Faculty of Economics and Business
Master Thesis
The effect of blockholders on the association of
earnings management and share price volatility
Name: Marina Tadić
Student number: 11112018 Thesis supervisor: Dr. Réka Felleg
Date: June 26, 2017
Word count: 15,016
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Statement of Originality
This document is written by student Marina Tadić who declares to take full responsibility for the contents of this document.
I declare that the text and the work presented in this document is original and that no sources other than those mentioned in the text and its references have been used in creating it.
The Faculty of Economics and Business is responsible solely for the supervision of completion of the work, not for the contents.
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The effect of blockholders on the association of earnings
management and share price volatility
Abstract
In this study, I examine the moderating effect of large shareholders, also known as blockholders, on the association of earnings management and share price volatility. In
addition, prior literature shows that the effect of blockholders is twofold namely: positive and negative. Using discretionary accruals as a proxy for accrual-based earnings management, I find that firms with the presence of blockholders that adjust accruals show an increase in stock price volatility. These results imply that investors uncertainty increases when firms with the presence of blockholders engage in accruals-based earnings management.
Furthermore, using abnormal levels of cash flows, production and discretionary expenses as proxies for real activities earnings management, I find that firms with the presence of at least one blockholder and controlling for multiple blockholders show a decrease in stock price volatility, when reporting abnormal levels of cash flows from operations in particular. The other two proxies of real activities earnings management show no significant relationship.
Keywords: Investor uncertainty, share price volatility, corporate governance, ownership structure, blockholders, accrual-based earnings management, real activities earnings management
Acknowledgements
I am grateful for the guidance and feedback received from my thesis supervisor Dr. Réka Felleg during my thesis process.
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Contents
1. Introduction 5
2. Literature review and hypothesis development 10
2.1. Agency theory 10 2.2. Blockholders 11 2.3. Earnings management 12 2.4. Investor uncertainty 14 2.5. Hypothesis development 15 3. Research methodology 17 3.1. Sample 17 3.2. Empirical model 17
3.2.1. Share price volatility 18
3.2.2. Blockholders 19
3.2.3. Earnings management (EM) 19
3.2.4. Control variables 21 4. Results 24 4.1. Descriptive statistics 24 4.2. Regression analysis 26 4.3. Additional tests 29 5. Conclusion 33 6. References 36 7. Appendix 39
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1. Introduction
This study examines the effect large investors, also known as blockholders, have on the association of earnings management and share price volatility. Specifically, examining whether blockholder has a moderating effect on the existing relation of earnings management and share price volatility.
Based on theory of Jensen and Meckling (1976) agency problems arise between the principal and the agent when shareholders invest resources in the company where the manager has decision rights, but needs to act in the shareholders’ interest to increase firm value. As a consequence, the problem of moral hazard and adverse selection arises, where the manager has access to the resources provided by the shareholders but has to act in the
shareholders’ and firms’ best interest. This separation of ownership, therefore, leads to
information asymmetry, where the manager has private information over the shareholder. The investors are faced with uncertainty due to this agency problem. This uncertainty enables the investor to make estimations of the firms’ future value. Due to these problems managers are able to use earnings management. To align their interest, the board of directors introduce corporate governance mechanism such as monitoring by large investors (Edmans, 2013). Investor uncertainty arises due to the information asymmetry between the manager and investors. Since the investor has no perfect knowledge about the firm, they rely on estimations of future cash flows they make based on available information. When uncertainty is high at a firm, investors may react more to new available information and generate asset price changes. As a result, investors that are risk-averse in general, will try to mitigate the risk by hedging and will require greater compensation for bearing more risk (Ozoguz, 2008; Kinder, 2002). Moreover, Bansal and Yaron (2004) argue that higher uncertainty results in lower valuation ratios and dividend price ratios. When investor uncertainty is greater more emphasis comes on the information the firm provides (Lang, Lins and Maffett, 2012). Therefore, firms that have higher transparency have lower transaction costs and greater liquidity of stocks. The available information results in investors making adjustments to their estimations which is the driver for stock price changes due to trading. Financial markets’ measure for stock price fluctuation variability is volatility.
However, due to agency problems, the manager’s discretion over earnings, and managers’ incentives, earnings management (EM) might be used by managers. Prior studies have shown evidence of firms using EM and affecting the share price, such as; managing the shares upward resulting in overvalued shares. They find that the longer the firm is overvalued
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the higher the amount of total earnings management is (Badertscher, 2011), managing earnings upward at initial public offering (IPO) to inflate issue price (Morsfield and Tan, 2006; Fan, 2007), managing earnings upward in the quarter and after the seasoned equity offerings (SEOs) (Rangan, 1998), or prior to a takeover bid (Botsari and Meeks, 2008) or beating the analysts’ forecast by the three types of thresholds; (1) zero earnings; (2) last year’s earnings, and (3) the consensus analyst forecast (Bartov, Givoly, Hayn, 2002; Das, Kim and Patro, 2011). The focus of these prior studies, however, is on the share price in relation to earnings management. In a recent study on idiosyncratic volatility and financial reporting quality measured by earnings quality, Rajgopal et al. (2011) find that worsening and decreasing earnings quality causes noisier earnings which are associated with higher stock return volatility. They argue that the lack of transparency of earnings result in more analysts’ forecasts putting more emphasis on other private information of the firm’s future performance. As a result, the financial reporting quality measured by earnings quality decreases. On the other hand, the working paper of Markarian and Gill-de-Albornoz (2012) and the extension paper of the study of Rajgopal et al. (2011), examines idiosyncratic risk in relation to income smoothing. They find that income smoothing actually reduces stock return volatility. In addition, Brockman and Sterling (2009) argue that blockholders increase the amount of firm-specific information into stock prices, since they have an information advantage. In addition to the findings that earnings management creates noisier earnings on which investors cannot rely (Rajgopal et al. 2011), Black (1976) argues that the relationship of the share price and volatility is negative.
The introduction of corporate governance by the firm’s boards to mitigate the use of earnings management then follows. Corporate governance controls are forced by the firm’s board of directors (Jensen et al., 1976), for monitoring by large shareholders or blockholders, that own at least 5 percent of firm’s outstanding common stocks and are not part of the board of directors. These controls can improve transparency by increasing the ability of
shareholders to determine the quality of management and the true value of the firm (Chung, Elder and Kim, 2010).
