The effect of organizational complexity on earnings forecasting behavior

The effect of organizational complexity on earnings forecasting behavior Jared Jennings Olin Business School Washington University in St. Louis St. Louis, MO 63130-6431 jaredjennings@wustl.edu Hojun Seo Olin Business School Washington University in St. Louis St. Louis, MO 63130-6431 hojun.seo@wustl.edu Lloyd Tanlu Michael G. Foster School of Business University of Washington Seattle, WA 98195 ltanlu@uw.edu Semptember, 2015 The authors would like to thank the following for helpful comments and suggestions: Bob Bowen, Dave Burgstahler, Elizabeth Chuk, Ed dehaan, Frank Hodge, Weili Ge, Amy Hutton, Michael Kimbrough, Valerie Li, Dawn Matsumoto, Sarah McVay, D. Shores, Jacob Thornock, and seminar participants at Boston College, Harvard University, University of Washington, University of Wisconsin-Madison, the 2009 International Symposium on Forecasting, and the 2011 American Accounting Association Annual Meeting. Earlier versions of this paper were circulated under the title Are Managers Unable or Unwilling to Revise Earnings Forecasts. We would also like to thank the research assistance of Robert Stoumbos. We are grateful for financial support from the Olin Business School and Foster School of Business.

The effect of organizational complexity on earnings forecasting behavior Abstract: This paper examines whether organizational complexity affects management s communication with external market participants. We specifically examine three aspects of organizational complexity business, geographical, and cost structure complexity. We find evidence consistent with geographical complexity reducing the quantity and quality of management s communication with external market participants. Specifically, we find that management is less likely to revise its initial forecasts and more likely to bundle its earnings forecasts with earnings announcements. We also find that management issues more pessimistic, less precise, and less accurate earnings forecasts when geographical complexity increases. Cost structure complexity also appears to reduce the quantity and quality of management s communication with external market participants. Our results suggest that cost structure complexity decreases the likelihood of management revising its initial forecasts and issuing more pessimistic and less precise, and less accurate earnings forecasts. This paper adds to the accounting and management literature by further examining how the structural organization of the firm affects the costs and benefits managers face when communicating financial information to external market participants. We also provide an explanation for why managers miss their own forecasts as well as why managers issue pessimistic forecasts. JEL Classification: M40; M41 Keywords: Organizational complexity; Management guidance; earnings forecasts 2

1. Introduction Organizational complexity is a structural variable that characterizes the operations and communication process of a firm (Anderson, 1999). Scott (1992) suggests that organizational complexity increases with the number of different firm elements that managers deal with simultaneously. These elements could include the activities or subsystems within the firm (Daft, 1992). Perrow (1967) suggests that it becomes more difficult for market participants to understand the firm s operations as organizational complexity increases. While the accounting literature has examined the role of organizational complexity in the evolution of various management control systems (e.g., Campbell, Datar, and Sandino, 2009; Dikolli and Vaysman, 2006), we have no evidence on how organizational complexity affects management s communication with external market participants. We directly add to both the accounting and management literature by examining how organizational complexity affects the quantity and quality of management s communication with constituencies external to the firm. An increase in the organizational complexity of the firm likely affects both the costs born and benefits received by management when providing relevant information to parties outside the firm. Prior to communicating information to parties outside the firm, managers must gather and process data to produce summary statistics that are useful in communicating the firm s actual or expected performance. As the firm becomes more organizationally complex, it becomes more difficult and costly for managers to aggregate and analyze relevant information, potentially reducing the quantity and quality of management s communication with external market participants, ceteris paribus. However, organizational complexity likely decreases external market participants ability to assess the synergies or lack of synergies created by a more operationally complex firm. As a result, the level of information asymmetry between 3

managers and external market participants likely increases with organizational complexity (Bushman et al., 2004), increasing the demand for information to assess the firm s performance. Based on the conflicting predictions above, it is appears to be an empirical question as to how the organizational complexity of the firm affects management s communication with external market participants. We examine how three distinct aspects of organizational complexity geographical, business, and cost structure complexity affect the quantity and characteristics of management s voluntary disclosure. 1,2 Similar to Bushman et al. (2004), we define geographic and business complexity as firm diversity in the geographical and industrial operations of the firm, respectively. Managers of firms that are either geographically or industrially dispersed gather and process information from a higher number of divisions within the firm. We define cost structure complexity as management s ability to forecast firm performance using information readily available to management such as the firm s net sales, which are regularly determined for marketing and production purposes (e.g., Mentzer and Cox, 1984; McHugh and Sparkes, 1983; Peterson, 1993; Klassen and Flores, 2001). If the firm s level of net sales is highly correlated with firm performance, management does not need to gather as much information about the related costs to provide useful information about the firm s performance. 3 1 To ensure that geographical, business, and cost structure complexity capture distinct aspects of organizational complexity, we examine the simple correlations between the variables and conclude that each organizational complexity proxy is capturing a distinct aspect of organizational complexity. The geographical and business complexity proxies are the most highly correlated (correlation = -0.04), which we believe is sufficiently low to conclude that each proxy is identifying different aspects of organizational complexity. 2 We are aware that there may be other facets of organizational complex firms that we do not explicitly examine in this paper. However, we believe that geographical, business, and cost structure complexity cover the more salient components of the overall organizational complexity of the firm. 3 Note that we opted to veer away from the textbook classifications of cost behavior as fixed or variable. These classifications are generally for short-run decision-making purposes. Over the long run, all costs are variable. As costs vary more with sales, costs are more predictable, thus, making earnings more predictable. The extant literature finds that most costs are generally sticky, thus suggesting that the classification of costs as fixed or variable does not apply in the long run. See Section 3.1.2 for a more extensive discussion on cost structure complexity. 4

Using a sample of firms that provide management forecasts between 2002 and 2014, we first examine how each of the three aspects of organizational complexity affects the quantity of management s voluntary communication with external market participants. We proxy for the quantity of management s communication using management earnings forecast revisions, which are more voluntary in nature compared to management earnings forecasts. Chen, Matsumoto, and Rajgopal (2011) provide evidence that stopping the issuance of management earnings forecasts is costly, making the issuance of a single management forecast for a fiscal quarter less discretionary once the firm decides to issue an initial forecast. Supporting Chen et al. (2011) s claim, we find that 76.78% of the quarters in which management provides an earnings forecast are preceded by a forecast in the previous quarter, adding further evidence to the nondiscretionary nature of management forecasts once they have be initiated by the firm. 4 However, we find that only 28.91% of the quarters in which management revises an earnings forecast are accompanied by a revision of a forecast in the previous quarter. We find that geographical and cost structure complexity reduces the likelihood and frequency of management earnings forecast revisions, suggesting that these types of complexity increase management s costs of aggregating, analyzing, and communicating information to external market participants. Interestingly, we find no evidence that business complexity decreases the likelihood of management revising its earnings forecasts, suggesting that the aggregate demand for firms that have higher business complexity out weight the additional costs associated with aggregating and assimilating information across more business segments. This finding is also consistent with prior studies 4 Contingent on management issuing (revising) a forecast in the current quarter, we isolate the I/B/E/S guidance database to all observations with non-missing total assets and sales, and at least one analyst following between 2002 and 2014 when calculating the percentage of firm/quarters in which management issues (revises) a forecast in the previous quarter. All observations with missing data in the previous period are set equal to zero. 5

documenting that greater diversification (i.e., business complexity measure) is not associated with increased asymmetric information (Thomas 2002; Clarke, Fee, and Thomas 2004). In our second test, we find that geographical complexity increases the likelihood of management bundling its final earnings forecast with the prior quarter s earnings announcement, which is also consistent with organizational complexity increasing management s costs of aggregating and analyzing firm information. Managers have heightened incentives to gather and process information about the subsequent reporting period around the current period s earnings announcement to answer analyst and investor questions about the firm s expected future performance during earnings conference calls (Rogers and Van Buskirk, 2013). 5 Therefore, if managerial incentives remain constant but organizational complexity increases management s costs of producing information in between reporting periods, management s last earnings forecast is more likely to be bundled with the prior period s earnings announcement. Even if the manager does not hold a conference call, managers likely find it less costly to collect data useful in forecasting earnings for the subsequent fiscal period while they are collecting the necessary data to report the current period s operating performance. We also provide evidence consistent with geographical and cost structure complexity decreasing forecast precision and increasing forecast pessimism. Forecasts that are less precise and more pessimistic reduce the need for managers to gather and process data to assess whether unexpected negative shocks necessitate management to update their forecasts in order to avoid 5 Rogers and Van Buskirk (2013) provide descriptive evidence that the number of management forecasts increase around the 2000-2001 period. They suggest that this increases is likely due to Regulation Fair Disclosure (Reg FD), which eliminated the communication of private information between managers and analysts. Instead of managers providing the earnings forecasts privately to analysts after Reg FD, they likely decided to provide the earnings forecast publicly, resulting in the perceived increase to the number of forecasts issued around the 2000-2001 period. Since our sample starts in 2002, we do not need to account for the pre/post effects of Reg FD. 6

future litigation. 6 Less precise and more pessimistic forecasts allow managers to absorb uncertainty in their forecasts and decrease the likelihood of missing their own forecast (Cyert and March, 1963; Merchant, 1985). As a result, the negative (positive) association between geographical/cost structure complexity and forecast precision (pessimism) is consistent with organizational complexity increasing the costs of producing relevant information that is communicated to external market participants. We also find that management forecasts are less accurate for firms that have higher geographical and cost structure complexity, which is likely a consequence of higher forecast pessimism. We find no results that business complexity is associated with forecast precision, accuracy, or pessimism. Lastly, we examine how investors demand for information affects the relation between organizational complexity and management s communication with investors. We find evidence that the negative relation between the number of revisions and geographical complexity is muted when institutional ownership is higher. We also find that managers are more precise and more accurate as geographical/cost structural complexity increases when institutional ownership is higher. Overall, the evidence in this paper is consistent with geographical and cost structure complexity reducing the quantity and quality of management s communication with external market participants. Interestingly, we find no evidence that business complexity affects management s communication. It is possible that the effect of external market participants increased demand for information and the effect of management s increased costs to produce 6 Lawyers frequently bring class action lawsuits against firms for failing to update existing disclosure. Cornerstone (2013) provides evidence that approximately 54% of lawsuits between 2009 and 2013 have allegations of false forward-looking information. Rogers and Van Buskirk (2009) suggest that plaintiff s attorneys frequently argue that the Private Securities Litigation Reform Act of 1995, which purportedly protected forward-looking disclosures, does not protect the defendant s forward-looking disclosures. 7

