Directors from Related Industries and Management Forecast Accuracy

Ruihao Ke Assistant Professor of Accounting

Cox School of Business Southern Methodist University Email: rke@mail.cox.smu.edu

Phone: 214-768-3702

Meng Li Assistant Professor of Accounting

School of Management University of Texas at Dallas

Email: mxl120531@utdallas.edu Phone: 972-883-2492

Yuan Zhang Associate Professor of Accounting

School of Management University of Texas at Dallas

Email: yxz122931@utdallas.edu Phone: 972-883-5828

June 2015

We thank David Aboody, Ashiq Ali, Bill Cready, Judson Caskey, Mei Feng, Umit Gurun, Doug Hanna, Yu Hou, Ningzhong Li, Zhejia Ling, Andrew MacKinlay, Brett Trueman, Clare Wang, Han Xia, Jieying Zhang, Yibin Zhou, and seminar participants at the University of Texas at Dallas, 2015 Tsinghua University International Corporate Governance Conference, 2015 Lone Star Accounting Research Conference, and 2015 AAA Annual Meeting for their comments and suggestions. We also thank Yue Zhang for providing data on the identity of firms’ major customers. All errors are our own.

Directors from Related Industries and Management Forecast Accuracy

Abstract: To provide accurate earnings forecasts, firms need not only internal information, but also information about external economic environment, such as their upstream and downstream industries. We investigate one party that can help firms obtain such information—directors who also serve as current directors or executives in the firms’ related industries (DRIs). We hypothesize and find that more DRIs on board are associated with more accurate management forecasts, and that this association is stronger when there is greater uncertainty in the firms themselves or in their related industries. Further analyses suggest that our findings are at least partly attributable to the advising role played by DRIs, and that DRIs are associated with improvement in other aspects of the firms’ information environment. Our study provides systematic evidence that external information is important in improving management forecast accuracy and that directors with such information can more effectively perform board functions.

Keywords: Directors; Related industries; External information; Management forecast accuracy.

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I. INTRODUCTION

Management earnings forecasts provide important information that market participants

use to form earnings expectations. Accurate management forecasts improve a firm’s reputation

for credible and transparent reporting (Hutton and Stocken 2009; Williams 1996). A large

literature has investigated the determinants of management forecast accuracy, particularly factors

affecting internal information gathering, such as internal control effectiveness (Feng, Li, and

McVay 2009) and organizational complexity (Jennings, Seo, and Tanlu 2014).

Firms, however, do not exist as isolated islands (Coase 1937). Their performance, and

thus their management forecast accuracy, are affected by outside forces such as competition

within their own industry as well as supply and demand shocks from upstream and downstream

industries (“related industries” hereafter). In fact, a survey of finance executives by PwC (CFO

Publishing 2011) indicates that the two most important causes for inaccurate management

forecasts are (1) uncertainty in the external business environment, and (2) difficulty in accessing

and incorporating external information. Yet, despite the importance of external information, it is

not well understood in the literature how firms acquire such information and to what extent

parties that possess this information affect management forecast accuracy.

The purpose of this study is to shed light on these issues, with a particular focus on one

party that is likely to possess valuable external information—directors who also serve as

directors or executives of firms from related industries (“DRIs” hereafter). Because of their close

ties to the firms’ related industries, DRIs can bring valuable information about these industries to

the firms. Indeed, when the coal-mining company Arch Coal appointed a DRI (Wesley Taylor)

to its board, the company stated, “He brings to our board… a keen understanding of the evolving

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needs of our principal customer base.”1 Because information is important for directors to perform

their two primary functions, advising and monitoring (Adams, Hermalin, and Weisbach 2010;

Harris and Raviv 2008; Hermalin and Weisbach 2003), we hypothesize that DRIs, as effective

advisors and/or monitors, help firms improve management forecast accuracy. Notice that this

hypothesis does not rely on boards’ direct involvement in the forecast formation process; as long

as DRIs communicate valuable external information to the boards/managers in various capacities,

for example, through discussions about strategic planning or board meetings on financial

performance, we would expect improvement in forecast accuracy.2

As advisors, DRIs have several ways of providing forecast-relevant information to

managers. They can directly update managers on unexpected shortages of supplies or decreases

in demand from customers that can slow down sales and decrease the bottom line; or they can

introduce managers to key contacts from the related industries. Even if the managers have

already obtained much of the information they need through alternative channels, DRIs, as

industry specialists, can provide expertise and insight on that information, thus helping the

managers form a more comprehensive view of the firms’ supply chain and respond accordingly.

