Debt maturity structure, institutional ownership and accounting conservatism: Evidence from Iranian listed companies

Mahdi Salehi (Faculty of Economics and Business Administration, Ferdowsi University of Mashhad, Mashhad, Iran)
Mohsen Sehat (Department of Economics and Administrative Sciences, Imamreza International University of Mashhad, Mashhad, Iran)

Asian Journal of Accounting Research

ISSN: 2443-4175

Article publication date: 5 August 2019

Abstract

Purpose

The purpose of this paper is to examine the impact of debt maturity structure and types of institutional ownership on accounting conservatism by using different financial variables and proxies.

Design/methodology/approach

Employing panel data analysis in the R programming language, the authors test their hypotheses on a sample of 143 (858 firm-year observations) companies listed on the Tehran Stock Exchange during 2011–2016.

Findings

Using Basu (1997) and Beaver and Ryan (2000) models as proxies for accounting conservatism, the findings suggest a non-significant relationship between accounting conservatism and debt maturity structure. Contrary to the primary expectation, the results indicate that short-maturity debts are also non-significantly and negatively associated with accounting conservatism in financially distressed firms. Finally, using both conservatism measures, the authors document that there is no significant relationship between both active and passive institutional ownership and accounting conservatism as well as debt maturity structure.

Originality/value

The current study is the first study conducted in a developing country like Iran, and the outcomes of the study may be helpful to other developing nations.

Keywords

Citation

Salehi, M. and Sehat, M. (2019), "Debt maturity structure, institutional ownership and accounting conservatism: Evidence from Iranian listed companies", Asian Journal of Accounting Research, Vol. 4 No. 1, pp. 35-51. https://doi.org/10.1108/AJAR-05-2018-0001

Publisher

:

Emerald Publishing Limited

Copyright © 2018, Mahdi Salehi and Mohsen Sehat

License

Published in Asian Journal of Accounting Research. Published by Emerald Publishing Limited. This article is published under the Creative Commons Attribution (CC BY 4.0) licence. Anyone may reproduce, distribute, translate and create derivative works of this article (for both commercial and non-commercial purposes), subject to full attribution to the original publication and authors. The full terms of this licence may be seen at http://creativecommons.org/licences/by/4.0/legalcode


1. Introduction

One of the most important financial issues in today’s growing capital markets is the quality of firms’ financial information. Higher quality financial information leads to sound financial decisions made by potential investors and also contributes to more appropriate allocation of financial resources. In this respect, it is of considerable importance for managers to maintain a proper level of conservatism when providing required financial information for interested investors, primarily due to the fact that future losses stemming from too optimistic estimates are much more serious than losing profitable opportunities arising from adopting too pessimistic valuation approaches. Creditors are among the interested users of financial information, and their returns from investing in the net value of firms’ total assets are typically asymmetrical. That is, creditors do not earn any additional return from their investment when the value of a firm’s net total assets is more than the nominal value of its debts, regardless of the amount of the overvaluation. However, these interested groups earn the sole net value of a firm’s total assets from their investment when the net assets are less than the nominal value of the debts. Therefore, creditors are more concerned about the undervaluation of assets than their overvaluation. From the creditors’ point of view, firms should report the least possible value for their net total assets, mainly because higher values of net total assets are generally regarded as unacceptable. The can be attained by recognizing losses in a more timely fashion than gains (or the adoption of conservative reporting practices). Accordingly, the present paper aims to investigate the impact of short-maturity debt and the type of institutional ownership (i.e. active vs passive) on different kinds of accounting conservatism (i.e. conditional and unconditional). Our primary inquiry is motivated based on the mixed results provided by prior literature, indicating similar roles for debt maturity structure, institutional ownership and accounting conditional and unconditional conservatism in resolving agency costs. Indeed, we aim to address the question of whether the degree of conservatism in financial reports is associated with the extent of agency problem arising from both debt financing and the presence of institutional investors within the corporate governance framework.

Although several empirical studies, to date, have examined accounting conservatism, the economic demands for conservatism are still under a long-running debate. Some papers, for instance, suggest that equity investors (shareholders) are more inclined toward conservative financial reporting as a corporate governance mechanism or tool (Ball, 2001; Watts, 2003; Ramalingegowda and Yu, 2012). Consistent with this notion, more recent studies document that greater financial conservatism is captured when there is greater separation of ownership and control (diffused ownership structure) as well as higher information asymmetry between managers and shareholders (LaFond and Roychowdhury, 2008; LaFond and Watts, 2008; Ramalingegowda and Yu, 2012). Prior literature also draws a clear distinction between individual (unsophisticated) investors whose trading goals are unrelated to information and institutional investors as sophisticated price-setters in capital markets who are more likely to value conservative financial reporting (Chan and Lakonishok, 1995; Sias et al., 2006; Ramalingegowda and Yu, 2012). The latter also adopt a direct monitoring approach rather than monitoring through accounting numbers, primarily due to greater access to both the managers and insider financial information (Carleton et al., 1998; Ke et al., 1999; Prendergast, 2002; Ramalingegowda and Yu, 2012). According to the argument of Ramalingegowda and Yu (2012), institutional characteristics inducing greater monitoring incentives among institutional investors and subsequently driving greater demand for the adoption of conservatism are concentration of share holdings, investment horizon and independence from firm management. The major role played by institutional investors within the corporate governance structure is also noteworthy. Specifically, institutions have the distinct privilege of controlling and monitoring firms’ corporate policies through their concentrated holdings, and, consequently, higher levels of institutional ownership lead to lower agency costs (Crane et al., 2016). The extant literature argues that institutional investors have the special privilege of monitoring corporate policies and lower costs of monitoring. Therefore, it is likely for firms with higher levels of institutional ownership to exploit the potential of lower agency costs (Jensen and Meckling, 1976). Prior literature also suggests that both active and passive institutional ownerships in the USA have increased to more than 60 percent over the past 40 years, with passive funds forming more than 20 percent of the public equity holdings (Aghion et al., 2013; Crane et al., 2016). Both the theory and empirical evidence confirm the active monitoring function of institutional investors inside the corporate governance structure and suggest that the process is rather difficult for more passive or less-informed investors (Almazan et al., 2005).

