1. Introduction
Privately held firms (firms that are not traded on public stock exchanges) play an important role in stimulating employment opportunities and contributing to economic growth worldwide, in both emerging economies and developed markets Ding, Liu, and Wu (2016). Berzins, Bøhren, and Rydland (2008) agree with the relative contribution of private firms, they show that private firms have about four times more employees, three times higher revenues and twice the amount of assets than public firms. Further, more than 99% of companies are not listed on a stock exchange in most countries (Berzins et al., 2008; Nagar, Petroni, & Wolfenzon, 2011).
Given the lack of access to public capital market, the capital structure of private firms comprises a combination of private equity and/or debt market. The availability of private equity is often limited to private firms that mainly depend on certain industries, geographies, or firm lifecycle stages (Hope & Vyas, 2017). In addition, private firms are more closely held and have greater managerial ownership, this suggests that debt financing is likely to be more important in private firms than public firms (Berger & Udell, 1998; Ding et al., 2016).
All firms have to publish a set of annual financial statements to inform their stakeholders. The accounting information plays an important role in credit decisions made by lenders because lenders predicted the repaying capacity of a firm by information of financial reports. High-quality financial information can reduce wrong selections and could be able to alleviate the agency conflict between lenders and borrowers arising from information asymmetry (Jensen & Meckling, 1976). Firm owners have unlimited upward potential with regard to returns, while creditors have fixed claims based on contractual agreements. As a result, lenders tend to focus on the future cash flows of borrowers to ensure fixed payments of interest and principal (Ding et al., 2016). In comparison to public firms, private firms disclose less non-accounting information whose increase the importance of financial accounting information to debt holders. Ball, Robin, and Sadka (2008) show that debt holders generate more demand than equity markets for financial reporting. The reason behind the demand is that debt holders of private firms have fewer information channels about borrowers’ financial information than creditors of public firms. (e.g., analysts, institutional investors, credit rating agencies, and media). This suggests that the quality of financial report information is important for debt holders especially for private firms. As result of higher-quality of financial report information, lenders can better predict the repaying capacity of a firm, which reduced the default and cost of debt for firms. It is important for both private firms and lenders that they can trust on guarantee the quality of financial reports.
The financial report quality is measured in accruals quality, an earnings quality metric borrowed from the accounting literature (Bharath, Sunder, & Sunder, 2008; Dechow & Dichev, 2002; Francis, LaFond, Olsson, & Schipper, 2005; Francis, Olsson, & Schipper, 2008; McNichols, 2002). Earnings quality is the precision of the earnings signal from the firm’s financial reporting system. Accruals provide information about future cash flows. The accruals and earnings will be more representative of future cash flows when the accruals process is free of estimation errors. Francis, Nanda, and Olsson (2008) show this measurement is a good proxy for overall reporting quality.
Current researches (Carmo, Moreira, & Miranda, 2016; Ding et al., 2016; Gray, Koh, & Tong, 2009; Hope, Thomas, & Vyas, 2013; Vander Bauwhede, De Meyere, & Van Cauwenberge, 2015) studied the impact of accrual quality on private firms’ cost of debt. They found that accrual quality is negatively related to those companies’ effective interest cost. The findings in these current papers are consistent with the idea that earnings are important for lenders in predicting repaying capacity of a firm (i.e., future cash flows). Less estimation error in accruals enhances earnings’ ability to predict future cash flows and reduce the cost of debt. They used data for private firms were only researched in Belgium, Portugal, China, US, Australia and not for German firms. In Germany, only five hundred of seven million companies are stock exchange list companies, this means that most of the German companies are private. This suggests that impact of accruals quality on the cost of debt for German firms is also important.
The purpose of this study is to contribute to the literature by testing whether and to what extent firms’ financial reports quality affects the cost of debt, specifically for German firms. This research gives the opportunity to reduce the gap in accrual quality literature of private in Germany. The practical contribution is that this research helps managers in understanding the important role of financial report quality for lenders in predicting the repaying capacity of a firm and determining the interest cost. The advantage of higher-quality financial information leads to a lower cost of debt for firms. The study focuses on the cost of debt because bank loans are a key source of private firms (Berger & Udell, 1998). The This study will answer the main question: “What is the impact of financial reports quality of private German firms that influence the cost of debt?". To answer this question, this study will examine the relationship between financial reporting quality and the cost of debt, in a sample of German private companies. The sample for this study includes ….. firm-year observations between the period 2007 and 2016.
