using four indicators: board size (BSZ), board independence (BDI), institutional ownership (ISO), and gender diversity (GDD). For robustness, this study constructs a board quality index using Principal Component Analysis (PCA) based on these four indicators. Earnings management, the second exogenous variable, is measured using two approaches. Following Mitra (2016), this study employs two measures of earnings quality: accrual quality (balance sheet approach) and absolute abnormal accruals (cash-flow approach).
This paper also includes firm-level characteristics as control variables, such as age, size, book-to-market ratio, and leverage (see Arian and Sands 2024a, 2024b). It is relevant to note that firm age and size help account for operational stability and resource availability. This study uses the book-to-market ratio to reflect the firm’s market valuation relative to its book value. Leverage is included to control for financial risk due to debt obligations. In our empirical investigation, these control variables help isolate the specific effects of corporate governance and earnings quality on idiosyncratic risk. The existing literature has identified R&D intensity as a determinant of stock return volatility (Chan et al. 2001). It is suggested R&D intensity should be included as a control variable in the empirical model, subject to data availability. For further details on the explanation and measurement of variables, please refer to Appendix A.

4. Empirical Analysis

4.1. Descriptive Statistics

Table 2 presents the summary statistics for the 75 firms listed on the Pakistan Stock Exchange over nine years (2010–2018). The statistics include all variables’ mean, standard deviation, maximum, minimum, and observations. The summary statistics are divided into three groups: the whole sample observation, between the firm’s observation, and within the observation period. According to Alodat et al. (2022), these statistical findings provide detailed descriptive information, enabling a comprehensive understanding and explanation of the data. The findings indicate that, on average, the idiosyncratic return volatility is 0.0169%. Earnings quality is measured using the cash-flow approach (Dechow and Dichev 2002) and the balance sheet approach (Jones 1991). The mean value for the balance sheet approach is −0.4811, while for the cash-flow approach, it is −0.1003. These values align with Latif et al. (2017) study. The study suggests that, on average, abnormal accruals should approximate zero, as negative and positive accruals offset each other over time.
The mean value of board independence (BDI) is 0.69, indicating that all corporations meet Pakistan’s corporate governance code, which recommends at least two or one-third of the board members be independent (Farooq et al. 2022). The average board size is 8.21, with a minimum of 6 and a maximum of 15, consistent with the Corporate Governance Guidelines 2012. The average institutional ownership is 0.66%, and the average gender diversity score is 0.5, with a maximum of 4, showing compliance with the board’s recommendation to include at least one female director.
The board quality index has a mean value of −0.78, with the lowest and highest levels being −16.83 and 2.11, respectively. The control variables’ average firm size, firm age, leverage, and book-to-market ratio are 16.17, 3.67, 0.32, and 1.09, respectively. The standard deviation, minimum, and maximum values vary based on the overall, between, and within observations for all variables.

4.2. Correlation Matrix

This study conducted a correlation analysis to examine the dynamic relationships between the response and explanatory variables and check for multicollinearity’s likelihood. The findings are presented in Table 3. Upon reviewing the correlation indices, it was found that the study does not suffer from significant multicollinearity issues, except for the correlations between ACF and ABS (0.88) and GDD and BQI (0.70). While perfect multicollinearity can indicate a serious issue or logical error, imperfect multicollinearity (where the correlation coefficient is nearly equal to 1) may be a data characteristic. Therefore, the study does not exclude ACF and GDD when performing the regression analysis.

4.3. Panel Unit Root

The panel unit root test examines variables’ stationarity properties to ensure the chosen methodology’s appropriateness. Chang et al. (2011) noted that testing for stationarity is essential for estimating a dynamic panel data model. Buck et al. (2008) argued that this test is crucial when the number of time-series observations is significantly lower than the number of cross-sectional units. Consequently, this study conducted a series of panel unit root tests to provide unbiased, valid, and reliable estimates. The panel unit root test was performed at both the level and first difference, based on four different criteria: (i) the Levin, Lin, and Chu (LLC) t–statistic, (ii) the IPS, (iii) the ADF–Fisher, and (iv) PP–Fisher χ2, following the methodology of Chen et al. (2012) and Olaniyi et al. (2017). It is important to note that LLC and Breitung assume a common unit root, while IPS, ADF–Fisher, and PP–Fisher χ2 assume individual unit root processes across cross-sectional units.
Table 4 presents the findings of the panel unit root test at both the level and first difference based on the mentioned criteria. The results indicate that all variables attain stationarity at the level, except for board and firm size. Conversely, all variables achieve stationarity at the first difference except for board size and firm age. Since most variables are significant at the first difference, the current study employs the generalised method of moments (GMM) framework proposed by Arellano and Bond (1991) to estimate the abovementioned four models.