In addition to the preceding discussion, we attempt to question the importance of short-maturity debts in explaining conservative financial reports. It has been argued that the inherent agency costs arising from the nature of debt financing such as information asymmetry and suboptimal investments could be mitigated by short-maturity debt covenants (Datta et al., 2005; Khurana and Wang, 2015). In this respect, Khurana and Wang (2015) argue that short-maturity debts are likely to affect the demand for accounting conservatism in two ways. On the one hand, based on the extant literature (e.g. Barnea et al., 1980; Leland and Toft, 1996), the authors demonstrate that the sensitivity of short-maturity debts in comparison with long-maturity debts in terms of valuation is lower and consequently propels managers to employ more monitoring mechanisms. This facilitates the shifting of investments toward less risky projects and accordingly lowers the need for debt-contracting as a potential contributing factor in the adoption of accounting conservatism. On the other hand, lenders are more inclined to demand more accounting conservatism when firm value decreases as a result of incomplete financial contracts, particularly debt contracts. At the same time, Khurana and Wang (2015) expect less demand for accounting conservatism when debt maturity is shorter, primarily due to the fact that lenders would be able to re-price debt when it is due for renewal and/or not renew the contract. Taking the results of Khurana and Wang (2015) together, the present paper posits a negative association between the debt maturity structure and accounting conservatism but, in contrast, finds a non-significant relationship.

Using a sample of 143 (858 firm-year observations) companies listed on the Tehran Stock Exchange (TSE) during 2009–2014, our findings, contrary to the results of prior literature, suggest that there is no significant relationship between debt maturity structure and the demand for conservative financial reports. This result is also confirmed for financially distressed firms. In addition, we show that both active (i.e. ownership by institutions that have an incentive to actively monitor management) and passive (i.e. ownership by institutions without corporate board representations) ownership structures are not significantly associated with accounting conservatism (both conditional and unconditional) as well as the debt maturity structure.

This paper proceeds as follows: the next section reviews prior literature on accounting conservatism and develops our hypotheses. Section 3 discusses our research design and sample selection procedure. Section 4 presents the estimation results. Finally, Section 5 concludes the paper.

2. Research background and hypothesis development

2.1 Accounting conservatism defined

As the old adage “anticipate no profits but anticipate all losses” goes, accountants are often inclined to require higher verifiability for recognizing good news as gains than for bad news as losses (Basu, 1997). Accordingly, conservative reporting is typically defined as the timely recognition of expected unfavorable events in income than the recognition of the effects of expected favorable events (Givoly et al., 2007). Lower cost or market accounting for inventories and the timely recognition of cost estimates leading to future expected losses on long-term contracts than those resulting in higher future profits are typical examples of financial conservatism (Basu, 1997). Nevertheless, from a broader perspective, conservatism is also defined and interpreted as accountants’ preference for the adoption of accounting methods leading to lower values of assets and revenues along with higher values of liabilities and expenses (Belkaoui, 1985; Basu, 1997).

2.2 Conditional vis-à-vis unconditional conservatism

Prior literature draws a sharp distinction between the two types of conservatism using different terminologies and/or several pairs of terms. In this respect, several papers have classified conservatism under income statement and balance sheet conservatism categories (e.g. Basu, 1997; Ball et al., 2000; Pae et al., 2004). Beaver and Ryan (2005), arguably, criticize this classification for being rather unrealistic, primarily due to the consistency of the impact of conservatism on the income statement and balance sheet when the firm employs clean surplus accounting. The authors refer to conditional and unconditional conservatism just as Ball and Shivakumar (2005) do. Other pairs of names used for classifying conservatism are ex post and ex ante as well as news-dependent and news-independent conservatism. Following Beaver and Ryan (2005) and Ball and Shivakumar (2005), we use conditional and unconditional terms to name the two types of conservatism. Based on the argument of Beaver and Ryan (2005), unconditional conservatism can be triggered when the expected unrecorded goodwill is captured through the accounting process of assets and liabilities such as immediate expensing of the costs of intangible assets, accelerated depreciation of property, plant, and equipment and the historical cost accounting for positive net present value projects. The authors also define conditional conservatism as the timely recognition of book values under sufficiently adverse circumstances than their recognition under favorable circumstances.

2.3 Active vis-à-vis passive institutional ownership

Prior studies suggest that institutional investors are not alike in terms of their monitoring functions. In other words, they have different incentives for actively monitoring corporate managers (Navissi and Naiker, 2006; Cornett et al., 2007). Accordingly, institutional ownership is generally comprised of both active and passive investors. Passive institutional investors possess a high portfolio turnover and transient trading strategy. They are interested only in short-term profits and their performance is evaluated primarily based on the short-term returns they generate (Navissi and Naiker, 2006). On the other hand, active institutional investors are more inclined to gain corporate board representation than passive ones and consequently exercise efficient monitoring (Navissi and Naiker, 2006).