The remainder of this paper is structured as follows. The second section of this paper conducts a literature review, including theories and outcomes of previous studies. Based on the literature the hypothesis is formulated. Section three describes the methodology, were the research model and its variables will be defined. This section ends with discussing the data collection and sampling. Section four presents the results. The fifth and last section is focusing on the conclusion and discussion of the results. Limitations of this research are also stated.
2. Literature review
2.1 Literature
In general, both public firms and private firms search for access to external capital with an acceptable cost (Ding et al., 2016). Public firms have access to public equity and debt markets while private firms do not have access to raise capital in the public equity markets. Also, the availability of private equity is often limited to private firms (Hope & Vyas, 2017). In the most countries, more than 99% are private firms, whose borrow over a half their capital from banks (Berzins et al., 2008; Nagar et al., 2011; Pacter, 2009). Relative to public firms, debt financing is likely to be more important in private firms (Berger & Udell, 1998). However, access to debt financing is always still difficult for private firms. A lack of collateral and social capital limited smaller private firms access to debt financing (Ding et al., 2016). The role of agency cost between lenders and borrowers plays a limiting factor in debt contracting for private firms. The extensive literature on debt contracting argues that properties of firms’ accounting information, such as accruals quality, disclosure, audit and other earnings properties can exacerbate or reduce the agency cost of debt, thereby impact debt pricing (Armstrong, Guay, & Weber, 2010; Shivakumar, 2013). In addition, financial information helps creditors in reducing adverse selection problems and assess the performance and financial position of the borrower.
2.1.1 Agency cost
Many debt research has developed with the agency-theory (e.g., Fama & Miller, 1972; Jensen & Meckling, 1976; Myers, 1977). Three central ideas emerge from this literature. In the first place, owners and/or managers have incentives to take actions in own interest to the disadvantages of outside capital providers. In anticipation of this behavior outside capital providers will price protect their claims. On which owners and/or managers react on the price protection and are willing to make monitoring and bonding costs, to restrict their ability to engage.
Smith and Warner (1979) expand on these ideas, they mention that there are four major sources of conflict which arise between debt holders and equity holders. First agency conflict is a conflict of interest between these two stakeholders over dividends. Debt holders are afraid that equity holders could increase their dividend payments, thereby reducing the resources available to pay off debt holders’ claims. As result, the claims are worthless. The second source of conflict is related to debt levels. When a firm increases there debt level, it reduces the probability that the firm will be repaid a loan. The third conflict is asset substitution, firms often have incentives to shift their asset mix toward riskier investments. As result, the wealth transfer from debt holders to equity holders. The fourth sources of conflict related to underinvestment. Firms with risky debt may forgo positive NPV projects if some or all of the value of the project goes toward the debt holders.
Information risk
The presence of asymmetric information leads to a disadvantage for the lender, because owners and/or managers have better inside information than debt holders. As result, the lender demands a higher return on the investment, which results in increased interest costs (Aldamen & Duncan, 2013; Easley & O'Hara, 2004; Lambert, Leuz, & Verrecchia, 2007, 2011). According to Easley and O'Hara (2004) information asymmetry drives information risk. However, Lambert et al. (2007) argue that information risk reflects the precision of information, instead of information asymmetry. They suggest that the quality of accounting information directly impacts the ability to assess the future cash flows and indirectly impact firm's real decisions. According to Carmo et al. (2016), information risk represents the likelihood that firm-specific information that is not available (asymmetric information) or is of poor quality (inaccurate information) for financing decisions. Which can lead that debt holders may not to lend, lend at higher interest rates or require the provision of guarantees.
