Alexandre Garel
email: agarel@audencia.com.

Jose M Martin-Flores

Arthur Petit-Romec
email: a.petit-romec@tbs-education.fr

Ayesha Scott
email: ayesha.scott@aut.nz

Thomas Bourveau

Francois Brochet

Alberta Di Giuli

Rüdiger Fahlenbrach

Bart Frijns

Scott Guernsey

Paul Hribar

Giri Kanagaretnam

Oguzhan Karakas

Alberto Manconi

Bartolomé Pascual-Fuster

Alireza Tourani-Rad

Institutional Investor Distraction and Earnings Management

Keywords: JEL classification: G23, G32, G34, M41 earnings management, investor distraction, monitoring intensity, discretionary accruals, real earnings management

In this study, we explore the implications of institutional investor distraction for earnings management. Our identification approach relies on a firm-level measure of institutional investor distraction that exploits exogenous attention-grabbing shocks to unrelated parts of institutional investors' portfolios. We find that firms with distracted institutional shareholders engage more in both accrual-based and real earnings management. Further analyses show that the association between investor distraction and earnings management is stronger in firms with low analyst coverage and weak board monitoring, as well as in firms where managing earnings upward allows meeting or just beating their earnings target. Collectively, our results suggest that managers exploit the loosening in monitoring intensity resulting from investor distraction by engaging in earnings management. Even in the presence of institutional investors with superior monitoring abilities, limited attention may induce insufficient monitoring of earnings management practices.

Introduction

The past decades have witnessed a well-documented increase in the ownership of institutional investors in U.S. corporations (e.g., [START_REF] Aghion | Innovation and institutional ownership[END_REF]. Institutional investors now own the great majority of U.S. firms and represent the most important set of shareholders. Existing evidence indicates that they are powerful shareholders who monitor managers and firms' decisions (e.g., [START_REF] Froot | Herd on the street: Informational inefficiencies in a market with short-term speculation[END_REF][START_REF] Hartzell | Institutional investors and executive compensation[END_REF][START_REF] Parrino | Voting with their feet: Institutional ownership changes around forced CEO turnover[END_REF][START_REF] Mccahery | Behind the scenes: The corporate governance preferences of institutional investors[END_REF]. Consistent with institutional investors' superior abilities to monitor and discipline managers, prior studies show that institutional ownership is negatively related to earnings management (e.g., [START_REF] Bushee | The influence of institutional investors on myopic R&D investment behavior[END_REF][START_REF] Chung | Institutional monitoring and opportunistic earnings management[END_REF][START_REF] Roychowdhury | Earnings management through real activities manipulation[END_REF][START_REF] Koh | Institutional investor type, earnings management and benchmark beaters[END_REF][START_REF] Khurana | The effects of hedge fund interventions on strategic firm behavior[END_REF]. Importantly, these studies show that institutional investors who are strong monitors (e.g., blockholders) deter earnings management. In this paper, we show that, when institutional investors (and in particular strong monitors) are distracted and shift their attention to unrelated parts of their portfolios, firms engage more in earnings management. That is, even in the presence of institutional investors, earnings management practices may be temporarily insufficiently monitored. 1 Attention is generally considered a resource in limited supply (e.g., [START_REF] Kahneman | Attention and effort[END_REF][START_REF] Baker | Behavioral corporate finance: An updated survey[END_REF]. Institutional investors, who often hold portfolios of hundreds or thousands of stocks, are subject to attention constraints. These attention constraints imply that institutional investors cannot monitor simultaneously all their portfolio firms with the same intensity (e.g., [START_REF] Kempf | Distracted shareholders and corporate actions[END_REF][START_REF] Schmidt | Distracted institutional investors[END_REF]. At certain points in time and for some of their portfolio firms, institutional investors may therefore become "distracted" shareholders and provide less than the otherwise optimal monitoring intensity. Prior studies investigating the role played by institutional investors in deterring earnings management generally consider that, at the firm level, the monitoring intensity depends on the level of institutional ownership. In comparison, an important refinement of our approach is to account for the possibility that two firms with the same level of institutional ownership may not be subject to the same 1 The literature has also investigated the link between investor monitoring and the quality of reporting in the context of private equity. For example, [START_REF] Cumming | Private equity returns and disclosure around the world[END_REF] find that, in the presence of information asymmetries and impediments to monitor effectively, fund managers systematically overreport returns of unsold deals. Consistent with this finding, we document that attention-driven lower institutional investor monitoring is associated with higher earnings management in publicly listed firms.

monitoring intensity depending on whether their respective institutional shareholders are distracted. 2 To measure investor distraction, we replicate the variable developed by [START_REF] Kempf | Distracted shareholders and corporate actions[END_REF]. Specifically, their approach exploits exogenous shocks (i.e., extreme returns) to unrelated industries by a given firm's institutional shareholders to identify periods where shareholders are likely to shift attention away from the firm. The measure of investor distraction aggregates the level of distraction of all the institutional investors at the firm level, overweighting the distraction of investors with superior means and incentives to monitor management [START_REF] Fich | Motivated monitors: The importance of institutional investors׳ portfolio weights[END_REF][START_REF] Edmans | Blockholders: A survey of theory and evidence[END_REF]. 3 Earnings management is a common way for managers to maximize their own benefits at the expense of long-term firm value. On the one hand, prior studies show that managers whose compensation is highly sensitive to stock prices have incentives to manipulate earnings to inflate stock prices (e.g., [START_REF] Cheng | Equity incentives and earnings management[END_REF][START_REF] Bergstresser | CEO incentives and earnings management[END_REF][START_REF] Cohen | Real and accrual-based earnings management in the pre-and post-Sarbanes-Oxley periods[END_REF]. On the other hand, extant literature shows that earnings management is detrimental to shareholders, as it reduces the quality of financial disclosure and increases the cost of capital (e.g., [START_REF] Aboody | Earnings quality, insider trading, and cost of capital[END_REF][START_REF] Kim | Real earnings management and cost of capital[END_REF], and ultimately decreases long-term firm value (e.g., [START_REF] Mizik | Myopic marketing management: Evidence of the phenomenon and its long-term performance consequences in the SEO context[END_REF][START_REF] Cohen | Accrual-based and real earnings management activities around seasoned equity offerings[END_REF][START_REF] Kothari | Managing for the moment: The role of earnings management via real activities versus accruals in SEO valuation[END_REF]. When institutional shareholders are distracted, managers may therefore react to the temporary loosening of monitoring intensity by engaging in income-increasing earnings management. 4In our empirical analysis, we analyze the relationship between institutional investor distraction and both accrual-based and real earnings management, which are the most common earnings management practices. Following [START_REF] Dechow | Detecting earnings management[END_REF], we measure accrual-based earnings management using the modified [START_REF] Jones | Earnings management during import relief investigations[END_REF] model. Following [START_REF] Roychowdhury | Earnings management through real activities manipulation[END_REF], we measure real earnings management using abnormal production costs, abnormal discretionary expenses, and abnormal operating cash flows. Using a large sample of U.S. listed firms for 1994-2016 period (30,661 firm-year observations), we find strong evidence that institutional investor distraction is positively associated with both accrualbased and real earnings management. The positive association between institutional investor distraction and earnings management is both statistically and economically meaningful. The increase in the discretionary accruals (real earnings management) associated with a one-standard deviation higher institutional investor distraction inflates ROA by about 0.73 percentage points (3 percentage points).

Importantly, the effect of institutional investor distraction on earnings management we document is arguably causal. By construction, the distraction measure we use is based on attention-grabbing shocks in unrelated industries and is therefore not related to the fundamentals of the firm.

In robustness checks, we conduct tests using alternative ways of measuring accrual-based and real earnings management [START_REF] Dechow | The quality of accruals and earnings: The role of accrual estimation errors[END_REF][START_REF] Mcnichols | Discussion of the quality of accruals and earnings: multiples[END_REF][START_REF] Larcker | Fees paid to audit firms, accrual choices, and corporate governance[END_REF][START_REF] Kothari | Performance matched discretionary accrual measures[END_REF][START_REF] Cohen | Accrual-based and real earnings management activities around seasoned equity offerings[END_REF][START_REF] Owens | Idiosyncratic shocks to firm underlying economics and abnormal accruals[END_REF][START_REF] Chen | Incorrect inferences when using residuals as dependent variables[END_REF]. We also include numerous additional control variables (e.g., performance, auditor, CEO, investor, analyst, stock returns, board, and share issuance variables) as well as industry-byyear fixed effects. Importantly, we control for the M&A activity of firms because prior studies show both that acquiring firms manage earnings upward in the periods prior to the merger agreement (e.g., [START_REF] Erickson | Earnings management by acquiring firms in stock for stock mergers[END_REF] and that M&A activity ramps up during investor distraction periods [START_REF] Kempf | Distracted shareholders and corporate actions[END_REF].

To further address omitted variable bias concerns, we conduct analysis on one-year changes in the variables that control away for unobservable and observable persistent differences across firms. The results from these tests confirm the robustness of the association between institutional investor distraction and earnings management.

To ensure that our results are not the product of randomness but are driven by our ability to correctly capture times when institutional investors are distracted, we have also conducted a placebo test. Key to our identification strategy of distracted investors is the way we determine the attention-grabbing industries. Following [START_REF] Kempf | Distracted shareholders and corporate actions[END_REF] and motivated by [START_REF] Barber | All that glitters: The effect of attention and news on the buying behavior of individual and institutional investors[END_REF], we use "extreme" industry returns (both positive and negative) to identify attention-grabbing events. We run a placebo test, in which, rather than focusing on the (true) distribution of industry returns, we randomly select the attention-grabbing industries. We then compute a measure of investor distraction based on these placebo attention-grabbing shocks and estimate our baseline regression using the placebo institutional investor distraction measure. We repeat this procedure 2,500 times and each time we save the coefficient estimate on the placebo investor distraction measure. The coefficient on our (true) measure of investor distraction lies well to the right of the distribution of placebo coefficients. The result of this placebo test lends support to our identification strategy.

In additional tests, we seek to further characterize the effect of investor distraction on earnings management by exploring how managers choose between accrual-based and real earnings management when institutional investors are distracted. [START_REF] Cohen | Real and accrual-based earnings management in the pre-and post-Sarbanes-Oxley periods[END_REF] provide evidence that accrual-based earnings management (real earnings management) increased (declined) prior the passage of Sarbanes-Oxley Act (SOX) in 2002 and declined (increased) after the passage of SOX, suggesting that firms switched from accrual-based to real earnings management methods after the passage of SOX. Consistent with [START_REF] Cohen | Real and accrual-based earnings management in the pre-and post-Sarbanes-Oxley periods[END_REF], we find that investor distraction has a stronger effect on real-earnings management after the passage of SOX.

