Earnings quality and financial flexibility: A moderating role of corporate governance

Abstract

The aim of this study is primarily to demonstrate how earnings quality is an influential determinant of financial flexibility. Secondly, how earnings quality affects financial flexibility. And finally, to provide evidence of the role of corporate governance between earnings quality and financial flexibility composing overall corporate governance index (CG-INDEX). This study considered unbalanced panel data from the year 2007 to 2020 from the database CSMAR yielding 14,088 firm-year observations. This study used liquidity as the proxy of financial flexibility, and also used a comprehensive index of corporate governance constructed by adopting the principal component analysis and STATA has been used for analyzing data. The study used System GMM regression for analysis and controls endogeneity by applying lag financial flexibility as an instrumental variable. The empirical results reveal that poor earnings quality significantly negatively influences the level of corporate financial flexibility. The results also demonstrate that corporate governance can significantly positively moderate the relationship between earnings quality and financial flexibility. This suggests that when the earnings quality is poor, firms are less likely to be financially flexible in holding liquidity. More specifically, firms with poor earnings quality will reduce their financial flexibility of firms; hence, firms need to provide high-quality earnings in order to be more financially flexible. Earnings quality is an important factor, which led the author to examine how earnings quality influences financial flexibility. Under the views of agency theory and positive accounting theory, poor earnings quality is a source of amplified shareholder’s concern of increased informational asymmetry, which may adversely affect the firm’s financial flexibility. Conversely, higher earnings quality reduces the information asymmetry which leads to higher financial flexibility. This study provides a way how to achieve financial flexibility with the assistance of corporate governance which is essential to combat financial crises and smooth business operations successfully.

Keywords:

• Earnings quality
• corporate governance
• financial flexibility

1. Introduction

Earnings quality has become a hot issue after a series of corporate failures due to the disclosure of non-authentic and temporary earnings (e.g., Hamdan, 2020). Accordingly, investors become overwhelmingly concerned in assessing firms’ disclosed earnings and use them in their financing decisions (García‐teruel et al., 2009; Hamdan, 2020; Vo & CHU, 2019). Better earning quality also referred to as “earnings fidelity” is inevitable information for investment decisions, which provides fruitful returns on the investment (e.g., Du et al., 2020; Farinha & Moreira, 2007). On the other hand, poor earnings quality signals issues of integrity and credibility of the reported earnings, and investors bear risks of unavailing return on their investments (Farinha & Moreira, 2007; Schipper & Vincent, 2003). Thus, earnings quality might influence a firm’s financial flexibility. However, how earnings quality instigates financial flexibility is yet to answer.

Corporate governance mechanisms are important mechanisms to mitigate moral hazards and information asymmetry concerning executives operating, financing, and investment selections (e.g., Fama & Jensen, 1983). Subsequently, effective governance mechanisms ensure the quality of financial reporting and pledge a firm’s higher level of reporting transparency, which entails minimal fraudulent reporting (e.g., Bazaz & Mashayekhi, 2010; Coffee & ca. 1999; Lambert, 2001), thereby may lead to high earnings quality.

The literature on the nexus between corporate governance and earnings quality suggests that the former can facilitate later and support manager’s effective financing strategies and investment decisions (e.g., Aldamen & Duncan, 2016; Habib & Jiang, 2015). Also, corporate governance can reduce fraudulent reporting (e.g., J. J. Chen & Zhang, 2014), because the audit committee is one of the dominant governance mechanisms to enhance the reliability and usefulness of financial statements (e.g., J. R. Francis et al., 1999; Srinidhi et al., 2011). In addition, earnings quality is strongly associated with corporate governance structures for the vigilance of an active independent board, female directors on the board, and audit committee (e.g.,Habib & Jiang, 2015; Klein, 2002; Srinidhi et al., 2011). Moreover, most of the studies on corporate governance and earnings quality nexus examine the relationship from the perspectives of financial reporting quality (e.g., Aldamen & Duncan, 2016; Bao & Lewellyn, 2017; Bhattacharya et al., 2003; Caramanis & Spathis, 2006; Gaio & Raposo, 2014; C. J. Chen & Jaggi, 2000; Jaggi et al., 2009; Srinidhi et al., 2011). Unlike the other studies in the literature, this study also examines the moderating effect of corporate governance on the relationship between earnings quality and financial flexibility.

Financial Flexibility has been defined as an intangible asset that provides a firm’s strength to face any impending unexpected events and contributes to maximizing the firm’s value (e.g., Arslan-ayaydin et al., 2014; Bolton et al., 2019; Cherkasova & Kuzmin, 2018; Denis & Mckeon, 2009; Ferrando et al., 2017; Ma & Y, 2016). The literature shows that the level of financial flexibility in terms of cash position depends on the trading-offs between the costs and benefits associated with hoarding of higher liquidity (Miller & D, 1966; Shin et al., 2018). Generally, the financing costs typically include the possibility of payment of tax and low returns on cash holdings (Bigelli & Sánchez-vidal, 2012). In contrast, the benefits are the savings on raising money through issuing new capital or disposal of assets, reducing the possibility of corporate failure, the abandoning of costly financing, and non-availability of alternative funding (e.g., Kim et al., 1998; Opler et al., 1999; da Da Cruz et al., 2019).

According to Myers and Majluf (1984), due to the presence of information asymmetry between investors and firms, external financing becomes expensive that leads to underinvestment or asset replacement (Jensen & Meckling, 1976). Moreover, for personal benefits, managers may maintain higher cash in the balance sheet for keeping optimal debt, risk, and dividends desired by the shareholders (Easterbrook, 1984).

In the developed market context, Sun et al. (2012) examine the relationship between earnings quality and financial flexibility (cash holding) and concluded with a significant negative relationship for the value of the cash but a positive relationship for cash holdings among these attributes. In contrast, the phenomenon that when there is a higher level of information asymmetry, firms hold a lower level of cash balances is consistent with the shareholder’s monitoring hypothesis when managers are controlled to maintain a huge cash level (e.g.,Shin et al., 2018) that could be misused in an opaque environment.

Emerging economies’ are countries where the stock market has gone through a radical reformation and introduction of corporate governance guidelines as well as setting regulatory bodies and regulations. For example, China went through radical reforms such as the board independence reforms in 2001 split-share structure reform in 2005 (e.g., LI et al., 2015), and corporate governance guidelines in 2006. Considering the introduction of several reforms in China, it becomes important to explore the association between earnings quality and financial flexibility. To the best of our knowledge, this study is the first to comprehensively investigate the relationship between earnings quality and financial flexibility in the context of China. We believe that it will fill the gap in the literature on earnings quality and financial flexibility, especially in emerging markets.

This study contributes to the finance literature in different dimensions: Firstly, the study will demonstrate how earnings quality is an influential determinant of financial flexibility. Secondly, how earnings quality affects financial flexibility. And finally, composing an overall corporate governance index (CG-INDEX) by PCA analysis, this study evidence on the role of corporate governance between earnings quality and financial flexibility in the emerging economies.

The paper is organized as follows. Section 2 reviews related literature and establish theoretical foundations of financial flexibility as well as developed hypotheses. Section 3 introduces data and methodology. Section 4 presents the empirical results and a discussion of the results. Finally, section 5 offers some conclusions, policy implications, limitations, and suggestions for future research.

2. Literature review

2.1. Theoretical foundation of financial flexibility

2.1.1. Liquidity preference theory and financial flexibility

John Maynard Keynes developed a theory called liquidity preference theory (hereafter LPT). The LPT suggests that considering the maturity of the investments, investors may demand diverse returns for their investments. This suggests that investors require a higher rate of return for long-term investments rather than short-term investments because of their preferences of holding liquidity (Keynes & Moggridge, 1973). The theory presumes that cash has speculative power and can provide additional benefits to firms. Although, firms are reluctant to sacrifice long period investments at lower interest rates, but they still prefer short-term investments at lower interest rates to enjoy the speculative power of liquidity and to reduce risk.

Firms prefer liquidity for three essential motives. First, a firm’s preference to hold liquidity is to deal with day-to-day transactions, which is referred to as transaction motive. Because of transaction motive, firms seek to hoard higher cash in order to meet their short-term obligations. Second, firms hold higher cash to safeguard from unexpected problems, which leads to precautionary motives. Finally, firms hold cash to exploit opportunities arising from unusual events like economic crisis, COVID-19 crisis, or better possible investment opportunities, which are called speculative motives (Huang-meier et al., 2016; Potì et al., 2020).

According to Keynes and Moggridge (1973), firms hold higher liquidity considering the benefits of savings from transaction costs as well as the motives of precaution. It is better to hoard cash because raising funds through an initial public offering, a right share offering, or bond issuance requires permission from the regulators and involves the underwriting and other costs. If firms have excess cash, managers can use it for smooth business operations as well as for achieving investment opportunities even if further sources of funding are not available (e.g.,Myers & Majluf, 1984). Also, in circumstances of mutual approved net debt, there is also an optimal volume of cash, but cash is not just net debt (Keynes & Moggridge, 1973).