2.1.2 Role of financial information
Financial information plays an important role in reducing information asymmetries and agency conflicts between borrows and lenders (Armstrong et al., 2010; Shivakumar, 2013). They emphasize that debt contracts typically contain covenants, often based on accounting information. These covenants restrict firms to increases dividend payments or the issuance of additional debt. In addition, lenders prevent borrowers from shifting risk by using security agreements to collateralize the firm’s assets. This limited firms capital expenditure and asset sale because in default of repaying the debt the collation assets are required to sales and be used to pay down the firm’s debt. However, it is more difficult to design debt contract based on accounting information that will force firms to commit to invest in all positive NPV projects or to maintain a given risk profile when exercising future growth options. Therefore lender used price protection through interest rates or reduction in the debt’s maturity, to reduce the costs that arise from underinvestment.
However, even in the absence of debt covenants, financial information plays a role in credit decisions, since banks assess the firms’ default risk based on financial information, in particular on earnings. The earnings quality is seen as a feature that enables lenders to predict better firms' future earnings and cash flows, as result lenders assess more accurately the firms’ default risk. The lenders values the earnings quality so high that it is reflecting the cost of the debt because higher earnings quality is expected to reduce lenders’ information risk (Carmo et al., 2016). Same as for default risk, information risk can lead to decisions not to lend, lend at higher interest rates or require the provision of guarantees. Consistent with the importance of financial information in credit decisions, Bharath et al. (2008) find that borrowers with more opaque financial statements face higher interest rates. A similar result is found by Minnis (2011), who studies a sample of US private firms and pointed that firms who volunteer their financial statements by audited are likely to get more attractive interest rate.
2.1.3 Earnings quality
According to Francis, et al. (2008), information quality is defined as ”the precision of a measure with respect to a valuation relevant construct. A higher quality information is more precise with respect to that construct". They also mention that financial reporting quality represents firms’ information quality. They suggest higher financial reporting quality should result in higher quality information that results in better capital decisions.
Earnings are a summary measure of firm performance produced under accounting accrual basis (Dechow, 1994) and one of the important source of firm-specific information in the financial statements (Lev, 1989). The earnings are used in stakeholders’ decisions, for example; in debt contracting, equity valuation or in executive compensation plans.
In prior studies (Dechow, 1994; Dechow, Kothari, & Watts, 1998) found that earnings; is a better measure of performance, is more closely reflect expected cash flows and better predict future operating cash flows than current cash flows. The reason for preferring earnings over current cash flow in predicting future cash flows is that accruals mitigate timing and mismatching problems in measuring cash flows over short time intervals (Dechow, 1994; Dechow et al., 1998). However, Dechow and Dichev (2002) argue that accruals are based on assumptions and estimates, if the assumptions and estimates are wrong, it must be corrected in future accruals and earnings. For example, if actual receivables are less than the original estimate, then both the actual cash collected and the correction of the estimation error must be recorded. In addition, Healy and Wahlen (1999) reported that managers can have incentives to manipulated earnings as result, that accruals and earnings are less informative. Thus, intentional, as well as unintentional errors, create noise in accruals which reduces the beneficial role of accruals (Dechow & Dichev, 2002).
Consistent prior studies, in this paper, higher-quality accruals and earnings will be more representative of future cash flows when the accruals are less affected by estimation errors. Higher-quality accruals and earnings enable lenders to make a better, more accurate, assessment of default risk and hence reduce information asymmetry.
2.1.4 Demand financial reporting quality
To get a general impression of the level of financial reporting quality of firms, the literature is consulted. The literature provides mix predictions on the financial reporting quality of private firms and public firms. Hope et al. (2013) explored the demand for high financial reporting quality between public firms and private firms in the US. The financial reporting quality depends on two opposite hypotheses ‘‘demand’’ and ‘‘opportunistic behavior’’ (Givoly, Hayn, & Katz, 2010). The demand hypothesis suggests that the demand for high-quality information is greater among public firms. Because public firms have greater ownership dispersion, greater owner-manager separation, and less managerial ownership on average than private firms (Hope et al., 2013). These ownership characteristic create more information asymmetry, while private firms most have only stakeholders like inside managers and debt holders. Thus, managers have incentives to provide highly reliable financial reporting quality of public firms. Ball et al. (2008); Ding et al. (2016) disagree that public firms have higher demand of financial reporting quality, because debt holders of private firms, in general, have fewer information channels about firms financial information than public firms stakeholders (e.g., analysts, credit rating agencies, media, institutional investors). They mention that financial reporting quality is more important for private firms. In addition, the opportunistic behavior hypothesis suggests that public firms are subject to capital market pressures which increase their incentives to manipulate accruals to meet earnings targets. Which decrease financial reporting quality of public firms. So, there are mix arguments in the level of financial reporting quality of private firms and public firms.