2.4 Debt maturity structure and accounting conservatism

To our knowledge, to date, prior literature on the relationship between accounting conservatism and the agency costs arising from debt financing has focused on different aspects of debts, such as debt maturity structure, the interest charged on the debts and in some cases, the extent of debt used in a firm’s capital structure (Ahmed et al., 2002; Zhang, 2008; Beatty et al., 2008; Khurana and Wang, 2015). The collective evidence provided by Ahmed et al. (2002), for instance, highlights the significant role of accounting conservatism in reducing corporate debt costs. Employing both market-based and accrual-based proxies for accounting conservatism, the authors indicate that corporate debt is negatively associated with the adoption of conservatism in financial reports, ceteris paribus. In a similar vein, Zhang (2008) recognizes mutual benefits for two sides of a debt contract, i.e., the lender and the borrower. Specifically, he predicts and finds that more conservative borrowers, as compared to their counterparts, enjoy lower initial interest rates offered by lenders as a result of their covenant violation and the consequent signaling of default risk. Using 3,641 private debt agreements with net worth covenants issued during 1994 to 2004, Beatty et al. (2008) documented the existence of conservative contract modifications, but not ubiquitously. Specifically, they provided some evidence that debt contracts are modified mostly when there are higher agency costs and lower litigation, tax and equity demands for conservatism. Recent evidence by Khurana and Wang (2015) focuses on the relationship between the debt maturity structure and accounting conservatism and indicates that the adoption of conservative accounting approach is negatively influenced by the short-maturity debt, which is also more pronounced among financially distressed firms. Ball and Shivakumar (2005) took into consideration the extent of debt used in the capital structure of a sample of the privately held and publicly traded companies of the UK and demonstrated that the publicly held companies of the UK were more likely to recognize losses in a timelier manner (i.e. adopt conservative financial reporting) than their counterparts.

It has been argued that shortening debt maturity could provide the ground for mitigating both the agency costs arising from debt financing and underinvestment problems. The latter can be fulfilled through facilitating frequent debt re-pricing and making debt mature before the growth options expire (Myers, 1977; Barnea et al., 1980; Khurana and Wang, 2015). However, although shortening debt maturity has the above-cited strengths, firms are required to consider the suboptimal liquidation risks stemming from too much refinancing and the consequent bankruptcy costs. Indeed, they have to trade off the benefits and costs of using short-maturity debt (Sharpe, 1991; Diamond, 1991; Khurana and Wang, 2015). Furthermore, accounting conservatism could be a curative mechanism through which lender–borrower conflicts and the agency costs of debt can be mitigated (Watts, 2003; Ball and Shivakumar, 2005; Khurana and Wang, 2015). Prior literature also suggests that accounting conservatism can act as much of a deterrent against managers’ incentives to undertake self-serving projects that do not enhance shareholder value. To put it more simply, accounting conservatism, to some extent, prevents managers from deferring the recognition of economic losses and thus pursuing negative net present value projects by providing timelier revisions of earnings and asset book values (Watts, 2003; Ball and Shivakumar, 2005; Khurana and Wang, 2015). In this context, Bushman et al. (2011) examined the relations between corporate investment behavior and the timeliness of accounting recognition of economic losses by using firm-level investment decisions for a sample of 25 countries and found that managers’ tendency toward investment spending is positively associated with investment opportunities in countries where conservative reporting is more pronounced. Based on above-cited arguments, we hypothesize that short-maturity debt contracting is likely to be negatively associated with the demand for accounting conservatism, particularly when it facilitates the monitoring function of lenders and discourages shifting toward risky projects. As Khurana and Wang (2015) point out, it is predictable from the incomplete financial contracts perspective that short-maturity debt financing is likely to negatively affect the demand for accounting conservatism. Therefore, we present the following hypotheses to examine our prediction:

H1.

Short-maturity debt is negatively associated with accounting conservatism.

H1a.

Short-maturity debt is negatively associated with conditional accounting conservatism.

H1b.

Short-maturity debt is negatively associated with unconditional accounting conservatism.

Despite the above predictions, several papers have focused on the conflicts between lenders and shareholders in the presence of default risk. In this case, several events cause the longevity of effective debt maturity such as choosing riskier investment projects, over-financing, lower equity financing and concealing problems from creditors (Myers, 2001; Khurana and Wang, 2015).

Myers (2001) asserts that where the default risk is very small or negligible, debts values are rarely influenced by news regarding the economic performance of corporations. However, as the level of default risk rises, the value of debts are further influenced, and, consequently, the creditors are provided with an incentive to receive the performance-related news of the corporations. Eisdorfer (2008) examines the relationship between investment and volatility and finds the expected negative relation between the two variables to be reversely affected by shareholders’ risk-shifting incentives. Indeed, the author offers two major results. The first result is related to the fact that financially distressed firms are faced with a weak negative relationship (and, in some cases, a positive relationship) between their investment intensity and volatility. The second finding reveals that the value-creation potential of investments in financially distressed firms is lower during times of high uncertainty. Based on the previously mentioned discussions, it can be concluded that financial distress (FD) is likely to influence the relationship between short maturity debts and accounting conservatism. Therefore, we posit the following hypotheses in the null form:

H2.

Short-maturity debt is negatively associated with accounting conservatism in financially distressed firms.

H2a.