information are offsetting each other when examining the effect of business complexity on management s communication. Our findings contribute to the accounting literature in several ways. First, we address the call for more research from Hirst, Koonce, and Venkataraman (2008) on (1) the specific features of the forecast, chosen by managers, following the decision to forecast (p. 37) as well as (2) on the interactions between forecast antecedents and properties. We directly address this call by providing evidence consistent with organizational complexity (a forecast antecedent) affecting management s earnings forecast revision behavior and forecast properties. We also provide evidence that organizational complexity affects management s decision to revise its earnings forecast, which is a decision that management makes after its decision to provide an initial forecast. This evidence helps academics as well as practitioners better understand how the structural organization of the firm can influence the amount and type of information that management provides to external market participants. Second, our study extends our understanding of why managers provide more (or less) disclosure, as well as why they miss their own forecasts (e.g., Lee, Matsunaga, and Park, 2012; Chen, 2004). An implicit assumption in many studies on management earnings forecasts is that managers can readily and cost-effectively obtain timely information to update investors on the firm s performance. Managers are assumed to then decide whether to disclose this information. This paper provides evidence that certain features of the firm s organizational design affect the costs that managers incur to obtain the information necessary to revise earnings forecasts in a timely manner. In other words, the lack of management disclosure may stem from a lack of management s ability to cost effectively gather and process information rather than a decision to withhold information from investors (Dye, 1985; Jung and Kwon, 1988). We provide evidence 8

consistent with the prediction that managers may be hindered by organizational factors (such as geographic and cost structure complexity) in collecting, analyzing, and communicating information to external market participants. This evidence provides additional evidence exploring the costs and benefits of providing voluntary disclosure, as called for by Beyer et al. (2010). Third, to the best of our knowledge we are among the first to introduce a more discretionary proxy for voluntary disclosure management forecast revisions. The prior accounting literature has primarily used the issuance of a management forecast as a measure for discretionary management disclosure. As suggested by Chen et al. (2011), the stoppage of providing management earnings forecasts is costly, incentivizing managers to continue providing forecasts once they have started. Based on our empirical findings, forecast revisions are more discretionary in nature and represent an alternative measure for discretionary management disclosure. The rest of the paper is organized as follows. Section 2 motivates the study and lays out our predictions. Section 3 describes the research design. Section 4 presents and discusses our results. Section 5 concludes and discusses potential extensions of this line of research. 2. Hypothesis development Organizational complexity is a construct that has been widely studied by management scholars (e.g., Damanpour, 1996; Campbell et al., 2009; Moldoveanu and Bauer, 2004; Robson, Katsikeas, and Bello, 2008). The management literature has described organizational complexity as a structural variable that characterizes the operations and communication processes within an organization (Anderson, 1999). Organizational complexity is said to increase with the number of 9

different elements that must be dealt with simultaneously by an organization (Scott, 1992) as well as with the number of activities and subsystems existing within an organization (Daft, 1992). Perrow (1967) broadly equates organizational complexity with the degree of ambiguity and difficulty in knowing and understanding an organization s operations. The accounting literature has documented that greater organizational complexity is associated with a reduction in corporate transparency, which is defined as the clarity of a firm s activities and performance to outsiders. For example, Bushman et al. (2004) provides evidence consistent with organizational complexity increasing ownership concentration and equity-based incentives for directors to reduce potential moral hazard problems that might arise due to reduced transparency in organizationally complex firms. Additionally, Duru and Reeb (2002) find that increased complexity stemming from international diversification makes it more difficult for analysts to forecast earnings, resulting in less accurate analyst forecasts. Organizational complexity can also impact how information is communicated between individuals within the firm. The prior literature has provided evidence that managers of diversified firms are faced with significant coordination and control challenges (e.g., Chase 1981, 1983; Karmarkar and Pitbladdo, 1995; Mittal et al., 2004). From an information processing perspective, March and Simon (1958) posit that managers have limited coordinative abilities, particularly in a multi-level organizational structure. They find this result regardless of whether sophisticated technologies exist to foster communication. 2.1. Organizational complexity and management s communication with investors We attempt to add to the existing accounting and management literature by examining how organizational complexity affects management s communications with market participants 10

outside the firm. 7 It is unclear whether organizational complexity ultimately improves or deteriorates management s communication with external market participants. Organizational complexity could encourage management to improve firm level disclosure if the overall information asymmetry between managers and investors increases with organizational complexity (Bushman et al., 2004). As the complexity of the organization increases, investors likely demand additional information to better assess and understand the efficiencies or inefficiencies that are achieved in a more complex organization. Denis et al. (1997) supports this prediction by positing that complexity (particularly in the form of corporate diversification) increases the agency problem between shareholders and management. Therefore, external market participants may demand additional information from the managers of organizationally complex firms to better assess the firm s overall operating performance and alleviate the increased information asymmetry between external market participants and managers. As a result, it is reasonable to predict that organizational complexity causes managers to improve the quantity and quality of communication with external market participants. We state our first hypothesis below in alternative form. H1a - Organizational complexity increases the quantity and quality of management s communication with external market participants. 7 Feng, Li, and McVay (2009) examine and find evidence consistent with ineffective internal controls decreasing the accuracy of management forecasts. In their analysis, they control for firm complexity and find a weak negative association between their composite complexity measure with the likelihood of management providing earnings guidance. We make a significant contribution beyond this paper in three specific ways. First, we decompose organizational complexity into specific components industry complexity, geographic complexity, and cost structure complexity to better understand what complexity component is affecting management s disclosure decisions. We believe that the investigation of each component provides a better understanding of what facets of organizational complexity ultimately affect financial reporting behavior. Second, we theorize and hypothesize on why organizational complexity affects the ability for management to analyze and communicate information to investors. Third, we thoroughly examine how several aspects (e.g., accuracy, precision, pessimism) of management forecasts are affected by organizational complexity, providing a more complete understanding of how organizational complexity affects management s communication with external market participants. 11

In addition to increasing investors demand for information, organizational complexity can also increase managers costs of collecting, analyzing, and communicating relevant information to external market participants. Dye (1985) and Jung and Kwon (1988) provide theoretical support that managers may not disclose relevant and useful information to investors because they do not have any to disclose. The manager s lack of information available to be disclosed may be due to the relevant information being too costly to obtain. As the number of elements, activities, or subsystems of the firm increases, management s challenge of aggregating, analyzing, and summarizing relevant information useful to external market participants increases, leading to higher information acquisition and processing costs, ceteris paribus. Therefore, it is reasonable to predict that organizational complexity impedes the dissemination of information throughout the organization and ultimately to interested market participants, resulting in a reduction to the quantity and quality of management s communications with external market participants, ceteris paribus. We state the alternative to Hypothesis 1a below. H1b - Organizational complexity decreases the quantity and quality of management s communication with external market participants. We do not necessarily have an ex ante prediction as to whether organizational complexity ultimately improves or deteriorates the level of voluntary communication between managers and external market participants. On one hand, if the increased costs of producing and communicating the manager s forecast to market participants outpaces the increase in market participants demand for information, we ultimately expect an increase in organizational complexity to reduce the quantity and quality of management s communication. On the other hand, if the increase in market participants demand for information outpaces the increased costs of producing and communicating the manager s forecast to market participants, then we expect 12

an increase in organizational complexity to increase the quantity and quality of management s communication. Therefore, the effect of organizational complexity on management s communication appears to be an empirical question, which we examine below. 3. Research design In this section, we first operationally define the proxies for each aspect of complexity that we examine in this paper. We then describe the model specification for testing our predictions along with the dependent and control variables. 3.1. Organizational complexity We examine three different aspects of organization complexity business complexity, geographical complexity, and cost structure complexity. While there are likely many other aspects of organizational complexity, we focus on these three aspects. We believe that these three aspects of organizational complexity cover the more salient components of the overall organizational complexity of the firm. Our decision to focus on certain aspects of organizational complexity is not without precedent. Bushman et al. (2004) focus on two aspects of organizational complexity geographical and business complexity to examine whether organizational complexity affects ownership concentration and equity-based incentives for directors. We discuss each aspect of organizational complexity, and our proxies for each, more extensively below. 3.1.1. Business and geographical complexity Habib et al. (1997) find that combining diverse operations creates information aggregation problems that can result in information asymmetry between different constituents external and internal to the firm. For example, a firm operating in only one industry allows 13

managers to focus and monitor that particular industry s trends and events affecting the firm s operations. A more diversified firm composed of business units operating in several industries requires managers to obtain, consolidate, and process information from each of the firm s segments and make decisions accordingly. We label this type of organizational complexity business complexity. Similarly, several papers point to the challenges of managing and monitoring firms that operate in international markets. Multinational firms face information aggregation problems due to the geographic dispersion of operations, different legal systems, and multiple currencies (e.g., Denis et al., 2002; Duru and Reeb, 2002). Different geographical markets also necessarily involve different cultures and languages, resulting in different customer bases and different employee standards. Mittal et al. (2004) argue that geographically dispersed customer bases makes coordinating activities within an organization much more challenging. Adler (1983) provides survey evidence that geographic dispersion leads to greater complexity with regard to managing employees. A particular managerial style does not uniformly motivate and improve the productivity of employees across cultures (e.g., Mendenhall and Oddou, 1985; Adler et al., 1986; Ralston et al., 1993). Thus, we believe dispersion in geographical segments is a component of organizational complexity, which we label geographical complexity. Similar to Bushman et al. (2004), we measure these aspects of organizational complexity by first computing the revenue-based Hirfindahl-Hirschman indices using the business (i.e., industry) and geographical segments for each firm. Each index is computed as the sum of squared sales in each industry or geographic segment divided by total firm sales. For example, in the case of industry complexity, the Hirfindahl-Hirshman index for a single firm operating in n different industries would be calculated as follows: 14

n ( Sales 2 industry Total Firm Sales ) industry=1 These complexity measures have a range between 0 and 1. Higher values of these indices indicate more industry and geographic concentration, and therefore less complexity. Furthermore, these measures increase with the concentration of firm sales in a given segment and decrease with the number of segments, ceteris paribus. For instance, consider two firms identical in size and number of segments. Firm A has 90 percent of its revenues generated by one segment and the remaining 10 percent of its sales split evenly between the two remaining segments. Firm B, on the other hand, has its revenues generated equally among its three segments. Firm A will have a higher Hirfindahl-Hirschman index, reflecting the higher concentration of its business in one segment and consequently signifying less complex operations. In our final step to calculate the complexity measures, we subtract each measure from 1. In doing so, we are creating variables for both industry (Business Complexityi,t) and geographic (Geographic Complexityi,t) complexity that still have values that range from 0 to 1, with higher values representing observations with greater complexity. 3.1.2. Cost structure complexity We define cost structure complexity as the extent to which expenses covary with revenues. Empirical evidence shows that managers keep close tabs on revenue projections for their respective organizations. Sales forecasts are used by management for production planning, budgeting, capital resource allocation, and profit planning (e.g., Mentzer and Cox, 1984; McHugh and Sparkes, 1983; Peterson, 1993; Klassen and Flores, 2001). In organizations with less complex cost structures, the operational costs move proportionately with revenues, such that earnings are more easily determined given a predicted change in revenues. For example, if a 15