Hope and Vyas (2017) mentioned the impact of debt financing on firms’ financial reporting that arises from two distinct mechanisms. The first mechanism is generally applicable during the credit-granting decision stage. In this stage, the role of financial reporting is an important factor in reducing information asymmetric between lenders and borrowers. The lenders demand high-quality financial reports from borrowers to reduce their information risk in forecasting future cash flows. The second mechanism pertains to maintaining the lenders demand high-quality financial reporting to increase debt-contracting efficiency.
2.1.5 Empirical evidence
Current researches studied the impact of accrual quality on private firms’ cost of debt. Van Caneghem and Van Campenhout (2012) examine the impact of differences in quantity and quality of information on Belgian SMEs financial structure. They found that both information quantity and quality are positively significant related to SME leverage. Based on this study Vander Bauwhede et al. (2015) studied the impact of accruals quality on Belgian SMEs cost of debt. They report that firms with lower accruals (earnings) quality are associated with higher interest costs. Similar findings are reported in the study by (Ding et al., 2016) that examines the relationship between earnings quality and Chinese privately held firms’ debt financing. They found that better earnings quality increases private firms’ access to debt financing and lowers their cost of debt. They also found that these effects are greater in under-developed provinces. Minnis (2011) found that firms who volunteer their financial statements by audited are received a more attractive interest rate. And Bharath et al. (2008) report that firms with more opaque financial statements face higher interest rates, both studies used a sample of US private firms.
The quality of accounting information is also important in merger and acquisitions. Acquiring firms may overpay for acquisitions when there is uncertainty in the value of the target in a lake of information. McNichols and Stubben (2015) examine whether acquisitions are more profitable for acquirers when the firms they target disclose higher-quality accounting information. They found that acquiring firms experience lower stock returns at the acquisition announcement when the value of the target firm is uncertain. However, the acquirer returns are higher when the target firm has higher accounting quality. So high-quality accounting information reduces information asymmetry between acquirers and target firms which lead to more profitable acquisitions. Raman, Shivakumar, and Tamayo (2013) agree with McNichols and Stubben (2015) that the payment is higher if the target firm's earnings quality is poor so the profit is lower. Also, they found that bidders are more likely to undertake negotiated takeovers when there is more asymmetric uncertainty.
2.2 Hypotheses development
In pursuance of answering the research question hypothesis will be developed based on the theory. Prior research suggests that improved transparency of financial information alleviates information risk, which leads to reduced limitations in access to external financing. Financial reporting quality represents firms’ information quality, the higher the information quality, the lower the information asymmetry between lenders and borrowers. In the first place, credible financial information enables lenders to assess the overall risk of the borrower and decisions whether not to lend, lend at higher interest rates or require the provision of guarantees. In the second place, reliable accounting information helps lenders to predict the repaying capacity of a firm, which provides a guideline for lenders to set the appropriate interest rate. Suppose information risk is reflected in interest rates, firms with poorer financial information quality tend to bear a higher cost of debt than firms with better financial information quality. Therefore the following hypothesis is expected.
H: High financial reporting quality is negatively associated with private firm’s cost of debt.
3. Methodology
This part will focus on the methods part of this research. It will start with a discussion of the research model, followed by a description of the variables (independent, dependent and control) and the way to measure these variables. An overview of the definitions of the variables in the research model can be found in Appendix A. This part ends with discussing the data collection and sampling.