Short-maturity debt is negatively associated with conditional accounting conservatism in financially distressed firms.

H2b.

Short-maturity debt is negatively associated with unconditional accounting conservatism in financially distressed firms.

Some recent studies indicate a positive association between the level of accounting conservatism and agency problems. LaFond and Roychowdhury (2008), for instance, examined the impact of managerial ownership on accounting conservatism employed by corporations and found that manager-owned firms with an accentuated state of ownership and control separation as well as agency conflicts are faced with lower conservatism. Likewise, LaFond and Watts (2008) demonstrate that there is a positive association between conservatism and information asymmetry arising from agency problems. These studies are indicative of how equity investors demand conservatism. Nevertheless, Ramalingegowda and Yu (2012) predict and find that institutional investors that are more likely to monitor corporate managers drive the demand for conservatism more than the individual investors. Indeed, institutional investors, as more sophisticated and important price-setters in capital markets, perceive and value corporate governance benefits of conservative financial reporting and, consequently, demand more conservative accounting from managers (Bartov et al., 2000; Chakravarty, 2001; Sias et al., 2006; Ramalingegowda and Yu, 2012). Prior literature also introduces some institutional characteristics inducing higher monitoring incentives such as long investment horizons, concentration of share holdings and independence from management (Shleifer and Vishny, 1986; Hartzell and Starks, 2003; Chen et al., 2007; Ramalingegowda and Yu, 2012). This leads to our next hypotheses as follows:

H3.

There is a significant association between institutional ownership and accounting conservatism.

H3a.

There is a significant association between active institutional ownership and conditional accounting conservatism.

H3b.

There is a significant association between passive institutional ownership and conditional accounting conservatism.

H3c.

There is a significant association between active institutional ownership and unconditional accounting conservatism.

H3d.

There is a significant association between passive institutional ownership and unconditional accounting conservatism.

Finally, based on the above-mentioned relationships, we posit the following hypotheses as well:

H4.

There is a significant association between institutional ownership and short-maturity debt.

H4a.

There is a significant association between active institutional ownership and short-maturity debt.

H4b.

There is a significant association between passive institutional ownership and short-maturity debt.

3. Research design

3.1 Data sources and sample selection procedure

We obtain our required data manually from the hardcopy financial statements held in the TSE library (Codal[1] and its supplementary software known as Rahavard Novin) for the period 2011–2016. To construct our sample for the paper’s hypotheses, we begin with all client-year observations present on the Codal database, i.e., a potential population of 2,982 firm-year observations. We then exclude delisted observations (696 firm-year observations), observations with missing or insufficient variable data (126 firm-year observations) and newly-listed observations[2] (408 firm year observations). We also exclude firms operating in banking industry as well as financial and investment institutions (894 firm-year observations) to calculate the variables used in our equations, primarily because financial institutions and banking industry have different reporting requirements that could influence the figures associated with dependent variables. This leaves us with a primary sample of 858 firm-year observations. Table I discusses the breakdown of sample attrition.

3.2 Measures of accounting conservatism

The present paper employs Basu’s (1997) earning-return model along with Beaver and Ryan’s (2000) book-to-market model to measure accounting conservatism. Basu’s (1997) model regresses earnings on positive (negative) stock returns (i.e. the sign of the return coefficient could be either positive or negative) to capture good or bad economic news as follows:

(1) N I j t = β 0 + β 1 N E G J T + β 2 R E T J T + β 3 N E G J T × R E T J T + ε ,
where NIjt is the annual income before extraordinary items of firm j in year t scaled by the market value of stockholders’ equity; RETJT is the buy-and-hold stock return of firm j over year t; NEGJT, is the indicator variable equal to 1 if RETJT is negative and 0 otherwise.

In Equation (1), the coefficient on NEGJT (β2) captures the timeliness of earnings concerning good news and the coefficient on the interaction variable of NEGJT×RETJT (β3) captures asymmetric timeliness regarding bad news vs good news (i.e. the measure of accounting conservatism). We use Equation (1) to measure conditional conservatism. Following Ismail and Elbolok (2011), we also incorporate MB (ratio of market value to book value of stockholders’ equity) in Equation (1) in order to measure unconditional conservatism.

Beaver and Ryan (2000) designed a model for measuring accounting conservatism by capturing the difference between the book value and market value of net total assets. To put it simply, the authors measured conservatism as the ratio of book to market value of stockholders’ equity:

(2) CON = Book Value of Stockholders Equity Market Value of Stockholders Equity × ( 1 ) .

3.3 Regression models

Following Khurana and Wang (2015), we use Basu’s (1997) modified model to empirically examine the relationship between short-maturity debts and conditional (unconditional) conservatism reflected in H1H1b as follows:

(3) N I t = β 0 + β 1 N E G t + β 2 S T t 1 + β 3 M B t 1 + β 4 L E V t 1 + β 5 S I Z E t 1 + β 6 N E G t × S T t 1 + β 7 N E G t × M B t 1 + β 8 N E G t × L E V t 1 + β 9 N E G t × S I Z E t 1 + β 10 R E T t + β 11 R E T t × S T t 1 + β 12 R E T t × M B t 1 + β 13 R E T t × L E V t 1 + β 14 R E T t × S I Z E t 1 + β 15 R E T t × N E G t + β 16 R E T t × N E G t × S T t 1 + β 17 R E T t × N E G t × M B t 1 + β 18 R E T t × N E G t × L E V t 1 + β 19 R E T t × N E G t × S I Z E t 1 .