Blockholders have multiple ways to exercise their control over a firm (Zhong, Gribbin and Zheng, 2007). Firstly, blockholders have influence through ‘voice’ which is the direct intervention where the investor has the possibility of monitoring the managers, suggesting a strategic change or even blocking a project. Basu, Paeglis and Rahnamaei, (2016) argue that blockholders with a monitoring role add value to the firm (Fama and Jensen, 1983; Shleifer and Vishny, 1997). This is also known as the ‘incentive alignment effect’. Secondly, they
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can govern through the ‘exit’ channel, which consists of selling their shares when the manager is not acting in alignment with the objectives, as a consequence the large investors can affect the share price. In sum, this could motivate the manager to increase firm value and act in shareholders’ interest (Edmans, 2013; Basu et al., 2016). Since, investors in general aim to reduce their risk of stock exposure because they are risk averse (Kinder, 2002), they can choose to sell shares after an increase in firm value when they are motivated in selling for personal gain exclusively (Zeckhauser and Pound, 1990). Instead of selling, they can choose to remain the holders of the stock. Particularly, when they have a strong preference to remain in control. This might be the case when more blockholders are present at one firm, and competition arises (La Porta, Lopez, Shleifer and Vishny, 2000). Rather than decreasing the agency problem the presence of blockholders can also intensify these problems. By the threat of intervention and decreasing the initiative of the manager where the presence of
blockholders result in a lower liquidity of stocks (Edmans, 2013). In addition, they can also reduce firm value by extracting capital. Blockholders, therefore, with a certain level of stocks have a tendency to expropriate the wealth minority shareholders and reduce firm value, which is also known as the ‘entrenchment effect’ (Fama et al., 1983; Shleifer et al., 1997; Edmans, 2013).
Linking the theoretical and empirical literature I expect the effect of blockholders on the relationship of earnings management and stock price volatility to be twofold. First, blockholders can have a positive effect on the relationship of EM and share price volatility. Informed blockholders have an information advantage over non-blockholders which
increases the quality and quantity of firm specific information through monitoring (Brockman
et al., 2009). Given that blockholders invest large amounts of capital, this incentivizes them
to bear the cost for obtaining new costly information which gives them access to more firm specific information. For that reason, blockholders are perceived as the group with
knowledge and capital, which investors might interpret as a guarantee that the firm is
performing well. Secondly, blockholders can have a negative effect on the association of EM and share price volatility. Since, blockholders are known for monitoring, investors might interpret this as bad where the manager of the firm is not acting in the shareholders’ best interest. On the contrary, investors may see the blockholder as controlling over the managers for own personal gain, which in turn results in higher volatility.
I extend prior literature by focusing on the effect of earnings management of both classifications, namely: accruals based accounting and real activities manipulation. Investors have gained experience finding and reacting to the signs of accruals EM and in some cases
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this can have a positive outcome, real earnings management is still difficult to discover. Therefore, this might result in a negative effect on the financial numbers investors rely on. Moreover, manipulation with real activities is based on the operating activities the manager has discretion over. Opposed to accruals based earnings management which are usually adjusted at fiscal-year end, the manager can manipulate real activities all year around. The difficulty of discovering and separating the manipulation from the normal business operating cycle increases. Since accruals manipulation in usually noticed by the auditor (Cohen and Zarowin, 2010), this could imply that investors can accept earnings management with the presence of blockholders as a guarantee. Consequently, this would result in a low volatility of the share price. Rather than accepting, investors can also reject the firm engaging in earnings management even though blockholders are present since they can make agency problems worse. In fact, blockholders might increase the risk investors perceive bearing which results in an increase of the share price volatility.
The US setting is appropriate to examine my research question because Bacidore and Sofianos (2002) argue that listed companies on the New York Stock Exchange (NYSE) exhibit a higher stock market liquidity than those outside the U.S. Furthermore, Denis and McConnell (2003) argue that blockholders have a more positive effect on firm value in countries with lower level of investor protections since it is more necessary to secure
managerial agency problems in these countries. Although, the US is marked as a country with high investor protection (Thomsen, Pedersen and Kvist, 2006), based on prior literature the effect can still be positive or negative, as discussed above.
Using data from 1996 to 2001, I find that blockholders have a positive or negative effect on the association of accruals-based EM, used as a proxy for EM based on the modified Jones model (1991), and stock price volatility. These findings imply that when blockholders are present and the firm engages in accruals based earnings management, investors perceive this firm of high risk and the stock price volatility increases. This implies, that blockholders are seen as controlling and increase the agency problem. In contrast, I find no such relationship when testing for real activities earnings management. Since, the main effect of real earnings management on share price volatility shows a positive significant relationship investors seem to recognize it happening, which results in a higher volatility. However, with the presence of block holder, investors do not seem to find this a problem since the relationship is not significant.
In additional analyses, I examine whether the association of real activities EM and stock price volatility would be affected by firms with two or more blockholders. Based on the
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main analysis I expect the relationship of two or more blockholders to be on the association of earnings management, real activities EM in particular, and share price volatility to be negative. However, my findings show that the firms with two or more blockholders engaging in real activities EM, of which the proxies include: abnormal cash flow from operations (CFO), abnormal production costs (PROD) and abnormal discretionary expenses (DISX), are only confirmed when the firm adjusts cash flow from operation is confirmed for accruals adjustment in a one tail test. Moreover, the results in the additional analyses also show that firms that engage in accruals-based EM with two or more blockholders present, have a decreasing effect on volatility. Therefore, the results might imply that the investors perceive the firm with one blockholder present as bad, but when more blockholders cover a firm the investors see this a positive.
I contribute to prior literature by examining the moderating effect of blockholders on the association of earnings management and share price volatility. To the best of my
knowledge, this study provides the first evidence on the moderating effect of large investors in the US. I contribute by confirming the existing association of blockholders and accruals based earnings management and add findings of the negative effects of blockholders to the limited literature. Moreover, I extend this existing association by real activities manipulation as well. The same holds for the existing association of accruals earnings management and stock price volatility which I also extend by real activities manipulation as well (Rajgopal et
al., 2011; Markarian et al., 2008). As a result, this study confirms the use of real activities
manipulation before the implementation of SOX, while prior literature has argued that REM has its foundation after SOX (Cohen et al., 2010; Zang, 2013). Since managers engage in earnings management to increase firm’s performance in the short-term, although this as implications for the performance in the long term. This study contributes to the survey of Graham et al. (2005) which shows that managers argue that they are prepared to make costly actions to reduce investors’ perception of risk by engaging in earnings management.
Therefore, these findings might be relevant for managers. Lastly, I add to the existing literature of incomplete information models to explain properties of asset returns and corporate policies (Berrada et al., 2012) by showing the implications earnings management have on the information role of accounting which might be relevant to regulators and accounting policy makers.
The next sections are formulated as follows. Section 2 describes the literature and the hypothesis. Section 3 discusses the research methodology including the sample, method and how the hypothesis will be tested. Section 4 documents the findings on the effect of
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blockholders on the association of earnings management and share price volatility. Lastly, section 5 concludes.
2. Literature review and hypothesis development
2.1.
Agency theory
Jensen et al. (1976) define agency theory as problems that arise when shareholders invest in a company where the executives have delegated decision rights to act in the shareholders’ interest to add value to the firm through daily operations. Moreover, the conflicts of interest between owners- managers and outside shareholders arise due to the managers acting in their own interest rather than increasing the value of the firm, therefore, the misalignment of interest occurs in situations where the managers have incentives to take excessive risk due to transferring investment strategies. The problem arises due to incentives of managers that have discretion over using the capital invested by the investors. This discretion is misused where instead of investing it either below the cost of capital or wasting it to other organizational inefficiencies (Jensen, 1986).