Next, we explore whether the association between institutional investor distraction and earnings management exhibits cross-sectional heterogeneity depending on the strength of other monitoring mechanisms. When investors are distracted, their lack of attention and the loosening in monitoring intensity may be at least partially compensated by the presence of other monitors such as boards. This should be particularly the case for real earnings management because it is under a relatively weaker regulatory focus than accruals management and thus requires greater outside monitoring (e.g., [START_REF] Graham | The economic implications of corporate financial reporting[END_REF][START_REF] Cohen | Accrual-based and real earnings management activities around seasoned equity offerings[END_REF][START_REF] Kothari | Managing for the moment: The role of earnings management via real activities versus accruals in SEO valuation[END_REF]. We focus on two key alternative monitoring mechanisms, namely analyst coverage and the board independence. Analysts represent an external source of monitoring and are able to influence corporate actions including earnings management [START_REF] Jensen | Theory of the firm: Managerial behavior, agency costs and ownership structure[END_REF][START_REF] Healy | Information asymmetry, corporate disclosure, and the capital markets: A review of the empirical disclosure literature[END_REF][START_REF] Yu | Analyst coverage and earnings management[END_REF][START_REF] Degeorge | Analyst coverage, earnings management and financial development: An international study[END_REF]Chen, Harford and Li, 2015). Similarly, independent directors have incentives to develop a reputation as skilled decision control experts in the corporate directorship market [START_REF] Fama | Agency problems and the theory of the firm[END_REF].

Prior empirical evidence indicates that independent board members are better able to influence the design of CEO compensation and to influence corporate decisions [START_REF] Core | Corporate governance, chief executive officer compensation, and firm performance[END_REF][START_REF] Kaplan | Executive compensation and corporate governance in the US: perceptions, facts and challenges[END_REF][START_REF] Peasnell | Board monitoring and earnings management: do outside directors influence abnormal accruals?[END_REF][START_REF] Klein | Audit committee, board of director characteristics, and earnings management[END_REF]). We find that the effect of investor distraction on real earnings management is significantly greater for firms with low analyst coverage and low board independence. These results are consistent with managers being able to take advantage of institutional investor distraction to a greater extent when monitoring from other sources is low.

To better identify firms that are more likely to have managed earnings upward, we follow [START_REF] Lo | Earnings management and annual report readability[END_REF] and consider both the process of earnings management and its outcome. We split our sample firms with positive abnormal accruals into two groups depending on whether they meet or just beat the earnings benchmark. Arguably, firms that have abnormal positive accruals and that happen to meet or just beat the earnings forecasts are more likely to have engaged in upward earnings management. We find that the positive relationship between investor distraction and positive abnormal accruals is mainly driven by firms that meet or just beat the earnings benchmark, which lends further support to investor distraction inducing income-increasing earnings management. We find similar results when we consider real earnings management.

Finally, we examine whether institutional investor distraction is associated with earnings restatement, which can be seen as a consequence of earnings management, as well as with long-term stock returns. To do so, we collect data on financial restatements for firms from Audit Analytics, which tracks financial restatement disclosures from 8-Ks, 10-Ks, 10-Qs, and other filings. The results show that institutional investor distraction statistically increases the probability of earnings restatement in firms with positive discretionary accruals. In contrast, we find that the association between institutional investor distraction and the probability of earnings restatement is not statistically significant for the subsample of firms with negative discretionary accruals. These results are consistent with our findings that institutional investor distraction mostly has an effect on income-increasing earnings management. We also find that investor distraction has a negative and statistically significant effect on future stock returns, which aggregate the effect of these different actions that managers may have taken while shareholders are distracted. Taken together our results for the effect of investor distraction on earnings management, financial restatements, and future stock performance indicate that the consequences of a temporary loosening in monitoring intensity may have important implications for long-term value creation in firms.

Our paper contributes to several streams of literature. First, our study contributes to the body of research in finance and economics documenting the effects of attention, and more generally information asymmetry, of institutional investors on various outcomes. For example, a large strand of that literature examines the role of investor attention for asset prices and stock returns (see [START_REF] Lim | Limited attention[END_REF] for a survey). In the private equity literature, studies examine the consequences of information asymmetry between institutional investors and fund managers. Cumming and Valz (2010) show that fund managers may report inflated valuations of private investee companies that are not yet sold to obtain more funds from the institutional investors. [START_REF] Cumming | Fund size, limited attention and valuation of venture capital backed firms[END_REF] show that investee firm valuations in the venture capital (VC) market are positively related to limited attention of VCs. In his review of investments in private equity funds, [START_REF] Phalippou | Investing in private equity funds: A survey[END_REF] argues that it is an open and interesting question to know if institutional investors are tricked by performance reports. [START_REF] Phalippou | The performance of private equity funds[END_REF] document that the performance of private equity funds as reported by industry associations or previous research is overstated, with a large part of performance being driven by inflated accounting valuation of ongoing investments. Our paper expands these results by showing that even in the case of publicly listed companies that are less opaque and have more transparent disclosure of their activities and performance, managers are able to take advantage of investor distraction to artificially boost their reported earnings.

Second, our paper is also related to more recent studies investigating the impact of institutional investor distraction on corporate actions for publicly listed companies. [START_REF] Kempf | Distracted shareholders and corporate actions[END_REF] show that managers become opportunistic when investors are distracted (i.e. firms make more value destroying acquisitions and managers are more likely to receive lucky grants). [START_REF] Schmidt | Distracted institutional investors[END_REF] show that distracted professional asset managers are significantly less likely to trade in other stocks. [START_REF] Chen | Institutional shareholders and corporate social responsibility[END_REF] and [START_REF] Liu | Monitoring the monitor: Distracted institutional investors and board governance[END_REF] show that shareholder distraction induces managers to invest less in CSR and weakens board governance. [START_REF] Abramova | Institutional Investor Attention and Firm Disclosure[END_REF] and [START_REF] Basu | The Effect of Investor Inattention on Voluntary Disclosure[END_REF] show that institutional investor attention affects firms' disclosure choices. Our paper contributes to this literature by relating investor distraction to earnings management, which represents an important corporate decision that has consequences for long-term value creation.

Third, our paper contributes to the corporate finance literature studying the links between ownership structure and the quality of earnings, and particularly earnings management. Prior studies document that institutional investors affect the quality of earnings reported by non-financial firms (e.g., [START_REF] Bushee | The influence of institutional investors on myopic R&D investment behavior[END_REF][START_REF] Chung | Institutional monitoring and opportunistic earnings management[END_REF][START_REF] Roychowdhury | Earnings management through real activities manipulation[END_REF][START_REF] Koh | Institutional investor type, earnings management and benchmark beaters[END_REF][START_REF] Kim | Drivers behind the monitoring effectiveness of global institutional investors: Evidence from earnings management[END_REF][START_REF] Dai | The governance transfer of blockholders: Evidence from block acquisitions and earnings management around the world[END_REF][START_REF] Khurana | The effects of hedge fund interventions on strategic firm behavior[END_REF]. We expand existing literature by showing that the intensity of the monitoring exerted by institutional investors on earnings management practices does not uniquely depend on the level of institutional ownership but also on investors' distraction. [START_REF] Hirshleifer | Limited attention, information disclosure, and financial reporting[END_REF] examine the consequences of limited attention for disclosure, financial reporting policy, and market trading. In their approach, investors sometimes neglect relevant aspects of the economic environments they face, such as strategic incentives of firms to manipulate investor perceptions. Our study on the effect institutional investor distraction on earnings management echoes, and at least partially addresses, two of the limitations highlighted by [START_REF] Hirshleifer | Limited attention, information disclosure, and financial reporting[END_REF]. First, the distraction measure we use makes it possible to more explicitly analyze how investors allocate attention. Motivated by [START_REF] Barber | All that glitters: The effect of attention and news on the buying behavior of individual and institutional investors[END_REF] and following [START_REF] Kempf | Distracted shareholders and corporate actions[END_REF], we assume that investors allocate more attention to firms in industries experiencing extreme returns.5 Second, our paper focuses on earnings management and expands the results of [START_REF] Hirshleifer | Limited attention, information disclosure, and financial reporting[END_REF] by providing evidence that managers can take advantage of limited shareholder attention, and the temporary looser monitoring intensity it induces, to engage in earnings management.

Finally, our paper adds to the long-standing literature in corporate finance and accounting investigating the determinants of earnings management. Recent contributions highlight the importance of stakeholder orientation [START_REF] Ni | Does stakeholder orientation matter for earnings management: Evidence from nonshareholder constituency statutes[END_REF], dividend policy [START_REF] He | Dividend policy and earnings management across countries[END_REF], CEO tenure [START_REF] Ali | CEO tenure and earnings management[END_REF], media news [START_REF] Chahine | Media news and earnings management prior to equity offerings[END_REF], languages [START_REF] Kim | Languages and earnings management[END_REF], and retaining employees [START_REF] Gao | Employee turnover likelihood and earnings management: evidence from the inevitable disclosure doctrine[END_REF].

The rest of the paper is organized as follows. Section 2 provides the background literature and hypothesis development. Section 3 describes our data and methodology. Section 4 presents the results and discusses them. Section 5 concludes.

Related Literature and Hypothesis Development

Managers have large discretion in financial reporting. Due to agency problems, they have incentives to manipulate earnings for their own benefits. For example, prior evidence shows that managers have incentives to inflate stock prices, which, in turn, increases their own wealth (e.g., [START_REF] Bergstresser | CEO incentives and earnings management[END_REF][START_REF] Cheng | Equity incentives and earnings management[END_REF][START_REF] Cohen | Real and accrual-based earnings management in the pre-and post-Sarbanes-Oxley periods[END_REF]. Managers manipulate earnings via two mechanisms: accrual-based and real earnings management. We discuss in detail how we measure these two forms of earnings management in the next section. Accrual-based earnings management refers to changing the accounting methods or estimates used when presenting a given transaction in the financial statements. Real earnings management refers to influencing real activities to manipulate earnings. Both forms of earnings management are detrimental to shareholders, as earnings management reduces the quality of financial disclosure and increases the cost of capital (e.g., [START_REF] Aboody | Earnings quality, insider trading, and cost of capital[END_REF][START_REF] Kim | Real earnings management and cost of capital[END_REF], and ultimately decreases firm long-term value (e.g., [START_REF] Mizik | Myopic marketing management: Evidence of the phenomenon and its long-term performance consequences in the SEO context[END_REF][START_REF] Cohen | Accrual-based and real earnings management activities around seasoned equity offerings[END_REF][START_REF] Kothari | Managing for the moment: The role of earnings management via real activities versus accruals in SEO valuation[END_REF]. [START_REF] Graham | The economic implications of corporate financial reporting[END_REF] show that 78% of executives report they would forego profitable investment opportunities to inflate near-term earnings.

Institutional investors play a central role in accounting choices [START_REF] Bird | Do institutional investors demand public disclosure?[END_REF][START_REF] Cumming | Private equity returns and disclosure around the world[END_REF]. In the context of earnings management, prior studies show that institutional ownership is negatively related to earnings management (e.g., [START_REF] Bushee | The influence of institutional investors on myopic R&D investment behavior[END_REF][START_REF] Chung | Institutional monitoring and opportunistic earnings management[END_REF][START_REF] Roychowdhury | Earnings management through real activities manipulation[END_REF][START_REF] Koh | Institutional investor type, earnings management and benchmark beaters[END_REF][START_REF] Khurana | The effects of hedge fund interventions on strategic firm behavior[END_REF]. Despite their superior abilities to monitor and discipline managers, institutional investors, who often hold a portfolio of hundreds or thousands of stocks, are subject to attention constraints (e.g., [START_REF] Sims | Implications of rational inattention[END_REF][START_REF] Kacperczyk | A rational theory of mutual funds' attention allocation[END_REF]. These attention constraints imply that institutional investors cannot monitor simultaneously all their portfolio firms with the same intensity [START_REF] Kempf | Distracted shareholders and corporate actions[END_REF]. When institutional shareholders are distracted away and loosen their monitoring, managers have greater leeway to maximize their private benefits. Our central hypothesis is that managers react to the temporary loosening of monitoring intensity, brought about by investor distraction, by engaging in upward earnings management.