In Equation (2), ST represents the extent of short-maturity debt in a firm’s capital structure. Following Fan et al. (2012) and Wang et al. (2010), we use the ratio of short-term debts to total debts as a proxy for the debt maturity structure. The market-to-book (MB) ratio is computed as the market value scaled by the book value of common equity. MB is used to measure unconditional conservatism and its relation with the debt maturity structure. LEV represents the financial leverage and is calculated as the book value of debts scaled by the book value of total assets. SIZE is a proxy for firm size and computed as the natural logarithm of market value of equity. To examine the relation between debt maturity structure and accounting conservatism, we also estimate the following model (Beaver and Ryan, 2000):

(4) C O N t = β 0 + β 1 S T t 1 + β 2 L E V t 1 + β 3 S I Z E t 1 + ε .

where CON is the accounting conservatism; ST is the extent of short-maturity debt in a firm’s capital structure; LEV is the financial leverage, calculated as the book value of debts scaled by the book value of total assets; SIZE is the firm size, computed as the natural logarithm of the market value of equity.

To test the association between short-maturity debts and conditional and unconditional conservatism in financially distressed firms (i.e. H2H2b), we employ Altman’s (1968) bankruptcy prediction model (Z-score). This model is dependent on five different ratios: namely, liquidity, solvency, profitability and activity:

(5) Z = α + 0 / 012 W C T A + 0 / 014 R O A + 0 / 033 E B T A + 0 / 006 M V T D + 0 / 999 S T A .

In Equation (5) WCTA represents the ratio of working capital to total assets; ROA, the ratio of retained earnings to total assets; EBTA, the ratio of earnings before income and tax to total assets; MVTD, the ratio of the market value of equity to book value of total debts; STA, the ratio of sales income to total assets; and Z, the overall Z index. The lower a firm’s Z-score, the higher its probability of bankruptcy. Indeed, Z values lower than 1.81 are indicative of bankruptcy. Based on the previous measure of FD or bankruptcy and following Khurana and Wang (2015), we estimate the following regression model to test H2H2b:

(6) N I t = β 0 + β 1 N E G t + β 2 S T t 1 + β 3 M B t 1 + β 4 L E V t 1 + β 5 S I Z E t 1 + β 6 F D + β 7 N E G t × S T t 1 + β 8 N E G t × M B t 1 + β 9 N E G t × L E V t 1 + β 10 N E G t × S I Z E t 1 + β 11 N E G t × F D + β 12 R E T t + β 13 R E T t × S T t 1 + β 14 R E T t × M B t 1 + β 15 R E T t × L E V t 1 + β 16 R E T t × S I Z E t 1 + β 17 R E T t × F D + β 18 R E T t × N E G t + β 19 R E T t × N E G t × S T t 1 + β 20 R E T t × N E G t × M B t 1 + β 21 R E T t × N E G t × L E V t 1 + β 22 R E T t × N E G t × S I Z E t 1 + β 23 R E T t × N E G t × F D + ε ,

where FD is our variable of interest which equals 1 if the firm is faced with FD, and 0 otherwise. The rest of the variables are similar to those defined in Equation (3). To test the relationship between the debts maturity structure and accounting conservatism by our second conservatism measure, we estimate the following Beaver and Ryan’s (2000) model:

(7) C O N t = β 0 + β 1 S T t 1 + β 2 F D + β 3 S T × F D + β 4 L E V t 1 + β 5 S I Z E t 1 + ε .

All of the variables included in Equation (7) are similar to variables used in Equations (3), (4) and (6). We examine the effect of active and passive institutional ownerships on accounting conservatism (H3H3d) by modifying Basu’s (1997) model as follows:

(8) N I t = β 0 + β 1 N E G t + β 2 R E T t + β 3 R E T t × N E G t + β 4 I N S T t + β 5 I N S T t × N E G t + β 6 I N S T t × R E T t + β 7 I N S T t × R E T t × N E G t + ε .

In Equation (8), the positive or negative sign of β7 indicates that greater (lesser) institutional ownership results in greater (lesser) accounting conservatism. Next, we draw a distinction between active and passive institutional ownerships and then modify the above-mentioned model:

(9) N I t = β 0 + β 1 N E G t + β 2 R E T t + β 3 M B t 1 + β 4 R E T t × N E G t + β 5 N E G t × M B t 1 + β 6 R E T t × M B t 1 + β 7 R E T t × N E G t × M B t 1 + β 8 A C I N S T t + β 9 A C I N S T t × N E G t + β 10 A C I N S T t × R E T t + β 11 A C I N S T t × R E T t × N E G t + β 12 I N A C I N S T t + β 13 I N A C I N S T t × N E G t + β 14 I N A C I N S T t × R E T t + β 15 I N A C I N S T t × R E T t × N E G t + ε .

In Equations (8) and (9), INST represents the percentage of shares held by institutional investors; ACINST, the percentage of shares owned by active institutional investors (i.e. the institutional investors with representatives on corporate board of directors); INACINST, the percentage of shares owned by passive institutional investors (i.e. the institutional investors without any board representation). The rest of variables are similar to previous equations. In addition to Equation (9), we attempt to examine the effect of active and passive institutional ownerships on the second measure of accounting conservatism proposed by Beaver and Ryan (2000) by estimating the following equation:

(10) C O N t = β 0 + β 1 A C I N S T t + β 2 I N A C I N S T t + β 3 L E V t 1 + β 4 S I Z E t 1 + ε .