Due to separation of ownerships and control, which leads to information asymmetry where the manager has more superior information over the shareholders, the alignment of interest of managers and shareholders usually involves agency costs, such as monitoring costs (Healy, Krishna & Palepu, 2001; Edmans, 2013). These agency costs result from
management’s shirking and perquisite consumption. As a consequence, managers can
expropriate wealth of minority shareholders which is related to agency problems and can take various forms, such as: theft of profits or just selling the output, assets or additional shares they control to another firm at below market price (La Porta et al., 1999).
In sum, agency problems arise due to information asymmetry between the investor and manager. The manager which has discretion over the invested capital by the investors, can misuse these resources for personal gain, because of incentives for reaching benchmarks granted by the firm. However, corporate governance might have a mitigating effect on agency problems and are, therefore, discussed in the next section.
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2.2.
Blockholders
Possible solutions to agency problems is corporate governance. Corporate governance key mechanism is the protection of outside investors through a legal system. (La Porta et al., 2000) As a result, this can improve transparency and reduce information asymmetries between inside and outside investors. This gives the shareholder the ability to monitor the quality of management and the true value of the firm (Chung et al., 2010).
Investors of more than 5% of stocks, also known as blockholders are a part of this corporate governance structure to ensure managers from misbehaving. These investors have an incentive due to stock outstanding and capital to bear the cost of monitoring and likewise reduce agency problems. These blockholders have multiple ways to exercise control over a firm. Firstly, through direct intervention - also known as ‘voice’ – undertaking any action to improve firm value such as helping the manager to create firm value by providing advice on strategic alternatives or blocking a merger (Edmans, 2013). Also, they can obtain direct intervention through firms’ operating, financing, investment, and governance decisions by getting a seat on the board or at top management (Dou, Hope, Thomas and Zou, 2016). Secondly, the investor can govern through selling shares – otherwise known as ‘exit’ and the “Wall Street Walk”- when the investor cannot intervene or use the ‘voice’ channel (Admati and Pfleiderer, 2009; Edmans and Manso, 2011). The investor can force the manager to show effort or threaten by leaving which results in a drive downward of the stock price (Edmans, 2013). However, unlike small investors blockholders cannot get rid of their large stock quickly, this requires a long-term strategy in general (Zhong et al., 2007). Nevertheless, a blockholder with capital over 5% leaving a firm, signals that the managers are not acting in line with the shareholder’s interest and will motivate more shareholders to leave the firm. By strengthening the disciplinary threat of removing management, this mechanism protects shareholder interests and decreases the ability to which management can turn to shirking, empire building, risk aversion, and perquisites (Bebchuk, Cohen and Ferrell, 2009). Lastly, the blockholder can have a negative effect on the firm value. Moreover, the negative effect can occur by extracting private benefits. Consequently, decreasing the conflict of interest between managers and investors the conflict of interest between the large and small shareholders can increase (Edmans, 2013).
However, there is an assumption in literature that large shareholders have greater power and stronger incentives to make sure the maximization of shareholders’ value is created. Although, the theoretical relationship between large owners and firm value is still
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unclear (Jensen et al., 1976; Zeckhouser et al., 1990; Thomson et al., 2005). Basu et al. (2016) argue that insider power has a negative influence on the firm value but insider
ownership has a positive one. Therefore, consistent with the reasoning that insiders use power to misuse the resources available and outsiders use their power to monitor. On the other hand, Klein (2002) argues that the presence of blockholders has a negative effect on the abnormal accruals when they are on the audit committee and influencing financing outcomes. Block holder’s ownership of more than a certain level may lead to entrenchment of owner-managers that misuse the resources of minority shareholders (Fama et al., 1983; Shleifer et al., 1997). Moreover, separate equity ownership may incentivize management to commit fraud
(Johnson, Ryan, and Tian, 2009), misrepresent corporate performance (Bar-Gill and
Bebchuk, 2002), or even reject positive but risky net present value projects (Smith and Stulz, 1985).
To conclude, blockholders are investors with more than 5% capital invested in the firm. Due to this large invested capital, these investors can exercise more control over a firm, or use the exit threat when the managers are not acting in line with the objectives. However, for a blockholder leaving a firm requires a long-term strategy due to large capital invested. Prior literature has shown mixed effects of blockholders on firms, which are positive in term of monitoring and negative in term of expropriating other shareholders or competing for control with other blockholders.
2.3.
Earnings management
Earnings management is defined by literature as the use of managerial discretion over generally accepted accounting principles (GAAP) accounting choices, earnings reporting choices, and real economic decisions to influence how underlying economic events are reflected in one or more measures of earnings (Walker, 2013; Dou et al., 2016). In addition, literature distinguishes two classifications, namely; accruals based management or
manipulation and real activities manipulation (Gunny, 2010; Zang 2012; Cohen et al., 2010). On the one hand, Gunny (2010) defines accrual management as when the manager uses GAAP accounting choices to hide true economic performance by adjusting accruals such as: receivables accounts, inventory, payable accounts, deferred revenue, accrued liabilities and prepaid expenses. Some examples of accrual manipulation within the GAAP accounting choices are: overly aggressive recognition of provisions or reserves, or acquired in process of Research and Development (R&D) in purchase acquisition, overstatements of restructuring
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charges and assets write-offs, change in accounting criteria. Moreover, recording sales before they are realized, backdating sales invoices or overstating inventory by recording fictitious inventory are examples of accrual manipulation violating accounting norms. (Walker, 2013; Gunny 2010; Cohen et al., 2010; Zang, 2012). Also, by using accrual manipulation the attention of the auditor will most likely be drawn but it will not have direct cash flow consequences (Cohen et al., 2010).
On the other hand, Roychowdhury (2006) defines real earnings management or real activities management as practices that are not in line with normal operational activities. These activities are motivated by managers to mislead some stakeholders into believing some financial reporting objectives have been met by using normal activities of operations. He argues that managers use these certain methods of real management during normal
operations, such as: price discounts and reduction on discretionary expenses, which in line with normal core business activities should not be a problem. Furthermore, real earnings activities manipulation encounter cutting R&D expenditures, cutting selling, overproducing inventory to reduce the cost of goods sold, selling fixed assets with a market value instead of book value (Walker, 2013). Therefore, these actions have a direct effect on the cash flow statement (Cohen et al., 2010). Since examining only one earnings management technique at a time cannot explain the overall effect of earnings management activities, and the outcome would likely be biased, therefore, managers use both methods based on their relative costs. (Fields, Lys and Vincent, 2001; Zang 2012)
Prior literature has showed that manager’s motives for engaging in earnings
management are: (1) earnings-based contracts or equity compensation that are linked to firm performance, (2) influencing information set by external investors for expectations of future cash flows, and (3) influencing information set by third parties with an interest in the firms’ performance (Walker, 2013). This motivation, though, triggers the use of earnings
management, to meet market expectations of shareholders (Walker, 2013). However, Bhojraj, Hribar, Picconi and McInnis (2009) argue that firms who engage in earnings management show a lower operating and stock market performance in the three subsequent years than firms that do not. This is in line with Ng (2011) who argues that higher earnings quality can reduce liquidity risk and the cost of capital. As well as, Lang et al. (2012) who argue that lower earnings management is related to greater liquidity, lower transaction costs and lower cost of capital. Moreover, Badertscher (2011) argues that a firm uses accruals manipulation in the early stages of overvaluation of the firm value and later on moves to real transactions in order to keep the overvalued equity. Furthermore, he shows that the longer the firm is
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overvalued the higher the amount of total earnings management is.