Hypothesis 1: When a firm's institutional shareholders are distracted, managers engage in income-increasing earnings management.

An alternative possibility is that managers take advantage of investor distraction to partially unwind prior upward earnings management. When institutional investors are distracted, managers may feel released from the pressure to report good performance and perceive reduced costs attached to reporting poorer performance. Consistent with this argument, prior evidence suggests that firms pre-announce their report dates well ahead of time when earnings are good to attract investor attention and on the contrary, do it at the very last moment when earnings are bad [START_REF] Boulland | Announcing the announcement[END_REF]. In the same vein, [START_REF] Koester | Attracting attention in a limited attention world: exploring the causes and consequences of extreme positive earnings surprises[END_REF] show that extreme positive earnings surprises are a successful method for attracting investors' attention. If this happens to be predominantly the case, we should observe that managers engage less in income-increasing earnings management when institutional shareholders shift their attention away from the firm.

Finally, a third possibility is that institutional investor distraction does not affect earnings management. It could be the case if managers are unable to detect investor distraction and therefore do not alter earnings management behavior. However, survey evidence suggests that managers generally know who their key shareholders are and interact frequently with them directly or through Investor Relations departments (e.g., [START_REF] Froot | Herd on the street: Informational inefficiencies in a market with short-term speculation[END_REF][START_REF] Parrino | Voting with their feet: Institutional ownership changes around forced CEO turnover[END_REF][START_REF] Hartzell | Institutional investors and executive compensation[END_REF][START_REF] Graham | The economic implications of corporate financial reporting[END_REF][START_REF] Mccahery | Behind the scenes: The corporate governance preferences of institutional investors[END_REF]. In addition, [START_REF] Kempf | Distracted shareholders and corporate actions[END_REF] show that, when a firm's investors are distracted, they are less likely to participate in conference calls and less likely to initiate a proposal in general meetings. 6 Managers further seem to modify their disclosure choices as a response to investor distraction. [START_REF] Abramova | Institutional Investor Attention and Firm Disclosure[END_REF] show that managers respond to institutional investor attention by increasing the number of forecasts and 8-K filings. [START_REF] Basu | The Effect of Investor Inattention on Voluntary Disclosure[END_REF] find that firms take advantage of investor inattention to disclose more aggressive non-GAAP performance metrics and to reduce the provision of management guidance.

Data and Variables

Data sources and sample construction

We use a combination of several databases for our empirical analysis. We obtain accounting data from Compustat, market data from CRSP, analyst data from I/B/E/S, and board and entrenchment data from Institutional Shareholder Services (ISS, formerly RiskMetrics). Data on accounting restatements are from Audit Analytics. Data on institutional investors are from Thomson Reuters 13F File. Institutional investors are required periodically to report their portfolio holdings. Using data from 13F File, for each firm, we can observe the pool of its institutional shareholders. Conversely, for each institutional investor, we can observe the pool of stocks that it concurrently holds. This feature of the data makes it possible to capture shifts in investor attention by identifying times where institutional investors are likely to shift their attention towards some of their portfolio firms and away from others. We require our sample firms to have available data to compute the variables of our baseline regression presented in section 3.4. Our main sample consists of 30,661 U.S. firm-year observations for our baseline regression examining accruals management (26,838 for our baseline regression examining real earnings management) corresponding to 3,917 unique firms over the 1994-2016 period.

Measurement of institutional investor distraction

Our main variable of interest is institutional investor distraction at the firm level, computed following [START_REF] Kempf | Distracted shareholders and corporate actions[END_REF]. It captures how much a given firm f 's institutional shareholders are distracted (i.e., shift their attention to unrelated parts of their portfolio) at a given point in time. We call this proxy Investor Distraction and define it so that higher values are associated with institutional shareholders that are more distracted. The intuition behind the computation of the measure is that a given investor i in firm f is more likely to be distracted if there is an attention-grabbing event in another industry, and if that other industry is important in investor i's portfolio. To illustrate this approach, consider two identical firms A and B in a given industry and year and with the same levels of institutional ownership.

Let's now assume that A's institutional shareholders have a relatively strong exposure to another industry (the banking industry, for example) and that B's institutional shareholders do not hold any bank stocks. If there is an attention-grabbing event in the banking industry, A's institutional shareholders will shift their attention towards banks. This implies that the monitoring intensity at firm A will (temporarily) decrease whereas it will remain constant at firm B. The investor distraction measure we use enables us to identify when, and for which firms, institutional investors are likely to be distracted and to loosen their monitoring intensity. We first compute an investor-level distraction score, and then aggregate the scores across all investors in the firm f. Specifically, we define institutional investor distraction for each firm f and calendar quarter q as:

= ∑ ∑ × × ∈ ! (1)
where " denotes the set of firm f 's institutional shareholders at the end of quarter q-1, # denotes a given Fama-French 12 industry, and # denotes firm f 's Fama-French industry. captures whether a distraction event occurs in an industry other than # , and captures how much investor i cares about the other industry. The weight captures how important investor i is for firm f. Intuitively, the investor distraction measure depends on whether shocks occur in other industries, whether investors care about those industries, and whether investors that are affected by the shock are potentially important monitors. We now explain the construction of these terms in greater detail.

is defined as the weight of industry # in the portfolio of investor i.

is an industry-level measure of whether something distracting is going on in industry # at quarter q. It is an

indicator variable that equals one if an industry has the highest or lowest return across all 12 Fama-French industries in a given quarter. An important advantage of this definition is that the industry shocks used in the computation of the investor distraction measure are not mechanically related to the fundamentals of the firm we are interested in, since the firm's own industry is excluded. Thus, it is a plausible candidate for identifying exogenous shocks to investor attention [START_REF] Kempf | Distracted shareholders and corporate actions[END_REF].

The product × measures for each investor i of firm f whether something distracting is going on in an unrelated industry ( ), and how much investor i cares about the unrelated industry ( ). Finally, we aggregate information across investors to obtain a firm-level distraction measure. As discussed previously, institutional investors differ in the level of intensity with which they monitor firms.

This implies that equal weighting is not appropriate and therefore, consistent with [START_REF] Kempf | Distracted shareholders and corporate actions[END_REF], we take a weighted average, with weights . We give more weight to investor i if (1) firm f has more weight in i's portfolio, and (2) if i owns a larger fraction of firm f 's shares. The former captures that investors will on average spend more time and effort analyzing the biggest positions in their portfolio [START_REF] Fich | Motivated monitors: The importance of institutional investors׳ portfolio weights[END_REF]. The latter captures that large shareholders (blockholders) have strong incentives to monitor [START_REF] Edmans | Blockholders: A survey of theory and evidence[END_REF].

We therefore define:

= $% &' ()* + ! ,$%'-./&0 + ! ∑ $% &' ()* + ! ,$%'-./&0 + ! + ∈ 1 ! (2)
Where, 2 34 is the fraction of firm f 's shares held by investor i, and

2"4 5ℎ is the market value weight of firm f in investor i's portfolio. Following [START_REF] Kempf | Distracted shareholders and corporate actions[END_REF], to minimize the impact of outliers, all stocks held by investor i in quarter q-1 are sorted by 2"4 5ℎ into quintiles, denoted 72"4 5ℎ . Similarly, firm f 's shareholders' stakes are sorted by 2 34 into quintiles, denoted 72 34 . We then scale by the term in the denominator so that the weights add up to one. Finally, we smooth the measure over the last four quarters to obtain a yearly construct (we label the resulting variable Investor Distraction).

In sum, the measure of investor distraction we use depends on whether shocks occur in other industries, whether investors care about those other industries, and whether investors that are most affected by the unrelated shock are potentially important monitors. This measure of institutional investor distraction has three appealing features for the purpose of this study. First, it is plausibly exogenous to the economics of the stocks for which investors will be considered as distracted. Second, it takes into account heterogeneity among institutional investors in their monitoring intensity. Third, [START_REF] Kempf | Distracted shareholders and corporate actions[END_REF] provide direct evidence suggesting that it captures well institutional investor distraction. More precisely, when institutional investor distraction is high, a firm's investors are less likely to participate in conference calls and less likely to initiate a proposal in general meetings.

Measurement of accrual-based earnings management

Our main model to estimate accrual-based earnings management is the expanded version of the Jones (1991)'s model including the inverse of total assets, the change in revenues, and the level of gross property, plant, and equipment as determinants of non-discretionary accruals as in [START_REF] Dechow | Detecting earnings management[END_REF].

We calculate Discretionary Accruals as the residuals from the following regression (industry subscripts omitted):

where i indexes firm and t indexes the year; ACCRUALS are total accruals, calculated as the difference between income before extraordinary items and operating cash flows.7 AT is the book value of total assets. 22O is the gross amount of property, plant, and equipment. (∆QOR -∆TQ) is the change in sales revenue minus the change in accounts receivable scaled by lagged total assets. We estimate regression (3) in each year for each two-digit SIC code industry and require each industry-year to have at least 20 observations. We define our main measure of accrual-based earnings management, Discretionary Accruals, as the residuals from these industry-year regressions. Discretionary Accruals is a signed measure so that positive values indicate income-increasing discretionary accruals.

For robustness purposes, we also employ alternative discretionary accruals models. First, we follow [START_REF] Dechow | The quality of accruals and earnings: The role of accrual estimation errors[END_REF] and regress total accruals on the lead, lag, and contemporaneous cash flow operation terms (CFO) scaled by lagged total assets. Second, we follow [START_REF] Mcnichols | Discussion of the quality of accruals and earnings: multiples[END_REF] and augment equation ( 3) with the additional controls suggested by [START_REF] Dechow | The quality of accruals and earnings: The role of accrual estimation errors[END_REF]. Third, we follow [START_REF] Larcker | Fees paid to audit firms, accrual choices, and corporate governance[END_REF] and augment equation ( 3) with the book-to-market ratio, included as a proxy for expected growth in the firm's operations. Fourth, [START_REF] Kothari | Performance matched discretionary accrual measures[END_REF] show that performance matching on return on assets controls for the effect of performance on measured discretionary accruals. We thus use discretionary accruals estimated based on equation (3) for a subsample of firms matched on two-digit SIC code, year and one-year-lagged ROA. Fifth, we follow Owens et al.