4.4. Regression Results

As shown in Table 5, four models are estimated using different independent variables. This study employs two approaches for measuring earnings quality. These approaches are estimated separately alongside corporate governance (CG) attributes in the first two models. Models 3 and 4 introduce the board quality index and two control variables—leverage and book-to-market ratio—into the regression equation instead of CG characteristics. All independent and firm-level control variables are included in models 3 and 4 (see Table 5). The findings in Table 5 support Hypothesis 1 (H1), indicating that earnings quality significantly correlates with idiosyncratic return volatility. These outcomes are consistent with earlier studies (Aman 2011; Mitra 2016; Hutton et al. 2009). Mitra (2016) also documented similar results, arguing that these findings align with the noise hypothesis, which suggests that higher earnings quality leads to lower idiosyncratic return volatility. Further, Morck et al. (2000) and Hutton et al. (2009) stated that outside stakeholders have limited access to firm-specific information when a corporation’s financial reporting quality is poor. As a result, they must make investment decisions based on industry or market-level public information, leading to lower idiosyncratic return volatility for such organisations.
Furthermore, this study also examines the influence of corporate governance (CG) attributes and the board quality index on idiosyncratic return volatility. The results indicate that only board independence has a negative and statistically significant impact on idiosyncratic return volatility, supporting Hypothesis 2 (H2). These findings are consistent with existing literature (Hussain and Shah 2017). Jiraporn and Lee (2018) argue that board independence helps develop an efficient governance system that discourages executives from making highly risky decisions. Similarly, Fareed et al. (2022) found that improved board independence reduces the likelihood of violations and connected transactions, such as illegal insider trading. Based on these studies, it is evident that board independence plays a critical role in governance mechanisms. Consequently, regulatory authorities worldwide have recognised its necessity and implemented regulations requiring a higher ratio of outside directors, such as the Sarbanes-Oxley Act of 2002 (SOX). Additionally, Anderson et al. (2004) documented that the presence of independent directors improves the validity of financial information and reduces financing costs, thereby decreasing stock price volatility.
The results indicate that board size, institutional ownership, and gender diversity do not have a statistically significant impact on idiosyncratic return volatility. As a result, the findings do not support H3, H4, and H5. The results of the study suggest that corporate governance mechanisms in emerging countries like Pakistan have an inconsistent influence on financial outcomes, and these findings are in line with the extant literature (see Swan and Forsberg 2014; John and Senbet 1998; Asghar et al. 2020; Patrick et al. 2015; Arya et al. 2003; Ghafoor et al. 2019; Akbar et al. 2017).
The study also includes four firm-level characteristics as control variables: size, age, book-to-market ratio, and leverage, drawn from previous studies (Zhou et al. 2017; Chen et al. 2012; Mitra 2016). The empirical outcomes in Table 5 reveal that only two control variables, firm size and age, are significantly aligned with the literature (Mitra 2016; Chen et al. 2012; Zhou et al. 2017). Firm size has an inverse and significant relationship with idiosyncratic return volatility, suggesting that smaller corporations have limited capacity to manage risks, while larger corporations have more pre-disclosure information available before earnings announcements (Rajgopal and Venkatachalam 2011; Brandt et al. 2010). Similarly, firm age also has an inverse and statistically significant correlation with idiosyncratic return volatility, indicating that younger corporations tend to have higher earnings uncertainty and, thus, greater idiosyncratic risk (Chen et al. 2012; Fareed et al. 2022).

5. Conclusions

The influence of corporate governance and earnings quality on idiosyncratic risk is a crucial finance study area. Idiosyncratic risk refers to the hazard inherent to a specific firm, independent of broader market movements. The existing literature (Cardoso et al. 2019; Fareed et al. 2022; Hussain and Shah 2017; Mitra 2016; Wu et al. 2020) reveals that corporate governance practices and earnings quality can impact idiosyncratic risk through different channels. This study identified a gap in this empirical literature since these studies have not addressed this issue collectively. To fill this gap, our study explores the impact of corporate governance and earnings quality on idiosyncratic risk within the context of developing countries, using data from 75 non-financial sector firms listed on the Pakistan equity market from 2010 to 2018. Our empirical estimation reveals that some corporate governance practices influence idiosyncratic risk.
Further, our empirical analysis reveals that higher earnings quality mitigates the idiosyncratic risk. These results support the noise hypothesis, demonstrating that better earnings quality leads to lower idiosyncratic risk. The outcomes of this study highlight the crucial role of some strong corporate governance mechanisms in emerging countries in risk management and contribute to a deeper understanding of idiosyncratic risk in corporate finance. Recognising and managing these interdependencies as corporations navigate an increasingly complex financial landscape is critical for informed decision-making and effective risk mitigation.