In sum, manager use EM to increase performance in the short-term to mislead the investors. The two classifications; accruals and real activities EM prior literature has shown are used interchangeably based on their relative costs. While in the short term, literature show that using earnings management over a period of years has a positive effect on the firm value and the stock market performance on the long term it has a negative effect. Due to EM information asymmetry between managers and investors rises. Consequently, the uncertainty about future performance increases as well, therefore, uncertainty will be discussed in the next section.
2.4.
Investor uncertainty
The theory of efficient markets covers the reason why stock prices change, it’s considered that new information becomes available, therefore, the prices move with the absence of noise of traders. Therefore, based on the efficient market hypothesis the market price reflects all historic information available (Kostanjcar, Jeren and Juretic, 2012). However, this would create predictable information for investors about future prices, where arbitrage opportunities arise. Furthermore, prior literature has shown that the variability of a stock is difficult to justify. Nevertheless, literature is conclusive on the fact that volatility arises due to trading and the impact this has on prices (Cutler, Poterba and Summers, 1989).
Hence, in an inefficient market the future about stock prices is not predictable. Uncertainty arises because investors are not able to predict the future perfectly, instead they make estimations based on the information currently available. Furthermore, this information is adjusted as more information becomes available (Ozogus, 2009). Due to this incomplete information, the expected returns based on perception by the investors includes aggregate risk exposure and forecast errors. Financial markets use the term volatility to measure the stock price fluctuation variability and risk and forecast errors, therefore, lead to a higher volatility in stock prices (Kostanjcar et al., 2012)
This volatility is important to investors since this implies the amount of risk they are exposed to (Hussainey, Mgbame, Mgbame, 2011). The risk of the exposures is high when the volatility is high since this means that a greater change of gain or loss will happen in the short run. Consequently, this stock is then marked as volatile. Due to these variations in price estimations are more difficult to make and uncertainty of the future price is higher. Since,
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investors are naturally risk averse they prefer less risk and label the investment with less risk as better (Kinder, 2002).
Prior literature has been focusing on the share price being adjusted up or down in relation to earnings management (Badertscher, 2011; Morsfield et al., 2006; Fan, 2007; Rangan, 1998; Botsari et al., 2008; Bartov et al., 2002; Das, Kim and Patro, 2011). Fewer research has been done on the consequences of earnings management on the share price volatility. Since, Pastor and Veronesi (2003) argue that uncertainty about firm’s average profitability has an influence on the stock return volatility, and find that when earnings disclosed are poor the uncertainty will likely be high, stock price volatility is used as a proxy for investors uncertainty. However, Vuolteenaho (2002) finds that cumulative volatility is mainly driven by expected return information, such as discount rates changes and that firm-level volatilities which are highly linked to changes in ex post cash-flow expectations, of which changes in realized earnings or returns are an example. Harris (2003) argues that an increase in firm-level volatilities can be explained by uninformed traders’ trading activity or an increase of ‘noise traders’ due to internet trading. Therefore, managing earnings results in estimation errors of investors. Likewise, investor uncertainty becomes higher which results in a higher volatility.
In conclusion, investors rely on available information to estimate future cash flow performance. On the other hand, managers engage in EM to conceal the true economic performance. As a result, these earnings adjustment investors cannot estimate and therefore, cannot relying on since they have errors included, which is indicated as noisy earnings. Since, investors are generally risk averse they will try to mitigate risk by trading or hedging and uncertainty rises due to estimation errors. This, therefore, increases the volatility of the stock.
2.5.
Hypothesis development
Prior research provides empirical evidence on the traditional theory of the outside block holder, a corporate governance mechanism, as having a monitoring effect to prevent managers from engaging in EM and increase firm value (Lins, 2003; Mitton, 2002; Maury and Pajuste, 2005). However, prior literature shows inconclusive findings since blockholders can increase agency problems as well (Thomsen et al., 2005; Edmans, 2010).
Based on prior literature the effect of blockholders on the association of earnings management and stock price volatility can be twofold. Firstly, this relationship can be
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engaging in EM as a well performing firm since blockholders are considered a corporate governance measure. In other words, the presence of blockholders can be interpreted by investors as a guarantee that the firm is performing well due to monitoring or exit threats. Although earnings are manipulated, the presence of blockholders justifies the use of EM. In this case, even though EM creates noisier earnings (Rajgopal et al., 2011) and the investors need to adjust their estimations (Kostanjcar et al., 2012), the presence of these blockholders signals that the firm is expected to generate reliable future benefits. Therefore, investors will not start trading the stocks of the company, in consequence, the volatility will decrease. On the other hand, the opposite effect of blockholders negatively influencing earnings management and share price volatility relationship, can be argued as well. Given that
blockholders are a corporate governance mechanism, the negative effect could be caused by the fact that the firm is not acting fittingly, therefore, monitoring is needed. Consequently, the presence of blockholders signals that the manager is not acting in the shareholders’ best interest. Additionally, investors could also perceive the presence of the blockholder as bad, due to misusing their influence or control. The manager might want to please these
blockholders, to avoid them from exiting the firm and sending out a signal of bad
performance, this may result in investors exiting the firm and influencing the share price which results in increasing the volatility, in turn.
Whether the presence of the blockholders results in a positive or negative effect on the association of earnings management and share price volatility, is therefore, an empirical question, leading to the following hypothesis:
H1: The presence of blockholders has an effect on the association of earnings management and share price volatility.
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3. Research
methodology
3.1.
Sample
To test whether blockholders have an effect on the relationship of EM and stock price
volatility the sample includes annual data for firms covering the period 1996 to 2001. Firstly, the accounting data is obtained through COMPUSTAT and data on daily stocks prices and returns are obtained from CRSP. Next, data on blockholders is obtained from the Dlugosz, Fahlenbrach, Gompers and Metrick (2006) standardized database. Lastly, analyst information is obtained through the I/B/E/S database. All databases are available through Wharton
Research Data Services (WRDS).
Table 1 shows the sample specifications; the initial sample consist of 91,376 observations. I exclude firms of regulated industries (SIC 4400-5000) and financial
institutions (SIC 6000-6999) because managers from these specific industries have different accounting rule which might result in different motivations for the manager to adjust numbers (e.g. Zang, 2012; Cohen et al., 2010).
I merge the block holder’s database with the merged COMPUSTAT and CRSP dataset, but exclude observations with insufficient data needed to compute my variables. The final sample is made up of 16,401 firm-year observations.
[ Insert Table 1 about here ]
3.2.