Finally, the following equation captures the effect of active and passive institutional ownerships on the debt maturity structure:

(11) S T = β 0 + β 1 N E G t + β 2 A C I N S T t + β 3 I N A C I N S T t + β 4 L E V t 1 + β 5 S I Z E t 1 + ε .

3.4 Specification tests (diagnostics) in panel data models

We conduct several diagnostic tests by using the R programming language to estimate the most appropriate models. What follows is a succinct explanation of these tests at 0.05 significance level: the results of F-limer specification test (P1<0.001; P2<0.001; P3<0.001; P4<0.001) confirms the preference of panel data model for all four models and for both accounting conservatism measures. The results of Hausman test indicates that all models are better fitted using the random effects model (P1: 0.497; P2: 0.962; P3: 0.961; P4: 0.204) for both conservatism measures. Next, we use Lagrange Multiplier Test (Breusch-Pagan) to choose the appropriate model between a random effects regression and a simple OLS regression. The null hypothesis in the LM test is that variances across entities are zero. That is, there is no significant difference across units (i.e. no panel effect). This test confirms the appropriateness of the pooled OLS model with time effects for all regression models (P1: 0.023; P2: 0.003; P3: 0.388; P4: 0.387) and for both conservatism measures. Finally, the results of specification tests indicate that the panel of random effects is the most appropriate model so far all models. To test the serial correlation of (the idiosyncratic component of) the errors in panel models, we also conduct the Breusch-Godfrey/Wooldridge test on the residuals of the (quasi-) demeaned model, which should be serially uncorrelated under the null of no serial correlation in idiosyncratic errors for both conservatism measures. The results suggest that our final models are fitted using the Panel Generalized Linear Model (PGLM). A brief summary of the above-cited tests is shown in Table II.

4. Results

4.1 Descriptive statistics

Table III reports the descriptive statistics of the variables used in the regression models. As it is evident in panel A of this table, current debts on average account for 86 percent of the total debts of the sample firms. Comparing the average and standard deviation of debt maturity structure (ST) and Beaver and Ryan (2000) conservatism measure also reveals a negative relationship between the two, which could provide supporting evidence for H2. In panels B and C of Table III, the proxy used for FD indicates that an average of 72 percent of sample firm are faced with FD. The indicator variable with respect to Basu’s (1997) model (NEG) demonstrates an average of 46 percent negative return on common equity of sample firms, suggesting the critical condition of most companies listed on the TSE. The average net income before extraordinary items of sample firms is 10 percent of market value of stockholders’ equity. Finally, Table III indicates that 57 percent of institutional investors are active institutional investors (i.e. with board representation) and 13 percent of them are passive institutional investors (i.e. without any representative on the corporate board of directors).

4.2 Estimation results

Table IV exhibits the estimation results of model (3) using PGLM. As it is shown in panel A of this table, while LEV is negatively (C: −0.936) and significantly (two-tailed p<0.001) associated with accounting conservatism, the coefficient on RET×NEG (C: 3.336) as well as the coefficient on RET×LEV (C: 0.283) suggest a significant and positive relation with accounting conservatism. In addition, the interaction variables of RET×NEG×LEV (C: −1.120; P: 0.017) and NEG×RET×SIZE (C: −0.158; P: 0.047) indicate a negative and significant relation with accounting conservatism. As the estimation results show, the rest of variables used in model (3) are not significantly associated with accounting conservatism. Based on preceding discussions and considering the non-significant coefficient on RET×NEG×ST, our results do not provide supporting evidence for H1. However, the results confirm the negative relationship between debt maturity structure and accounting conservatism.

Panel B of Table IV also suggests that LEV (C: 2.933; p<0.001) and SIZE (C: 0.311; p<0.001) are negatively and significantly associated with accounting conservatism computed by Beaver and Ryan’s (2000) model (at 0.01 significance level). Furthermore, the figures reported in panel B are also indicative of the significance of the coefficient on ST at 0.05 of significance level. Therefore, our findings provide supporting evidence for H1. That is, accounting conservatism measured by Beaver and Ryan’s (2000) model is negatively influenced by debt maturity structure.

The figures shown in Table V are the estimation results of models (6) and (7) using PGLM. Panel A of this table indicates that LEV and the interaction variables of RET×NEG×LEV as well as RET×NEG×SIZE are negatively and significantly associated with accounting conservatism measured by modified Basu’s (1997) model. Since the coefficient on the interaction variable of RET×NEG×ST×FD (C: 0.321; P: 0.882) is non-significant, our findings do not support H2. Nevertheless, our findings confirm the negative relation between debt maturity structure and accounting conservatism. Panel (B) of Table V indicates a significant relationship between all the variables used in model (7) and accounting conservatism measured by Beaver and Ryan’s (2000) model. In this respect, the significant coefficients on ST (C: −1.122; p<0.001) and FD (−1.245; p<0.001) provide support for H2.

Table VI indicates the estimation results of model (9) and (10) using PGLM. As it is evident in panel A of Table VI, the ratio of MB value of stockholders’ equity and passive institutional investors (INACINST) is significantly associated with accounting conservatism (at 0.05 of significance level). However, since the coefficients on the interaction variables of INACINST×RET×NEG (C: 0.860; P: 0.076) and ACINST×RET×NEG (C: −1.578; P: 0.104) are not statistically significant, H3 is not supported. Panel B of Table VI indicates that LEV and SIZE are significantly and positively associated with accounting conservatism measured by Beaver and Ryan’s (2000) model. However, these figures do not show any significant relationship between active (C: −0.152; P: 0.387) and passive (C: −0.243; P: 0.329) institutional investors (ACINST and INACINST) and accounting conservatism. Accordingly, H3 is not supported by our findings.