3.1 Research Model
In line with previous studies (e.g., Carmo et al., 2016; Ding et al., 2016; Francis et al., 2005; Gray et al., 2009; Minnis, 2011; Vander Bauwhede et al., 2015), the relationship between the cost of debt and earnings quality is tested using an Ordinary Least Squares (OLS) regression model. When the relationship between the dependent and an independent variable is unknown, in most cases OLS is a useful method to measure the relationship (Chumney & Simpson, 2006). OLS is a linear regression model, which means that the OLS line is a straight line. OLS contains the following variables: intercept, residual, dependent variable, independent variables and control variables. The dependent variable in this research is cost of debt. The independent variable is accrual quality (AQ). The model includes also a set of variables in order to control for the effect of other determinants of the cost of debt. Some variables are measured more than one measurement in order to check robustness. The regression model can be specified as follows. Where i and t index firms and year, respectively.
Cost of debti,t+1 = β₀ + β₁ * AQi,t + β₂ * Sizei,t + β₃ * CF performancei,t + β₄ * Agei,t + β₅ * Leveragei,t + β₆ * Interest coveragei,t + β₇ * Asset tangibilityi,t + β₈ * Negative equityi,t + β₉ * Growthi,t + β₁₀ *Maturityi,t + β₁₁ * Industryi,t + ɛi,t (1)
3.2 Variables
The choice of variables used in this study are based on prior studies that have examined the relationship between quality of private firm earnings and the cost of debt (e.g., Carmo et al., 2016; Ding et al., 2016; Francis et al., 2005; Gray et al., 2009; Minnis, 2011; Vander Bauwhede et al., 2015).
3.2.1 Dependent variable
This study examines whether the quality of firms earnings affects the cost of debt of German private firms. This means that the dependent variable in this study is the cost of debt, whose measured in two different ways. The first measurement is as follows: interest expense in year t+1 divided by the average interest-bearing debt outstanding during years t and t + 1. This ratio is a good proxy for the cost of debt (Carmo et al., 2016; Ding et al., 2016; Francis et al., 2005; Gray et al., 2009). The second measurement of cost of debt is interest expense in year t+1 divided by the average long-term debt in years t and t + 1 (Minnis, 2011).
3.2.2 Independent variable
The independent variable is AQ. The approach to measuring the precision of financial statement information is based on the model developed by Dechow and Dichev (2002). The advantage of this model is that the measure focuses on the strength of the relationship between current accruals and past, present, and future cash flows. The model recognizes that the timing of a firm’s economic results often differs from the timing of the related cash flows (Gray et al., 2009). McNichols (2002) modified the Dechow and Dichev model and add the change in revenue and the level of property, plant and equipment (PPE) in the model. McNichols (2002) arguing that the change in sales and PPE are important in determining current accruals. She shows that adding these variables to the Dechow and Dichev model, the explanatory power significantly increases and thus reducing measurement error. Francis et al. (2008) show this measure to be a good proxy for overall reporting quality. The accruals quality is measured in two steps. The first step is following the model that estimated cross-sectionally in each industry-year combination using OLS.
ACCi,t = α₀ + α₁ * CFOi,t + α₂ * CFOi,t-1 + α₃ * CFOi,t+1 + α₄ * ΔREVi,t + α₅ * GPPEi,t + ɛi,t
(2)
Cost of debti,t+1 = β₀ + β₁ * AQi,t + β₂ * Sizei,t + β₃ * CF performancei,t + β₄ * Agei,t + β₅ * Leveragei,t + β₆ * Interest coveragei,t where ACC i,t is firm i’s accruals (net income – cash flow from operations) in year t; CFO t, CFO t-1 and CFO t+1 are the cash flows from operations in year t, t – 1, and t + 1, respectively; ΔREV i,t the change in total revenue and GPPE i,t, the gross property, plant, and equipment. All variables in Equation (2) are scaled by average total assets from year
t ) 1tot
where ACC i,t is firm i’s accruals (net income – cash flow from operations) in year t; CFO t, CFO t-1 and CFO t+1 are the cash flows from operations in year t, t – 1, and t + 1, respectively; ΔREV i,t the change in total revenue and GPPE i,t, the gross property, plant, and equipment. All the variables are standardized by average total assets, and winsorized, at the 1st and 99th percentiles each year to mitigate the impact of outliers (Francis et al., 2008). The industry- and year AQ measure are computed as the standard deviation of the residuals in year t. It reflects the accruals that are not related cash flows realized in the current, prior or future year, not to the change in net sales and the gross value of property, plant and equipment. With mean a greater standard deviation is considered to reflect lower AQ. In the second step, the AQ measure from the model must be multiplied with -1 to ease the interpretation of this variable.