Overall, LPT suggests that financial flexibility in liquidity might act as a source of liquidity’s transaction motive, precautionary motive, and speculative motive (Huang-meier et al., 2016; Potì et al., 2020; Whalen, 1966).

2.1.2. Inventory management theory and financial flexibility

Baumol (1952) introduced inventory management theory (hereafter IMT) to the nexus of Keynes’s liquidity management theory. Unlike LPT, the IMT focuses on transaction motive and provides a new way to demonstrate it. The IMT theory explains the transaction motive of cash from an inventory management perspective and suggests that firms can hold cash as liquid assets to facilitate business transactions. Also, the theory states that maintaining goods as inventory involves cost, and thus business prefers to seek an optimal inventory level to minimize costs associated with hoarding inventory. Similarly, firms should keep an optimal level of cash or liquidity in order to minimize the costs associated with holding liquid assets (Itzkowitz, 2013; Karni, 1973).

Moreover, IMT suggests that firms should keep cash or liquid inventory as short-term deposits instead of keeping them in the firm’s vault. By having money in short-term deposits and investment in bonds, firms can earn interest instead of keeping these assets in the balance sheet, which will minimize the firm value. Further, IMT advocates that firms should hold large cash reserves in order to bridge the gap between earnings and spending on a daily basis because surplus cash provides financial flexibility to firms (Bigelli & Sánchez-vidal, 2012; Shah, 2011; Wang et al., 2014).

Thus, among earlier works on liquidity, Baumol (1952) proposed a model of weighted benefits of cash holdings, which suggests that cash is an asset and it should be managed like other tangible assets. The study further suggests that the conversion of tangible assets into cash is not free. In fact, this conversion involves higher costs, which indicate that cash holdings and additional debt capacity are critical drivers of an organization’s financial flexibility.

2.1.3. Positive accounting theory and financial flexibility

Positive Accounting Theory (hereafter PAT) predicts possible accounting actions and choices of accounting policies taken by managers in the real world (Watts & Zimmerman, 1986). The theory indicates that managers’ choice of accounting policies depends on three things: 1) bonus plan hypothesis; 2) debt covenant; and 3) political costs (Watts & Zimmerman, 1986). These three things create agency costs and political costs associated with earnings quality. Figure 1 portrays the theoretical basis of earnings quality.

Figure 1. Theoretical basis of earnings quality.

PAT considers that managers are rational (like shareholders) and report earnings in their best interest if they have an opportunity to do so. When managers have executive stock options (ESOs), they are likely to be opportunistic by showing excessive earnings to pile up bonuses that are entirely based on the managerial performance (e.g., Alves, 2012; Hanlon et al., 2003). Also, in the presence of contractual agreements with employees, suppliers, and capital providers, firms attempt to minimize the costs of negotiation, renegotiation, moral hazards, and monitoring performance, including the bankruptcy costs and financial distress (e.g., Christensen et al., 2009). Efficient contracts lead to a better stewardship role of financial reporting by motivating executives to act in shareholders’ interest and minimizing the cost of moral hazards (e.g.,Slotte, 2018). Thus, the debt covenants are likely to influence the earnings quality due to the diverse accounting variables that affect the reported earnings. For example, employees’ promotion depends on the firm’s earnings or achieving the target to control costs; thus, contracts with the supplier may be based on the firm’s liquidity or lenders protection costs, including maintenance of specific ratios (such as debt-equity ratio and lower working capital ratios).

On the other hand, political costs are incurred when firms are facing political pressure (e.g., Cahan, 1992; Na’im & Hartono, 1996). For example, larger firms may face pressure and adopt higher performance standards due to their enormous size and power. When such types of firms are highly profitable, they are forced to maintain compulsory environmental and social responsibility costs as well as increased pressure of tax payments. To lessen the burden of costs, these firms might produce low-quality earnings (Gaio & Pinto, 2018; Hashmi et al., 2018; Sadiq & Othman, 2017).

2.2. Hypothesis development

Earnings quality is a significant indicator of information asymmetry which demonstrates that higher earnings quality minimizes information asymmetry and resolves problems like adverse selection and moral hazards (Bushman & Smith, 2001; Shin et al., 2018). According to J. Francis et al. (2005), higher earnings quality enhances trust among shareholders and mitigates the consequences of information asymmetry. Conversely, poor earnings quality fuels uncertainty about the firm’s financial health and exacerbates suspicions on the reported earnings (Mansali et al., 2019). This study argues that firms with lower earnings quality will show lower financial flexibility by holding less liquidity as these firm’s weak manager’s monitoring exacerbates cash spending. Conversely, firms with higher earnings quality will show higher financial flexibility by holding more liquid assets and become financially flexible to exercise the precaution motive, transaction motive, agency motive, and speculative motive of liquidity preference theory.

The literature on earnings quality is not very widespread and provides little evidence on how earnings quality is associated with financial flexibility (FF). Among early studies, Opler et al. (1999) argue that the firm’s liquidity is positively associated with its growth opportunities and risk, but negatively associated with its size. They also document that companies having easier access to capital markets tend to hold less cash. This finding also suggests that managers hold more cash in their balance sheets due to unavailability or higher cost of external financing, especially when they have higher future investment opportunities to support their high growth rate for their business activities (D’mello et al. (2008); Bigelli and Sánchez-vidal (2012).

Lower earnings quality fuels informational asymmetries between the firm and external investors, which can further increase the cost of external financing, which provides incentives for managers to hold more cash to avoid external funding (Myers & Majluf, 1984). Under this situation, underinvestment or asset exchange can also trigger firms to hold substantial cash reserves to avoid external financing, and managers can also reduce net debt and inherent financial risks of not paying dividends due to the agency costs between managers and shareholders (Easterbrook, 1984; Opler et al. (1999)). On the other hand, higher earnings quality minimizes information asymmetry between firms and investors entailing lower adverse selection and external financing costs (Mansali et al., 2019). In addition, better earnings quality downsizes agency problems, and produces a better informational environment that could mitigate the cost of monitoring excreted by stockholders, and thus improve project selection and minimize to hold precautionary cash that affects the financial flexibility of cash but produce the opportunity to raise cash from potential investors (García‐teruel & Martínez‐solano, 2008).

However, the consensus regarding earnings quality and cash holding is contextual, mixed, and fragmented. For, example, Farinha et al. (2018) investigated the earnings quality disclosure as a determinant of financial flexibility in the UK covering the period of 1998–2015 and reported that poor earnings quality and financial flexibility are positively associated. It indicates that poor earnings quality fuels information asymmetry and firms hold more cash (Dittmar et al., 2003; García‐teruel et al., 2009; Ozkan & Ozkan, 2004), and use them for financial flexibility. In addition, Sun et al. (2012) investigate US firms over 1980–2005 and evidence that poor earnings quality has a positive relation with cash level but a negative association with the value of the corporate cash holdings. Similarly, Dechow and Dichev (2002) and García‐teruel et al. (2009) provide evidence that a higher level of earnings quality minimizes the level of cash holding so does financial flexibility. On the other hand, Xiong et al. (2020) evidence that the firm’s internal information quality (IIQ) is negatively related to their cash holding. However, this opposite association is observed among complex firms (i.e., firms with more business segments), non-state-owned firms, and firms with poor corporate governance.

2.2.1. Earnings quality and financial flexibility

Previous studies show that cash holdings and information asymmetries are positively correlated (Dittmar & Mahrt-smith, 2007; Ozkan & Ozkan, 2004). Information asymmetries are a significant determinant of the firms’ cost of capital (Bhattacharya et al., 2003). Consistent with this view, studies suggest that the quality of accounting earnings also affects a company’s cost of financing (Easley & O’hara, 2004). Accounting earnings are composed of cash and accruals components. However, when earnings are produced with principal accruals, they will not be very persistent, resulting in poor information quality (lower quality) compared to when they are primarily based on cash flows (e.g., Farinha et al., 2018).

The literature on earnings quality and financial flexibility provides antagonistic results. Higher earnings quality provides higher financial flexibility by providing access to external funds (Shin et al., 2018). On the other hand, when firms generate and disclose lower quality earnings, they should accumulate substantial cash levels to become less dependent on external financing. The reason is when the earnings quality is poor, firms find it difficult to raise external funds and consequently involve in high-level costs. Thus, managers may have incentives to hold an optimal level of cash that may act as a defense strategy to deal with the working capital shortage in the future. Moreover, low-quality earnings may motivate managers to show a better and reliable financial position with a high level of cash reserves to resource providers to increase their confidence to diminish the firm’s cost of financing. This view is consistent with the pecking-order theory that suggests that due to the presence of information asymmetries, firms will exploit internally generated funds instead of external funds (Myers & Majluf, 1984).