firm s expenses are highly and positively correlated with its revenues, a firm does not have to evaluate the expenses as extensively for a given level of production to predict earnings. As a result, the firm can more easily predict earnings based on an estimated level of production. If costs do not move synchronously with revenues, then predicting earnings requires managers to estimate revenues along with a more extensive evaluation and estimation of expenses. Costs that do not vary as highly with production are more likely to remain static within a relevant range of production then significantly increase or decrease as production either rises above or sinks below relevant ranges. 8 To complicate matters, firms with a high percentage of static costs do not necessarily have uniform relevant ranges for each static cost. Therefore, as the firm s estimated production increases or decreases, the manager must evaluate each static cost within each cost s relevant range to estimate the expense. In addition, if production is estimated to be just below the production threshold where a significant static cost increases, managers could significantly underestimate expenses and overestimate earnings if actual production turns out to be just above the threshold. As a result, a relatively small change in expected production could significantly affect predicted earnings, incentivizing management to evaluate and estimate the firm s predicted expenses more closely. Based on the above, the costs that management incurs to predict the financial performance of the firm is likely impacted by its cost structure complexity, ceteris paribus. We measure cost structure complexity by calculating the correlation between revenues and operating income before depreciation (both scaled by total assets in quarter t-4) measured over the prior 12 quarters (a minimum of 8 observation is required). If costs and margins are fairly consistent, then earnings are more likely to follow revenues in a relatively predictable way, 8 As noted in footnote 3, we do use the typical textbook definition of fixed and variable costs because all costs are variable in the long run; therefore, we refer to costs that are constant within a relevant range as static costs. 16

thus making earnings forecasts more easily derived from revenue forecasts. Lower correlations between revenues and operating income suggest that costs are unpredictable, resulting in a less predictable relation between earnings and sales. Similar to our industry and geographic complexity variables, we multiply these correlation values by -1 so that the Cost Structure Complexityi,t variable has values ranging between -1 and 1. Higher values correspond to less predictable earnings and higher cost structure complexity. 3.2. Model specification We use several tests to examine whether organizational complexity affects the quantity and quality of management s communication with external market participants. We first examine whether organizational complexity affects the likelihood of management revising its earnings forecasts. Second, we examine how organizational complexity affects the timing of managerial earnings forecasts. We then examine whether the precision, pessimism, and accuracy of managerial guidance is affected by organizational complexity. 3.2.1. Forecast revisions We focus on revisions in an attempt to identify a more discretionary component of management s voluntary communication with external market participants. The extant literature on earnings guidance suggests that once managers start making regular earnings forecasts, they continue to do so quarter after quarter, since it is costly for them to discontinue providing these forecasts (Chen et al., 2011). Because of these market pressures to continue providing earnings forecasts once the practice is started, we argue that earnings forecast revisions are much more discretionary in nature relative to the issuance of an initial forecast. We also find empirical evidence (untabulated) supporting this conjecture. We find that approximately 76.78% of all management forecasts are preceded by a forecast in the prior quarter, while only 28.91% of 17

management forecast revisions are preceded by a revision in the prior quarter. This evidence provides some support for the more discretionary nature of earnings revisions relative to initial earnings forecasts. Furthermore, based on practitioner surveys on budgeting and reporting processes, we assume that firms regularly prepare budgets and make quarterly forecasts of their earnings. 9 This suggests that internal planning and control systems are in place to allow managers to, at regular specified intervals (such as quarterly budget meetings or reviews), gather information about future firm performance required to prepare these initial quarterly forecasts. However, many managers may not be able to cost effectively aggregate data in a timely manner about the different segments of the organization in between reporting periods to ascertain whether initial forecasts need to be revised. 10 Based on the preceding arguments, we believe that managerial earnings revisions are a more discretionary measure for management s communication with external market participants. We use the following specification to examine whether the three components of organizational complexity geographical, industry, and cost structure complexity affect managerial forecast revisions. If organizational complexity increases investors demand for information, we expect organizational complexity to increase the likelihood of management revising its forecast. Contrarily, we expect the likelihood of management revising its forecasts to decrease as organizational complexity increases if organizational complexity increases management s costs of aggregating and processing the firm s information. 9 Survey evidence (Umapathy, 1987; Develin and Partners, 2005) point to the ubiquitous use of budgets by organizations throughout the US and UK. 10 The 2007 Centage/IOMA survey on budgeting practices finds that Microsoft Excel is the predominant tool used in budgeting and reporting. Unlike integrated accounting system, it is much more difficult to use Excel to aggregate information from several segments of the organization, and thus would make it difficult to managers to obtain firmwide information, especially during ad hoc periods. 18

DEPVARi,t = α + β1 Geographic Complexityi,t + β2 Business Complexityi,t + β3 Cost Structure Complexityi,t + β4 lndays_eai,t + β5 SalesVoli,t-1 + β6 STD_Analystsi,t-1 + β7 Litigation Riski,t-1 + β8 HHIi,t + β9 lncoveragei,t-1 + β10 INSTOWNi,t-1 + β11 lnsizesi,t-1 + β12 Book-to-Marketi,t-1 + β13 ROAi,t + β14 SalesGrowthi,t + β15 Fourth Quarteri,t + β16 First_FEi,t + β17 Lagged DEPVARi,t-1 + ΣIndustry FE + ΣYear FE + εi,t (1) We use two related dependent variables (Revisei,t and #Revisionsi,t) to test our first hypothesis. The Revisei,t variable is equal to one if management revises an earnings forecast for firm i during quarter t. The #Revisionsi,t variable is equal to the number of times firm i revises earnings forecasts during quarter t. We use the multivariate logistic regression when the Revisei,t variable is the dependent variable and an ordinary least squared regression when the #Revisions,t variable is the dependent variable. We regress the Revisei,t (#Revisionsi,t) variable on the complexity variables (Geographic Complexityi,t, Business Complexityi,t, and Cost Structure Complexityi,t) and other control variables. The complexity variables are as previously defined and are expected to have significant and negative (positive) coefficients if organizational complexity decreases (increases) the propensity of management revising its initial earnings forecast. We include several additional independent variables to control for other external factors that could influence a manager s propensity to revise forecasts. We divide the control variables into five categories quality of accounting and control systems, uncertainty, demand for information, litigation risk, and other variables influencing management disclosure. We briefly discuss these factors and the associated proxies below. The degree of integration and information sharing between the various differentiated units of an organization is facilitated by accounting and control systems, particularly interactive control systems, which are information systems that managers use to facilitate the flow of information throughout the organization (Simons, 1995). The quality of these control systems 19

affects the degree of integration in a decentralized organization. If an organization has accounting and control systems in place that are appropriately and sufficiently sophisticated for the degree of differentiation between the organization s subunits, then we expect information to flow efficient within the organization. We proxy for accounting system sophistication by counting the number of days between the end of the quarter and the earnings announcement date and taking the natural logarithm of the number (lndays_eai,t). We argue that firm-quarter observations with higher values of the lndays_eai,t variable represent longer periods between the quarter-end and closing of the books for financial reporting purposes. A sophisticated accounting system that is capable of quickly integrating information from the different parts of an organization allows for the books to be closed more quickly than a firm with a less sophisticated accounting system, ceteris paribus, and likely results in a shorter lag between the quarter-end and earnings announcement date. Uncertainty in macroeconomic factors and volatility in product demand affect managers willingness and ability to provide earnings forecasts. Waymire (1985) documents that earnings volatility due to environmental uncertainty surrounding a firm makes performance difficult to forecast and consequently reduces a manager s ability to make credible and accurate earnings projections. A manager that faces a greater degree of uncertainty with regard to future firm performance is likely going to find it more difficult to prepare and revise an earnings forecast that is both accurate and informative. 11 We proxy for environmental uncertainty using the coefficient of variation (standard deviation divided by the mean) in sales over the prior 12 11 Chen et al. (2011) find that of all the firms that discontinue guidance, the most popular reason provided is the amount of uncertainty (or the difficulty in predicting the future). An example of a firm that refused to provide an earnings forecast is Progressive Insurance. The company has been reluctant to provide earnings guidance due to the high degree of uncertainty that surrounds the firm and the industry as a whole. According to the company s controller in 2001, Progressive could not commit to delivering a point estimate and any range we might offer (derived from our internal forecasts) would be much wider than ranges investors have come to expect. To get within a 5-10 cent range in a 12-month period would be a miracle. (Hutton and Weber, 2001, p. 7). 20

quarters preceding quarter t (SalesVoli,t-1) and the level of analyst forecast dispersion for firm i preceding quarter t (STD_Analystsi,t-1). 12 Several additional firm characteristics affect investors demand for information. Healy et al. (1999) find that analysts seek disclosure of forward-looking information and tend to cover firms when their disclosure is more forthcoming. We use analyst following, measured by the natural logarithm of the number of analysts following firm i preceding quarter t (lncoveragei,t-1) as a proxy for information demand. In addition, institutional investors likely demand better quality disclosure. Healy et al. (1999) also suggest that institutional investors are attracted to firms that analysts rate as having better disclosure practices. Therefore, we include the percentage of institutional ownership (INSTOWNi,t-1) as an additional independent variable to help control for investors demand for information. We include the revision indicator (Revisei,t-1) or the number of revisions (#Revisionsi,t-1) for firm i during quarter t-1 as an independent variable to control for the possibility that management pre-commits to revising earnings forecasts through past behavior. Healy and Palepu (2001) document that litigation risk faced by managers can have two effects on disclosure. On one hand, the threat of litigation can deter disclosure, particularly of forward-looking information (Francis et al., 1994). On the other hand, inadequate or untimely disclosures potentially increase litigation risk, thereby incenting managers to improve disclosure (e.g., Skinner, 1994; Kasznik, 1999). Following Wang (2007), we use an indicator variable (Litigation Riski,t) that takes on a value of 1 if a firm is in a high litigation risk industry and experiences an earnings decrease of more than 20 percent relative to the same quarter of the 12 We selected to use the standard deviation in revenues as opposed to earnings in our tests in order to more directly capture demand uncertainty facing the organization. However, we have also used earnings volatility (which is highly correlated with the volatility in revenues) in unreported tests and get similar results. 21