Finally, the estimation results of model (11) reported in Table VII suggest that active (C: 0.0008; P: 0.978) and passive (C: −0.006; P: 0.895) institutional ownerships are not significantly associated with accounting conservatism and consequently H4 is not supported as well.

5. Conclusions

The present paper examines the relation between a firm’s overall the debt maturity structure and accounting conservatism in financially distressed and non-distressed firms. In this respect, we employ two different measures for accounting conservatism often used in prior literature, i.e., Basu’s (1997) earning-return model as well as Beaver and Ryan’s (2000) book-to-market model. We argue that short-maturity debts contributes to the reduction of suboptimal investment problems inherent in debt financing and information asymmetry and mitigates agency costs of debts. Indeed, we expect that short-maturity debt covenants lead to less conservative financial reporting, primarily because conservatism per se can trigger debt covenant violations. However, our findings are in contradiction to our primary expectation and suggest a non-significant relation between the two in both financially distressed and non-distressed firms. This finding is inconsistent with the results reported by Khurana and Wang (2015) who found a negative and significant relationship.

Our study also extends an emerging empirical literature examining institutional investors’ demand for accounting conservatism (LaFond and Roychowdhury, 2008; LaFond and Watts, 2008; Ramalingegowda and Yu, 2012). To put it simply, we shed further light on prior studies by providing evidence that institutional investors as a major group of investors who demand conservatism could significantly influence the extent of conservatism employed in financial reports. Again, our results did not provide supporting evidence for preceding expectation as they indicated a non-significant relationship between both active and passive institutional ownerships and accounting conservatism. Likewise, the relationship between institutional ownership and accounting conservatism was found to be non-significant.

Sample attrition

Initial population of industrial firms with required data for estimating variables derived from the TSE database for the sample period 2011–2016 2,982
Less: Delisted observations 696
Less: Observations with missing variable data 126
Less: Observations operating in banking industry as well as financial and investment institutions 894
Less: Newly-listed observations 408
Equal: Total observations in sample 858

The summary of specification tests in panel data models

Specification test Null hypothesis Hypothesis (Basu) Hypothesis (B&R) Test result
F-limer OLS model is appropriate 1, 2, 3 & 4 1, 2, 3 & 4 Preference of panel data model
F-limer OLS model with time effects is appropriate 1, 2, 3 & 4 1, 2, 3 & 4 Preference of panel data model
Hausman Random effects model is appropriate 1, 2, 3 & 4 1, 2, 3 & 4 Appropriate
LM Pooled OLS model with individual effects is appropriate 1, 2, 3 & 4 1, 2, 3 & 4 Inappropriate
LM Pooled OLS model with time effects is appropriate 1, 2, 3 & 4 1, 2, 3 & 4 Appropriate
LM Pooled OLS model with individual and time effects is appropriate 1, 2, 3 & 4 1, 2, 3 & 4 Inappropriate
Breusch–Godfrey/Wooldridge No serial correlation in idiosyncratic errors 1, 2, 3 & 4 1, 2, 3 & 4 The residuals are serially correlated

Descriptive statistics

Variable n Min. Max. Mean Median SD
Panel A: descriptive statistics of 858 firm-year observations over 2009–2014
NI 858 −3.306 1.827 0.097 0.165 0.412
ST 858 0.156 1.000 0.858 0.915 0.154
RET 858 −0.784 6.992 0.077 0.014 0.687
MB 858 −277.242 87.069 1.104 1.463 12.919
LEV 858 −0.758 6.596 0.670 0.685 0.507
SIZE 858 9.254 18.629 12.985 12.994 1.501
CON 858 −5.148 8.509 −0.699 −0.613 0.965
ACINST 858 0.000 0.990 0.573 0.680 0.311
INACINST 858 0.000 0.820 0.126 0.065 0.161
Panel B: descriptive statistics of financially distressed observations (FD=1)
NI 616 −3.310 1.830 0.936 0.170 0.432
ST 616 0.160 1.000 0.857 0.920 0.160
RET 616 −0.780 4.520 0.077 0.000 0.465
MB 616 −277.240 87.070 0.668 1.435 15.011
LEV 616 0.140 3.060 0.678 0.665 0.243
SIZE 616 9.250 18.630 13.295 13.240 1.461
CON 616 0.000 0.710 −0.699 −0.610 0.968
Panel C: descriptive statistics of non-financially distressed observations (FD=0)
NI 242 −2.070 1.650 0.104 0.150 0.357
ST 242 0.230 1.000 0.757 0.890 0.135
RET 242 0.780 6.600 0.075 0.000 0.598
MB 242 −7.112 43.400 2.219 1.500 3.915
LEV 242 0.150 2.770 0.651 0.640 0.283
SIZE 242 9.450 15.970 12.208 12.085 1.297
CON 242 −3.770 5.370 −0.689 −0.605 0.946