Empirical model
To test the hypothesis: ‘whether blockholders have an effect on EM and stock price volatility’ I use a regression model following Rajgopal et al. (2011). Given that my hypothesis is
different, I modify it. The following regression is used to test the hypothesis:
!"#$% = ()+ +,-.//01#"23$,%5,+ +6789$,%5,+ +:;89$,%5,+ β=789$,%5,∗
-.//01#"23$,%5,+ β?;89$,%5,∗ -.//01#"23$,%5,+ +@!2A"$,%5,+ +BC7C7#$,%5,+ +D;"7$,%5,+ +E19$,%5,+ +,)FGH8$,%5,+ +,,#8!$,%5,+ +,6IJ;C"!8;$,%5,+
+,:1GK4$,%5,+ +MNOP-.//QNR + GS-.RTP0-.//QNR + U$%
(1)
The main variables are defined as follows:
VOL is the standard deviation of the annual return of stock which is the proxy for stock price volatility based on Chen, Du, Li and Ouyang (2013).
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BLOCK is the indicator variable which indicates 1 when the firm has at least one blockholder, and 0 otherwise.
AEM is the proxy for accrual based EM, based on the modified Jones model.
REM is the total proxy of the three proxies based on the Roychowdhury (2006) model which consist of: abnormal cash flow from operations (CFO), abnormal production costs (PROD) and abnormal discretionary expenses (DISX). Subsequently, CFO and DISX are multiplied by -1 and added to PROD to have one measure. The measures of these proxies are shown separately in tables.
The regression includes control variables following prior literature (Rajgopal et al., 2011; Markarian et al., 2012).
3.2.1. Share price volatility
Following prior literature, uncertainty faced by investors is represented by the risk in stock prices. The variability of the stock price i.e. investors’ risk is measured by stock price volatility. To measure volatility, I use the model of Chen et al. (2013). While the model of Rajgopal et al. (2011) is also used for stock return volatility, it is not suitable for my test since they make use of average monthly stock return data, while annual is needed due to merging with annual accounting data from Compustat. Moreover, they make a distinction between systematic and unsystematic risk faced by investors. Although, they use the share price volatility and idiosyncratic risk (unsystematic risk) terminology interchangeably, their actual measure is focused only on the systematic risk. Since, I make no distinction between the two risks faced by investors and focus on annual data due to merging with accounting data, I use the model of Chen et al. (2013) to estimate annual volatility.
The first model represents the variance of stock return data and the second model is the standard deviation of the stock return data, which represents the volatility. The model used for stock price volatility (2):
!VWXOP$,% = Y,ln(PNT.PS$,]6)
!"#$,% = Y5,, Y, (PNT.PS$,]− 987C$,])6 (2)
;NT.PS$,] = the daily return of stock i in day k of year t;
987C$,] = is the annual average of all daily stock returns of firm I in year t; S = is the number of trading days in year t.
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3.2.2. Blockholders
Following Dlugosz et al. (2006) the database on WRDS covers the years 1996-2001. Since, the focus is only on outside blockholders, the variables of stocks held by directors, affiliated entities, ESOPs and officers are excluded from the dataset. Blockholders (BLOCK) are measured as an indicator variable that equals 1 if the firm has a block holder, and 0 otherwise.
3.2.3. Earnings management (EM)
Following prior literature, I use both EM classification; accruals based earnings management (AM) and real activities manipulation (REM) (Zang, 2012; Cohen et al., 2010). Firstly, for the accrual based EM the modified Jones model (1991) is used with discretionary accruals as a proxy. Next, for real activities manipulation the model of Roychowdhury (2006) is used, which is the sum of the following three proxies: the abnormal levels of production costs, discretionary expenditure and discretionary accruals.
Both models are estimated with fixed effect for year and industry based on the 2-digit SIC industry code, with the requirement of at least 8 observations (Cohen et al., 2010). This is to control for industry-wide changes in economic conditions which could have an effect on variables to allow coefficients to vary across time. To avoid extreme observations resulting from estimation errors, I winsorize all the variables with 5% and 95% for top and bottom.
3.2.3.1. Accruals earnings management (AEM)
Following prior literature, the proxy for accruals earnings management is the modified Jones model (1991), which is the most powerful test of earnings management (Dechow and Sloan, 1995). This modified model assumes that all changes in credit sales in the event period are due to EM, opposed to the original model which assume discretion was not exercised over revenue. The modified model takes this discretion into account when measuring the discretionary accruals (Dechow et al., 1995). The modified Jones model for discretionary accruals is based on the following cross-sectional model estimated for each industry and year grouping:
I722% = ∝, a,
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I722% = total accruals in year t divided by total assets in year t-1 ∆;8!% = revenues in year t minus t-1 scaled by total assets by t-1;
∆;82% = net receivables in year t minus t-1 scaled by total assets at t-1;
ff8% = gross property, plant, and equipment in year t scaled by total assets t-1;
7%5, = total assets at t-1;
∝,, ∝6, ∝: = firm-specific parameters;
Abnormal values are the residuals of the comparison of the normal values and the actual levels. Therefore, the discretionary accruals (DACC) are the difference between total accruals and abnormal accruals (NDACC). Calculating the discretionary accruals as follows:
g722% = I722%− Cg722% (4)
3.2.3.2. Real earnings management (REM)
Following prior studies, measurement of real earnings management is based on the model of Roychowdhury (2006). In fact, three manipulation methods including their effects on the abnormal levels are used (Roychowdhury, 2006; Cohen et al., 2010). To determine if real manipulation has occurred, the abnormal cash flow from operations R_CFO, abnormal level of production (R_PROD) and the abnormal level of discretionary expenses (R_DISX) are calculated. Abnormal values are the residuals of the comparison of the normal values and the actual levels. Therefore, the following model is used (Roychowdhury, 2006):
hijb abcd = +) + +, , abcd + +6 klmnb abcd + +: ∆klmnb abcd + U% (5)
2A"% = Cash flow from operations in year t, 7%5, = Total assets in year t-1,
FOWN% = Total sales in year t,
∆FOWN% = F% - F%5, , sales in period t minus sales in period t-1, +) , +, , +6 , +: = Parameters to be estimated, namely the betas, U% = Residuals in year t.
The abnormal cash flow from operations is the actual CFO minus the normal CFO calculated using estimated coefficients from the formula above (Cohen et al., 2010; Zang, 2012).
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Production costs are defined as the sum of cost of goods sold (COGS) and the change in inventory during the year (∆GC!%) (Cohen et al., 2010).
The normal level of COGS is: hjokb abcd = +) + +, , abcd + +6 klmnb abcd + U% (6) Inventory growth: ∆pqrb abcd = +) + +, , abcd + +6 ∆klmnb abcd + +: ∆klmnbcd abcd + U% (7)
Normal levels of production in period t combined (equation 6 and 7): stjub abcd = +) + +, , abcd + +6 klmnb abcd + +: ∆klmnb abcd + += ∆klmnbcd abcd + U% (8)
Normal level of discretionary expenses in period t: upkvb abcd = +) + +, , abcd + +6+ klmnbcd abcd + U% (9)
gGFw% = discretionary expenditure, the sum of: advertising expenses, R&D expenses and, SG&A expense in period t
To calculate the total effect of real earnings management, I follow Cohen et al. (2010) and Zang, (2012) and modify by combining the three (CFO, PROD, and DISX) individual measures into one comprehensive metric of real earnings management activities. The total real earnings management measure is combined by multiplying DISX and CFO with -1, resulting in a higher value of real activities manipulation (Zang, 2012). Subsequently, I then add them to PROD which results in the REMtotal variable.