6. Scope for Future Studies

It is acknowledged that our study utilised data from 75 non-financial Pakistani firms, which may limit the generalisability of the results to other economies. As a future research direction, we recommend expanding this empirical investigation to include the latest dataset from global data sources. This would enhance the generalizability of the findings to different countries and markets. Additionally, including the R&D intensity as a control variable is suggested in future empirical models, subject to the data’s availability.

Author Contributions

Conceptualisation, A.A. (Asif Ali) and H.U.R.; methodology, H.U.R.; software, A.A. (Asif Ali); validation, A.A. (Adam Arian), H.U.R. and J.S.; formal analysis, H.U.R.; investigation, A.A. (Asif Ali) and H.U.R.; resources, J.S. and A.A. (Adam Arian); data curation, A.A. (Asif Ali) and H.U.R.; writing—original draft preparation, A.A. (Asif Ali); writing—review and editing, H.U.R., A.A. (Adam Arian), and J.S.; visualisation, A.A. (Adam Arian) and J.S.; supervision, A.A. (Adam Arian) and J.S.; project administration, A.A. (Asif Ali); funding acquisition, A.A. (Adam Arian) and J.S. All authors have read and agreed to the published version of the manuscript.

Funding

This research received no external funding.

Data Availability Statement

The raw data supporting the conclusions of this article will be made available by the authors on request.

Acknowledgments

We are grateful to Hareem Mirza for her administrative support.

Conflicts of Interest

The authors declare no conflicts of interest.

Appendix A. Variables, Abbreviations and Their Definitions

VariableAbbreviationDefinition
Idiosyncratic RiskIDRSee Equation (5) in Section 3
Earnings Quality
Accrual QualityABSSee (Mitra 2016)
Absolute Abnormal AccrualsACFSee (Dechow and Dichev 2002)
Corporate Governance
Board IndependenceBDIThe ratio of non-executive directors to total directors on board
Board SizeBSZTotal members on the board
Institutional OwnershipISOThe proportion of shares held by the institution to the total shares outstanding
Gender DiversityGDDRatio female directors to total directors
Board Quality IndexBQIIt is measured through PCA by using corporate governance indicators e.g., BDI, BSZ, ISO, and GDD.
Control Variables
Firm AgeLFATotal number of years since the business established
Firm SizeLFSLn (Total asset)
LeverageLVGThe proportion of debt to equity
Book-to-market ratioBTMThe proportion of book value of equity to market value of equity