However, under the view of positive accounting theory, in the presence of information asymmetry, managers focus on accounting policies to focus their benefits on performance bonuses, and or in personal contractual agreements with suppliers, and costs related to the regulation changes. Also, due to personal interests, managers might overuse cash to satisfy suppliers or third parties like government, environmental agencies, and make short-term risky investments to ensure profit maximization. In this situation, corporate managers may incline to provide less information in the reported earnings, which may challenge external financing and provide a negative relationship with financial flexibility. Thus, this study develops the following hypothesis:

Hypothesis H1: Firms with poor earnings quality have a negative impact on financial flexibility.

2.2.2. The moderating role of corporate governance

According to the Board (2010), the principal objective of financial reporting is to provide economic information to stakeholders so that they can make their investment decisions. The reported earnings in the financial statements are the primary source of information to investors, analysts, and managers. Importantly, the theoretical perspectives advocate that standard corporate governance mechanisms can alleviate information asymmetry and reduce the information risk (e.g., Latif et al., 2017), and may influence the relationship between earnings quality and financial flexibility.

Managers possess more internal information compared to outside shareholders due to the isolation of ownership and control, which leads to information asymmetry between insiders and outsiders. According to Watts and Zimmerman (1986), the theory of positive accounting infers that managers act for their best interests, which increases opportunistic behavior. A strong governance system can restrain managerial opportunistic behavior and diminish the risks of poor earnings quality and financial flexibility (Akileng, 2014). Also, corporate governance in emerging countries possesses weaker invertors protection but attempts to provide better earnings quality (Aldamen & Duncan, 2016; Gaio & Raposo, 2014), and may act as a moderator of earnings quality and financial flexibility.

Corporate governance may improve earnings quality and financial flexibility by enhancing the monitoring over the management (e.g., Karamanou & Vafeas, 2005). The board of directors is one of the governance mechanisms that can ensure effective monitoring and encourage management to disclose higher quality earnings to stakeholders (e.g., Karamanou & Vafeas, 2005). Also, the board of directors is engaged in the process of generating earnings, and they possess the power to maintain and disclose quality earnings (e.g., Alves, 2014). Alongside this, independent directors play a pivotal role in ensuring effective monitoring, which leads to minimizing managerial opportunistic behavior, thereby increasing earnings quality and financial flexibility (e.g., Alves, 2014; Beasley, 1996). Moreover, independent directors are not employees of the firm who don’t employ human and intellectual capital in the organization like executive directors do. But independent directors have interests and incentives to preserve their honor and reputation by ensuring higher quality financial reporting and sensible financial flexibility (e.g., Srinidhi et al., 2011).

Ownership concentration is also one of the governance mechanisms that influence firms to report higher quality earnings, which may consequently influence financial flexibility. For example, foreign investors’ ownership in firms of emerging countries is considered sophisticated investors. They possess better skills and knowledge to analyze financial statements and can play a better-monitoring role in reducing managers’ opportunistic behavior (e.g., Bao & Lewellyn, 2017; Vintilă et al., 2014). Thus, foreign ownership contributes to a higher quality of earnings and financial flexibility (e.g.,Vintilă et al., 2014). Hence, external ownership concentration plays a significant role in reporting earnings through monitoring the information (e.g., Ajinkya et al., 2005; Chung & Zhang, 2011) and may instigate financial flexibility.

The role of the audit committee as another component of corporate governance may improve the earnings quality and financial flexibility by recruiting a reputable auditor, such as Big4. Audit committee attempt to ensure high-quality audit reports, which entails an examination of the adequacy of internal controls, oversight of the internal audit activities, and appraising accounting disclosure and policy choices (e.g., Sulaiman et al., 2018), which may lead to the increasing interest of investors to invest in the firms. In addition, the board’s independence, along with audit quality may lead to better earnings quality and in turn financial flexibility (e.g., Srinidhi et al., 2011).

Based on the above arguments, the ownership concentration, in the form of external ownership concentration, including foreign ownership and institutional ownership, are mostly equipped with sophisticated skills and ability. They can increase monitoring of the firms’ activity, which reduces information asymmetry and may lead to higher earnings quality and better financial flexibility. Moreover, a reputable auditor may assist firms in providing quality earnings by examining the financial reports, especially when there is a higher demand for quality earnings. Consequently, it enhanced investors’ confidence. Under these situations, this study presumes that the corporate governance mechanisms can positively moderate the relationship between earnings quality and financial flexibility.

H2: Corporate governance (CG-INDEX) can positively moderate the relationship between poor earnings quality and financial flexibility.

3. Data and methodology

This study selects firm-level data from China Stock Market and Accounting Research Database (CSMAR). This study selects data from CSMAR as it is one of the widely recognized reputable databases for publicly-listed Chinese firms (Carney et al., 2018). The CSMAR database contains detailed data of Chinese listed and unlisted A, B, and H categories firms. However, considering the main aim of the study, we considered only A-Share Listed manufacturing companies’ data as they are identical in feature and reasonable to produce the best research outcome. Thus, the study undertakes A-Share firms listed in the Shanghai and Shenzhen Stock exchanges over the period 2007 to 2020. This study chooses 2007 as the starting year because the Chinese stock market has undergone radical reforms of board independence in 2001, the split share structure reforms in 2005, and corporate governance guidelines in 2006. In addition, the availability of the corporate governance data on the CSMAR database also restricted to begin the sample from the year 2007.

This study follows several steps to select the final sample for analysis. First, the financial institutions are excluded, such as banks, investment trusts, and insurance companies from the sample because they have different accounting rules and regulations. The data of mining, oil, and gas firms are also dropped because of their differences in operations. Therefore, the sample only includes non-financial listed firms operating in China. Second, firms that have no data available for our sample period are also excluded. Third, firm-year observations with missing data on main independent and control variables are eliminated. The final sample comprised of 2,034 firms yielding 14,088 firm-year observations. We also reduce the effect of potential outliers by winsorizing all continuous variables at a 1% level at both tails.

3.1. Definition and measurement of variables

3.1.1. Dependent variable: Financial flexibility

Although the literature is widespread, there is still no single measure, which can fully explain the term financial flexibility, and thus, it is a greater challenge to decide how to measure financial flexibility. Prior studies advocate that financial flexibility can be obtained through cash holdings (e.g., Almeida & Campello, 2007; T. Chen et al., 2017; R. R. Chen et al., 2020; zhou & Li, 2018), line of credit with banks (Sufi, 2007), and commercial paper (Kahl et al., 2008). Some of the academic literature also defines financial flexibility as “untapped borrowing power,” which suggests that low leverage can increase untapped borrowing power (e.g.,Denis and Sibikov; Marchica & Mura, 2010). Considering the aforementioned discussion, for measuring the accurate financial flexibility of a firm, one has to consider the best proxy from diverse sources used by firms to avail financial flexibility.

Among the recent studies, Nadarajah et al. (2018), zhou and Li (2018), and Arslan-ayaydin et al. (2014) argue that financial flexibility is a proxy of the firm’s cash holdings (liquidity) and leverage. They suggest that firms with high cash holding and low leverage are more flexible as such types of firms have exceptional ability to raise external funds. Conversely, firms with high leverage and lower cash positions indicate that firms are less flexible. Consistent with this approach, Ma and Y (2016) measure financial flexibility by considering the firm’s liquidity, leverage, and internal funds. Specifically, following Harford et al. (2008) and Farinha et al. (2018), this study uses cash holding as a proxy of financial flexibility, because cash is the most liquid asset and can best represent financial flexibility.

3.1.2. Independent variables

Earnings quality (EQ)

There is no universal definition of earnings quality because it entails different perspectives to diverse financial statement users. For example, from the viewpoint of the auditor, fraudulent reporting is an outcome of earnings quality problems because of unusual items and lack of transparency in the reported earnings, even if it satisfies all of the formalities of Generally Accepted Accounting Principles (GAAP; e.g.,Dechow & Schrand, 2004). On the contrary, the regulators view high-quality earnings when it satisfies the rules of GAAP. Similarly, creditors might view earnings as high-quality when they are effortlessly transformable into cash flows. At the same time, the compensation committee can term high-quality earnings when it reflects the executive’s real performance and is less likely to be influenced by factors other than managerial control (e.g.,Dechow & Schrand, 2004). However, this study defines earnings quality in similarity with SFAC¹ as those earnings where more information is provided about “the features of a firm’s financial performance that are relevant to a specific decision made by a specific decision-maker” (p. 344).