previous fiscal year. 13,14 In addition, we include management s first forecast error (First_FEi,t) as an additional predictor of litigation risk. The First_FEi,t variable is defined as actual earnings minus the first management forecast for quarter t, scaled by stock price three-days before the forecast date. 15 Kasznik (1999) suggests that large management forecast errors are costly and that firms who overestimate their forecasts are subject to greater potential litigation costs than those that underestimate their forecasts. To control for other factors that the literature has identified as affecting voluntary disclosure, we include the following variables: firm size, growth opportunities, profitability, sales-based Herfindahl-Hirschman Index, and a fourth quarter indicator variable. Prior literature documents a positive association between discretionary disclosures and (a) size (Baginski and Hassell, 1997; Bamber and Cheon, 1998) as well as (b) growth opportunities (Bamber and Cheon, 1998). We proxy for size using the natural log of market value of equity (lnsizei,t-1). We proxy for growth opportunities using the book to market ratio for firm i at the beginning of quarter t (Book-to-Marketi,t-1) and sales growth (SalesGrowthi,t), which we define as sales from quarter t divided by sales from quarter t-4. We also control for the firm s profitability by including ROAi,t as an independent variable, which we define as return on assets for firm i in quarter t less return on assets in quarter t-4. We define return on assets as net income before extraordinary items for firm i in quarter t (IBES-reported) scaled by total assets in quarter t-4. We include the sales-based Herfindahl-Hirschman Index (HHIi,t) measured as the sum of squared market shares of all firms in the same industry during quarter t to control the effect of product 13 Matsumoto (2002) identified high litigation risk industries as biotechnology (SIC codes 2833-2836), electronics (3600-3674), retailing (5200-5961), computers (7370-7374), and R&D services (8731-8734). 14 We also use an alternative measure of litigation risk proposed by Kim and Skinner (2012) obtain qualitatively similar results. 15 In additional unreported robustness tests, we also scale by actual and forecasted EPS. We also use a truncated First_FE i,t variable where we set all positive forecast errors to zero, because the likelihood of litigation is significantly less for underestimated forecasts. Results are qualitatively similar with these alternative measures. 22

market competition on firms disclosure policies. We also include an indicator variable for the fourth fiscal quarter (Fourth Quarteri,t) for firm i during quarter t since managers are more likely to provide voluntary disclosure toward the end of the fiscal year. We also include industry fixed effects (Fama-French 12 industries) and year fixed effects to mitigate concerns that unobservable industry-wide factors and time-varying common shocks confound our results. We cluster all the standard errors by firm and calendar quarters to correct the standard errors for both serial and cross-sectional correlation (Petersen, 2009). 3.2.2. Forecast bundling In this section, we examine how organizational complexity affects the timing of management s communication with investors. If organizational complexity increases external market participants demand for information, we anticipate that the likelihood of managers bundling their final earnings forecast for the subsequent period with the current period s earnings announcement to decrease. We expect that a higher demand for information by external market participants to encourage management to provide additional information during the fiscal period as the firm s production estimates are realized to confirm market expectations. However, if organizational complexity increases the costs managers bare in aggregating and processing information, then we expect managers to be more likely to bundle their final earnings forecasts for the subsequent period with the current period s earnings announcement for two reasons. First, the benefits of providing an earnings forecast for the subsequent period around the current period s earnings announcement are likely higher, allowing managers to offset the higher costs of aggregating and processing information for an organizationally complex firm to be offset with the increased benefits. Rogers and Van Buskirk (2013) provide anecdotal evidence that managers who hold earnings conference calls are more likely to prepare 23

earnings forecasts for subsequent periods in anticipation of analyst and investor questions that are likely to be asked during the conference call, suggesting that managers have an incentive to prepare a forecast for the subsequent period to answer related questions. Second, the costs of aggregating and processing the information needed to prepare the subsequent period s earnings forecast around the current period s earnings announcement are likely lower. As managers are aggregating and processing information about the firm to report the firm s operating performance, managers likely find it more cost effective to simultaneously gather and analyze information about the subsequent period s expected performance. We use equation (2) to test whether organizational complexity changes the likelihood of management bundling its final earnings forecast around the prior quarter s earnings announcement. Bundlei,t = α + β1 Geographic Complexityi,t + β2 Business Complexityi,t + β3 Cost Structure Complexityi,t + β4 lndays_eai,t + β5 SalesVoli,t-1 + β6 STD_Analystsi,t-1 + β7 Litigation Riski,t-1 + β8 HHIi,t + β9 lncoveragei,t-1 + β10 INSTOWNi,t-1 + β11 lnsizesi,t-1 + β12 Book-to-Marketi,t-1 + β13 ROAi,t + β14 SalesGrowthi,t + β15 Fourth Quarteri,t + β16 First_FEi,t + β17 Lagged DEPVARi,t-1 + ΣIndustry FE + ΣYear FE + εi,t (2) The Bundlei,t variable is an indicator variable equal to one when the manager issues the last earnings forecast for quarter t during the five day window surrounding the earnings announcement for quarter t-1. Using a multivariate logit model, we expect a positive (negative) coefficient on the Geographic Complexityi,t, Business Complexityi,t, and Cost Structure Complexityi,t variables if organizational complexity increases (decreases) the likelihood of managers bundling the future period s earnings forecast with the current period s earnings announcement. 24

When the Bundlei,t variable is the dependent variable, we include similar control variables as those described in Section 3.2.1. We expect negative coefficients on the lncoveragei,t-1, STD_Analystsi,-1, SalesVoli,t-1, INSTOWNi,t-1, and lnsizei,t-1 variables. Firms with greater uncertainty (STD_Analystsi,t-1, SalesVoli,t-1), larger firms (lnsizei,t-1), higher institutional ownership (INSTOWNi,t-1), and higher analyst coverage (lncoveragei,t-1) all increase the demand for management to provide information throughout the quarter. We expect a positive coefficient on the lndays_eai,t variable, suggesting that less sophisticated information systems increase management s cost of gathering information on ad hoc days not around the earnings announcement. We expect a negative coefficient on the Litigation Riski,t and First_FEi,t variables. We anticipate litigation risk to increase the likelihood of managers providing information throughout the quarter to reduce the likelihood of litigation. 3.2.3. Forecast precision In this section, we examine whether organizational complexity increases or decreases the precision of managerial earnings forecasts. Organizational complexity increases the precision of managerial earnings forecasts if external market participants demand more precise information as information asymmetry increases, ceteris paribus. However, if organizational complexity increases the manger s costs of gathering and processing information, we expect organizational complexity to decrease the precision of managerial earnings forecasts. We use equation (3) to examine this relation. Specificityi,t = α + β1 Geographic Complexityi,t + β2 Business Complexityi,t + β3 Cost Structure Complexityi,t + β4 lndays_eai,t + β5 SalesVoli,t-1 + β6 STD_Analystsi,t-1 + β7 Litigation Riski,t-1 + β8 HHIi,t + β9 lncoveragei,t-1 + β10 INSTOWNi,t-1 + β11 lnsizesi,t-1 + β12 Book-to-Marketi,t-1 + β13 ROAi,t + β14 SalesGrowthi,t (3) 25

+ β15 Fourth Quarteri,t + β16 #Revisionsi,t + β17 Horizoni,t + ΣIndustry FE + ΣYear FE + εi,t The Specificityi,t variable is equal to the difference between the top of the forecast range less the bottom of the forecast range multiplied by -1 and scaled by stock price three days before the forecast date for all range forecast, and is equal to zero for point estimates (Vashishtha 2014). Hence, the greater value of the Specificityi,t variable indicate more specific forecasts and greater forecast informativeness. We use ordinary least squares to test the prediction. If organizational complexity increases management s costs of aggregating and processing information, we expect a negative coefficient on the Geographic Complexityi,t, Business Complexityi,t, and Cost Structure Complexityi,t variables in equation (3). However, if organizational complexity increases external market participants demand for information, then we expect a positive coefficient on the Geographic Complexityi,t, Business Complexityi,t, and Cost Structure Complexityi,t variables. In equation (3) we include several control variables also found in equations (1) and (2). We expect a positive coefficient on the lncoveragei,t-1, INSTOWNi,t-1, and lnsizei,t-1 variables, suggesting that managers provide more precise information when there is a higher demand for information from external market participants. We anticipate a negative coefficient on the SalesVoli,t-1 and STD_Analystsi,t-1 variables, suggesting that a more uncertain economic environment reduces management s ability to precisely predict earnings. We also predict a negative coefficient on the lndays_eai,t variable, suggesting that firms with less sophisticated information systems provide less precise forecasts. In addition to the control variables previously described, we include the #Revisionsi,t and the Horizoni,t variables as additional independent variables. The Horizonsi,t variable is equal to the number of days between the last management forecast and the earnings announcement date for quarter t. We expect the coefficient on the Horizoni,t variable to be negative, suggesting that management is more likely to provide precise 26

guidance the closer they get to the earnings release. We also expect management to have more precise forecasts the more they revise their forecasts during the quarter, suggesting a positive coefficient on the #Revisionsi,t variable. 3.2.4. Forecast pessimism and accuracy We now turn our attention toward the effect of organizational complexity on forecast pessimism and accuracy. Similar to the preceding tests, if organizational complexity increases external market participants demand for information, we expect managers to provide more accurate information to market participants. However, if organizational complexity increases the costs that managers incur when gathering and analyzing information, we expect managers to be more pessimistic in their forecasts to absorb negative shocks that might negatively affect expected earnings. Managers are less likely to miss pessimistic forecasts, reducing the likelihood of future litigation brought by shareholders for not updating firm disclosure in a timely manner. Cornerstone (2013) reports that approximately 54% of lawsuits between 2009 and 2013 bring allegations that management provided false forward-looking information, which could be cited if management does not update the firm s earnings forecasts after negative earnings shocks in a timely manner. 16 We use equation (4) to examine whether forecast accuracy and pessimism are affected by the firm s organizational complexity. DEPVARi,t = α + β1 Geographic Complexityi,t + β2 Business Complexityi,t + β3 Cost Structure Complexityi,t + β4 lndays_eai,t + β5 SalesVoli,t-1 + β6 STD_Analystsi,t-1 + β7 Litigation Riski,t-1 + β8 HHIi,t + β9 lncoveragei,t-1 + β10 INSTOWNi,t-1 + β11 lnsizesi,t-1 + β12 Book-to-Marketi,t-1 + β13 ROAi,t + β14 SalesGrowthi,t + β15 Fourth Quarteri,t + β16 #Revisionsi,t + β17 Horizoni,t (4) 16 Rogers and Van Buskirk (2009) suggest that plaintiff s attorneys frequently argue that the Private Securities Litigation Reform Act of 1995, which purportedly protected forward-looking disclosures, does not protect the defendant s forward-looking disclosures. 27