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Table 1. Sample selection.
Table 1. Sample selection.
Selection Criteria Observations
Observations during the period 2009–2018122
Subtract: Observations with missing data on earnings quality−38
Subtract: Observations with missing data of other variables−9
Final Sample75
Note: The procedure followed for the sample selection is summarised in this panel.
Table 2. Descriptive statistics.
Table 2. Descriptive statistics.
Variable MeanStd. Dev.MinMaxObservations
IDROverall0.020.010.000.11N = 675
Between 0.010.000.04n = 75
Within 0.01−0.020.09T = 9
ABSOverall−0.480.70−2.919.13N = 675
Between 0.51−1.482.62n = 75
Within 0.48−3.766.02T = 9
ACFOverall−0.100.23−0.963.47N = 675
Between 0.17−0.471.11n = 75
Within 0.17−1.392.26T = 9
BDIOverall0.700.190.001.04N = 750
Between 0.160.180.94n = 75
Within 0.100.231.09T = 10
BSZOverall8.221.766.0015.00N = 750
Between 1.726.7015.00n = 75
Within 0.45.2211.32T = 10
ISOOverall0.670.210.001.00N = 750
Between 0.180.290.96n = 75
Within 0.110.111.02T = 10
GDDOverall0.560.870.004.00N = 750
Between 0.780.003.20n = 75
Within 0.39−0.643.76T = 10
BQIOverall−0.780.99−16.832.11N = 750
Between 0.75−2.391.24n = 75
Within 0.65−15.221.75T = 10
LFSOverall16.181.4911.8120.32N = 750
Between 1.4513.0519.80n = 75
Within 0.4014.917.50T = 10
LFAOverall3.680.782.087.61N = 750
Between 0.752.507.25n = 75
Within 0.25−1.304.23T = 10
BTMOverall1.092.3−18.4621.14N = 750
Between 1.83−8.574.55n = 75
Within 1.40−10.1217.68T = 10
LVGOverall0.320.280.800.99N = 750
Between 0.150.150.71n = 75
Within 0.240.471.21T = 10
Note: The abbreviations used in the above table are as follows: IND denotes idiosyncratic risk volatility, ABS represents earnings quality measured using the balance sheet approach, and ACF indicates earnings quality measured using the cash-flow approach. BDI stands for board independence, BSZ for board size, ISO for institutional ownership, and GDD for gender diversity. BQI refers to the board quality index, LFS to the natural log of firm size, LFA to the natural log of firm age, BTM to the book-to-market value, and LVG to leverage.
Table 3. Correlation analysis.
Table 3. Correlation analysis.
ABSACF BDI BQI BSZ BTM GDD ISO LFA LFS LVG
ABS1
--
ACF0.88 **1
49.2--
BDI0.000.001
−0.070.01--
BQI0.020.00−0.22 **1
0.420.06−5.93--
BSZ0.040.040.45 **−0.4 **1
1.110.9313.1−11.4--
BTM0.020.00−0.13 **0.05−0.18 **1
0.610.09−3.431.28−4.82--
GDD0.01−0.00−0.26 **0.70 **−0.17 **−0.001
0.24−0.20−6.9725.45−4.57−0.02--
ISO−0.04−0.00−0.09 *−0.21 **0.01−0.20 **−0.10 **1
−1.07−0.40−2.40−5.490.26−4.20−3.10--
LFA−0.07−0.000.24 **−0.15 **0.14 **−0.10 **−0.20 **0.11 **1
−1.77−0.806.35−3.953.71−1.50−4.802.91--
LFS−0.23 **−0.20 **0.25 **−0.16 **0.43 **−0.10−0.10−0.000.001
−6.22−4.306.61−4.2512.5−3.10−1.9−0..051.20--
LVG0.020.06−0.02−0.02−0.08 *0.22 **−0.1 *0.08 *0.10−0.101
0.561.46−0.48−0.60−2.055.79−2..042.031.60−1.50--
Note: The values reported above are the correlation coefficients and t–statistics. The abbreviations are defined as follows: IND denotes idiosyncratic risk volatility, ABS represents earnings quality using the balance sheet approach, ACF indicates earnings quality using the cash-flow approach, BDI stands for board independence, BSZ for board size, ISO for institutional ownership, GDD for gender diversity, BQI for the board quality index, LFS for the natural log of firm size, LFA for the natural log of firm age, BTM for the book-to-market value, and LVG for leverage. Correlations are significant at the 0.01 (**) and 0.05 (*) levels.
Table 4. Panel unit root tests.
Table 4. Panel unit root tests.
At Level At First Difference
LLCIPSADF–FisherPP–FisherLLCIPSADF–FisherPP–Fisher
IDR−11.20−2.71212.43306.50−24.73−7.74336.79664.74
0.000.000.000.000.000.000.000.00
ABS−14.95−4.90261.42513.87−17.53−7.54331.39731.35
0.000.000.000.000.000.000.000.00
ACF−19.02−6.07288.19530.66−24.14−9.06366.98759.89
0.000.000.000.000.000.000.000.00
BDI−7.22−0.61164.85201.72−23.03−6.70286.36634.64
0.000.270.090.000.000.000.000.00
BSZ−2.02−0.0927.0154.69−1.00−1.8529.85104.68
0.020.470.520.000.160.030.020.00