This study used the industry cross-sectional approach primarily suggested by Dechow and Dichev (2002) extended by J. Francis et al. (2005) and Farinha et al. (2018) to measure earnings quality. The proposed model illustrates that accruals help to match past, present, and future cash flows, and the standard deviation of the estimated residuals is a scale of the earnings quality of the firm. A higher value of the measures is an indicator of lower earnings quality, whereas a lower value indicates higher-earning quality. This study used the regression model for estimation of earnings quality followed by Farinha et al. (2018) in the UK, and Raman et al. (2013) in the US presented in Model (a). This study calculates the absolute value of the standard deviation of the error term for the specific firms and specific time following the method proposed by Dechow and Dichev (2002). The higher the standard deviation, the greater uncertainty of information presented by earnings, entailing lower earnings quality for the specific company “a higher value of the measure used below is an indicator of lower earnings quality, whereas a lower value indicates higher-earning quality”. Therefore, according to our hypothesis, we expect that poor earnings quality can reduce financial flexibility. The earnings quality is measured using the accruals quality as follows:

(a) $\mathit{\text{Earnings}}\mathit{\text{Quality}}\left(\mathit{\text{EQ}}\right)=\mathit{\text{AC}}{C}_{\mathit{\text{it}}}={\beta }_0+{\beta }_1\mathit{\text{CFLO}}{W}_{\mathit{\text{it}}-1}+{\beta }_2\mathit{\text{CFLO}}{W}_{\mathit{\text{it}}}+{\beta }_3\mathit{\text{CLFO}}{W}_{t+1}+{\beta }_4\mathrm{\Delta }\mathit{\text{RE}}{V}_{\mathit{\text{it}}}+{\beta }_5\mathit{\text{PP}}{E}_{\mathit{\text{it}}}+\mu +\delta + {\epsilon }_{\mathit{\text{it}}}$(a)

where EQ is the Earnings Quality of the firm i^th in year t. ACC_i,t is the total accruals of the company i^th in year t. EQ is the difference between current assets changes in two consecutive years and cash & cash equivalents, current liabilities, and depreciation & amortization (CA—Cash & Cash Equivalents—CL—Dep & Amortization; e.g.,Botsari & Meeks, 2008). CFLOW_i,t is the cash flow from the operation of the firm i^th in year t. It is measured as net operating income (before extraordinary items) subtracted by total accruals for firms i^th in year t. ΔREV_i,t is the total revenue of the i^th firms in the year t^th changes. PPE denotes the total property, plant, and equipment of firm i^th in year t.

Cash flow (CFLOW)

Cash flow is the best indicator of a firm’s financial health, and investors and analysts often use it for evaluating the company’s performance. Firms are likely to pile up a substantial amount of cash when they have high cash flows to prevent future shocks or earnings fluctuations, as well as liquidity deficits. Moreover, in the presence of asymmetric information, the firm’s cost of financing increases, and the firms are likely to prefer internal resources financing under such situations (Myers & Majluf, 1984). Overall, cash flow is an important indicator of a firm’s financial health and indicates a direct link with financial flexibility. This study measures cash flow as net operating income plus depreciation and amortization divided by total assets.

Research and development expenditure (R&D)

According to Mikkelson and Partch (2003), companies with high cash levels have more significant investment expenditure, especially R&D expenditure. The increase in R&D expenditures indicates more information asymmetry leading to additional cost of external financing (Farinha et al., 2018). The increments of the cash to assets ratio can be explained by the increase in R&D expenditure (He & Wintoki, 2016). Thus, we expect that firms with more considerable cash holdings will have higher R&D expenditures. We measure R&D expenses as total R&D expenditure divided by total assets.

3.1.3. Control variables

Book to market (MTB)

Book to market ratio (MTB) also indicates the growth opportunities of the firm, and it is often associated with higher agency costs due to shareholders–debt holders conflicts (Kim & Sorensen, 1986). It implies that external financing becomes more expensive in the presence of agency conflicts. Besides, in the presence of higher cash holdings, firms might avail higher growth opportunities (Ozkan & Ozkan, 2004).

MTB ratio is considered as another control variable to examine the relationship between corporate governance, earnings quality, and financial flexibility since financial flexibility allows managers to fund potential future investments. MTB is related to the proxies of the firms’ growth. Following Arslan-ayaydin et al. (2014), this study calculated the MTB as the book value of the total assets minus the book value of equity + market value of equity and then divided by total assets.

Financial constraints (FCONST)

Multi-index is considered as a combination of several indexes to calculate the level of financial strengths (Farre-Mensa & Ljungqvist, 2016). The measurement of financial strengths, also referred to as Altman’s Z-Score (Z = 1.2X₁ + 1.4X₂ + 3.3X₃ + 0.6X₄), is the most widely used measure of financial constraints (Halteh et al., 2018b; Mittoo & Bancel, 2011). The Z–Score is the combination of cash ratios, earnings before interest and taxes (EBIT) ratios, retained earnings ratio, the book to market ratio, and sales ratio.

If a firm has a Z-Score value of more than 2.70, it is considered a more financially flexible firm. On the other hand, a Z-Score value below 1.81 is considered that the firm is financially inflexible and has a high risk of bankruptcy. However, if Z-Score values are between 1.81 and 2.70, it is regarded as an uncertain zone, which indicates safe for bankruptcy for the next two years (e.g.,Halteh et al., 2018a). We consider Z-Score as a control variable, as suggested by prior studies (e.g.,Ma and Y (2016); Arslan-ayaydin et al., 2014). This study computed FCONST (Z-Score) = 1.2*(cash & cash equivalent minus trade-payables)/total assets +1.4*(retained earnings/total assets)+3.3*EBIT/TA+0.999* sales/total assets (Altman et al., 2014).

Firm size (FSIZE)

This study considers firm size (FSIZE) as a control variable as large firms have greater access to external funds than small firms and are more financially flexible (e.g., Almeida et al., 2014; Faulkender & Wang, 2006). Further, large firms tend to be less risky, which helps them to have the ability to take more debt (e.g., Frank & Goyal, 2003). Additionally, if the firms are “too-big-to-fail,” then they can avail more funds with the help of government guarantees (e.g., Jokipii & Milne, 2008). This study measured FSIZE as the natural logarithm of net assets (e.g., Ang & Smedema, 2011) to examine the relationship between corporate governance and financial flexibility.

Further, to examine the relationship between earnings quality and financial flexibility, this study controls for FSIZE, and measures it as the natural logarithm of the sales revenue of the firm. Size plays a significant role in raising external funds. It is because a larger size lowers the borrowing costs as it is less likely to fail and is more diversified, entailing easier access to external financing. For these reasons, as well as with the results reported by Opler et al. (1999), this study expects an antagonistic relationship between firm size and financial flexibility.

Assets tangibility (TANG)

Assets tangibility is an important factor that can influence the firm’s financing and investment decisions (Almeida & Campello, 2007). A constraint firm’s flexibility is sensitive to its asset’s tangibility compared to an unconstrained firm. Marchica and Mura (2010) argue that asset tangibility can influence a firm’s flexibility. Tangibility can reduce information asymmetry due to the fact that a firm can pay-off off its tangible assets in case of bankruptcy (Chung & Zhang, 2011). Following the studies of Nadarajah et al. (2018), Denis and Mckeon (2016), and Marchica and Mura (2010), we measure asset tangibility (TANG) as the ratio of net property, plant, and equipment to total assets.

Short term debts (SDEBT)

Short-term debt (SDEBT) is a financial obligation that is in less than one year. This study measures SDEBT by summing all short-term financial obligations, and the portion of long-term obligation payable in the current year scaled by total assets (Farinha et al., 2018). According to Hall et al. (2014), a firm’s cash retention and exploiting debt is not linear, which indicates that to a specific limit of debt financing, firms are inclined to minimize cash reserves. Still, when the leverage ratio becomes substantially higher, firms become aware and start to hoard cash to avoid any unexpected events like bankruptcy. This study expects that short-term debt should have a nonlinear relationship with the preservation of cash as LDEBT is supplied mostly with collateral (Bartholdy et al., 2012).

Long term debts (LDEBT)

Long-term debts are the firm’s financial obligations that are due to be paid in more than one year. This study constructs the long-term debts by summing up all long-term financial obligations and divided by the firm’s total assets. According to Hall et al. (2014), LDEBT and cash holdings are negatively associated, because a high level of LDEBT reduces the cash holdings owing to the burden of the increased cost of funds to employ in liquidity. However, a higher long-term debt enhances the fixed financing costs and increases the possibility of bankruptcy. To avoid bankruptcy, firms may increase their cash holding.

Dividends (DIV)

This study measures dividend payouts using a dummy variable, which is set to one (1) if the firm pays dividends, otherwise zero (0). The outcome of this variable on the effect of cash holding is considered ambiguous. The reason behind this is when firms can pay dividends they hold less cash because they need to pay dividends. However, dividend-paying firms hold a good position in the stock market, and their stock prices less often fluctuate. But, failing to pay or cut dividends may adversely affect share prices. Thus, to maintain a good market position and higher stock prices, firms may hold more cash to pay continuous dividends.

3.1.4. Moderating variable: Corporate governance

In this study, we aim to examine the moderating effect of corporate governance on the association between poor earnings quality and financial flexibility. As already mentioned previously, we have constructed CG-INDEX using PCA analysis. For constructing CG-INDEX, we have selected seven different governance attributes, which can best represent corporate governance mechanisms. These variables include ownership structure such as internal ownership concentration (OCI) and external ownership concentration (OCE); board structure such as board size (BSIZE) and board independence (BIND); board leadership structure including CEO-Duality, CEO & executive compensations, and the relationship between top ten shareholders relationships (SR) i.e., whether top ten shareholders are related with each other (e.g., Aldamen & Duncan, 2016; Aldamen et al., 2012; Latif et al., 2017; Shahwan, 2015; Srinidhi et al., 2011). Table 1 provides Variables details for earnings quality and financial flexibility.