+ ΣIndustry FE + ΣYear FE + εi,t The DEPVARi,t is equal to either the forecast error (FEi,t) or the absolute value of the forecast error (Abs_FEi,t) for firm i in quarter t. The FEi,t variable is calculated by the last management earnings forecast for firm i during quarter t less actual earnings as provided by IBES scaled by stock price three days prior to the date of management forecast. The Abs_FEi,t variable is calculated by taking the absolute value of the FEi,t variable. Lower values of the FEi,t variable suggests a more pessimistic forecast, while higher values of the Abs_FEi,t variable suggests a less accurate forecast. If organizational complexity decreases forecast accuracy (increases forecast pessimism) in management s earnings forecasts, we expect to find a significant and positive (negative) coefficient on the Geographic Complexityi,t, Business Complexityi,t, and Cost Structure Complexityi,t variables in equation (4). We include the same control variables as in equation (3). When the dependent variable is the FEi,t (Abs_FEi,t) variable we expect the coefficient on the SalesVoli,t and STD_Analystsi,t variables to be negative (positive), if firms are more pessimistic (less accurate) in periods of greater economic uncertainty. We expect the coefficient on the Horizoni,t variable to be positive if forecasts with long horizon are more optimistic and less accurate. We expect investors demand for information (lnsizei,t-1, lncoveragei,t-1, INSTOWNi,t-1) to improve accuracy. We have no prediction for the coefficients on the lndays_eai,t, HHIi,t, Fourth Quarteri,t, SalesGrowthi,t, Book-to-Marketi,t-1, #Revisioni,t, and ROAi,t variables in either the FEi,t or Abs_FEi,t regressions but include them for completeness. 4. Results 4.1. Sample 28

We start with all firm-quarter observations from I/B/E/S and Compustat with sufficient data to calculate each of the variables in our model specifications. We collect all quarterly management earnings forecasts from the IBES guidance database from 2002 through 2014. We excluded forecasts made before 2002 due to confounding effects that could affect our results present before Regulation Fair Disclosure (Reg FD). Prior to Reg FD, management could privately communicate information about the firm s performance to financial intermediaries. We also require the firm to have made an earnings announcement during quarter t to be included in our sample and to have at least two analysts following. Following prior studies (e.g., Rogers and Stocken 2005), we do not include forecasts made after the fiscal quarter end (i.e., pre-earnings announcements). Our full sample includes 21,336 firm-quarter observations. Descriptive statistics for the sample can be found in Table 1. Our sample seems to consist of firms with relatively high analyst following (approximately 10.6 analysts) and institutional ownership (60.7% of shares are held by institutions). We note that approximately 10.8% of our sample consists of firms who revise their forecasts during the quarter. Finally, we note that approximately 79.9% of the firm-quarters bundle their last earnings forecast for the quarter with the prior quarter s earnings announcement. We provide the Pearson and Spearman correlation values between the dependent and main independent variables of interest in Table 2. We first point out that correlations between our complexity variables are generally low, suggesting that each organizational complexity variable is measuring a different aspect of complexity, which could ultimately have different impacts on managerial disclosure. We find univariate evidence that the Revisei,t variable is negatively associated with the Geographic Complexityi,t and Cost Structure Complexityi,t variables, providing preliminary evidence that business and cost structure complexity increase 29

the costs that managers incur to gather and analyze information useful to external market participants. We also find evidence that geographic and cost structure complexity decreases the precision of management earnings forecasts. 4.2. Forecast revision results Table 3 presents results examining the relation between organizational complexity and management forecast revisions. In column (1) through column (4), the Revisei,t variable is the dependent variable and we estimate the multivariate logistic regression. The #Revisionsi,t variable is the dependent variable in column (5) and use ordinary least square estimation. In column (1) through column (3), we include each complexity variable separately, and in column (4), we include all complexity measures simultaneously in the same regression. In column (4), we find a significantly negative coefficient on both the Geographic Complexityi,t and Cost Structure Complexityi,t variable, suggesting that earnings forecast revision behavior is decreasing in geographical complexity and cost structure complexity. This explanation is consistent with managers of more geographically complex and cost structure complex firms incurring higher costs to aggregate, analyze, and produce information useful to investors between reporting periods. We find no multivariate evidence that business complexity affect management earnings revisions. In addition, we find a positive and statistically significant coefficient on First_FEi,t, implying that there is a higher likelihood of revision when the initial forecast is too optimistic. We also find that litigation risk (Litigation Riski,t) is positively associated with the likelihood of revision. Larger firms tend to revise more often (as evidenced by the positive and statistically significant coefficients on lnsizei,t-1) The coefficient on the lndays_eai,t variable is negative and 30

significant, suggesting that firms with more sophisticated accounting systems face lower costs of aggregating and processing information that is used to revise forecasts during the fiscal period. In column (5), we find similar results using the number of forecast revisions as a dependent variable. The Geographic Complexityi,t and the Cost Structure Complexityi,t variables are significantly negatively associated with the number of forecast revisions while the Business Complexityi,t variable is not statistically associated with the number of forecast revisions. 4.3. Forecast bundling results In Table 4, we present the multivariate logit results with the Bundlei,t variable as the dependent variable. We find a significant and positive coefficients at the 1% level on the Geographic Complexityi,t variable in column (3), suggesting that geographic complexity increase the likelihood of management bundling its final earnings forecast of the quarter with the prior quarter s earnings announcement. We find no evidence that business complexity and cost structure complexity have an effect on the likelihood of management bundling its final earnings forecast with its previous quarter s earnings announcement. We find that firms with higher litigation risk (Litigation Riski,t), higher first forecast error (First_FEi,t), higher analyst forecast dispersion (STD Analystsi,t) are less likely to bundle. The coefficients on the lnsizei,t and the lncoveragei,t-1 variables are negative, possibly suggesting that investors demand for information reduces the likelihood of management bundling their forecasts with earnings announcements. We also find that the lndays_eai,t variable has a positive coefficient, which suggests that less sophisticated information processing technology increase management s information acquisition costs resulting in more bundling. 4.4 Forecast precision results 31

In Table 5, we present results with the Specificityi,t variable as the dependent variable. We find a negative and significant coefficient at the 1% level on both the Geographic Complexityi,t and Cost Structure Complexityi,t variable, suggesting that geographic and cost structure complexity decreases the likelihood of management providing precise forecasts. This evidence is consistent with geographical and cost structure complexity increasing management s costs of gathering and analyzing firm information that is subsequently communicated to external market participants. We find no evidence that business complexity affects the likelihood of management providing a point forecast. In line with our predictions, the coefficients on the control variables STD_Analystsi,t-1 and SalesVoli,t-1 variable are significantly negative, suggesting uncertainty decreases the precision of forecasts. Also in line with our predictions, the coefficients are positive on the control variables that proxy for investor demand for information (INSTOWNi,t-1 and lnsizei,t-1). 4.5. Forecast pessimism and accuracy results In Panel A of Table 6, we present the multivariate regression results when FEi,t is included as the dependent variable. We find evidence that geographic complexity and cost structure complexity are associated with more pessimistic forecasts, which is manifested by a negative and significant (10% level) coefficient on the Geographic Complexityi,t and Cost Structure Complexityi,t variables. Once again, this evidence is consistent with geographical and cost structure complexity increasing the costs of gathering and processing information that is subsequently communicated to external market participants. We find no evidence that business complexity is associated with forecast pessimism. We find that managers of firms with high uncertainty (STD_Analystsi,t-1 and SalesVoli,t-1) are more pessimistic in their forecasts. We also 32

find a significant and positive coefficient on the Horizoni,t variable, suggesting that managers issue more optimistic forecasts when the forecast is issued earlier in the quarter. In Panel B of Table 6, we present the multivariate regression results examining the relation between organizational complexity and the accuracy of managerial earnings guidance. The coefficient on the Geographic Complexityi,t and the Cost structure Complexityi,t variable are positive and significant at the 10% and 1% level, respectively, providing evidence that geographic and cost structure complexity increases the forecast error of management s voluntary disclosure (i.e., less accurate forecasts). This evidence is likely by-product of increased forecast pessimism for geographically and cost structure complex firms. We find no evidence that business complexity has any effect on the accuracy of management forecasts. Consistent with expectations, we find that managers of large firms (lnsizei,t-1) and firms with high institutional ownership (INSTOWNi,t-1) have more accurate forecasts. We also find that high uncertainty firms (SalesVoli,t-1 and STD Analystsi,t) are more likely to have less accurate forecasts. 4.5. Summary of results In summary, we find consistent evidence that geographic and cost structure complexity increases the costs that managers incur to gather and process information that is subsequently communicated to external market participants. However, we find no evidence that business complexity affects management s communication with external market participants. The lack of consistent evidence for business complexity could be because it has no effect on management s communication. Alternatively, the lack of results could be due to the effect of the increased external market participants demand for information offsetting the effect of the increased information acquisition costs that managers incur for more organizational complex firms. We are unable to disentangle these two competing explanations in this study. 33