(2016) and incorporate in (3) an indicator variable that equals one if CFO is less than zero and equals zero otherwise, the firm's abnormal stock return during fiscal year t (based on the CRSP equal-weighted market index), and an indicator variable that equals one if the firm's abnormal stock return is less than zero and equals zero otherwise. Sixth, [START_REF] Chen | Incorrect inferences when using residuals as dependent variables[END_REF] show that the two-step procedure used to examine determinants of constructs such as discretionary accruals generates biased coefficients and standard errors that can lead to incorrect inferences. As a final robustness test, we use a one-step procedure where we directly regress total accruals on the regressors of the previously cited accruals models plus the firm-level variables of interest and control variables.

Measurement of real earnings management

Following prior literature (e.g., [START_REF] Roychowdhury | Earnings management through real activities manipulation[END_REF][START_REF] Cohen | Accrual-based and real earnings management activities around seasoned equity offerings[END_REF][START_REF] Kothari | Managing for the moment: The role of earnings management via real activities versus accruals in SEO valuation[END_REF], we measure real earnings management using abnormal production costs, abnormal discretionary expenses, and abnormal operating cash flow. Specifically, using the model developed by [START_REF] Dechow | The relation between earnings and cash flows[END_REF] and implemented by [START_REF] Roychowdhury | Earnings management through real activities manipulation[END_REF], we estimate abnormal production costs as follows:

%:/ +,?

8@ +,? ! = D B + D 8@ +,? ! + D G =8<H +,?

8@ +,? !

+ D I U=8<H +,? 8@ +,? ! + D V U=8<H +,? ! 8@ +,? ! + N ,* (4)
where i indexes the firm and t indexes the year; PROD is the sum of the cost of the goods sold and the change in inventory from year t-1 to year t; AT is the firm's book assets; SALES is the firm's sales revenue; and ε is the error term. We estimate equation ( 4) for each two-digit SIC code industry in each year and require each industry-year to have at least 20 observations. Abnormal Production Costs are defined as the regression residuals from the industry-year regressions. Overproduction refers to producing more goods than necessary to increase earnings. Higher values of Abnormal Production Cost indicate more real earnings management.

Further, we estimate abnormal discretionary expenses as follows:

=W +,? 8@ +,? ! = D B + D 8@ +,? ! + D G =8<H +,? 8@ +,? ! + N ,* (5)
where DISX is the sum of R&D, advertising and selling, general and administrative expenses. Following [START_REF] Cohen | Accrual-based and real earnings management activities around seasoned equity offerings[END_REF], in our main regression analysis, we set R&D and advertising expenses to zero if they are missing. All the other variables are defined in Equation ( 4). We conduct the same industry-year regressions and defined Abnormal Discretionary Expenses as the residuals from the regressions. Managers have the discretion to cut R&D, advertising and selling, general and administrative expenses to increase reported earnings. Lower values of Abnormal Discretionary Expenses indicate more real earnings management. We multiply it by minus one so that higher values indicate more real earnings management.

While setting missing values for R&D and advertising expenditures to zero when SG&A is available is a standard approach, recent findings show that this retreatment may be problematic. In particular, [START_REF] Koh | Missing R&D[END_REF] investigate whether missing R&D expenditures in financial statements indicate a lack of innovation activity and find that 10.5% of missing R&D firms file and receive patents.

For robustness purposes, we also compute a measure of abnormal discretionary expenses (Raw Abnormal Discretionary Expenses) for which we do not set R&D and advertising expenses to zero if they are missing.

Last, we estimate abnormal operating cash flow as follows:

9 / +,?

8@ +,? ! = D B + D 8@ +,? ! + D G =8<H +,?

8@ +,? !

+ D I U=8<H +,? 8@ +,? ! + N ,* (6)
where CFO is the firm's operating cash flow. All the other variables are as defined in Equation ( 4). We conduct the same industry-year regressions and define Abnormal Operating Cash Flow as the residuals from these regressions. Abnormal operating cash flow captures sales manipulation reflecting managers' attempts to increase sales during the year by offering "limited-time" price discounts or more lenient credit terms.8

Lower values of Abnormal Operating Cash Flow indicate more real earnings management. We multiply it by negative one so that higher values indicate more real earnings management.

To capture the aggregate effects of real earnings management9 , we follow [START_REF] Roychowdhury | Earnings management through real activities manipulation[END_REF] and combine the three individual measures as follows:

Q X O 5 Y 5 Z = T[ Z X 2 \] ^ + T[ Z X 3_ 5 ^ ℎ "X 4 + T[ Z X ` Oa_ (7)
For robustness purposes, we follow [START_REF] Cohen | Accrual-based and real earnings management activities around seasoned equity offerings[END_REF] and also compute the two following measures of real earnings management:

QOY 1 = T[ Z X 2 \] ^ + T[ Z X ` Oa_ (8) QOY 2 = T[ Z X ` Oa_ + T[ Z X 3_ 5 ^ ℎ "X 4 (9)

Empirical model

The main objective of our empirical analysis is to test the effect of investor distraction on earnings management. In our baseline analysis, we use the following specification:

O 5 Y 5 Z ,* = D B + D ,* + D G ] X 34 ℎ _ ,* + D I d ,* + D V T5 ,* + D e f 5 ,* + D g " a \ T ,* + D h Y i j i ,* + D k X l 4 ℎ ,* + D m \. X ,* + D B ^"3 ,* + D \. ^"3 ,* + D G o _5 ] X 4 ℎ _ ,* + q * + \ r + s ,* (10)
where i indexes firm, t indexes the year, and ε is the error term. Earnings Management is either accrual-based earnings management (Discretionary Accruals) or real earnings management (Real Earnings Management). Yt denotes year fixed effects, and Indj denotes industry fixed effects based on two-digit SIC codes. The coefficient of interest is D , which captures the effect of investor distraction on earnings management. A positive and significant coefficient would suggest that when institutional shareholders are distracted and loosen their monitoring, managers engage in income-increasing earnings management. In all our tests, we control for Institutional Ownership, measured as the percentage of a firm's shares held by institutional investors. Our approach therefore examines whether controlling for institutional ownership, the level of distraction of a firm's institutional investors influences earnings management.

We further include a large set of potential determinants of earnings management used in prior studies (e.g., [START_REF] Bushee | The influence of institutional investors on myopic R&D investment behavior[END_REF][START_REF] Cheng | Equity incentives and earnings management[END_REF][START_REF] Bergstresser | CEO incentives and earnings management[END_REF][START_REF] Roychowdhury | Earnings management through real activities manipulation[END_REF][START_REF] Koh | Institutional investor type, earnings management and benchmark beaters[END_REF][START_REF] Yu | Analyst coverage and earnings management[END_REF][START_REF] Cohen | Accrual-based and real earnings management activities around seasoned equity offerings[END_REF][START_REF] Ali | CEO tenure and earnings management[END_REF]. d ,* is the natural logarithm of the firm's book assets at the beginning of year t. T5 ,* is the natural logarithm of the number of years elapsed since the company's incorporation. f 5 ,* is total debt divided by total assets at the beginning of year t. " a \ T ,* is net property, plant, and equipment divided by total assets. Y i j i ,* is the market value of equity divided by the book value of equity.

X l 4 ℎ ,* is the annual percentage increase in the firm's sales at the beginning of year t.

\. X ,* is the standard deviation of sales scaled by total assets over the last three years. ^"3 ,* is the ratio of the firm's cash flow from operation to its book assets. \. ^"3 ,* is the cash flow volatility measured as the standard deviation of CFO over the last three years. Finally, we control for the fraction of the firm's stock owned by the five largest institutional investors (o _5 ] X 4 ℎ _) as a measure of ownership concentration to ensure that our results are not driven by standard measures of institutional ownership structure [START_REF] Kempf | Distracted shareholders and corporate actions[END_REF].

Descriptive statistics

Table 1 presents the descriptive statistics of the variables we use throughout our empirical analysis.

We winsorize all continuous variables at the 1 st and 99 th percentiles. The mean value of the investor distraction measure is 0.14 in line with [START_REF] Kempf | Distracted shareholders and corporate actions[END_REF]. The mean institutional ownership is about 60% in line with prior studies on institutional ownership (e.g., [START_REF] Koh | Institutional investor type, earnings management and benchmark beaters[END_REF]. The summary statistics for Discretionary Accruals and Real Earnings Management are consistent with those reported in prior studies (e.g., [START_REF] Roychowdhury | Earnings management through real activities manipulation[END_REF][START_REF] Cohen | Accrual-based and real earnings management activities around seasoned equity offerings[END_REF][START_REF] Kim | Real earnings management and cost of capital[END_REF][START_REF] Ali | CEO tenure and earnings management[END_REF][START_REF] Lo | Earnings management and annual report readability[END_REF]. Regarding the control variables, the descriptive statistics show that the average firm in our sample has total assets of approximately $528.5 million, a leverage ratio of 21%, a proportion of fixed assets of 28%, a sales growth of 3%, and a market-to-book ratio of 2.88.

Results

Institutional investor distraction and earnings management

To examine the relationship between investor distraction and earnings management, we estimate Equation (10) using ordinary least square regressions. The standard errors of these regressions as well as those of all other regressions in the paper are clustered by firm and robust to heteroscedasticity. Table 2 reports our baseline results. In Columns 1 and 2, we examine whether investor distraction is associated with accrual-based earnings management (Discretionary Accruals). The results show that the coefficient on Investor Distraction is positive and significant at the 1% level, suggesting that firms have greater abnormal accruals when institutional shareholders are distracted. In Columns 3 and 4, we examine whether investor distraction is associated with real earnings management. The results show that the coefficient on Investor Distraction is positive and significant at the 1% level, suggesting that firms engage in income-increasing real earnings management when institutional shareholders are distracted.

The effect of investor distraction on earnings management is also economically significant. The Insert Table 2

about here

The coefficients on the control variables have the expected sign. In line with prior studies, we find a negative association between institutional ownership and real earnings management (e.g., [START_REF] Bushee | The influence of institutional investors on myopic R&D investment behavior[END_REF][START_REF] Roychowdhury | Earnings management through real activities manipulation[END_REF][START_REF] Koh | Institutional investor type, earnings management and benchmark beaters[END_REF][START_REF] Kothari | Managing for the moment: The role of earnings management via real activities versus accruals in SEO valuation[END_REF]. Our results therefore indicate that investor distraction is associated with earnings management even after accounting for differences in institutional ownership. This finding lends empirical support to the idea that the monitoring intensity exerted by 10 When we restrict our analysis of real earnings to abnormal discretionary expenses (as reported in Table 3), we document a total ROA overstatement associated with a one-standard deviation increase in investor distraction of about 2 percentage points. [START_REF] Ali | CEO tenure and earnings management[END_REF] find that in the early years of CEO's service, discretionary accruals and abnormal discretionary expenses increase total ROA overstatement by about 1.5 percentage points. [START_REF] Cohen | Accrual-based and real earnings management activities around seasoned equity offerings[END_REF] report that ROA overstatement by firms, through discretionary accruals and abnormal discretionary expenses, is about 2 percentage points in the seasoned equity offering year. The economic effect we document varies across the specification we use. Our most conservative estimate is a total ROA overstatement of about 1 percentage point when we restrict real earnings to abnormal operating cash flows and use [START_REF] Owens | Idiosyncratic shocks to firm underlying economics and abnormal accruals[END_REF]'s discretionary accruals models.

institutional shareholders depends not only on their level of ownership but also on their level of distraction.