Table 1. Variables details for earnings quality and financial flexibility nexus

Variables	Acronym	Definitions	Predicted Sign
Dependent variable used as a proxy for financial flexibility
Financial Flexibility	FF	Cash and cash equivalents are divided by total assets (Farinha et al., 2018).	?
Independent variables
Earnings quality	EQ	Poor earnings quality measures following literature (Raman et al., 2013; Farinha et al., 2018; Dechow & Dichev, 2002).	?
Cash flow	CFLOW	Net operating income plus depreciation and amortization scaled by total assets.	+
Firm size	FSIZE	Natural logarithm of total sales revenue.	-
Liquidity	Liquidity	Receivable + inventory-accounts payable.	-
R&D expenditure	R&D	R&D expenditure divided by total assets.	+
Control variables
Market to book ratio	MTB	It is calculated as (book value of the total assets-book value of equity + market value of equity) scaled by total assets.	+
Financial constraints	FCONST	Z-Score = 1.2 * (cash & cash equivalent—trade payables)/total assets + 1.4 * (retained earnings/total assets) + 3.3 * EBIT/TA + 0.999 * sales/total assets (Altman et al., 2014).	+
Dividends	DIV	If paid 1 otherwise 0.	±
Short term debt	SDEBT	Total short term debt divided by total assets	±
Long term debt	LDEBT	Total long term debt divided by total assets	?
Moderating variables
Corporate governance	CG-INDEX	It is measured by Principal Component Analysis (PCA; e.g.,Aldamen & Duncan, 2016).	+

3.2. Endogeneity and reverse causality

According to Roberts and Whited (2013), endogeneity is a critical issue in finance research, which creates significant obstacles in empirical studies. Similarly, Brown et al. (2011) argue that endogeneity bias is a major concern and severe issues can occur when examining the relationship of corporate governance to other related issues in finance and accounting. Moreover, endogeneity bias confers that one or more explanatory variables are missing from the econometric model, which creates biased and inconsistent estimates for all variables (Roberts & Whited, 2013). And thus, it is essential to address the endogeneity issues when examining the relationship between corporate governance, earnings quality, and financial flexibility. To this point, Antonakis et al. (2014) suggest that fixed effect estimation is a reliable technique to control unobservable heterogeneity (missing variables). However, OLS and fixed effects estimation techniques may sometimes lead to inconsistent and biased findings but partly address the endogeneity problems (Wooldridge, 2010).

Therefore, two-stage least squares (2SLS) regressions are commonly used in the literature to solve the problem. The condition of using 2SLS is to identify the exogenous variables in the first stage that are not related to the second-stage dependent variable. Notably, the extant literature in the domain of earnings quality, corporate governance, and financial flexibility, and firm value doesn’t comprehensively provide evidence in support of 2sls, because of difficulties replying on instrumental variables. Considering the aforementioned issues related to endogeneity in corporate governance research, an alternative approach of adding lag variables is widely used to control endogeneity in finance (Harford et al., 2008; Marchica & Mura, 2010; Sun et al., 2012).

To address endogeneity issues, the System-GMM estimation technique would be the best approach in terms of consistency by including lagged values of the dependent variable (liquidity; Blundell & Bond, 1998). But, how many lag values should be included in the model to solve the endogeneity problem is an important question. The answer is provided by Wintoki et al. (2012), who suggest that a one-year lag is enough to capture the dynamic endogeneity issues of the variables. Similarly, reverse causality issues can be eliminated by using lag variables (Leszczensky & Wolbring, 2018). Therefore, we employ a year lag value of financial flexibility (FF) variable to address the endogeneity and reverse causality problems in the relationship between poor earnings quality and financial flexibility as well as the moderation effect of corporate governance on the earnings quality and financial flexibility relationships. Following Duru et al. (2016) and Wintoki et al. (2012), we address the endogeneity and reverse causality applying the following equation:

(b) $Y_{\mathit{\text{it}}}= {\beta }_1{Y}_{(\mathit{\text{it}}-1)}+ {\beta }_2{X}_{\mathit{\text{it}}}+ \mu + \delta + {\epsilon }_{\mathit{\text{it}}}$(b)

3.3. Econometric models

Empirical models for earnings quality and financial flexibility

This section describes the econometric models (System GMM) used to investigate the relationship between earnings quality and financial flexibility. Further, this section uses liquidity as the dependent variable representing financial flexibility and three different independent variables and six control variables, and one lag variable along with time and industry fixed effects to test the hypotheses. In addition, these models have used liquidity as the dependent variable and employing an alternative method of fixed effect OLS to do robustness checks by using unused debt capacity as dependent variables representing financial flexibility in additional analysis to verify the results.

Model-1

(1) $F{F}_{\mathit{\text{it}}}={\beta }_0+{\sum }_{k=0}^n+{\beta }_{i,k{Y}_{i,k,t}}+\mu +\delta +{\epsilon }_{\mathit{\text{it}}}$(1)

Where, FF_i,t denotes financial flexibility which is the vector of the dependent (endogenous) variable of the i^th firm’s cash ratio over the t^th period. Y_i,k,t is the matrix of n^th firms independent (explanatory) variables that include the earning quality proxy, β₀ is the intercept, β_i,k is the matrices of coefficients. µ is industry effects,$\delta$ is Year effects, and ɛ_i,t is a vector of error terms.

Model-2

(2) $F{F}_{\mathit{\text{it}}}={\beta }_0+{\beta }_{1}E{Q}_{\mathit{\text{it}}}+{\beta }_{2}F{F}_{\mathit{\text{it}}-1}+{\beta }_3\mathit{\text{CFLO}}{W}_{\mathit{\text{it}}}+{\beta }_{4}R\mathrm{\&}{D}_{\mathit{\text{it}}}+{\beta }_5\mathit{\text{FSIZ}}{E}_{\mathit{\text{it}}}+{\beta }_6\mathit{\text{MT}}{B}_{\mathit{\text{it}}}+{\beta }_7\mathit{\text{FCONS}}{T}_{\mathit{\text{it}}}+{\beta }_8\mathit{\text{SDEB}}{T}_{\mathit{\text{it}}}+{\beta }_9\mathit{\text{LDEB}}{T}_{\mathit{\text{it}}}+{\beta }_{10}\mathit{\text{DI}}{V}_{\mathit{\text{it}}}+\mu +\delta + {\epsilon }_{\mathit{\text{it}}}$(2)

Moderating effect of corporate governance

This section describes the econometric models (System GMM) used to investigate the moderating effect of corporate governance on the relationship between earnings quality and financial flexibility. Further, in analyses, liquidity is used as the dependent variable representing financial flexibility and three different independent variables and six control variables, and one lag variable along with time and industry fixed effects to test the hypotheses.

Model-3

(3) $\begin{array}{rl}& F{F}_{\mathit{\text{it}}}={\beta }_0+{\beta }_{1}E{Q}_{\mathit{\text{it}}}\ast \mathit{\text{CG}}-\mathit{\text{INDE}}{X}_{\mathit{\text{it}}}+{\beta }_{2}E{Q}_{\mathit{\text{it}}}+{\beta }_3\mathit{\text{CG}}-\mathit{\text{INDE}}{X}_{\mathit{\text{it}}}+{\beta }_{4}F{F}_{\mathit{\text{it}}-1}+\\ & {\beta }_5\mathit{\text{CFLO}}{W}_{\mathit{\text{it}}}+{\beta }_{6}R\mathrm{\&}{D}_{\mathit{\text{it}}}+{\beta }_7\mathit{\text{FSIZ}}{E}_{\mathit{\text{it}}}+{\beta }_8\mathit{\text{MT}}{B}_{\mathit{\text{it}}}+{\beta }_9\mathit{\text{FCONS}}{T}_{\mathit{\text{it}}}+{\beta }_{10}\mathit{\text{SDEB}}{T}_{\mathit{\text{it}}}+\\ & {\beta }_{11}\mathit{\text{LDEB}}{T}_{\mathit{\text{it}}}+{\beta }_{12}\mathit{\text{DI}}{V}_{\mathit{\text{it}}}+\mu +\delta + {\epsilon }_{\mathit{\text{it}}}\end{array}$(3)

Where, Financial Flexibility (FF_i,t) is the dependent (endogenous) variable of the i^th firm’s cash ratio over t^th period. CG-INDEX_i,t is the corporate governance index of the firm i^th over the t^th period. CFLOW is the cash flow of i^th firm over t^th period. R&D_i,t is the research and development expenditure of i^th firm over t^th period. FSIZE_i,t is the firm size of i^th firm over t^th period. MTB_i,t is the book to market ratio of the i^th firm over t^th period. FCONST_i,t is the financial constraints of the i^th firm over t^th period. SDEBT_i,t is the short-term debt ratio of the i^th firm over t^th period. LDEBT_i,t is the long-term debt ratio of the i^th firm over t^th period. FF_t-1 is the one-period lag value of financial flexibility. DIV is the dividend payout of the i^th firm over t^th period. µ denotes industry effects,$\delta$ is the year effects, and ɛ_it is the error terms.