3.2.4. Control variables
Cash flow volatility (VCFO)
Following Vuolteenaho (2002) and Rajgopal et al. (2010), that argue that cash flow news in stock return volatility are related due to expected and unexpected cash flow I control for variances in cash flows which can be correlated with stock price volatility. This variable is calculated for each firm as the variance of annual operating cash flow scaled by total assets over the five years’ window of that firm. The relation with stock price volatility is expected to be positive.
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Analyst following
Following Zang (2012), which argues that analyst can put pressure on managers to issue guidance. Analyst following are defined as the log of 1 plus the number of analyst following the firm. The relation with stock price volatility is expected to be positive.
Operating performance (ROA)
Following Rajgopal et al. (2010) and Zang (2012) I include operating performance to control for firm performance which can be correlated with both volatility an earnings management. ROA is defined as the net income divided by total assets. The relation with stock price volatility is expected to be negative.
Book-to-market (BM)
Following Rajgopal et al. (2010) and Skinner & Sloan (2002), argue that firms with higher growth opportunities experience greater stock price volatility and they are more likely to engage in earnings management. The book-to-market ratio is defined as the ratio of book value of equity, defined as total assets minus total liabilities, divided by the market value of equity. The relation with stock price volatility is expected to be negative.
Size
Following Rajgopal et al. (2010) and Dechow et al. (2002), argue that small firms experience higher stock price volatility and bigger firms tend to be more conservative in accounting decision which can reflect in accruals. Size is defined as the natural logarithm of total assets. The relation with stock price volatility is expected to be negative (Xu and Malkiel, 2015).
Leverage (LEV)
Following Rajgopal et al., (2010) and DeFond & Jiambalvo (1994), argue that firms that have a higher leverage are more likely to experience financial distress or tend to manipulate
accruals because of their concerns of debt covenant violations. Financial leverage is defined as the ratio of long-term debt to book value of total assets. The relation with stock price volatility is expected to be positive.
23 Stock turnover (TURNOVER)
Following Brockman et al. (2009) and Vo (2016) I include stock turnover to control for the amount of stocktrading. Turnover is defined as the total number of shares traded in a year divided by the average number of shares outstanding in a firm. The relation with stock price volatility is expected to be negative.
BIG4
Following Myers, Myers and Omer (2003), to control for earnings management they argue that auditors place a greater constraint on EM and BIG4 auditors are faced with litigation and reputation issues. BIG4 will be measured as an indicator variable that equals 1 if the firm has the auditor of the audit firms; PwC, EY, Deloitte, KPMG, and 0 otherwise the relation with stock price volatility is expected to be negative.
I winsorize all the variables with 5% and 95% for top and bottom to get a normal distribution of my dataset. To examine the hypothesis and to compare firm-years with the rest of the sample I use a robust Ordinary Least Square (OLS) regression clustering by firm. Moreover, year and industry dummies are added to control for e.g. industry specific heterogeneity.
To confirm the hypothesis which is non-directional, β12 should be significant to confirm the effect of blockholders on the association of accruals based EM and stock price volatility. Furthermore, β13 should be significant to confirm the effect of blockholders on the
association between real activities manipulation and stock price volatility. The hypothesis is confirmed if at least one of the two βs is significant. A negative coefficient β12 and/or β13 for H1 would be consistent with the expectation that blockholders have a negative effect on the association of EM and stock price volatility, while a positive coefficient β12 and/or β13 for H1 would be consistent with the expectations that blockholders have a positive effect on the relationship of EM and stock price volatility.
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4. Results
4.1.
Descriptive statistics
In table 2 the descriptive statistics are shown. The dependent variable; VOL has a mean of 0.032 and a median of 0.028 indicating the average share price volatility in the period from 1996 to 2001. Although, share price volatility shows a minimum and maximum of [0.012; 0.0668] similar to Markarian et al. (2012) it is not in line with Rajgopal et al. (2011). The different outcomes might be due to difference in calculation of share price volatility and time period which for their paper is from 1962 to 2001. The mean of the discretionary accruals (AEM) is 0.006 (median= 0.013). For comparison, this is similar to Rajgopal et al. (2011) which indicates that 0.006 of firms in the sample have discretionary accruals. Real earnings management has a mean of -0.014 (median= 0.003) including: abnormal levels of cash flows (CFO) which have a mean of 0.0048 (median= 0.0121), abnormal production costs (PROD) which have a mean of -0.0065 (median= -0.0014) and the abnormal discretionary expenses (DISX) which have a mean of 0.0035 (median= -0.0026). Prior literature on real earnings management shows similar results (Gunny, 2010). The indicator variable on firms that have at least one blockholder (BLOCK) has a mean of 0.2292, indicating that the sample consist of 23% of firms with blockholders. In comparison, Dou et al. (2016) shows a mean of 35%, the difference might have occurred due to Dou et al. (2016) hand collecting data of blockholders. The variance of the cash flows has a mean of 26.855 and a median of 4.418. Analyst following shows a mean of 46.107 (median=17), of which can be argued that the sample consist of 46% of firms that are followed by analysts. ROA has a mean of -0.211 (median= 0.0379) and BM has a mean of 0.0006 (median= 0.0005). Moreover, size shows a mean of 5.166 (median= 5063.082) and LEVERAGE has a mean of 0.1717 (median= 0.1078) indicating that firms have 17% of long-term debt over their total assets. The stock turnover (TURNOVER) has a mean of 104.42 (median= 64.245), which indicates that the firms in the sample has high trades of stock. Lastly, the indicator variable of BIG4 has a mean of 0.6981 indicating that that the sample consist of 70% of firms that have a BIG4 auditor.
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Table 3.1 presents the correlation matrix. I include both measures of share price volatility in the correlation matrix to show that the two measures of share price volatility are highly correlated (0.9771, p < 0.01) and are basically the same measure, since it is close to 1. The indicator variable for blockholder shows a significant, negative correlation (-0.3245, p < 0.01) with stock price volatility. This confirms that the presence of blockholders has a positive effect on stock price volatility, indicating that the presence of blockholders reduces the risk investors face, therefore, reduces stock price volatility. Similarly, blockholders show a mitigating effect on both methods of earnings management, namely: AEM (-0.0252, p < 0.01) and REM (-0.0326, p < 0.01) indicating that their presence restricts the managers from manipulating the outcomes of earnings. In addition, Dou et al., (2016) argues that the
increasing monitoring ability of these shareholders suggests that they will be more likely to select firms with lower quality of reported earnings. Moreover, the two classification of earnings management: (1) the discretionary accruals (AEM) are negatively correlated with share price volatility (VOL) (-0.1347, p < 0.01) indicating that higher levels of discretionary accruals by management causes a decrease in stock price volatility, which argues against the results of Rajgopal et al. (2011). Prior literature has shown that adjusting earnings introduces “noisy earnings” which results in investors relying on other information which results in lower earnings quality. This correlation suggests that accruals adjustment can reduce stock price volatility. On the contrary, (2) real activities manipulation (REM) show a positive significant correlation (0.0151, p < 0.10) indicating that when REM is used this results in an increase of stock price volatility. The increase of stock price volatility can be explained by the fact that manipulating with real activities is perceived as bad in prior literature
(Rowchodhury, 2006; Zang, 2012; Cohen et al., 2010). In comparison with prior literature, REM shows a positive significant correlation (0.1410, p < 0.01) with AEM, indicating that managers use both classification of earnings management (Cohen et al., 2010; Zang, 2012).