Overall, our main findings are consistent with the hypothesis that when institutional shareholders shift their attention away and become distracted, managers engage in upward earnings management.

Importantly, the effect of institutional investor distraction on earnings management is arguably causal. By construction, the distraction measure we use captures times where a firm's institutional shareholders shift their attention away from the firm due to exogenous shocks in unrelated parts of their portfolios.

Furthermore, while prior studies show that managers may have incentives to attract investor attention when they know that their firms will report good earnings (e.g., [START_REF] Koester | Attracting attention in a limited attention world: exploring the causes and consequences of extreme positive earnings surprises[END_REF][START_REF] Boulland | Announcing the announcement[END_REF], this association is unlikely to create a severe endogeneity concern in our setting because it works against finding a positive relationship between investor distraction and earnings management.

The results from Table 2 are consistent with both investor distraction decreasing downward earnings management and increasing upward earnings management. Therefore, in Table 3, we split the sample into subsamples with positive and negative discretionary accruals. The results show that the positive association between investor distraction and discretionary accruals exists only for the subsample with positive (i.e., income-increasing) discretionary accruals. The results are consistent with the idea that the loosening in monitoring intensity should manifest itself in accentuating managers' intention to boost reported earnings.

Insert Table 3 about here

Next, we focus on the three individual real earnings management components that enter in the computation of the measure of real earnings management used in Table 2, namely, abnormal discretionary expenditures, abnormal production costs, and abnormal operating cash flow. Results reported in Columns

(3), (4), and (5) of Table 3 indicate the association between investor distraction and real earnings management is homogenous across the three types of real activities manipulation documented by [START_REF] Roychowdhury | Earnings management through real activities manipulation[END_REF].

Robustness checks

In this section, we conduct numerous tests to check the robustness of our findings to alternative specifications and potential omitted variables. Table 4 presents the results. For the sake of brevity and clarity, we report only the coefficients for investor distraction throughout the table.

As described in Section 3.2., our primary measure of accrual-based earnings management is discretionary accruals based on the modified Jones (1991) model by [START_REF] Dechow | Detecting earnings management[END_REF]. We now consider alternative ways of calculating discretionary accruals to ensure that our main findings are not driven by our choice of discretionary accruals model. We re-estimate Equation ( 10 4 presents the results. The coefficient on investor distraction is positive and significant in all the regressions (it varies between 0.078 and 0.167 versus a baseline coefficient of 0.183). These results suggest that our findings are insensitive to the use of alternative discretionary accruals models.

Further, we assess the robustness of our results to alternative measures of real earnings management. [START_REF] Cohen | Accrual-based and real earnings management activities around seasoned equity offerings[END_REF] derive two other measures of real earnings management focusing on specific combinations of the three components of our main measure of real earnings management (i.e., abnormal discretionary expenses, abnormal production costs, and abnormal operating cashflows). As reported in Panel B of Table 4, our results hold when we replace Real Earnings

Management by REM 1 or REM 2. As discussed in the real earnings measurement section, while it is standard to replace missing values for R&D and advertising by zeros, this practice raises some concerns.

When we remove from our sample firms with missing R&D or advertising values and re-compute our main measure of real earnings management, we find that the association between investor distraction and real earnings management remains positive and significant.11

Although our results show a positive association between investor distraction and earnings management, it is possible that both are simultaneously determined by other variables that are omitted from the regression. To alleviate this concern, we include in Panel C of Table 4 several additional control variables in our baseline regression (10). We use different sets of additional control variables. Motivated by [START_REF] Kothari | Performance matched discretionary accrual measures[END_REF], we first control for accounting performance by including a dummy variable indicating whether the firm has experienced a loss during the year (Loss Dummy), and contemporaneous return on assets (ROA).12 Second, we control for stock return variables by including the cumulative monthly stock return over the fiscal year (Momentum) and stock return volatility, calculated as the standard deviation of monthly stock returns over the past three years (Stock Return Volatility). Third, we augment our baseline specification with auditor variables. [START_REF] Lennox | Earnings management, audit adjustments, and the financing of corporate acquisitions: Evidence from China[END_REF] find that audits help to detect and correct earnings overstatements. More specifically, we include a Big Four dummy variable (Big Four Dummy) and a dummy variable that equals one if the firm is audited by an industry-specialist auditor, where an industry-specialist auditor is the auditor with the largest market share in the firm's two digit SIC industry during the year (Audit Industry Specialization Dummy). Fourth, we control for board characteristics likely to affect earnings management (e.g., [START_REF] Klein | Audit committee, board of director characteristics, and earnings management[END_REF][START_REF] Peasnell | Board monitoring and earnings management: do outside directors influence abnormal accruals?[END_REF]Fich and Shivdansani, 2006;[START_REF] Adams | Women in the boardroom and their impact on governance and performance[END_REF][START_REF] Srinidhi | Female directors and earnings quality[END_REF]. We include board size (Board Size), independence (Board Independence), gender diversity (Board Pct. Female), age (Board Avr. Age), firm-specific experience (Board Avr.

Tenure), and busyness (Board Avr. Busyness). Fifth, we control for CEO incentives and CEO entrenchment.

We include the Incentive Ratio from [START_REF] Bergstresser | CEO incentives and earnings management[END_REF] measuring the sensitivity of CEO compensation to the stock price. In addition, we also control for CEO Duality, a dummy variable for whether the CEO is also the chairman of the board and for CEO entrenchment, using the Entrenchment Index computed following [START_REF] Bebchuk | What matters in corporate governance?[END_REF]. Di Meo, Lara, and Surroca (2017) provide evidence suggesting that entrenched managers are less prone to engage in earnings management activities.

Sixth, we include institutional investor controls. Prior literature shows that monitoring intensity varies across institutional investor types and depends on investor concentration (e.g., [START_REF] Bushee | The influence of institutional investors on myopic R&D investment behavior[END_REF][START_REF] Chung | Institutional monitoring and opportunistic earnings management[END_REF][START_REF] Koh | Institutional investor type, earnings management and benchmark beaters[END_REF]. We include as controls investor ownership concentration (Institutional Holdings HHI), institutional blockholder ownership (Institutional Blockholder Ownership), and Fich et al. ( 2015)'s measure of motivated monitors' ownership (FHT Proportion of Monitoring Institutions). Seventh, we include analyst variables such as analyst coverage and earnings forecasts dispersion [START_REF] Yu | Analyst coverage and earnings management[END_REF][START_REF] Degeorge | Analyst coverage, earnings management and financial development: An international study[END_REF].

Finally, we control for M&A activities (M&A Dummy) and the issuance of shares (Net Share Issuance)

because prior literature argues that in certain situations CEOs have greater incentives to manage earnings and provide evidence that they do. More specifically, [START_REF] Erickson | Earnings management by acquiring firms in stock for stock mergers[END_REF] find that acquiring firms manage earnings upward prior to merger agreements and Chen, Thomas, and Zhang (2016) find evidence that performance -reflected in earnings and cash flows -is transferred from targets to acquirers around acquisitions. [START_REF] Cohen | Accrual-based and real earnings management activities around seasoned equity offerings[END_REF] and [START_REF] Kothari | Managing for the moment: The role of earnings management via real activities versus accruals in SEO valuation[END_REF] show that firms engage in earnings management around season equity offerings. All variable definitions are provided in the Appendix. As reported in Panel C of Table 4, our results are robust to the inclusion of each of these additional control variables.

To further mitigate omitted variable concerns, we consider a first-difference regression model.

More precisely, we replace all variables in Equation ( 10) by their first differences. This analysis allows us to control away for unobservable and observable persistent differences across firms that could drive both earnings management and the distraction of a firm's institutional investors. Panel D of Table 4 reports the results of the first-difference regressions and shows that a within-firm change in investor distraction is associated with changes in both accrual-based earnings management and real earnings management. In Panel D, we further add industry-year fixed effects. The rationale for checking the robustness of our results to the inclusion of industry-year fixed effects is twofold. First, the abnormal earnings models we use are derived from industry-year regressions and there may be marked differences in the model accuracy across industry-year observations. Second, we want to ensure that our results cannot be explained by any variable that does not vary across firms within a given industry and year, such as industry-wide investment opportunities, the state of the business cycle, or industry-wide stock returns that we use to identify attention-grabbing stocks. Our results are robust to the introduction of industry-year fixed effects.

Insert Table 4 about here

Key to our identification strategy of distracted investors is the way the attention-grabbing industries are defined. Following [START_REF] Kempf | Distracted shareholders and corporate actions[END_REF] and motivated by [START_REF] Barber | All that glitters: The effect of attention and news on the buying behavior of individual and institutional investors[END_REF], we use "extreme" industry returns (both positive and negative) to identify attention-grabbing events. To ensure that our results are driven by our ability to accurately capture institutional investor distraction rather than by randomness, we have conducted a placebo test in which, rather than focusing on the (true) distribution of industry returns, we randomly select the attention-grabbing industries. We then compute a measure of investor distraction based on these random attention-grabbing shocks and estimate our baseline Equation (10) using the placebo investor distraction measure. We save the coefficient estimate on the placebo investor distraction measure. We repeat this procedure 2,500 times. The distribution of the standardized coefficients is presented in Figure 1.a for accrual-based earnings management and in Figure 1.b for real earnings management. In Figure 1.a, the actual standardized coefficient based on our baseline regression from Table 2, Column 2, lies well to the right of the distribution of standardized coefficient estimates based on placebo investor distraction measures. In Figure 1.b, we observe a similar pattern for real earnings management. Overall, these placebo tests alleviate the concern of a spurious association between investor distraction and earnings management.

Insert Figure 1 about here Finally, an implicit assumption in the computation of the measure of investor distraction introduced by Kempf et al. ( 2017) is that extreme industry returns, be they positive or negative, attract investor attention to the same extent. As an additional robustness test, we examine whether our results are sensitive to the sign of the extreme returns. To do so, we create measures of investor distraction based on positive extreme returns only, labelled Investor Distraction (Top), and on negative extreme returns only, labelled Investor Distraction (Bottom). We then reproduce our baseline regressions using these two measures of investor distraction as independent variables of interest. The results are reported in Table 5 and show our main results hold for measures of investor distraction based on positive extreme returns only and negative extreme returns only.

Insert Table 5 about here

Additional analysis

Investor distraction and the choice between accrual-based and real earnings management

Our baseline results from Section 4.1. indicate that institutional investor distraction is positively associated with both accrual-based earnings management and real earnings management. This evidence supports the hypothesis that managers are able to take advantage of the loosening in monitoring to inflate earnings. In this section, we seek to further characterize the effect of investor distraction on earnings management by exploring how managers choose between accrual-based and real earnings management when institutional investors are distracted. [START_REF] Cohen | Real and accrual-based earnings management in the pre-and post-Sarbanes-Oxley periods[END_REF] provide evidence suggesting that firms increase the use of real earnings management methods after the passage of the Sarbanes-Oxley Act (SOX).