4. Results and discussion

4.1. Descriptive statistics

Table 2 shows summary statistics of all variables used in analyses. The statistics show that the financial flexibility (FF) on average is approximately 19% of the total assets (TA) of Chinese firms. The mean value of earnings quality (EQ) is 0.16 and the standard deviation is 0.11, indicating the poor earnings quality (e.g., Sun et al. (2012). These findings are similar to the prior studies conducted by Deng et al. (2017) who reported mean EQ 0.13 and standard deviation 0.15 in China. The mean value of PL is 0.85%, indicating the profitability of the Chinese firms during the sample period. The mean value of firm size (FSIZE) is 9.45.

Table 2. Summary statistics

Variables	N	Mean	Std. Dev.	Min	Max
FF	13,756	0.190	0.140	0.000	1.000
EQ	13,756	0.160	0.114	0.001	0.823
PL	13,757	0.850	0.360	0.000	1.000
FSIZE (ln)	13,756	9.450	0.530	7.030	11.860
SDEBT	13,756	0.600	4.290	0.000	32.660
LDEBT	13,756	0.330	5.450	−0.040	49.290
R&D	13,756	0.230	4.510	−1.000	28.970
CFLOW	13,756	0.050	0.100	−5.060	1.230
MTB	12,692	0.790	0.910	0.010	21.190
FCONST	13,756	0.930	3.860	−20.130	8.640
CG-INDEX	10,428	0.000	1.000	−0.470	16.510
DIV	10,269	0.510	0.500	0.000	1.000

Notes: Liquidity (FF) is a proxy of financial flexibility. PL is the firm’s profitability status. FSIZE is the log value of firm size. EQ is the earnings quality. SDEBT represents the short-term debts, whereas LDEBT is the long-term debts. R&D exp. is the research and development expenditure. CFLOW is the cash flows of the firm. MTB is the market to book ratio. FCOSNT is a financial constraint. CG-Index is the corporate governance index. DIV is the dividend payment.

Further, the statistics indicate that firms keep on average 60% of the total assets as short-term debt (SDEBT), whereas 33% of the total assets as long-term debt (LDEBT). The mean value of R&D is 0.23, indicating the intensity of research and development expenditure during the sample period. The mean value of the MTB ratio 0.79%, indicating the growth opportunities. The mean value of FCONST is 0.93, indicating the financial health of the Chinese firms, which suggests a relatively lower Z-Score value, as a sign of non-flexibility. The mean value of DIV is 0.51, suggesting the dividend payment in Chinese companies.

4.2. Multicollinearity analysis

This study follows the following steps to check the multicollinearity issues. Firstly, by adopting Parson’s pairwise correlation matrix, and secondly, by conducting VIF tests.

4.2.1. Pearson’s pairwise correlation matrix

Table 3 represents the results of the Pearson correlation matrix between variables. We document that the highest correlation is between SDEBT and LDEBT, which is 0.876. However, this value seems to have no issues because both of the variables are strongly related to each other. Further, according to prior studies (Bryman & Cramer, 1997; Hasan et al., 2014), the simple correlation between independent variables is not an issue if the value is less than 0.9. Thus, the correlation matrix advocates that multicollinearity is not a problem in our analysis.

Table 3. Pearson’s pairwise correlation matrix

	Variables	1	2	3	4	5	6	7	8	9	10	11	12
1	FF	1.000
2	EQ	−0.128*	1.000
3	CG-INDEX	0.118*	0.043*	1.000
4	R&D	−0.015	−0.008	−0.007	1.000
5	CFLOW	0.232*	0.078*	0.040*	−0.063*	1.000
6	FSIZE	−0.189*	−0.151*	−0.094*	−0.103*	0.043*	1.000
7	SDEBT	0.004	0.001	0.068*	0.076*	0.087*	−0.163*	1.000
8	LDEBT	−0.007	−0.007	0.064*	0.072*	0.111*	−0.100*	0.876*	1.000
9	DIVP	0.006	−0.019	0.033*	0.003	0.027*	0.057*	0.023*	0.026*	1.000
10	MTB	−0.202*	−0.124*	−0.087*	−0.032*	−0.176*	0.468*	−0.083*	−0.066*	0.024*	1.000
11	FCONST	0.103*	0.074*	0.010	−0.282*	0.359*	0.167*	−0.537*	−0.572*	0.011	−0.009	1.000
12	PL	0.193*	0.105*	0.047*	−0.046*	0.490*	0.064*	−0.044*	−0.036*	0.010	−0.168*	0.189*	1.000

Note: The above table shows the PW correlations between dependent, independent, and control variables. Liquidity, internal funds (IF), and unused debt capacity (UDC) are the proxy of financial flexibilities is the earnings quality, CG-INDEX is the corporate governance index, R&D. is the research and development expenditure, CFLOW is the cash flow, FSIZE is the firm size SDEBT is the short term debts, LDEBT is the long term debts, DIVP is the dividend payout, MTB is the book to market ratio, FCONST is the financial constraint (Z-Score), and PL is the firms’ profitability.* represents the significance at the 0.05 level.

4.2.2. Variance inflation factor

Table 4 provides Variance Inflation Factor Tests (VIF) results. The table shows that the highest value is of VIF is 2.440, which is far less than 5 (as a rule of thumb suggested by Gujarati and Porter (2003)). These results confirm that there are no multicollinearity issues in data sets, hence the regression results will be not affected by any multicollinearity problem.

Table 4. Results of variance inflation factor

Variables	VIF	1/VIF
SDEBT	2.440	0.411
LDEBT	2.300	0.434
CFLOW	1.920	0.522
FCONST	1.770	0.565
FSIZE (ln)	1.450	0.688
MTB	1.370	0.732
PL	1.330	0.754
R&D	1.140	0.878
Lag Liquidity	1.130	0.883
EQ	1.060	0.943
DIV	1.020	0.983
Mean VIF	1.540

4.3. Empirical results

4.3.1. Earning quality and financial flexibility

This study investigates the influence of earnings quality on the level of corporate financial flexibility, and the results are reported in Table 5. We also reported in the descriptive analysis that the average earnings quality (residuals of accruals quality) was 0.16, indicating poor earnings quality. The estimation results are obtained from Equation (2)(2) $F{F}_{\mathit{\text{it}}}={\beta }_0+{\beta }_{1}E{Q}_{\mathit{\text{it}}}+{\beta }_{2}F{F}_{\mathit{\text{it}}-1}+{\beta }_3\mathit{\text{CFLO}}{W}_{\mathit{\text{it}}}+{\beta }_{4}R\mathrm{\&}{D}_{\mathit{\text{it}}}+{\beta }_5\mathit{\text{FSIZ}}{E}_{\mathit{\text{it}}}+{\beta }_6\mathit{\text{MT}}{B}_{\mathit{\text{it}}}+{\beta }_7\mathit{\text{FCONS}}{T}_{\mathit{\text{it}}}+{\beta }_8\mathit{\text{SDEB}}{T}_{\mathit{\text{it}}}+{\beta }_9\mathit{\text{LDEB}}{T}_{\mathit{\text{it}}}+{\beta }_{10}\mathit{\text{DI}}{V}_{\mathit{\text{it}}}+\mu +\delta + {\epsilon }_{\mathit{\text{it}}}$(2) discussed in the Data and Methodology (Section 3), and the results are presented in Table 5 show that the coefficient of EQ is −0.228 and is highly significant. The result indicates that poor earnings quality (EQ) has a significant negative effect on the level of corporate financial flexibility (β = −0.228, p-value <0.001). The negative coefficient implies that a 1 unit increase in poor earnings quality will reduce financial flexibility by approximately 0.228 units. The negative coefficient also explains that the decrease in residuals of earnings quality increases the level of corporate financial flexibility. The results contribute to the findings of prior studies, such as Farinha et al. (2018) for UK firms and Sun et al. (2012) for US firms. This finding also supports Hypothesis H1, that poor earnings quality has a significant negative relationship with financial flexibility.

Table 5. Relationship between earnings quality and financial flexibility

Variables	Dependent Variable = Financial flexibility
	(1)
EQ	−0.228***
	(−19.98)
FFt-1	0.439***
	(49.42)
CFLOW	0.173***
	(11.51)
R&D	−0.001
	(−1.31)
FSIZE	−0.058***
	(−17.57)
MTB	−0.007***
	(−4.36)
FCONST	0.001**
	(2.60)
SDEBT	−0.001
	(−0.07)
LDEBT	0.002
	(1.39)
DIV	−0.004
	(−1.41)
Year fixed effect	Yes
Industry fixed effect	Yes
Constant	0.699***
	(22.23)
Adjusted R^2	0.419
N	8935

Notes: Financial flexibility (FF) is measured using liquidity, computed as cash and cash equivalent divided by total assets. This study constructs Earnings Quality (EQ)by considering accounting accruals that match past, present, and future cash flows and the standard deviation of the estimated residuals is a scale of earnings quality of the firms. Numbers in parenthesis are t-statistics.