Estimation results for H1 by using PGLM

Variable Coefficient SD t-statistic p-value
Panel A: estimation results by using modified Basu’s (1997) model
Constant 0.551 0.267 2.061 0.039**
NEG 0.394 0.477 0.825 0.409
ST 0.096 0.151 0.640 0.522
MB 0.002 0.001 1.604 0.108
LEV −0.963 0.107 −8.977 <0.001***
SIZE 0.011 0.016 0.720 0.471
NEG×ST −0.083 0.257 0.321 0.748
NEG×MB −0.003 0.003 −0.756 0.450
NEG×LEV 0.077 0.193 0.403 0.687
NEG×SIZE −0.029 0.028 −1.056 0.291
RET 0.587 0.556 1.056 0.290
RET×ST −0.212 0.346 −0.612 0.540
RET×MB 0.009 0.010 0.846 0.397
RET×LEV 0.283 0.168 1.690 0.047**
RET×SIZE −0.048 0.033 −1.443 0.148
RET×NEG 3.336 1.451 2.298 0.021**
RET×NEG×ST −0.173 0.898 −0.192 0.847
RET×NEG×MB −0.010 0.012 −0.861 0.389
RET×NEG×LEV −1.120 0.470 −2.381 0.017**
RET×NEG×SIZE −0.158 0.079 −1.986 0.047**
Panel B: estimation results by using Beaver and Ryan’s (2000) model
Constant −6.392 0.430 −14.862 <0.001***
ST −0.358 0.185 −1.932 0.053*
LEV 2.933 0.134 21.773 <0.001***
SIZE 0.311 0.027 11.398 <0.001***

Notes: *,**,***Statistical significance at 10, 5 and 1 percent, respectively

Estimation results for H2 by using PGLM

Variable Coefficient SD t-statistic p-value
Panel A: estimation results by using modified Basu’s (1997) model
Constant 0.936 0.334 2.802 0.005***
NEG 0.172 0.621 0.276 0.782
ST −0.299 0.285 −1.050 0.293
MB 0.002 0.001 1.638 0.101
LEV −1.007 0.108 −9.289 <0.001***
SIZE 0.004 0.017 0.248 0.803
FD −0.334 0.292 −1.143 0.253
NEG×ST 0.222 0.522 0.427 0.669
NEG×MB −0.002 0.003 −0.755 0.451
NEG×LEV 0.033 0.204 0.163 0.870
NEG×SIZE −0.028 0.030 −0.933 0.351
NEG×FD 0.332 0.526 0.631 0.527
RET 0.906 1.057 0.857 0.391
RET×ST −0.435 1.136 −0.383 0.701
RET×MB 0.005 0.013 0.432 0.665
RET×LEV 0.296 0.169 1.749 0.080
RET×SIZE −0.052 0.034 −1.523 0.127
RET×NEG 3.017 2.071 1.457 0.145
RET×NEG×ST×FD −0.321 2.165 −0.148 0.882
RET×NEG×MB −0.005 0.014 −0.423 0.672
RET×NEG×LEV −1.398 0.498 −2.807 0.005***
RET×NEG×SIZE −0.179 0.083 −2.144 0.032**
Panel B: estimation results by using Beaver and Ryan’s (2000) model
Constant −5.560 0.486 −11.579 <0.001***
ST −1.122 0.331 −3.382 <0.001***
FD −1.245 0.356 −3.498 <0.001***
ST×FD 1.113 0.392 2.834 0.0045***
LEV 2.899 0.132 21.899 <0.001***
SIZE 0.320 0.026 12.053 <0.001***

Notes: *,**,***Statistical significance at 10, 5 and 1 percent, respectively

Estimation results for H3 by using PGLM

Variable Coefficient SD t-statistic p-value
Panel A: estimation results by using modified Basu’s (1997) model
Constant 0.169 0.070 2.387 0.017**
NEG −0.126 0.123 −1.031 0.303
MB 0.004 0.001 2.710 0.006***
RET −0.002 0.079 −0.025 0.980
ACINST 0.046 0.096 0.481 0.630
INACINST −0.400 0.174 −2.299 0.021**
NEG×MB −0.001 0.003 −0.376 0.707
RET×MB −0.002 0.008 −0.205 0.837
RET×NEG 0.119 0.342 0.349 0.726
RET×NEG×MB 0.005 0.010 0.554 0.579
ACINST×NEG 0.172 0.161 1.066 0.286
ACINST×RET −0.081 0.138 −0.592 0.553
ACINST×RET×NEG 0.860 0.482 1.774 0.076
INACINST×NEG 0.126 0.308 0.408 0.683
INACINST×RET 0.482 0.296 1.629 0.103
INACINST×RET×NEG −1.578 0.973 −1.621 0.104
Panel B: estimation results by using Beaver and Ryan’s (2000) model
Constant −6.682 0.393 −16.982 <0.001***
ACINST −0.152 0.176 −0.865 0.387
INACINST −0.243 0.248 −0.976 0.329
LEV 2.955 0.134 21.929 <0.001***
SIZE 0.317 0.028 11.418 <0.001***

Notes: *,**,***Statistical significance at 10, 5 and 1 percent, respectively

Estimation results for H4 by using PGLM

Variable Coefficient SD t-statistic p-value
Constant 0.985 0.068 14.528 <0.001***
ACINST 0.0008 0.032 0.026 0.978
INACINST −0.006 0.046 −0.131 0.895
LEV −0.051 0.024 −2.137 0.032**
SIZE −0.007 0.005 −1.518 0.129

Notes: *,**,***Statistical significance at 10, 5 and 1 percent, respectively

Notes

2.

That is those companies that had been listed on the TSE after the fiscal year of 2011.

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Corresponding author

Mahdi Salehi can be contacted at: mehdi.salehi@um.ac.ir