We therefore expect that investor distraction to have a stronger effect on real earnings management after the passage of SOX. In Table 6, we re-estimate our baseline regressions separately prior to and after the passage of SOX in 2002. The results from Columns 1 and 2 show that the effect of investor distraction on accrual-based earnings management is slightly more pronounced before SOX. The Wald test of coefficient equality shows that the coefficient prior to SOX is statistically higher only at the 20% level.

However, the results from Columns 3 and 4 show that the effect of investor distraction on real earnings management is significantly more pronounced after SOX. The Wald test of coefficient equality shows that the coefficient for investor attention is statistically higher at the 3% level, indicating that the effect of investor distraction on real earnings management is more pronounced after the passage of SOX in 2002.

Overall, the results from Table 6 provide some insights on how managers choose between accrual-based and real earnings management and are consistent with prior literature highlighting an increase in the use of real earnings management post SOX (e.g., [START_REF] Cohen | Real and accrual-based earnings management in the pre-and post-Sarbanes-Oxley periods[END_REF].

Insert Table 6 about here

The distraction of strong monitors

A key feature of the measure of investor distraction introduced by [START_REF] Kempf | Distracted shareholders and corporate actions[END_REF] and that we use is that it gives more weight to investors that are strong monitors. Strong monitors are either i)

investors that own a larger fraction of the firm's shares or ii) investors for which the firm has more weight in their portfolio. On top of blockholders or large shareholders, the measure takes into account that investors will on average spend more time and effort analyzing the biggest positions in their portfolio [START_REF] Fich | Motivated monitors: The importance of institutional investors׳ portfolio weights[END_REF].

While the measure of investor distraction gives more weight to investors that are strong monitors, it does not take into account differences in monitoring intensity among strong monitors (i.e., large shareholders and investors for which the focal company represents an important fraction of the portfolio).

In this section, we explore whether the distraction of large shareholders matters more than the distraction of investors for which the company represents an important position. To do so, we construct two alternative measures of investor distraction. The first measure labelled Investor Distraction (Stake) considers that strong monitors are those with a large stake in the company. The second measure labelled Investor Distraction (Relative Importance) considers that strong monitors are those for which the company represents an important portion of their portfolio.

We then re-estimate our main regressions replacing our baseline measure of investor distraction with these two alternative measures. The results are reported in Table 7. We find that the coefficients for Investor Distraction (Stake) are positive and statistically significant at the 1% level, indicating that the distraction of large shareholders or blockholders leads to more accrual-based earnings management and real earnings management. These results indicate that the loosening in monitoring intensity is especially strong when large shareholders are distracted. We also find that the coefficients for Investor Distraction (Relative Importance) are positive and statistically significant at the 1% level. These results are consistent with the fact that investors monitor more the biggest positions in their portfolio and that their distraction results in more earnings management. Overall, these results indicate that the distraction of two types of strong monitors considered separately lead to more earnings management.

Insert Table 7about here

Other sources of monitoring

In this section, we explore whether the effect of institutional investor distraction exhibits crosssectional heterogeneity depending on the strength of other monitoring mechanisms. When investors are distracted, the presence of other monitoring mechanisms may partially compensate the loosening in monitoring intensity brought about by investor distraction. 13 This should be particularly the case for real earnings management because it is under a relatively weaker regulatory focus than accruals management and thus requires greater monitoring (e.g., [START_REF] Graham | The economic implications of corporate financial reporting[END_REF][START_REF] Cohen | Accrual-based and real earnings management activities around seasoned equity offerings[END_REF][START_REF] Kothari | Managing for the moment: The role of earnings management via real activities versus accruals in SEO valuation[END_REF]. We hence concentrate on this type of earnings management in our tests. We focus on two key complementary sources of monitoring, namely analysts and the independent directors.

First, analysts represent an external source of monitoring [START_REF] Jensen | Theory of the firm: Managerial behavior, agency costs and ownership structure[END_REF][START_REF] Healy | Information asymmetry, corporate disclosure, and the capital markets: A review of the empirical disclosure literature[END_REF]. Using exogenous shocks to analyst coverage (broker closures and mergers), [START_REF] Chen | Do analysts matter for governance? Evidence from natural experiments[END_REF] provide evidence indicating that financial analysts play an important governance role in monitoring managers. 14 Second, board independence is another important source of monitoring. In particular, independent directors have incentives to develop a reputation as skilled decision control experts in the corporate directorship market [START_REF] Fama | Agency problems and the theory of the firm[END_REF]. Prior empirical evidence indicates that independent board members are better able to influence the design of CEO compensation and to influence corporate decisions (Core et al., 1999; Kaplan, 2012; Peasnell et al. 2005). 15 In Table 8, we estimate our baseline regression for different subsamples of firms sorted by analyst monitoring and board monitoring. Specifically, we define firms with analyst coverage above (below) the median as firms with high (low) analyst monitoring. Similarly, we define firms with board independence above (below) the median as firms with high (low) board monitoring. The results from Columns 1 and 2

show that the effect of investor distraction on earnings management is more pronounced for firms with low analyst coverage. The Wald test of coefficient equality shows that the coefficient for investor distraction is statistically higher at the 7% level. The results from Columns 3 and 4 show that the effect of investor distraction on earnings management is more pronounced for firms with low board independence. The Wald test of coefficient equality shows that the coefficient for investor distraction is statistically higher at the 9% level. Overall, the results are consistent with the conjecture that, in the absence of other sources of monitoring, investor distraction offers managers increased leeway to engage in earnings management. 16

14 Empirically, [START_REF] Yu | Analyst coverage and earnings management[END_REF] finds that firms followed by more analysts manage their earnings less. [START_REF] Degeorge | Analyst coverage, earnings management and financial development: An international study[END_REF] find that in countries with high financial development, increased within-firm analyst coverage results in less earnings management. 15 Board independence is not the only board characteristic that is likely to influence board monitoring. Prior evidence indicates that female directors exert stronger monitoring over managers [START_REF] Adams | Women in the boardroom and their impact on governance and performance[END_REF], specifically in the context of earnings management [START_REF] Srinidhi | Female directors and earnings quality[END_REF]. Prior literature also shows that firms with busy boards are associated with weaker corporate governance (Fich and Shivdansani, 2006). In unreported tests, we find similar results if we classify firms in low and high board monitoring according to board busyness, female representation, and the proportion of independent directors. 16 Consistent with our prior that alternative sources of monitoring should mostly matter for real earnings management because it is under a relatively weaker regulatory focus than accruals management, we do not find significant differences in the association between institutional investor distraction and discretionary accruals when we split firms according to their level of analyst or board monitoring.

Insert Table 8 about here

Institutional investor distraction, earnings management, and earnings benchmarks

To improve the identification of firms that are likely to have managed earnings upwards purposely, we follow [START_REF] Lo | Earnings management and annual report readability[END_REF] and consider both the process of earnings management and its outcome. Arguably, one of the main incentives for managers to manage earnings upwards is to meet or beat the earnings benchmark. 17 In Table 9, we explore whether among firms with positive discretionary accruals (real earnings management), the association between institutional investor distraction and earnings management is more pronounced for firms that meet or just beat the earnings benchmark. We focus on analyst forecasts as our measure of earnings benchmark. Prior research indicates that meeting analyst forecasts is a more important benchmark than meeting prior year's earnings or avoiding losses [START_REF] Dechow | Why are earnings kinky? An examination of the earnings management explanation[END_REF], and that the market perceives missing analyst forecast as a more negative signal than a decrease in year-over-year earnings [START_REF] Brown | A temporal analysis of quarterly earnings thresholds: Propensities and valuation consequences[END_REF]. We identify firms that meet or just beat the earnings benchmark as firms whose earnings per share surprise is within the [$0, $0.03] range (e.g., [START_REF] Brown | A temporal analysis of earnings surprises: Profits versus losses[END_REF][START_REF] Dhaliwal | Last-chance earnings management: Using the tax expense to meet analysts' forecasts[END_REF][START_REF] Bhojraj | Making sense of cents: An examination of firms that marginally miss or beat analyst forecasts[END_REF][START_REF] Call | Short-term earnings guidance and accrual-based earnings management[END_REF]). 18

Insert Table 9 about here Table 9 reports the results of the subsample regressions. We find that the positive relationship between investor distraction and income-increasing discretionary accruals (income-increasing real earnings management) is limited to firms that have just met or beaten the earnings benchmark, which provides further evidence that investor distraction induces upward earnings management.

17 [START_REF] Burgstahler | Earnings management to avoid earnings decreases and losses[END_REF] provide evidence that firms manage reported earnings to avoid earnings decrease and losses. [START_REF] Degeorge | Earnings management to exceed thresholds[END_REF] show how thresholds induce specific types of earnings management. [START_REF] Graham | The economic implications of corporate financial reporting[END_REF] reports that some CFOs admit managing earnings in order to meet earnings benchmarks. [START_REF] Bhojraj | Making sense of cents: An examination of firms that marginally miss or beat analyst forecasts[END_REF] confirm survey evidence suggesting managers engage in myopic behavior to beat benchmarks 18 We compute the earnings per share surprise as the difference between the actual earnings per share and the last I/B/E/S consensus forecast. Our results are robust to the use of different cut-offs such as [$0, $0.01] and [$0, $0.05]. Our results remain qualitatively the same if we use the difference between the actual earnings per share and the average of all analysts' latest forecast made within [-180,-4 days] to define earnings surprises as done for instance in [START_REF] Caskey | Earnings expectations and employee safety[END_REF].

Institutional investor distraction, earnings restatement, and stock performance

In previous sections, we have shown that institutional investor distraction has a strong effect on earnings management. In this section, we examine whether institutional investor distraction is associated with earnings restatement, which can be seen as a consequence of earnings management. To do so, we collect data on financial restatements for firms in the S&P1500 from Audit Analytics. Audit Analytics tracks financial restatement disclosures from 8-Ks, 10-Ks, 10-Qs, and other filings. Since the coverage of Audit Analytics starts in 2000, our analysis of financial restatements is based on the period 2000-2016.

Table 10, Panel A reports regressions of Restatement on the institutional investor distraction variable and controls. We estimate this regression using a linear probability model including our usual control variables as well as firm and year fixed effects. Given that our results from Table 3 indicate that institutional investor distraction is mainly associated with income-increasing earnings management but not with income-decreasing earnings management, we estimate the regression separately for firms with positive and negative discretionary accruals. The results show that institutional investor distraction statistically increases the probability of earnings restatement in firms with positive discretionary accruals. The association is statistically significant at the 5% level. In contrast, we find that the association between institutional investor distraction and the probability of earnings restatement is not statistically significant for the subsample of firms with negative discretionary accruals.

Finally, we examine whether institutional investor distraction has an effect on long-run stock returns. Both accrual and real earnings management are likely to be detrimental to long-term value creation. While cutting R&D and advertising expenses to increase short-term earnings will be valuedestroying in the long run (e.g., lower innovation and lower brand recognition), accrual-based earnings management, especially when leading to restatements, may also have negative consequences over the long run. Moreover, when shareholders are distracted, managers may take actions and engage in forms of earnings management that are not necessarily observable. Long-run stock returns aggregate the effect of these different actions. Table 10, Panel B reports regressions of stock returns in excess of the market return (value-weighted CRSP return) over the 24 months following the end of the fiscal year. 19 The results

show that investor distraction has a negative and statistically significant effect on future stock returns.