* p < 0.05, ** p < 0.01, and *** p < 0.01

For other variables, the coefficient of CFLOW is 0.173 and is highly significant. The result indicates that cash flow (CFLOW) has a significant positive relationship with the level of corporate financial flexibility (β = 0.173, p-value <0.001). The coefficient of CFLOW indicates that a 1 unit increase in the cash flow will increase FF by about 0.173 units. However, the results suggest that R&D expenditure has no significant effect on the level of corporate financial flexibility.

Table 5 also reports the results of other control variables included in the regression model. It shows that the coefficient of firm size (FSIZE) is −0.058 and is highly significant, which indicates that FSIZE negatively influences corporate financial flexibility. The coefficient of the book to market ratio (MTB) is—0.007 and is highly significant. These findings imply that the financial flexibility (Liquidity) is lower for large firms and also for firms having a higher book-to-market ratio. Further, the coefficient of financial constraints (FCONST) represented by the Z-Score value is 0.001 and is significant. These results suggest that firms with higher Z-Score values are more financially flexible. However, we find no significant effects of short-term debts (SDEBT), long-term debts (LDEBT), and dividends (DIV) on the level of financial flexibility (FF).

4.3.2. Moderating role of corporate governance

This section investigates the moderating role of corporate governance (CG-INDEX) on the relationship between earnings quality (EQ) and the level of corporate financial flexibility (FF) for all sample firms. To find whether the earnings quality increases or decreases the level of corporate financial flexibility by the moderation of corporate governance (CG-INDEX), we employ fixed effect regression and apply the System-GMM. Since we are using the System-GMM model, it is necessary to use instrumental variables. Therefore, we use lag-FF as an instrumental variable and assume that it can be a more suitable variable to control for endogeneity issues.

The results are estimated using Equation (3)(3) $\begin{array}{rl}& F{F}_{\mathit{\text{it}}}={\beta }_0+{\beta }_{1}E{Q}_{\mathit{\text{it}}}\ast \mathit{\text{CG}}-\mathit{\text{INDE}}{X}_{\mathit{\text{it}}}+{\beta }_{2}E{Q}_{\mathit{\text{it}}}+{\beta }_3\mathit{\text{CG}}-\mathit{\text{INDE}}{X}_{\mathit{\text{it}}}+{\beta }_{4}F{F}_{\mathit{\text{it}}-1}+\\ & {\beta }_5\mathit{\text{CFLO}}{W}_{\mathit{\text{it}}}+{\beta }_{6}R\mathrm{\&}{D}_{\mathit{\text{it}}}+{\beta }_7\mathit{\text{FSIZ}}{E}_{\mathit{\text{it}}}+{\beta }_8\mathit{\text{MT}}{B}_{\mathit{\text{it}}}+{\beta }_9\mathit{\text{FCONS}}{T}_{\mathit{\text{it}}}+{\beta }_{10}\mathit{\text{SDEB}}{T}_{\mathit{\text{it}}}+\\ & {\beta }_{11}\mathit{\text{LDEB}}{T}_{\mathit{\text{it}}}+{\beta }_{12}\mathit{\text{DI}}{V}_{\mathit{\text{it}}}+\mu +\delta + {\epsilon }_{\mathit{\text{it}}}\end{array}$(3) presented in the Data and Methodology section. The System-GMM estimation results are reported in Table 6. The fixed effect estimation results show that the coefficient of EQ is −0.215 and is highly significant. The results indicate that poor earnings quality (EQ) reduces the level of financial flexibility (β = −0.215, p < 0.001) for all sample firms. The results further explain that with a 1 unit increase in poor earnings quality, the firm’s level of financial flexibility will reduce by 0.215 units. The results also indicate that a decrease in residuals of earnings quality improves the level of corporate financial flexibility. In addition, the coefficient of corporate governance is 0.018 and is highly significant, indicating that a 1 unit increase in corporate governance can enhance financial flexibility by approximately 0.018 units.

Table 6. Corporate governance role on earnings quality-financial flexibility

Variables	Dependent variable = Financial flexibility
	(1)
EQ	−0.215***
	(−17.43)
CG-INDEX	0.018***
	(7.14)
EQ*CG-INDEX	0.059***
	(5.07)
FF t-1	0.463***
	(47.18)
CFLOW	0.110***
	(5.72)
R&D	0.004*
	(2.16)
FSIZE	−0.051***
	(−13.97)
MTB	−0.007***
	(−3.46)
FCONST	0.006***
	(5.26)
SDEBT	−0.001
	(−0.13)
LDEBT	0.002
	(1.52)
DIV	−0.002
	(−0.70)
Industry fixed effects	Yes
Year fixed effects	Yes
Constant	0.624***
	(17.86)
Adjusted R^2	0.424
N	7142

Following Farinha et al. (2018), Financial Flexibility (FF) is measured based on cash holding, which is cash and cash equivalent divided by total assets. Following Dechow and Dichev (2002), this study constructs Earnings Quality(EQ) considering accounting accruals that match past, present, and future cash flows and the standard deviation of the estimated residuals is a scale of earnings quality of the firms. Corporate Governance Index (CG-INDEX) is prepared using Principal Component Analysis (PCA) suggested by Aldamen and Duncan (2016). Numbers in parenthesis are t-statistics.

* p < 0.05, ** p < 0.01, and *** p < 0.001

Further, the coefficient on the interaction term (EQ*CG-INDEX) is 0.059² and is highly significant, which implies that corporate governance significantly moderates the relationship between earnings quality and financial flexibility. These results indicate that with a 1 unit increase of EQ*CG-INDEX, the level of corporate financial flexibility enhances approximately 0.059 units. Thus, Hypothesis H2 is supported, that is, corporate governance (CG-INDEX) can positively significantly moderate the relationship between earnings quality and financial flexibility.

This finding supports the monitoring role of corporate governance, which suggests that a useful board can enhance the effect of earnings quality on financial flexibility (e.g.,Karamanou & Vafeas, 2005). These results provide evidence of the effective board characteristics in the Chinese firms, such as the independent directors, who play an important role in corporate decisions, by disseminating positive information to the general public (e.g.,Habib & Jiang, 2015). Thus, such types of boards reduce information asymmetry as well as managerial opportunistic behavior (e.g.,Alves, 2014) and help firms to be more financially flexible. Besides, diverse ownership concentration can also help firms to reduce information asymmetry by alleviating managerial opportunistic behavior (e.g.,Bao & Lewellyn, 2017; Vintilă et al., 2014).

For other variables, the coefficient of cash flow (CFLOW) is 0.110 and is highly significant. The results indicate that CFLOW has a significant positive relationship with the level of corporate financial flexibility (β = 0.110, p-value <0.001). The coefficient of CFLOW indicates that a 1 unit increase in the cash flow will increase FF by about 0.110 units. The coefficient R&D expenditure is 0.004 and is significant at 5%.

Table 6 also reports the results of other control variables included in the regression model. It shows that the coefficient of firm size (FSIZE) is −0.051 and is highly significant. The coefficient of the book to market ratio (MTB) is—0.007 and is highly significant. These findings imply that the financial flexibility is lower for large firms and also for firms having a higher book-to-market ratio. Further, the coefficient of financial constraints (FCONST) represented by the Z-Score value is 0.006 and is significant. These results suggest that firms with higher Z-Score values are more financially flexible. However, we find no significant effects of short-term debts (SDEBT), long-term debts (LDEBT), and dividends (DIV) on the level of corporate financial flexibility (FF).

4.4. Discussion of the results

4.4.1. Earnings quality and financial flexibility

Our findings contribute to the existing literature by empirically investigating the relationship between poor earnings quality and financial flexibility in the emerging market of China. We have predicted that poor earnings quality adversely influences financial flexibility (liquidity). Consequently, we have evidence that poor earnings quality has a strong negative relationship with financial flexibility for overall firms. The negative co-efficient also explains that the decrease in residuals (high quality) of earnings quality increases the level of corporate financial flexibility. Further, this study also contributes to the accounting and finance literature, especially to emerging markets by documenting an inverse relationship between poor earnings quality and financial flexibility as opposed to a developed market (Farinha et al., 2018).

Earnings quality (EQ) is also considered as the accuracy of financial reporting (Ball & Shivakumar, 2008; J. Francis et al., 2005), which also influences the information asymmetry (Leuz & Verrecchia, 2000), wherein poor EQ instigates information asymmetry and strong earnings quality mitigates information asymmetry (Bushman & Smith, 2001). Thus, poor earnings quality exacerbates cash spending due to the weak monitoring of managers and increased information asymmetry, which supports the spending hypothesis as well as the prediction of positive accounting theory.