[ Insert Table 3.1 about here ]
Table 3.2 shows the correlation matrix for the three proxies of REM separately. The indicator variable of firms with blockholders show a negative significant correlation for all three of the proxies used. However, the proxy for abnormal cash flows from operations (CFO) (-0.2354, p < 0.01) shows a negative and significant relationship opposed to the abnormal production costs (PROD) (0.0146, p < 0.10) and abnormal discretionary expenses (DISX) (0.0789, p < 0.01) with stock price volatility. These results indicate that when
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abnormal CFO are reported, stock price volatility decreases which can be interpreted as positive. On the contrary, when abnormal production costs or discretionary expenses are reported this actually has an increasing effect on share price volatility, indicating that this effect is negative. In sum, univariate analysis suggests that blockholders have a restricting effect on managers’ use of EM and on the REM proxies but show different effects on share price volatility.
[ Insert Table 3.2 about here ]
The control variables show in table 3.1 that stock price volatility is negatively and significantly correlated with the variable for variance of cash flow (-.2710, p < 0.01) and is opposed to what is expected following Rajgopal et al. (2011). As well as, analysts following (-0.2710, p < 0.01) and are in accordance with Roychowdhury’s (2006) findings that more sophisticated investors restrict the use of earnings management, ROA (-0.0129, p < 0.10), leverage (LEV) (-0.1207, p < 0.01) and auditor (BIG4) (-0.1318, p < 0.01) are negative and significantly correlated, indicating that they reduce stock price volatility. On the other hand, a significant, positive correlation is found with the book-to-market ratio (BM) (0.0280, p < 0.01) and stock turnover (TURNOVER) (0.2636, p < 0.01), following Rajgopal et al. (2011) these signs were not expected.
Table 4 shows the variance inflaction factor (VIF), respectively. In this table the highest, of the variables of interest, is the control variable SIZE (VIF =2.55) which does not indicate multicollinearity.
[ Insert Table 4 about here ]
4.2.
Regression analysis
Table 5 shows the results of the multivariate analysis. The regression analysis has an adjusted R-squared of 0.5222. This indicates that the variation in stock price volatility is explained by 52.2% by the model of which can be stated that the model shows to be a good fit. In
comparison, Rajgopal et al. (2011) shows a R-squared of 0.11 on average in the period from 1994 to 2001, which implies that the variation in idiosyncratic risk is explained by 11% by the model. My adjusted R-squared is higher due to the fact that I have added variables to test my hypothesis focused on the moderating effect of blockholders on the association of
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Firstly, the results in model 1 show the main effect of the indicator variable of blockholders (BLOCK) to have a negative and significant coefficient (β1= -0.0010, p= 0.001). This indicates that the presence of blockholders mitigates the uncertainty faced by investors resulting in a decrease of the stock price volatility. These results confirm the
positive effect of blockholders, known for monitoring, and implies that blockholders serve as a guarantee to investors that the firm is performing well (Dou et al., 2016).
Secondly, the main effect of accruals based earnings management (AEM) measured by discretionary accruals have a negative and significant coefficient (β2= -0.0247, p= 0.000). This shows that when AEM is used, in absence of blockholders, the stock price volatility decreases. This implies that investors do not find the use of AEM to create “noisy earnings” as suggested by Rajgopal et al. (2010). A possible explanation could be the difference in measurement of stock price volatility. Hence, these results are in line with Markarian et al. (2008) who find that discretionary income smoothing reduces idiosyncratic risk. In contrast, when blockholders are present at the firm, measured as the interaction of blockholders and AEM, the relationships show to be positive and significant (β4= 0.0071, p= 0.009). These results show that firms with blockholders which make use of AEM, leads to an increase of stock price volatility. This implies that investors find the presence of blockholders riskier opposed to when they are not at a firm. Moreover, these results suggest that investors
interpret the presence of blockholders as negative, which is in line with prior literature which suggest that shareholders with more rights exert more pressure on managers (Zhao and Chen, 2008) or due to the fact that the firm is in need of monitoring (Edmans, 2013). Therefore, the investor uncertainty rises which results in an increase of share price volatility. In sum, these results indicate that blockholders have a negative effect on the association of EM and stock price volatility, therefore, I find support for my hypothesis.
Thirdly, I find, in absence of blockholders, a positive and significant relationship in the main effect for the total variable of real activities earnings management (REM) (β3= 0.0034, p= 0.000). This indicates that investors find the stocks of firms to be of high risk when managers use REM, which results in an increase of stock price volatility. In
comparison, Cohen et al. (2008) argues that managers used AEM exceedingly before SOX, which decreased after the implementation while regulation has a constraining effect on the use of accruals. Consequently, this led to the use of REM after SOX. However, the results show that in the period before SOX from 1996 and 2001 which I cover, firms already engage in both methods of earnings management which is showed in the main effect on share price volatility, as discussed above. Specifically, in model 2 the main effect of the proxies of REM
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are shown separately. I find a negative and significant coefficient (β3,= -0.0569, p= 0.000)
for the first REM proxy, which is abnormal cash flows from operations (CFO) but abnormal production costs (PROD) and discretionary expenses (DISX) show no significant
relationship. This shows that the results of the total variable of REM are merely driven by the proxy abnormal CFO. In contrast, the interaction of blockholders on REM and share price volatility shows no significant coefficient. For that reason, the investors uncertainty seems to not be affected by the presence of blockholders when they make use of REM, since there is no effect on the stock price volatility. Particularly, the proxies of REM in model 2 show a positive and significant coefficient (β5,= 0.0142, p= 0.002) of abnormal CFO, when the blockholders are present at the firm. However, the other two proxies show not significant relationship. These results might imply that when the manager reports abnormal CFO, the errors in investors’ estimations occur and the risk they face rises and they start to trade the shares to mitigate their risk exposure which increases stock price volatility. However, this does not hold for the REM proxies PROD and DISX. Since, the main effect of these two proxies was not present as well as in the interaction, this could imply that investors do not distinguish manipulation by adjusting production costs and discretionary expenses, from the normal operating cycle Dou et al. (2016)
Lastly, the following control variables show a positive significance: variance of cash flows (VCFO) and aligned with Rajgopal et al. (2011) since stock returns are related to unexpected and expected cash flow news. As well as, analyst following (NANAL) which is in line with the findings of Cohen et al. (2010) which argue that when more analysist follow the firm, this motives the manager to use more earnings management especially when a benchmark needs to be reached. Moreover, book- to-market (BM), leverage (LEV), stock turnover (TURNOVER) are positive significant as well but all do not have the hypothesized sign. On the other hand, negative and significant are size, return on asset (ROA) and BIG4 which was expected.