Taken together our results for the effect of investor distraction on earnings management, financial restatements, and future stock performance indicate the consequences of a temporary loosening in monitoring intensity may have important implications for understanding long-term value creation in firms.

Conclusion

We examine the effect of institutional investor distraction on earnings management behavior by identifying times when institutional investors experience attention-grabbing shocks in unrelated parts of their portfolios. We find that institutional investor distraction is positively associated with both accrualbased earnings management and real activities earnings-management, suggesting that managers exploit institutional investor distraction to engage in upward earnings management.

Prior studies document a negative association between the percentage of institutional ownership and earnings management. A key novelty of our paper is to show that the monitoring intensity of institutional investors on earnings management may temporarily decrease and weaken this association.

While the superior degree of sophistication of institutional investors contribute to deter earnings management, the management of large portfolios also exposes them to attention constraints that prevent them from simultaneously monitoring earnings management in all their portfolio firms with the same intensity.

Our results have important implications for understanding the potential limitations in the corporate governance role played by institutional investors including those which are strong monitors (e.g., blockholders). Even when the presence of institutional investors is high, earnings management practices may be temporary insufficiently monitored. This finding stresses the importance of complementary monitoring role played by financial analysts and independent directors in curbing earnings management and contributes to explain the persistence of earnings management practices.

Together with a growing body of studies investigating how firms respond to institutional investor distraction, our results also suggest that managers do not passively endure institutional monitoring but also adapt to the variation in institutional monitoring and time their opportunistic behaviors accordingly.

share of a hypothetical CEO's total compensation that would come from a one percentage point increase in the value of the equity of his company. The 'delta' of the CEO's option portfolio follows the Core and Guay (2002) approach, they are taken from Lalitha Naveen's website (Coles et al. 2006).

CRSP Entrenchment Index

We follow Bebchuk, Cohen, and Ferrell (2009). This proxy for managerial entrenchment is a count of the number of six antitakeover provisions that a firm has in place (thus a higher value of the entrenchment index means worse corporate governance): dual class, poison pill, classified board, amended bylaw, limited amend charter, and golden parachute.

ISS

Institutional Blockholder Ownership

Institutional blockholder ownership expressed as a percentage of a firm's total shares outstanding. Whereby we define a blockholder as an institutional investor owning at least 5% of the shares of a given firm.

13F

Institutional Holdings HHI Concentration of institutional holdings. Computed as the Herfindahl-Hirschman Index of the holdings of institutional investors expressed as a percentage of a firm's total shares outstanding.

13F FHT Proportion of Monitoring Institutions

Computed following [START_REF] Fich | Motivated monitors: The importance of institutional investors׳ portfolio weights[END_REF] as the institutional monitor ownership expressed as a percentage of a firm's total shares outstanding. Whereby institutional monitors are defined as institutional investors for which the weight of the firm in their portfolios is equal or greater to 10%. Dummy variable that code for whether the firm has issued at least 5% more shares net of share repurchases over the fiscal year.

Compustat

Alternative measures of investor distraction

Investor Distraction (Top)

Alternative measure of investor distraction computed using extreme positive industry returns only as attention-grabbing shocks.

13F, CRSP

Investor Distraction (Bottom)

Alternative measure of investor distraction computed using extreme negative industry returns only as attention-grabbing shocks.

13F, CRSP

Investor Distraction (Stake)

Alternative measure of investor distraction computed assuming that strong monitors are investors with a large stake the company. This distraction measure is weighted only based on an investor's stake in the company.

Investor Distraction (Relative Importance)

Alternative measure of investor distraction computed assuming that strong monitors are investors for which the company represents an important fraction of their portfolio. This distraction measure is weighted only based on the relative importance of the firm in the investor's portfolio. Yes Yes This table presents the results of robustness tests on the relation between earnings management and institutional investor distraction. We require each firm-year observation to have non-missing values in the baseline analysis and winsorize all variables at both the 1 st and 99 th percentiles. Standard errors are reported in parentheses. They are robust to heteroscedasticity and clustered by firm. Constant, industry fixed effects based on 2-digit SIC codes, and year fixed effects are included. ***, **, and * denote statistical significance at the 1%, 5%, and 10% levels, respectively. Variable definitions are provided in the Appendix. Control variables are those used in Table 2. Additional control variables used in Panel C are described in the main text. the results of a regression analysis on the relation between different earnings management variables and institutional investor distraction. We use two variations of our core measure of investor distraction. In Columns 1 and 3, we report the regression results for a measure of investor distraction based on the highest extreme industry returns only. In Columns 2 and 4, we report the regression results for a measure of investor distraction based on the lowest extreme industry returns only. We require each firm-year observation to have non-missing values in the baseline analysis and winsorize all variables at both the 1 st and 99 th percentiles. Standard errors are reported in parentheses. They are robust to heteroscedasticity and clustered by firm. Constant, industry fixed effects based on 2-digit SIC codes, and year fixed effects are included. ***, **, and * denote statistical significance at the 1%, 5%, and 10% levels, respectively. Variable definitions are provided in the Appendix. the results of a regression analysis on the relation between different earnings management variables and institutional investor distraction. For accruals and real earnings management, this table reports the regression results for subsample of observations before and after the passage of the SOX, in 2002. We require each firm-year observation to have non-missing values in the baseline analysis and winsorize all variables at both the 1 st and 99 th percentiles. Standard errors are reported in parentheses. They are robust to heteroscedasticity and clustered by firm. Constant, industry fixed effects based on 2-digit SIC codes, and year fixed effects are included. ***, **, and * denote statistical significance at the 1%, 5%, and 10% levels, respectively. Variable definitions are provided in the Appendix. the results of regressions of restatements on institutional investor distraction and control variables estimated separately for firms with positive and negative discretionary accruals. Regressions are estimated using a linear probability model. In both columns, the dependent variable is a dummy variable that is equal to 1 if the earnings of firm i for fiscal year t end up being restated and 0 otherwise. We require each firm-year observation to have non-missing values in the baseline analysis and winsorize all continuous variables at both the 1 st and 99 th percentiles. Standard errors are reported in parentheses. They are robust to heteroscedasticity and clustered by firm. Constant, firm fixed effects, and year fixed effects are included. ***, **, and * denote statistical significance at the 1%, 5%, and 10% levels, respectively. Variable definitions are provided in the Appendix. 2, whereby the placebo measures of investor distraction are obtained by using random industry shocks instead of extreme industry returns. We compute 2,500 placebo measures of investor distraction, run 2,500 times the regression 2 of Table 2, and store the resulting placebo coefficients for investor distraction. The placebo coefficients are standardized by removing the mean of the distribution and dividing them by the standard deviation of the distribution. 2, whereby the placebo measures of investor distraction are obtained by using random industry shocks instead of extreme industry returns. We compute 2,500 placebo measures of investor distraction, run 2,500 times the regression 4 of Table 2, and store the resulting placebo coefficients for investor distraction. The placebo coefficients are standardized by removing the mean of the distribution and dividing them by the standard deviation of the distribution.

Actual Standardized Coefficient based on

Actual Standardized Coefficient based on

  change in discretionary accruals associated with a one-standard deviation increase in investor distraction increases ROA by about 0.73 percentage points (0.04*0.183). The change in real earnings management associated with a one-standard deviation increase in investor distraction increases ROA by 3 percentage points (0.04*0.76). Thus, the total ROA overstatement associated with a one-standard deviation increase in investor distraction is of about 3.73 percentage points, which represents 22 percent of the ROA standard deviation for our sample firms (0.17). The economic effect of investor distraction compares favorably in magnitude to other determinants of earnings management recently uncovered in the literature.10

  ) using discretionary accruals, which are calculated following[START_REF] Dechow | The quality of accruals and earnings: The role of accrual estimation errors[END_REF],[START_REF] Mcnichols | Discussion of the quality of accruals and earnings: multiples[END_REF],[START_REF] Larcker | Fees paid to audit firms, accrual choices, and corporate governance[END_REF],[START_REF] Kothari | Performance matched discretionary accrual measures[END_REF],[START_REF] Owens | Idiosyncratic shocks to firm underlying economics and abnormal accruals[END_REF][START_REF] Chen | Incorrect inferences when using residuals as dependent variables[END_REF], respectively. Panel A of Table

  Standard deviation of the one-year ahead earnings forecasts issued by the analyst covering the firm. I/B/E/S M&A Dummy Dummy variable that indicates whether the firm has undertaken a M&A within the fiscal year. SDC Net Share Issuance

Figure 1 .

 1 Figure 1. Placebo tests

Figure 1 .

 1 Figure 1.b Real earnings management

Table 1 . Summary Statistics

 1 This table presents the summary statistics of the variables for the sample period 1994-2016. We require each firmyear observation to have non-missing values in the baseline analysis and winsorize all continuous variables at both the 1 st and 99 th percentiles. Variable definitions are provided in the Appendix.

	13F, CRSP

Table 2 . Institutional Investor Distraction and Earnings Management: Baseline Results

 2 This table presents the results of our baseline regression analysis on the relation between earnings management and institutional investor distraction. We require each firm-year observation to have non-missing values in the baseline analysis and winsorize all variables at both the 1 st and 99 th percentiles. Standard errors are reported in parentheses. They are robust to heteroscedasticity and clustered by firm. Constant, industry fixed effects based on 2-digit SIC codes, and year fixed effects are included. ***, **, and * denote statistical significance at the 1%, 5%, and 10% levels, respectively. Variable definitions are provided in the Appendix.

		Discretionary Accruals	Real Earnings Management
		(1)	(2)	(3)	(4)
	Investor Distraction	0.260***	0.183***	0.781***	0.752***
		(0.037)	(0.032)	(0.131)	(0.110)
	Institutional Ownership		-0.012		-0.263***
			(0.009)		(0.044)
	Top 5 Ownership		-0.021		0.353***
			(0.018)		(0.078)
	Size		-0.004***		0.038***
			(0.001)		(0.006)
	Age		0.021***		0.059***
			(0.002)		(0.012)
	Leverage		-0.034***		0.130***
			(0.008)		(0.036)
	Fixed Assets		0.248***		0.301***
			(0.013)		(0.039)
	Market-to-Book		0.000		-0.018***
			(0.000)		(0.002)
	Sales Growth		0.008**		-0.113***
			(0.004)		(0.011)
	Std. Sales		0.001		0.285***
			(0.007)		(0.033)
	CFO		-0.112***		-0.954***
			(0.014)		(0.060)
	Std. CFO		-0.492***		-3.223***
			(0.063)		(0.279)
	Observations	30,661	30,661	26,838	26,838
	Year Fixed Effects	Yes	Yes	Yes	Yes
	Industry Fixed Effects	Yes	Yes	Yes	Yes
	Firm Cluster	Yes	Yes	Yes	Yes
	Adjusted R-squared	0.165	0.244	0.0186	0.173

Table 3 . Institutional Investor Distraction and Earnings Management: Different Aspects of Earnings Management

 3 This table presents the results of a regression analysis on the relation between different aspects of earnings management and institutional investor distraction. We require each firm-year observation to have non-missing values in the baseline analysis and winsorize all variables at both the 1 st and 99 th percentiles. Standard errors are reported in parentheses. They are robust to heteroscedasticity and clustered by firm. Constant, industry fixed effects based on 2digit SIC codes, and year fixed effects are included. ***, **, and * denote statistical significance at the 1%, 5%, and 10% levels, respectively. Variable definitions are provided in the Appendix.