On the other hand, better earnings quality instigate firms to hoard more cash leading to the availability of external finance, which assists firms to raise more funds from external sources entailing increased financial flexibility. These findings are also supported by the theory of inventory management, which suggests that firms’ should hold a minimal or optimum level of cash to mitigate the cost related to holding liquidity (e.g., opportunity costs, carry-on costs, management discretion costs, etc.), and suggests that firms should invest cash in short term rather than long term to balance the gap between earnings and spending (Baumol, 1952). These findings also support the precaution motive, transaction motive, and agency motive of Liquidity Preference Theory. For precautionary motives, firms maintain liquidity for being financially flexible to meet unexpected contingencies, so firms hold liquidity to shield themselves against the likelihood of cash shortfalls, thus reducing cash flow uncertainty. For transactional motives, firms need liquidity to face their current expenses. Moreover, liquidity could prevent underinvestment costs and enable firms to undertake their profitable investment projects without raising outside funds at high transaction costs.

Like other emerging countries, China has undergone radical regulation changes since 2000 and has established several corporate governance standard mechanisms. This study expects that reported earnings quality (EQ) was worse before the implementation of the reforms. However, after the implementation of standard governance mechanisms and other regulatory measures, the earnings quality has improved and reached an average of 16%. This study shows that poor earnings quality has a significant negative impact on financial flexibility. It means with an increase of one unit in poor earnings quality (ceteris paribus), the financial flexibility will reduce by approximately 0.228 units. It is evident from the literature that poor earnings quality instigate informational asymmetry and agency problems (e.g., Jensen & Meckling, 1976) and put pressure on managers to focus on short term goals by enforcing them to spending cash, which can lead to misappropriation of liquidity (e.g., He & Wintoki, 2016). Conversely, high-quality financial reporting reduces the information asymmetry and increases earnings quality (Bushman & Smith, 2001), which in turn releases pressure on managers leading to hoarding more cash, which also increases the availability of external financing and may focus on the long-term goals of the organization.

4.4.2. Moderating role of corporate governance

This study contributes to the existing literature by empirically investigating the moderating effect of corporate governance on the association between earnings quality and financial flexibility. The study finds a significant positive moderating effect of corporate governance on the association between poor earnings quality and financial flexibility. Specifically, corporate governance positively moderates the negative effect of poor earnings quality on financial flexibility.

The aforementioned results provide support for several predictions. The agency theory predicts that in the presence of excess cash, managers disgorge or do excessive investments to pursue private benefits. This prediction is supported by our results that poor earnings quality has a strong negative relationship with financial flexibility measured in terms of liquidity. Similar conclusions have been drawn by (Sun et al., 2012). This proposition is also supported by the notion of signaling theory that better earnings quality signals the firm’s perspective by reducing information asymmetry between managers and investors, which instigates financial flexibility.

However, the Resource-based view suggests that corporate governance can assemble talented personnel with special expertise which enhances effective communication between the firm and industries (Pugliese et al., 2014). As a consequence, a firm can devise strategies by overviewing theories and existing literature, which may contribute to a positive interaction between earnings quality and financial flexibility relationship. Also, corporate governance can improve earnings quality by enhancing governance practices, such as board independence, diversity, as well as enhancement of monitoring. Consequently, it provides a better signal to the firm and interacts positively between earnings quality and financial flexibility.

In addition, corporate governance tends to influence the firm’s strengths, skills, and ability by incorporating a diverse ownership structure, skilled audit committees, and an effective board of directors. Chinese corporate governance system is unique since the board of directors is monitored by the supervisory board, and independent directors can disclose important board decisions to the general public (Habib & Jiang, 2015), which enhances the effective monitoring of the board of directors including managers. As a consequence, corporate governance minimizes information asymmetry and lessens managerial opportunistic behavior which in turn leads to the positive effect of earnings quality on financial flexibility.

5. Conclusions and policy implications

This study addressed several critical issues in the emerging market of China, i.e., the effects of earnings quality on financial flexibility, and how corporate governance can moderate the relationship between earnings quality and financial flexibility. This study used an unbalanced panel data of 14,088 firm-year observations of 2,034 Chinese A-share firms listed in the Shanghai and Shenzhen stock exchanges considering the period 2007 to 2020. The key objectives of the research were: to investigate the relationship between earnings quality and financial flexibility, and whether corporate governance modifies the relationship between earnings quality and financial flexibility in the emerging economy of China.

The results of earnings quality and financial flexibility are as follows. First, this study finds that earnings quality is a dominant predictor of financial flexibility, showing a negative relationship with financial flexibility. It implies that poor earnings quality will reduce the firm’s financial flexibility and better earnings quality will increase financial flexibility. The empirical results also show that corporate governance (CG-INDEX) significantly positively moderates the relationship between poor earnings quality and financial flexibility. This evidence supports the notion that corporate governance can play a crucial role and it can weaken the negative effect of earnings quality on financial flexibility. Overall, the findings suggest that corporate governance significantly moderates the association between earnings quality and the level of corporate financial flexibility.

5.1. Policy implications

The findings provide implications especially for emerging markets since our analyses are based on the emerging market of China. This study considers China as an emerging economy because it is one of the largest emerging economies having a unique set of corporate governance structures. Besides, firms are highly monitored by the government, which in turn reduces information asymmetry and leads to earnings quality and financial flexibility. Thus, the results of this study offer several implications for researchers, managers, regulatory agencies, and governments which may be useful for both emerging and developed countries.

Earnings quality is an important factor, which led the author to examine how earnings quality influences financial flexibility. The pecking order theory suggests that internal funds are the most important source of financing when the market is imperfect and there are higher information asymmetries within the market. In addition, positive accounting theory predicts that in the presence of manager’s bonus options, their privilege over debt contracts, and related political costs, firms managers can exaggerate their actual earnings which increases informational asymmetry. Under the views of the aforementioned theories, poor earnings quality is a source of amplified shareholder’s concern of increased informational asymmetry, which can adversely affect the firm’s financial flexibility. Conversely, higher earnings quality reduces the information asymmetry which leads to higher financial flexibility.

This study provides implications as it documented that poor earnings quality negatively influences the firm’s financial flexibility, indicating that firms with poor earnings quality are less likely to be financially flexible since lower quality earnings will make them less flexible. The findings suggest that earnings quality is relatively important because it can provide a better ability to borrow funds, but it is necessary to hold a substantial level of liquidity due to the firm’s transaction, speculative, and precautionary motives suggested by liquidity preferences and inventory management theories. Especially in crises situation, like the recent COVID-19 pandemic, the hoarding of cash reserves becomes more important to survive and utilize better investment opportunities. Thus, this study suggests firms to improve earnings quality but should also hoard a substantial amount of cash for their needs. However, increased cash levels can lead to agency problems by motivating managers to use excess cash for personal benefits. Hence, we recommend a strong monitoring governance system that can reduce agency issues, as our findings also indicated that corporate governance can reduce the negative effect of poor earnings quality on financial flexibility.

5.2. Limitations and further studies

This study has some limitations and future studies can address those limitations. First, this study is limited to considering the emerging market of China. Hence, it is possible that the results obtained from Chinese firms might be different for other emerging and developed countries. Therefore, future studies can consider a large sample of firms considering several different economies.

Second, this study is limited to considering the manufacturing firms yielding eight industries. Hence, other industries, like financial, mining, oil, and gas, are not considered due to the differences in operating, regulatory, and financial characteristics. Hence, it is suggested that other types of firms should be also considered in analyses while examining the context of financial flexibility. In addition, studies can also consider examining the phenomenon of financial flexibility in state-owned firms, small and medium enterprises, and private firms, which may provide fruitful findings.

Finally, this research uses secondary data for measuring financial flexibility and it is known from the literature that financial flexibility is an intangible asset that is difficult to measure. Hence, there is no comprehensive measure that can completely measure financial flexibility. Thus, future studies can incorporate primary data by semi-structured questionnaires interviewing employees, managers, and other stakeholders, which might provide a more clear understanding of financial flexibility.

Acknowlegement

This article is prepared based on the PhD Thesis of Dr. Md. Rashidul Islam.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Additional information

Funding

The authors received no direct funding for this research.

Notes

1. Statement of Financial Accounting Concepts are a set of guidelines issued by the FASB-Financial Accounting Standards Board.

2. The co-efficient of independent variable (CG) and dependent variable (FF) is significantly negative, and the coefficient of moderating variable (CG-Index) with financial flexibility is significantly positive. However, the interaction of CG-Index with financial flexibility is significantly positive (see.,García‐sánchez et al., 2018. Board of directors and CSR in banking: the moderating role of bank regulation and investor protection strength. Australian Accounting Review, 28, 428–445, Duru, A., Iyengar, R. J. & Zampelli, E. M. 2016. The dynamic relationship between CEO duality and firm performance: The moderating role of board independence. Journal of Business Research, 69, 4269–4277, Rezaee et al., 2020. Environmental disclosure quality and risk: the moderating effect of corporate governance. Sustainability Accounting, Management and Policy Journal.