5. Cross-Sectional test: Institutional ownership As previously discussed, our primary argument is that organizational complexity increases the costs of gathering, processing, and communicating information that is timely and relevant to external market participants, ceteris paribus. In this section, we investigate whether the managers of organizationally complex firms are more likely to provide forward-looking information to external market participants when the demand for information increases. We measure investors demand for information using the percentage of shares owned by institutional investors. Prior studies argue that institutions prefer greater transparency in the corporate information environment, encouraging managers to improve voluntary disclosures (Healy et al., 1999; Bushee and Noe, 2000; Ajinkya et al., 2005; Boone and White, 2014). Hence, we examine whether firms with greater organizational complexity revise earnings forecasts more, issue less pessimistic forecasts, and provide more precise and accurate forecasts when the institutional ownership is higher. To address this prediction, we create an indicator variable equal to one if the fraction of shares owned by institutional investors is greater than the sample median, zero otherwise (INSTDUMi,t-1). We interact this indicator variable with our complexity measures to examine whether the relationship between organizational complexity and management s communication with external market participants varies with the level of institutional ownership. Table 7 presents the results. In Panel A, we use the number of earnings forecast revisions as the dependent variable. Consistent with our expectations, we find a positive coefficient on the interaction term, Geographic Complexityi,t INSTDUMi,t, suggesting that managers in geographically complex firms respond to investors higher information demands and that the 34

negative association between geographic complexity and management revision frequencies is attenuated when investors demand more information. In Panel B, we present the results examining whether the association between organizational complexity and various properties of management earnings forecasts is tempered when investors demand for information is higher. We find that higher geographic and cost structure complexity results in management forecasts that are more (less) precise and more (less) accurate when the institutional ownership is high (low). We find that higher geographic and cost structure complexity is associated with management forecasts that are less pessimistically biased in column (2) but the coefficients are statistically insignificant. In sum, the results in Table 7 suggest that investors demand for information can incentivize managers to improve the frequency and properties of voluntary disclosure as geographic and cost structure complexity increases. 6. Conclusion and directions for future research In this paper, we investigate how organizational complexity affects management s communication with external market participants. Specifically, we examine whether different aspects of organizational complexity (geographic complexity, business complexity, and cost structure complexity) are associated with management s propensity to revise earnings forecasts, forecast timing, and forecast quality. Using earnings forecasts made between 2002 and 2014, we find evidence that the propensity to revise forecasts is negatively associated with greater geographical and cost structure complexity. We also find that managers of firms with high geographic complexity are more likely to bundle their final earnings forecast with the previous quarter s earnings announcement. In addition, we find evidence that geographic and cost 35

structure complexity decreases the accuracy and precision of management forecasts. Finally, we find that managers issue more pessimistic forecasts when geographic and cost structure complexity increase. This evidence is consistent with geographical and cost structure complexity increasing management s cost of gathering and processing information that is subsequently communicated to investors. We contribute to the accounting literature by providing additional evidence that the costs associated with aggregating, analyzing, and communicating information within the firm affect management disclosure practices. Our study is among the few that attempts to link managerial and financial accounting research by examining how internal organizational factors, such as organizational complexity, is associated with financial reporting practices. Our study also helps us to better understand the reasons why managers miss their own forecasts (Lee et al., 2012; Chen, 2004). Finally, our study provides additional evidence on how specific organizational characteristics influence managers decision to forecast and forecast properties. Although our results largely corroborate our hypotheses, the measures that we use for organizational complexity are all publicly available measures that are relatively noisy. Future research may find it useful to better understand the relation between the finer components of organizational complexity and management disclosure. For example, the number of product lines, the number of business segments used in managing the business internally (as opposed to those that are disclosed to investors in SEC filings), and the geographical distance between different segments all are ways through which complexity can be more precisely captured. Fieldbased studies can more aptly study these tools that enhance information processing and transfer within organizations. Although costly in terms of data collection and analysis, field studies may 36

help elucidate the process by which organizational complexity affects the aggregation, analysis, and communication of information to external market participants. 37

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Appendix A. Variable Definitions Variables Geographic Complexity i,t Business Complexity i,t Cost Structure Complexity i,t Days_EA i,t SalesVol i,t-1 STD_Analysts i,t-1 Litigation Risk i,t HHI i,t Coverage i,t-1 INSTOWN i,t-1 lnsize i,t-1 Book-to-Market i,t-1 ROA i,t Descriptions Geographic Complexity i,t is equal to one minus the geographic segment Hirfindahl-Hirschman index for firm i in quarter t. The Hirfindahl- Hirschman index is sum of squares of sales in each geographic segment divided by total firm sales. Business Complexity i,t is equal to one minus the firm/business segment Hirfindahl-Hirschman index for firm i in quarter t. The Hirfindahl- Hirschman index is sum of squares of sales in each business segment divided by total firm sales. Cost Structure Complexity i,t is equal to the negative one multiplied by the correlation between firm revenues scaled by total assets in quarter t-4 and operating income before depreciation scaled total assets in quarter t-4 over the 12 quarters preceding the observation for firm i in quarter t (a minimum of 8 observations is required). Days_EA i,t is equal to the number of days between the end of the quarter and earnings announcement date for firm i in quarter t. When this variable is used in the regression equation, we take the natural logarithm of this variable (i.e., lndays_ea i,t). SalesVol i,t-1 is equal to the standard deviation of sales scaled by the mean value of sales for the 12 quarters preceding quarter t for firm i (a minimum of 8 observations is required). STD_Analysts i,t is equal to the standard deviation of analyst forecasts for firm i at the beginning of quarter t. Litigation Risk i,t is equal to one if firm i experiences an earnings decrease of more than 20 percent in quarter t compared to quarter t-4 and is in a high risk industry (SIC codes 2833-2836, 3600-3674, 5200-5961, 7370-7374, 8731-8734). HHI j,t is Herfindahl-Hirschman Index measured as the sum of squared market shares of all firms in the same two-digit SIC industry during quarter t. Coverage i,t-1 is equal to the number of analysts following firm i at the beginning of quarter t. When this variable is used in the regression model, we take the natural logarithm of this variable (i.e., lncoverage i,t-1). InstOwn i,t-1 is equal to the percentage of firm i's stock held by institutional investors at the beginning of quarter t. lnsize i,t-1 is equal to the natural logarithm of firm i s market value of equity at the beginning of quarter t. Book-to-Market i,t-1 is measured by firm i s book value of equity divided by the market value of equity at the beginning of quarter t. ROA i,t is return on assets for firm i in quarter t minus return on assets for firm i in quarter t-4. Return on assets in quarter t is measured as IBESreported Actual EPS divided by average total assets per share for firm i in 43

SalesGrowth i,t Fourth Quarter i,t Revise i,t #Revisions i,t Bundle i,t FE i,t Abs_FE i,t First_FE i,t Horizon i,t Specificity i,t quarter t. SalesGrowth i,t is equal to firm i s net sales in quarter t divided by net sales in quarter t-4. Fourth Quarter i,t is an indicator variable equal to one if the fiscal quarter is fourth quarter, zero otherwise. Revise i,t is equal to one if firm i revises its initial earnings forecast during quarter t. #Revisions i,t is equal to the number of forecast revisions following the initial earnings forecast for firm i during quarter t. Bundle i,t is an indicator variable equal to one when the last earnings forecast is issued for firm i and quarter t in the same day as the earnings announcement for quarter t-1. FE i,t is equal to IBES-reported actual earnings per share subtracted from the last management earnings forecast prior to earnings announcement during quarter t for firm i, scaled by stock price 3 days prior to the date of management forecast. Abs_FE i,t is equal to the absolute value of the FE i,t variable for firm i in quarter t. First_FE i,t is equal to IBES-reported actual earnings per share subtracted from the first management earnings forecast for firm i during quarter t, scaled by stock price 3 days prior to the date of management forecas. Horizon i,t is equal to the number of days between end of the quarter and the management earnings forecast date for firm i during quarte t divided by 365. Specificity i,t is equal to the difference between the top of forecast range and the bottom of forecast range divided by stock price 3 days before the date of management forecast. This variable is multiplied by negative one. If the management forecast is a point forecast, this variable is set to zero. 44

Table 1 Descriptive Statistics This table reports descriptive statistics for the sample with available information. The sample period ranges from 2002 to 2014. All continuous variables are winsorized at the 1 and 99th percentiles. All variables are defined in the Appendix. Variables N Mean Std Q1 Median Q3 Geographic Complexity i,t 21,336 0.360 0.277 0.014 0.414 0.602 Business Complexity i,t 21,336 0.086 0.176 0.000 0.000 0.013 Cost Structure Complexity i,t 21,336-0.724 0.326-0.937-0.842-0.647 Days_EA i,t 21,336 29.438 10.614 23 28 34 SalesVol i,t-1 21,336 0.185 0.123 0.100 0.152 0.234 STD_Analysts i,t-1 21,336 0.017 0.018 0.010 0.010 0.020 Litigation Risk i,t 21,336 0.129 0.335 - - - HHI i,t 21,336 0.064 0.063 0.035 0.043 0.065 Coverage i,t-1 21,336 10.653 7.010 5 9 14 INSTOWN i,t-1 21,336 0.607 0.359 0.367 0.747 0.894 lnsize i,t-1 21,336 7.293 1.536 6.202 7.191 8.277 Book-to-Market i,t-1 21,336 0.475 0.299 0.266 0.412 0.617 ROA i,t 21,336 0.000 0.015-0.005 0.000 0.005 SalesGrowth i,t 21,336 1.122 0.249 1.001 1.091 1.202 Fourth Quarter i,t 21,336 0.265 0.441 - - - Revise i,t 21,336 0.108 0.310 - - - #Revisions i,t 21,336 0.124 0.384 0.000 0.000 0.000 Bundle i,t 21,336 0.799 0.401 - - - Frist_FE i,t 21,336-0.126 1.166-0.351-0.097 0.000 FE i,t 21,336-0.153 1.107-0.344-0.099 0.000 Abs_FE i,t 21,336 0.600 1.636 0.058 0.166 0.493 Horizon i,t 21,336 0.239 0.087 0.214 0.249 0.260 Specificity i,t 20,837-0.266 0.537-0.253-0.116-0.046 45

Table 2 Correlation Matrix This table reports Pearson (Above) / Spearman (Below) correlations of main variables. Sample period ranges from 2002 to 2014. All continuous variables are winsorized at 1 st and 99 th percentiles. Significance level at 1% is bolded. All variables are defined in the Appendix. Variables (1) (2) (3) (4) (5) (6) (7) (8) (9) (10) (11) (12) (13) (14) (15) (16) (1) Geographic Complexity i,t - 0.05-0.04-0.10 0.00 0.12 0.12 0.19-0.04 0.02-0.03-0.11-0.03 0.01 0.01-0.05 (2) Business Complexity i,t 0.01 - -0.04-0.01-0.11 0.00 0.09 0.22 0.00 0.00-0.03 0.02 0.00-0.02-0.01 0.07 (3) Cost Structure Complexity i,t -0.06-0.01-0.04 0.08-0.09 0.00-0.15-0.01 0.04 0.03-0.04-0.04 0.09 0.04-0.10 (4) Days_EA i,t -0.10-0.02 0.07-0.02-0.21 0.07-0.26 0.14-0.05 0.02-0.01 0.07 0.08 0.37-0.09 (5) Litigation Risk i,t 0.00-0.12 0.05 0.02 - -0.04-0.12-0.22 0.14-0.14-0.12 0.06 0.00 0.10 0.00-0.19 (6) Coverage i,t-1 0.09 0.02-0.13-0.20-0.06 - -0.05 0.67-0.23-0.01 0.02 0.05 0.01-0.11-0.05 0.17 (7) INSTOWN i,t-1 0.10 0.05 0.00 0.11-0.13 0.06-0.03-0.02 0.00 0.03-0.10 0.04-0.06 0.04 0.10 (8) lnsize i,t-1 0.18 0.21-0.15-0.26-0.22 0.70 0.07 - -0.37 0.04 0.04 0.05 0.03-0.19-0.07 0.32 (9) Book-to-Market i,t-1-0.03 0.03 0.00 0.14 0.12-0.26-0.03-0.36 - -0.14-0.23-0.02 0.04 0.17 0.03-0.31 (10) ROA i,t 0.03 0.00 0.03-0.04-0.20-0.02-0.01 0.04-0.14-0.41-0.02-0.17-0.09 0.01 0.08 (11) SalesGrowth i,t -0.03-0.03 0.03 0.02-0.17 0.06 0.06 0.07-0.29 0.38 - -0.01-0.03-0.06 0.02 0.16 (12) Revise i,t -0.10 0.02-0.04-0.04 0.06 0.06-0.09 0.05-0.02-0.02-0.01-0.01 0.00-0.24 0.04 (13) FE i,t -0.03-0.02-0.01 0.03 0.06 0.07 0.02 0.06-0.03-0.27-0.09 0.02-0.12 0.07 0.03 (14) Abs_FE i,t -0.01-0.03 0.05 0.09 0.10-0.22-0.08-0.28 0.21 0.05-0.06-0.01-0.54-0.04-0.60 (15) Horizon i,t 0.05-0.03 0.04 0.43-0.01-0.06 0.05-0.07 0.02 0.01 0.02-0.26 0.03 0.07 - -0.02 (16) Specificity i,t -0.06 0.10-0.06-0.13-0.19 0.28 0.10 0.40-0.41 0.09 0.26 0.07-0.01-0.24-0.06-46