ISO−10.33−4.45254.64311.95−17.7−8.45347.51633.27
0.000.000.000.000.000.000.000.00
GDD−5.58−1.4137.0134.93−4.20−2.1135.1574.79
0.000.080.040.070.000.020.010.00
BQI−20.44−3.41198.83296.52−12.1−5.06268.75669.91
0.000.000.000.000.000.000.000.00
LFA−101.94−903.751344.711344.71119376−894.131344.711344.71
0.000.000.000.001.000.000.000.00
LFS−3.773.95119.61173.01−9.43−3.12224.69396.82
0.001.000.970.100.000.000.000.00
LVG−6.91−2.37200.97294.95−15.32−6.47311.24704.63
0.000.010.000.000.000.000.000.00
BTM−8.17−0.77166.15217.01−12.68−3.98237.05477.55
0.000.220.170.000.000.000.000.00
Note: IND, ABS, ACF, BDI, BSZ, ISO, GDD, BQI, LFS, LFA, BTM, and LVG represent idiosyncratic risk volatility, earnings quality using the balance sheet approach, earnings quality using the cash-flow approach, board independence, board size, institutional ownership, gender diversity, board quality index, natural log of firm size, natural log of firm age, book-to-market value, and leverage, respectively. The reported values are the t–statistics and their corresponding p–values. LLC, IPS, ADF–F, and PP–F stand for the following tests: (1) Levin, Lin, and Chu, (2) Im, Pesaran, and Shin W-stat, (3) ADF–Fisher Chi–square, and (4) PP–Fisher Chi–square.
Table 5. Impact of EQ and CG indicators on idiosyncratic risk volatility.
Table 5. Impact of EQ and CG indicators on idiosyncratic risk volatility.
Model_01Model_02Model_03Model_04
VariableCo-eff.t-StatCo-eff.t-StatCo-eff.t-StatCo-eff.t-Stat
IDR Lag−0.19 ***−2.83−0.20 ***−3.15−0.20 ***−3.25−0.20 ***−3.36
ABS0.01 **1.82 0.000.89
ACF 0.061.52 0.020.53
BDI−0.08 **−2.19−0.07 **−2.04
BSZ0.021.140.021.39
ISO0.000.040.010.42
GDD0.01−0.760−0.70
BQI 0.00−0.880.00−0.8
LFA−0.09 ***−2.86−0.10 ***−2.86−0.06 **−2.40−0.06 *−1.97
LFS0.02 **2.510.02 **2.390.01 **2.050.01 *1.74
LVG 0.00−0.880.00−0.84
BTM 0.000.600.000.65
J-statistic 12.37 13.8 17.57 18.57
Prob(J-statistic) 0.82 0.74 0.55 0.48
Instrument rank 27.00 27.00 27.00 27.00
Arellano-Bond Serial Correlation Test
AR (1)
M-Statistic −2.83 −2.57 −3.33 −2.91
Prob. 0.00 0.01 0.00 0.00
AR (2)
M-Statistic −1.39 −0.39 −0.54 −0.28
Prob. 0.16 0.69 0.58 0.77
Note: The second lag of IDR is reported. ***, **, and * indicate significance levels at 1%, 5%, and 10%, respectively. The first difference transformation is applied for the panel GMM, and if the innovations are i.i.d., the transformed innovations follow an integrated MA (1) process. Seventy-five cross-sections are included in all four models, with a total of 450 balanced panel observations. Two lags of the dependent variables are used, and the second lag is reported in all four models. The lagged dependent variables are specified as regressors, and period dummy variables (period fixed effects) are not included. This study uses 2-step (update weights once) GMM iterations with the White period. This research has carefully considered these GMM limitations and ensured our empirical analysis’s robustness.
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MDPI and ACS Style

Rahman, H.U.; Ali, A.; Arian, A.; Sands, J. Does Corporate Governance and Earning Quality Mitigate Idiosyncratic Risk? Evidence from an Emerging Economy. J. Risk Financial Manag. 2024, 17, 362. https://doi.org/10.3390/jrfm17080362

AMA Style

Rahman HU, Ali A, Arian A, Sands J. Does Corporate Governance and Earning Quality Mitigate Idiosyncratic Risk? Evidence from an Emerging Economy. Journal of Risk and Financial Management. 2024; 17(8):362. https://doi.org/10.3390/jrfm17080362

Chicago/Turabian Style

Rahman, Habib Ur, Asif Ali, Adam Arian, and John Sands. 2024. "Does Corporate Governance and Earning Quality Mitigate Idiosyncratic Risk? Evidence from an Emerging Economy" Journal of Risk and Financial Management 17, no. 8: 362. https://doi.org/10.3390/jrfm17080362

APA Style

Rahman, H. U., Ali, A., Arian, A., & Sands, J. (2024). Does Corporate Governance and Earning Quality Mitigate Idiosyncratic Risk? Evidence from an Emerging Economy. Journal of Risk and Financial Management, 17(8), 362. https://doi.org/10.3390/jrfm17080362

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