		Positive Discretionary Accruals Subsample	Negative Discretionary Accruals Subsample	Abnormal Discretionary Expenditures	Abnormal Production Costs	Abnormal Operating Cash Flow
		(1)	(2)	(3)	(4)	(5)
	Investor Distraction	-0.152***	0.037	0.373***	0.286***	0.110***
		(0.032)	(0.031)	(0.067)	(0.051)	(0.025)
	Institutional Ownership	-0.028***	-0.022**	-0.114***	-0.123***	-0.033***
		(0.007)	(0.010)	(0.025)	(0.020)	(0.008)
	Top 5 Ownership	0.014	-0.031*	0.150***	0.165***	0.039***
		(0.014)	(0.018)	(0.047)	(0.036)	(0.015)
	Size	-0.004***	-0.008***	0.026***	0.017***	-0.005***
		(0.001)	(0.001)	(0.003)	(0.003)	(0.001)
	Age	-0.010***	0.011***	0.032***	0.021***	0.010***
		(0.002)	(0.002)	(0.007)	(0.006)	(0.002)
	Leverage	0.028***	0.004	0.089***	0.030*	0.006
		(0.006)	(0.008)	(0.020)	(0.016)	(0.006)
	Fixed Assets	-0.038***	0.243***	0.158***	0.130***	0.008
		(0.007)	(0.014)	(0.022)	(0.019)	(0.008)
	Market-to-Book	0.000	0.000	-0.010***	-0.007***	-0.000
		(0.000)	(0.000)	(0.001)	(0.001)	(0.000)
	Sales Growth	0.016***	0.022***	-0.072***	-0.023***	-0.019***
		(0.004)	(0.004)	(0.008)	(0.005)	(0.003)
	Std. Sales	0.024***	0.042***	0.121***	0.125***	0.047***
		(0.005)	(0.006)	(0.020)	(0.016)	(0.007)
	CFO	-0.008	-0.082***	0.313***	-0.467***	-0.819***
		(0.012)	(0.012)	(0.040)	(0.025)	(0.012)
	Std. CFO	0.362***	-0.092*	-1.929***	-1.099***	-0.341***
		(0.056)	(0.055)	(0.182)	(0.129)	(0.047)
	Observations	21,230	9,431	26,838	26,838	26,838
	Year Fixed Effects	Yes	Yes	Yes	Yes	Yes
	Industry Fixed Effects	Yes	Yes	Yes	Yes	Yes
	Firm Cluster	Yes	Yes	Yes	Yes	Yes
	Adjusted R-squared	0.150	0.308	0.274	0.190	0.609

Table 4 . Institutional Investor Distraction and Earnings Management: Robustness Checks

 4

	Discretionary Accruals	Real Earnings Management
	(1)	(2)

Table 5 . Institutional Investor Distraction and Earnings Management: Positive Versus Negative Industry Returns

 5

		Accruals Management	Real Earnings Management
		(1)	(2)	(3)	(4)
	Investor Distraction (Top)	0.206***		0.699***
		(0.038)		(0.115)
	Investor Distraction (Bottom)		0.126***		0.748***
			(0.040)		(0.132)
	Observations	30,661	30,661	26,838	26,838
	Controls (Table 2)	Yes	Yes	Yes	Yes
	Year Fixed Effects	Yes	Yes	Yes	Yes
	Industry Fixed Effects	Yes	Yes	Yes	Yes
	Firm Cluster	Yes	Yes	Yes	Yes
	Adjusted R-squared	0.243	0.243	0.169	0.169
	This table presents

Table 6 . Institutional Investor Distraction and Earnings Management before and after SOX

 6

		Accruals Management	Real Earnings Management
		(1)	(2)	(3)	(4)
	Sample period:	<=2002	>2002	<=2002	>2002
	Investor Distraction	0.237***	0.189***	0.613***	0.999***
		(0.049)	(0.041)	(0.193)	(0.123)
	Observations	9,737	20,924	8,199	18,639
	Controls (Table 2)	Yes	Yes	Yes	Yes
	Year Fixed Effects	Yes	Yes	Yes	Yes
	Industry Fixed Effects	Yes	Yes	Yes	Yes
	Firm Cluster	Yes	Yes	Yes	Yes
	Wald test (1) > (2)	0.20
	Wald test (4) > (3)			0.03
	Adjusted R-squared	0.223	0.279	0.223	0.161
	This table presents

Table 10 . Institutional Investor Distraction and Earnings Restatements

 10

	Panel A. Restatements
		OLS
	Earnings Restatement	Discretionary	Discretionary
		Accruals	Accruals
		>0	<0
		(1)	(2)
	Investor Distraction	0.141**	-0.025
		(0.057)	(0.097)
	#Observations	12,758	5,666
	Control variables of Table 2	Yes	Yes
	Year Fixed Effects	Yes	Yes
	Firm Fixed Effects	Yes	Yes
	Adjusted R-squared	0.0122	0.0237
	This table presents

Table 2

 2

	, Column 2
	=
	2.85

Table 2

 2

	, Column 4
	=
	2.54

As we explain in the empirical methodology, we systematically control for the level of institutional ownership in all our tests, hence the effect of investor distraction on earnings management we document cannot be attributed to differences in institutional ownership.

In auxiliary analyses, we analyze the effect of investor distraction on earnings management considering a variety of distraction measures. For example, we construct separate measures of distraction for investors that have a large stake in the company and those for which the firm represents a large fraction of their portfolio. We also construct measures of investor distraction based on positive extreme returns only and on negative extreme returns only.

Our main hypothesis and empirical analysis relates to income-increasing earnings management because it is likely to be the outcome of an opportunistic behavior of managers when institutional investors are distracted.[START_REF] Lafond | Discussion of "CEO reputation and earnings quality[END_REF] suggests that if one is concerned about self-serving practices of managers, the focus should be on income-increasing earnings management.

This approach can be further justified on the grounds that extreme return periods are periods when learning about uncertainty can be particularly beneficial (e.g.,[START_REF] Kacperczyk | A rational theory of mutual funds' attention allocation[END_REF].

[START_REF] Dehaan | Market (in) attention and the strategic scheduling and timing of earnings announcements[END_REF] provide evidence that managers report bad news after market hours, on busy days, and with less advance notice. Their results further suggest that managers notice and take advantage of periods of low attention.

We focus on operating accruals because they have a direct impact on earnings and have relatively high subjectivity (see[START_REF] Larson | Defining, measuring, and modeling accruals: a guide for researchers[END_REF] for a guide on the use of accruals).

[START_REF] Chapman | An investigation of earnings management through marketing actions[END_REF] show that soup manufacturers roughly double the frequency and change the mix of marketing promotions (price discounts, feature advertisements, and aisle displays) at the fiscal quarter-end when they have greater incentives to boost earnings.

As discussed by[START_REF] Srivastana | Improving the Measures of Real Earnings Management[END_REF], models that measure REM do not control for differences in competitive strategy. Hence, a spurious correlation may exist between earnings management and a firm characteristic that varies with competitive strategy. In our case, this concern is minor because our core results hold for both accrualbased earnings management and real earnings management, and we do not expect investor distraction to vary according to the competitive strategy of a firm.

This restriction reduces the number of firm-year observations to 8,155.

Our results hold when we include one-year-lagged ROA instead of ROA and when we use a matching approach instead of adding performance repressors to our main specification.

The monitoring exerted by institutional investors, board of directors, and analysts are likely to play a complementary role in holding managers accountable and ultimately enhance firm performance. However, in the particular context of our study, the question we ask is whether the temporary loosening of monitoring intensity brought about by investor distraction can be substituted (instantaneously and at no cost) by other monitors.

We obtain qualitatively similar results if we calculate stock returns in the 36 months following the end of fiscal year.

This work was achieved through the Laboratory of Excellence on Financial Regulation (Labex ReFi) supported by PRES heSam under the reference ANR-10-LABX-0095. Jose M. Martin-Flores gratefully acknowledges financial support from the Spanish Ministry of Science and Innovation (Project PID2019-111066GA-I00).

Appendix: Variable Definitions

Variables Description Source Baseline Variables

Discretionary Accruals

Signed discretionary accruals computed using an expanded version of the [START_REF] Jones | Earnings management during import relief investigations[END_REF]'s model including the inverse of total assets, the change in revenues, and the level of gross property, plant and equipment as determinants of nondiscretionary accruals as in [START_REF] Dechow | Detecting earnings management[END_REF]. A detailed description is provided in Section 3.

Compustat Real Earnings Management

We follow [START_REF] Roychowdhury | Earnings management through real activities manipulation[END_REF] and combine (-) abnormal cash flow, (-) abnormal discretionary expenses, and (+) abnormal production costs. A detailed description is provided in Section 3.

Compustat Investor Distraction

We compute the measure of investor distraction following [START_REF] Kempf | Distracted shareholders and corporate actions[END_REF]. We compute an investor-level distraction score, and then aggregate across all institutional investors in the firm, weighting investors' individual distraction by their incentives and means to monitor. A detailed description is provided in Section 3. table presents the results of a regression analysis on the relation between different earnings management variables and institutional investor distraction. We use two variations of our core measure of investor distraction. In Columns 1 and 3, we report the regression results for a distraction measure weighting the individual distraction of each institutional investor by her stake in the firm only (and not weighting it by the relative importance of a firm in the investor's portfolio), namely Investor Distraction (Stake). In Columns 2 and 4, we report the regression results for a distraction measure weighting the individual distraction of each institutional investor by the importance of the firm in its portfolio only (and not weighting it by the stakes investors have in the firm), namely Investor Distraction (Relative Importance). We require each firm-year observation to have non-missing values in the baseline analysis and winsorize all variables at both the 1 st and 99 th percentiles. Standard errors are reported in parentheses. They are robust to heteroscedasticity and clustered by firm. Constant, industry fixed effects based on 2-digit SIC codes, and year fixed effects are included. ***, **, and * denote statistical significance at the 1%, 5%, and 10% levels, respectively. Variable definitions are provided in the Appendix. table presents the results of a regression analysis on the relation between long-term returns and institutional investor distraction. Our main dependent variable is cumulative abnormal returns which are cumulative returns in excess of the market return over the 24 months following the end of the fiscal year. We require each firm-year observation to have non-missing values in the baseline analysis and winsorize all continuous variables at both the 1 st and 99 th percentiles. Standard errors are reported in parentheses. They are robust to heteroscedasticity and clustered by firm. Constant, industry fixed effects based on 2-digit SIC codes, and year fixed effects are included. ***, **, and * denote statistical significance at the 1%, 5%, and 10% levels, respectively. Variable definitions are provided in the Appendix.