Table 3 The effects of organizational complexity on management forecast revisions This table presents the results from the regression of the management forecast revision variable on the complexity measures and control variables. The dependent variable in column (1) through column (4), Revise i,t, is an indicator variable equal to one if management revises an earnings forecast for firm i during quarter t, zero otherwise. In column (5), the dependent variable is #Revisions i,t and defined as the number of times firm i revises earnings forecasts during quarter t. All other variables are defined in the Appendix and all continuous variables are winsorized at the 1 st and 99 th percentiles. Standard errors are clustered by firm and calendar quarter. Robust t- statistics are in parentheses. *, **, and *** represent significance level at the 10%, 5%, and 1%, respectively. Revise i,t #Revisions i,t Independent Variables (1) (2) (3) (4) (5) Geographic Complexityi,t -0.657*** - - -0.687*** -0.059*** (-4.315) - - (-4.536) (-4.096) Business Complexityi,t - 0.305-0.336 0.035 - (1.419) - (1.584) (1.535) Cost Structure Complexityi,t - - -0.247** -0.278*** -0.025** - - (-2.498) (-2.851) (-2.571) lndays_ea i,t -0.329*** -0.336*** -0.321*** -0.338*** -0.033** (-2.936) (-3.017) (-2.843) (-3.065) (-2.310) SalesVol i,t-1 0.466 0.516* 0.441 0.409 0.053 (1.575) (1.687) (1.440) (1.377) (1.585) STD_Analysts i,t-1-2.141-1.795-1.683-2.137-0.172 (-1.413) (-1.205) (-1.124) (-1.427) (-1.124) Litigation Risk i,t 0.518*** 0.517*** 0.531*** 0.530*** 0.048*** (6.673) (6.657) (6.818) (6.733) (4.686) HHI i,t -0.130 0.099 0.118-0.246-0.016 (-0.191) (0.153) (0.177) (-0.367) (-0.172) lncoverage i,t-1 0.075 0.097 0.083 0.088 0.013 (0.978) (1.243) (1.077) (1.129) (1.555) INSTOWN i,t-1-0.237** -0.260** -0.249** -0.248** -0.035** (-1.966) (-2.100) (-2.043) (-2.044) (-2.414) lnsize i,t-1 0.159*** 0.115*** 0.121*** 0.139*** 0.009** (3.932) (2.762) (3.045) (3.287) (1.989) Book-to-Market i,t-1-0.046-0.104-0.100-0.065-0.006 (-0.391) (-0.887) (-0.856) (-0.551) (-0.483) ROA i,t 1.144 1.064 1.212 1.370 0.173 (0.691) (0.638) (0.719) (0.812) (0.886) SalesGrowth i,t -0.090-0.061-0.052-0.076-0.009 (-0.750) (-0.505) (-0.431) (-0.635) (-0.841) Fourth Quarter i,t 0.829*** 0.833*** 0.829*** 0.834*** 0.104*** (9.770) (9.779) (9.761) (9.844) (8.799) First_FE i,t 0.157*** 0.158*** 0.158*** 0.156*** 0.021*** (6.900) (6.907) (6.906) (6.740) (5.534) Lagged Dependent variable i,t-1 1.433*** 1.449*** 1.448*** 1.427*** 0.282*** (13.316) (13.367) (13.534) (13.258) (10.718) Constant -1.733*** -1.628*** -1.846*** -1.824*** 0.189*** (-3.291) (-3.103) (-3.451) (-3.445) (3.152) Industry Fixed Effects Yes Yes Yes Yes Yes Year Fixed Effects Yes Yes Yes Yes Yes Observations 21,336 21,336 21,336 21,336 21,336 Pseudo R-squared / Adj. R-squared 0.147 0.145 0.146 0.148 0.147 47

Table 4 The effects of organizational complexity on management forecast bundling This table presents the results from the regression of the Bundle i,t variable on the complexity measures and control variables. Bundle i,t is an indicator variable equal to one when the last management earnings forecast is issued for firm i and quarter t in the same day of the earnings announcement for quarter t-1. All other variables are defined in the Appendix and all continuous variables are winsorized at the 1 st and 99 th percentiles. Standard errors are clustered by firm and calendar quarter. Robust t-statistics are in parentheses. *, **, and *** represent significance level at the 10%, 5%, and 1%, respectively. Bundle i,t Independent Variables (1) (2) (3) (4) Geographic Complexityi,t 0.800*** - - 0.800*** (3.754) - - (3.736) Business Complexityi,t - -0.319 - -0.328 - (-1.162) - (-1.205) Cost Structure Complexityi,t - - -0.069-0.035 - - (-0.705) (-0.350) lndays_ea i,t 0.674*** 0.680*** 0.671*** 0.687*** (5.320) (5.357) (5.266) (5.473) SalesVol i,t-1 0.204 0.151 0.143 0.184 (0.739) (0.522) (0.510) (0.672) STD_Analysts i,t-1-4.525*** -4.882*** -4.905*** -4.462*** (-3.043) (-3.202) (-3.224) (-3.008) Litigation Risk i,t -0.395*** -0.398*** -0.395*** -0.392*** (-5.117) (-5.201) (-5.196) (-5.077) HHI i,t 1.111 0.807 0.731 1.171 (1.438) (1.059) (0.960) (1.517) lncoverage i,t-1-0.183** -0.207** -0.193** -0.198** (-2.219) (-2.496) (-2.319) (-2.407) INSTOWN i,t-1 0.179 0.201 0.191 0.190 (1.417) (1.553) (1.487) (1.488) lnsize i,t-1-0.145*** -0.094** -0.110** -0.132*** (-3.132) (-2.091) (-2.395) (-2.902) Book-to-Market i,t-1-0.126-0.061-0.074-0.115 (-0.933) (-0.458) (-0.558) (-0.852) ROA i,t 2.861 2.956* 3.042* 2.841 (1.618) (1.711) (1.766) (1.587) SalesGrowth i,t -0.128-0.159-0.155-0.128 (-1.072) (-1.321) (-1.294) (-1.062) Fourth Quarter i,t -0.413*** -0.416*** -0.412*** -0.417*** (-5.401) (-5.471) (-5.401) (-5.480) First_FE i,t -0.107*** -0.110*** -0.111*** -0.107*** (-4.199) (-4.219) (-4.293) (-4.176) Constant -0.887-0.993* -0.963-0.984* (-1.497) (-1.686) (-1.618) (-1.679) Industry Fixed Effects Yes Yes Yes Yes Year Fixed Effects Yes Yes Yes Yes Observations 21,336 21,336 21,336 21,336 Pseudo R-squared 0.123 0.120 0.120 0.124 48

Table 5 The effects of organizational complexity on management forecast specificity This table presents the results from the regression of the Specificity i,t variable on the complexity measures and control variables. Specificity i,t is defined as the difference between the top of the range forecast and the bottom of the range forecast scaled by stock price 3 days before the date of management forecast. If the management forecast is a point forecast, this variable is set to zero. This variable is multiplied by -1. All coefficients in this table are multiplied by 100. All other variables are defined in the Appendix and all continuous variables are winsorized at the 1 st and 99 th percentiles. Standard errors are clustered by firm and calendar quarter. Robust t-statistics are in parentheses. *, **, and *** represent significance level at the 10%, 5%, and 1%. Specificity i,t Independent Variables (1) (2) (3) (4) Geographic Complexityi,t -0.150*** - - -0.162*** (-3.188) - - (-3.433) Business Complexityi,t - 0.013-0.015 - (0.253) - (0.285) Cost Structure Complexityi,t - - -0.117*** -0.124*** - - (-3.386) (-3.608) lndays_ea i,t -0.011-0.009-0.006-0.009 (-0.418) (-0.329) (-0.197) (-0.312) SalesVol i,t-1-0.511*** -0.501*** -0.530*** -0.542*** (-5.589) (-5.490) (-5.712) (-5.849) STD_Analysts i,t-1-2.048*** -1.985*** -1.955*** -2.026*** (-4.219) (-4.059) (-3.965) (-4.154) Litigation Risk i,t -0.113*** -0.113*** -0.105*** -0.106*** (-3.594) (-3.539) (-3.384) (-3.429) HHI i,t -0.050 0.015 0.012-0.067 (-0.404) (0.122) (0.099) (-0.528) lncoverage i,t-1-0.013-0.009-0.008-0.011 (-0.772) (-0.541) (-0.482) (-0.657) INSTOWN i,t-1 0.154*** 0.153*** 0.152*** 0.151*** (5.184) (5.163) (5.203) (5.163) lnsize i,t-1 0.088*** 0.081*** 0.077*** 0.084*** (6.737) (6.539) (6.420) (6.617) Book-to-Market i,t-1-0.312*** -0.322*** -0.328*** -0.319*** (-6.533) (-6.763) (-6.941) (-6.749) ROA i,t -0.912-0.947* -0.860-0.815 (-1.612) (-1.671) (-1.500) (-1.427) SalesGrowth i,t 0.302*** 0.309*** 0.315*** 0.308*** (7.767) (7.764) (7.870) (7.912) Fourth Quarter i,t -0.032** -0.032** -0.033*** -0.032** (-2.487) (-2.515) (-2.581) (-2.532) #Revisions i,t 0.038*** 0.041*** 0.039*** 0.035*** (3.009) (3.306) (3.091) (2.770) Horizon i,t 0.150** 0.144** 0.143** 0.149** (2.134) (2.021) (1.999) (2.123) Constant -0.846*** -0.848*** -0.919*** -0.915*** (-5.486) (-5.424) (-5.655) (-5.665) Industry Fixed Effects Yes Yes Yes Yes Year Fixed Effects Yes Yes Yes Yes Observations 20,837 20,837 20,837 20,837 Pseudo R-squared 0.210 0.207 0.211 0.216 49