[ Insert Table 5 about here ]
In conclusion, the results show that the presence of blockholders affects the
association of earnings management and share price volatility when the firm uses AEM. This implies that investors find firms with blockholders riskier due to the increase in uncertainty, therefore, an increase in stock price volatility. A possible explanation is that the presence of blockholders signals that the firm is in need of monitoring, which could imply that the firm is performing badly. Since, the need for monitoring could signal that the manager is not acting
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in alignment with the shareholder’s best interest. Similarly, the investors might perceive the blockholders to misuse their control or influence over the managers. In this case, the
managers would want to please the present blockholders preventing them from exiting the firm. However, no relationship is found when the firm engages in REM. Nevertheless, the
results show that my hypothesis is supported.
4.3.
Additional tests
In my main analyses, I hypothesized and shown that the presence of blockholders has a negative effect on the association of earnings management, particularly accruals based earnings management, and stock price volatility. In developing my hypothesis, I have shown that there can be a positive, and negative effect on the stock price volatility. Moreover, my hypothesis is focused on the presence of blockholders, but the difference in the size of
stockholdings by blockholders were not taken into account. Prior literature has been focusing on firms with multiple blockholders and has shown that power or control can have different outcomes which might give different results.
Prior research has shown that the effect of blockholders can either be positive or negative. On the one hand, a firm with one block holder can have a positive effect on the share price volatility, while one blockholder will more likely use their power to increase firm value since the ownership stakes depends on the value of their shares (Basu et al., 2016). On the other hand, having one block holder can be perceived as negative, since the block holder can exert more power over the managers (Dou et al., 2016). In turn the manager can try to please the block holder to preventing them from leaving, since this would signal that the firm’s performance is not as expected (Edmans, 2013). However, the presence of multiple blockholders can have a positive effect as well. When more blockholders are present they can monitor the firm and each other, also known as peer monitoring, which can increase the quality of monitoring resulting in lower stock price volatility (Edman, 2013). In addition, the presence of multiple blockholders might be perceived as negative. When multiple
blockholders are present at a firm each blockholder becomes less influential and can lower incentives to monitor (Schleifer et al., 1986). Moreover, due to high agency costs the free-riding problem among blockholders can start to play a role or competition can arise.
Therefore, in the following I examine whether the level of blockholders has an effect on the association of earnings management and share price volatility.
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To examine the effect of multiple blockholders on the association of earnings management and stock price volatility, I perform an additional test. I use the original regression model (1) and include an indicator variable for two or more blockholders
(MULTIBLOCK). This indicator variable is 1 when the firm has two or more blockholders, and 0 when firm has either one block holder or zero. The following regression model will be used:
VOL}~ = α)+ β,BLOCK},~5,+b6MULTIBLOCK},~5,+ β:AEM},~5,+ β=REM},~5,+ β? AEM},~5,* BLOCK},~5,+ β@ REM},~5,* BLOCK},~5,+
βB AEM},~5,* MULTIBLOCK},~5,+ βD REM},~5,* MULTIBLOCK},~5,+ Controls + ε}~
(10) Where:
MULTIBLOCK is the indicator variable that is added to the modified regression model (1), which is specified as 1 when the firm has two or more blockholders, and 0 otherwise. I examine the regression coefficients of the effect multiple blockholders on the association of both earnings management methods, AEM and REM, and share price volatility. I expect the coefficient of the interaction of blockholders and AEM, β?, to be positive since the results of the main regression showed a positive effect. This indicates that the presence of blockholders has an increasing effect on share price volatility when earnings management is used by the manager while investors cannot rely on the earning outcomes. I expect the coefficient of the interaction of multiple blockholders and REM, β@, to be positive
since the main effect has shown that using REM has an increasing effect on share price volatility. Moreover, prior literature has marked the use of REM to have a negative effect on earnings. In addition to my findings, which have shown that investors find the use of AEM to create noisier earnings, therefore, I expect when multiple blockholders are present and firms engage in REM this will result in a positive coefficient.
Table 6 shows the descriptive statistics of the indicator variable MULTIBLOCK. The sample consist of 16,401 observations as does the main analysis, of which the mean is 0.145. This indicates that the sample consist of 14.5% of firms with two or more blockholders on average. The main regression showed the mean of .2292 for total blockholders, this implies that 8.4% are firms with the presence of one blockholder on average.
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In table 7 the correlation matrix is shown. MULTIBLOCK is highly correlated with BLOCK, the indicator variable of the main analysis, (0.7559, p < 0.05) showing that the value is almost 1 indicating that they are the same measures. Specifically, MULTIBLOCK shows to be correlated with all the variables of interest in the same direction as BLOCK does. Stock price volatility shows a negative and significant correlation (-0.2331, p < 0.01), which implies that multiple blockholders have a decreasing and mitigating effect on stock price volatility. MULTIBLOCK shows a negative and significant correlation with AEM (-0.0172, p < 0.05) and REM (-0.0183, p < 0.05). This indicates that the presence of multiple
blockholders mitigates the use of earnings management.
Specifically, when REM is separated in the three proxies, MULTIBLOCK shows to be positively significantly correlated (0.0742, p < 0.01) with abnormal CFO. However, MULTIBLOCK shows to be negatively correlated with abnormal PROD (-0.0171, p < 0.05) and DISX (-0.0133, p < 0.10). This implies that when firms use more of CFO when there are multiple blockholders present, opposed to the use of abnormal PROD and DISX which show to be used less.
[ Insert Table 7 about here ]
In table 8 the results of the additional test’s regression are shown. The total
explanatory power of the model specified by the adjusted R-squared, is 0.5598. This points out that the variation in stock price volatility is explained by 56% by the model.
Firstly, model 1 shows that the main effect of the indicator variable of the presence of blockholder (BLOCK), opposed to the main analysis, appears to have no significant
coefficient. In contrast, the variable for firms with multiple blockholders (MULTIBLOCK) shows a negative and significant relationship (β2= -0.0007, p < 0.10). This indicates that multiple blockholders have a mitigating effect on the stock price volatility, by peer monitoring or competition among each other.
Secondly, in the main effect AEM shows a negative and significant coefficient (β3= -0.0247, p < 0.01), which is not different from the main analysis. This indicates that when AEM is reported by managers this results in a decrease in stock price volatility, where the use of AEM is shown to be positive for the firm. However, this relationship when influenced by the presence of blockholders (BLOCK), shows a positive and significant coefficient (β5= 0.0083, p < 0.05), suggesting that the presence of blockholders are perceived as negative, which confirms the results of the main analysis. In addition, the interaction of AEM with the presence of multiple blockholders (MULTIBLOCK) shows no significant relationship. This