3.2.3 Control Variables
In line with prior studies (e.g., Carmo et al., 2016; Ding et al., 2016; Francis et al., 2005; Gray et al., 2009; Minnis, 2011; Vander Bauwhede et al., 2015), that have examined private firms’ cost of debt includes firm-level control variables. These control variables are firm size, cash flow (CF) performance, firm age, leverage, interest coverage, asset tangibility, negative equity, firm growth, maturity and industry dummies.
Larger, more mature and more cash-generating firms are bearing in general less financial risk. Larger firms are more diversified and mature firms have longer-standing relationships with their banks and are more likely to have established a respectable reputation, the expectation to go bankrupt is less than smaller and/or younger firms (Psillaki & Daskalakis, 2009). So, cash flow, size, and firm age are expected to be negative to cost of debt. CF performance is computed as the cash flow from operations divided by total assets. Size is measured in two ways, first as the natural logarithm of total assets and second as the natural logarithm of net sales. Firm age is defined as the natural logarithm of the number of years a firm has been in business.
Higher leverage firms expect to pay a higher average interest rate because leverage is positively associated with debt-related agency conflicts and financial risk. For example, owners with little equity have greater incentives to engage in asset substitution (Jensen & Meckling, 1976). However, firms that borrow large amounts experience an advantage in attractive interest rates (Francis et al., 2005; Minnis, 2011; Vander Bauwhede et al., 2015). The first measurement of leverage is as follows; total interest bearing debt divided by total assets. The second measurement of leverage is long-term debt divided by total assets.
Higher values for interest coverage and asset tangibility indicate less financial risk (Vander Bauwhede et al., 2015). The extent of tangible assets is important because tangible assets could be liquidated to repay outstanding debts in event of default (Minnis, 2011). The expectation is that both variables have a negative coefficient. The first measurement of interest coverage is calculated as follows: earnings before interest and depreciation and amortization (EBITDA) divided by interest expense. The second calculation is earnings before interest and tax (EBIT) divided by the interest expense. Asset tangibility is calculated as the net value of property, plant and equipment divided by total assets.
According to Minnis (2011), the unique characteristics of firms with negative equity positions must be taken into account. A negative equity position indicates to negative past performance, as a result, the firm access riskier. The effect of negative equity expects a positive to cost of debt. Negative equity is measured using a dummy variable which equals 1 if the book value of equity is negative, 0 otherwise.
A rapid growth associated with more agency problems and risk, through growth opportunities firms take more risk in their investments (Minnis, 2011; Myers, 1977; Vander Bauwhede et al., 2015). Furthermore, firms with more stable cash flows in the future are better predictable in capital requirements than firms with growth potential (Psillaki & Daskalakis, 2009). Growth is expected positively with the cost of debt. Growth is measured as the year-over-year percentage growth in sales.
Further, following prior studies (Bharath et al., 2008; Dennis, Nandy, & Sharpe, 2000; Vander Bauwhede et al., 2015), the model includes a measure of debt maturity. Debt maturity is included for two reasons. First, to control potential interdependencies between the interest rate and maturity (Dennis et al., 2000). Second, to control the prior finding that in the case of private debt (compared to public debt), financial information quality not only impacts debt pricing but also debt maturity (Bharath et al., 2008). Because in conflicting theories on debt maturity, there is no prediction relationship between the cost of debt and debt maturity. Debt maturity is calculated as all debt with an initial maturity of more than 1 year divided by total debt.
The final control variable is industry dummy (Chen, Hope, Li, & Wang), it included to control industry effect. The industry dummies are based on the sections of the Primary NACE Rev. 2 codes. Just like in previous studies (Minnis, 2011; Vander Bauwhede et al., 2015), the independent variable and all control variables except for firm age are winsorized at the 1st and 99th percentile, to mitigate the impact of outliers.