Table 6 The effects of organizational complexity on management forecast bias and error This table presents the results from the regression of the management forecast bias / error variable on the complexity measures and control variables. FE i,t in Panel A is equal to IBES-reported actual earnings per share for firm i in quarter t subtracted from the last management earnings forecast during quarter t scaled by stock price three days before the date of management forecast. Abs_FE i,t in Panel B is the absolute value of the FE i,t variable. All coefficients in this table are multiplied by 100. All other variables are defined in the Appendix and all continuous variables are winsorized at the 1 st and 99 th percentiles. Standard errors are clustered by firm and calendar quarter. Robust t-statistics are in parentheses. *, **, and *** represent significance level at the 10%, 5%, and 1%. Panel A Forecast Bias Forecast Bias i,t Independent Variables (1) (2) (3) (4) Geographic Complexityi,t -0.167* - - -0.180* (-1.662) - - (-1.759) Business Complexityi,t - -0.089 - -0.086 - (-0.730) - (-0.706) Cost Structure Complexityi,t - - -0.120* -0.127* - - (-1.817) (-1.907) lndays_ea i,t 0.145*** 0.150*** 0.151*** 0.151*** (2.699) (2.783) (2.765) (2.779) SalesVol i,t-1-0.414*** -0.407*** -0.434*** -0.448*** (-2.849) (-2.805) (-2.892) (-2.972) STD_Analysts i,t-1-2.882*** -2.795*** -2.779*** -2.838*** (-3.299) (-3.180) (-3.143) (-3.227) Litigation Risk i,t 0.002 0.003 0.010 0.010 (0.044) (0.067) (0.240) (0.235) HHI i,t 0.225 0.326 0.294 0.236 (0.682) (0.946) (0.871) (0.703) lncoverage i,t-1-0.043-0.044-0.039-0.046 (-1.444) (-1.511) (-1.279) (-1.595) INSTOWN i,t-1 0.097* 0.100* 0.095* 0.097* (1.736) (1.776) (1.694) (1.757) lnsize i,t-1 0.072*** 0.068*** 0.060*** 0.071*** (3.454) (3.601) (3.153) (3.717) Book-to-Market i,t-1 0.213** 0.207** 0.197** 0.211** (2.531) (2.482) (2.395) (2.486) ROA i,t -14.846*** -14.891*** -14.786*** -14.747*** (-8.556) (-8.616) (-8.560) (-8.516) SalesGrowth i,t 0.271*** 0.278*** 0.285*** 0.276*** (3.663) (3.717) (3.807) (3.744) Fourth Quarter i,t -0.031-0.032-0.032-0.033 (-1.299) (-1.353) (-1.351) (-1.366) #Revisions i,t 0.053* 0.058* 0.055* 0.051 (1.685) (1.818) (1.727) (1.617) Horizon i,t 0.826*** 0.822*** 0.823*** 0.831*** (4.152) (4.120) (4.148) (4.186) Constant -1.573*** -1.598*** -1.649*** -1.668*** (-5.877) (-5.914) (-5.713) (-5.775) Industry Fixed Effects Yes Yes Yes Yes Year Fixed Effects Yes Yes Yes Yes Observations 21,336 21,336 21,336 21,336 Pseudo R-squared 0.058 0.058 0.059 0.060 50

Panel B Forecast Error Forecast Error i,t Independent Variables (1) (2) (3) (4) Geographic Complexityi,t 0.285 - - 0.325* (1.636) - - (1.811) Business Complexityi,t - 0.110-0.101 - (0.618) - (0.569) Cost Structure Complexityi,t - - 0.394*** 0.408*** - - (2.806) (2.847) lndays_ea i,t 0.091 0.083 0.075 0.077 (1.164) (1.043) (0.923) (0.957) SalesVol i,t-1 1.517*** 1.504*** 1.597*** 1.621*** (4.631) (4.607) (4.789) (4.822) STD_Analysts i,t-1 5.597*** 5.454*** 5.370*** 5.487*** (5.012) (4.850) (4.790) (4.935) Litigation Risk i,t 0.156** 0.155* 0.131* 0.132* (1.961) (1.936) (1.728) (1.747) HHI i,t 0.477 0.317 0.373 0.492 (1.077) (0.682) (0.827) (1.074) lncoverage i,t-1 0.006 0.006-0.004 0.007 (0.117) (0.103) (-0.079) (0.128) INSTOWN i,t-1-0.293*** -0.296*** -0.286*** -0.288*** (-2.994) (-3.019) (-3.006) (-2.997) lnsize i,t-1-0.137*** -0.128*** -0.109*** -0.128*** (-3.518) (-3.689) (-3.272) (-3.665) Book-to-Market i,t-1 0.529*** 0.541*** 0.568*** 0.545*** (4.076) (4.273) (4.633) (4.347) ROA i,t -5.102** -5.029** -5.360** -5.435** (-2.318) (-2.278) (-2.414) (-2.455) SalesGrowth i,t -0.312*** -0.324*** -0.344*** -0.329*** (-2.901) (-3.011) (-3.182) (-3.065) Fourth Quarter i,t 0.023 0.024 0.026 0.026 (0.717) (0.775) (0.821) (0.837) #Revisions i,t 0.034 0.026 0.035 0.042 (0.748) (0.557) (0.748) (0.930) Horizon i,t 0.354 0.362 0.354 0.340 (1.366) (1.385) (1.348) (1.308) Constant 0.959** 0.992** 1.200** 1.221** (2.103) (2.152) (2.340) (2.396) Industry Fixed Effects Yes Yes Yes Yes Year Fixed Effects Yes Yes Yes Yes Observations 21,336 21,336 21,336 21,336 Pseudo R-squared 0.090 0.089 0.095 0.097 51

Table 7 Cross sectional variation: Institutional ownership This table presents the results from cross-sectional tests using institutional ownership. All dependent variables are specified in the top row of the table and all other variables are defined in the Appendix and all continuous variables are winsorized at the 1 st and 99 th percentiles. Standard errors are clustered by firm and calendar quarter. Robust t- statistics are in parentheses. *, **, and *** represent significance level at the 10%, 5%, and 1%, respectively. Panel A Forecast Revisions #Revisions i,t Independent Variables (1) Geographic Complexity i,t -0.099*** (-4.324) Business Complexity i,t 0.047 (1.219) Cost Structure Complexity i,t -0.027* (-1.886) INSTDUM i,t -0.019 (-1.151) Geographic Complexity i,t INSTDUM i,t 0.081*** (2.928) Business Complexity i,t INSTDUM i,t -0.017 (-0.367) Cost Structure Complexity i,t INSTDUM i,t 0.008 (0.498) lndays_ea i,t -0.032** (-2.253) SalesVol i,t-1 0.055* (1.667) STD_Analysts i,t-1-0.180 (-1.179) Litigation Risk i,t 0.049*** (4.836) HHI i,t -0.032 (-0.325) lncoverage i,t-1 0.013 (1.592) INSTOWN i,t-1-0.039* (-1.863) lnsize i,t-1 0.009** (2.042) Book-to-Market i,t-1-0.005 (-0.384) ROA i,t 0.158 SalesGrowth i,t (0.810) -0.008 (-0.759) Fourth Quarter i,t 0.104*** (8.863) First_FE i,t 0.020*** (5.365) Lagged Dependent variable i,t-1 0.281*** (10.712) Constant 0.198*** (3.248) Industry Fixed Effects Yes Year Fixed Effects Yes Observations 21,336 Pseudo R-squared 0.148 52

Panel B Forecast Characteristics Specificity i,t Forecast Bias i,t Forecast Error i,t Independent Variables (1) (2) (3) Geographic Complexity i,t -0.267*** -0.279** 0.546** (-3.614) (-2.192) (2.165) Business Complexity i,t -0.003-0.006-0.078 (-0.030) (-0.039) (-0.300) Cost Structure Complexity i,t -0.202*** -0.195* 0.699*** (-3.770) (-1.872) (3.045) INSTDUM i,t 0.144*** 0.062-0.495*** (3.025) (0.644) (-3.053) Geographic Complexity i,t INSTDUM i,t 0.206*** 0.205-0.461* (2.903) (1.492) (-1.839) Business Complexity i,t INSTDUM i,t 0.054-0.132 0.268 (0.596) (-0.721) (0.973) Cost Structure Complexity i,t INSTDUM i,t 0.195*** 0.167-0.714*** (3.503) (1.451) (-2.996) lndays_ea i,t -0.008 0.151*** 0.078 (-0.285) (2.781) (0.974) SalesVol i,t-1-0.523*** -0.438*** 1.575*** (-5.803) (-2.963) (4.765) STD_Analysts i,t-1-2.038*** -2.867*** 5.553*** (-4.190) (-3.283) (5.042) Litigation Risk i,t -0.097*** 0.014 0.110 (-3.231) (0.349) (1.533) HHI i,t -0.133 0.198 0.605 (-1.024) (0.565) (1.258) lncoverage i,t-1-0.013-0.045 0.009 (-0.810) (-1.568) (0.169) INSTOWN i,t-1 0.062 0.093-0.159 (1.431) (1.169) (-1.196) lnsize i,t-1 0.083*** 0.071*** -0.124*** (6.689) (3.732) (-3.643) Book-to-Market i,t-1-0.317*** 0.215** 0.536*** (-6.692) (2.543) (4.258) ROA i,t -0.814-14.731*** -5.540** (-1.394) (-8.502) (-2.493) SalesGrowth i,t 0.309*** 0.278*** -0.330*** (7.935) (3.769) (-3.057) Fourth Quarter i,t -0.032*** -0.032 0.026 (-2.607) (-1.350) (0.824) #Revisions i,t 0.031** 0.048 0.051 (2.458) (1.468) (1.103) Horizon i,t 0.142** 0.837*** 0.336 (2.039) (4.181) (1.275) Constant -0.901*** -1.680*** 1.291** (-5.699) (-5.814) (2.488) Industry Fixed Effects Yes Yes Yes Year Fixed Effects Yes Yes Yes Observations 20,837 21,336 21,336 Pseudo R-squared 0.223 0.061 0.103 53