Therefore, we expect that managers can use the information provided by DRIs to better

anticipate and manage external uncertainties from the related industries, and, as a result, make

more accurate management forecasts relative to firms without DRIs.

DRIs may also contribute to more accurate management forecasts in their capacity as

monitors. Theoretical work argues that informed boards can better evaluate managerial decisions

1 http://news.archcoal.com/, July 28, 2005. At the time, Wesley Taylor was a director in a firm that belonged to a downstream industry of Arch Coal. For more details, please see Appendix A. 2 Although firms differ, there is anecdotal evidence that at least some boards review the forecasts before they are disclosed to investors. For example, CFO of General Electric, Keith Sherin (2010), stated that, “We [present] forecasts and budgets in summary form to our board. Those budgets also provide the basis for the annual update with investors that takes place each December and presents our outlook for the next year.”

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and thus more effectively monitor the managers (Adams and Ferreira 2007; Harris and Raviv

2008). DRIs can bring important information about related industries to the boards (even if the

information may not be new to the managers), narrowing the information gap between the boards

and the managers. Knowing that the boards are equipped with relevant information from DRIs,

the managers are more likely to incorporate in their forecasts information about the related

industries in an unbiased fashion; otherwise, they risk being questioned by the boards. Therefore,

effective monitoring by DRIs can also contribute to more accurate management forecasts.

However, despite the valuable information they possess about the related industries, DRIs

may contribute little to forecast accuracy if they mainly advise on major corporate decisions, or

if they are ineffective as monitors (Jensen 1993; Mace 1986). Furthermore, firms without DRIs

may compensate for their lack of information about the related industries by hiring directors who

possess expertise in other areas that contribute to forecast accuracy. Thus, it is ultimately an

empirical question whether firms with DRIs issue more accurate forecasts relative to other firms.

Following the literature, we define related industries with the Input-Output (IO)

Benchmark table provided by Bureau of Economic Analysis (Ahern and Harford 2014; Dass,

Kini, Nanda, Onal, and Wang 2014). The table provides trade flows across about 400 detailed

industries that cover the whole economy. One industry is deemed to be related to another

industry if it provides a significant proportion of the input or consumes a significant proportion

of the output of the other industry.3 Once we identify related industries, we use the employment

information on directors provided by the BoardEx database to identify DRIs. Our sample

includes 2,017 unique firms or 9,253 firm-years for the period of 2002-2011, about 20% of

which have DRIs on board. We test our hypothesis based on the proportion of DRIs on board.

3 As explained in detail in Section 3, we use 5% of total input and output as the cutoff point for related industries. Our results are qualitatively similar when we use 1% or 10% as alternative cutoff points (see Section 4.5).

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Consistent with the view that DRIs provide valuable information about the related

industries to the boards/managers, we find that more DRIs are associated with significantly more

accurate management forecasts. After controlling for known determinants of management

forecast accuracy, having one more DRI who is a director (executive) from related industries in a

board of 10 directors is associated with a 12.3% (24.6%) increase of forecast accuracy in a

median firm in our sample.

If DRIs improve management forecast accuracy by providing relevant information about

the related industries, we expect greater improvement in firms facing higher levels of uncertainty.

In such circumstances, the information from DRIs is likely to be more valuable in helping

managers anticipate operational changes or helping boards narrow the information gap with

managers, leading to more effective advising or monitoring by the boards. We examine three

specific aspects of uncertainty: uncertainty in the firms’ related industries, uncertainty in the

firms themselves, and uncertainty due to the firms’ adverse financial conditions. Our cross-

sectional analyses find results consistent with our expectation.

We perform several tests to examine whether our results are driven by endogeneity. That

is, the choice of DRIs itself is endogenously affected by factors that also influence management

forecast accuracy. First, we use a propensity score matched sample to control for observable

differences between firms with and without DRIs. Second, we use the Heckman selection model

to correct for the potential bias introduced by the firms’ decision to have DRIs. Third, we

conduct 2SLS estimations using the supply of potential DRIs as an instrument. While the supply

of DRIs is likely to be associated with the proportion of DRIs in a firm, there is no clear

economic rationale for it to directly affect management forecast accuracy. Fourth, we examine a

subsample of firms whose proportion of DRIs changes in two adjacent years only because

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directors get on or off the boards of related industry firms, which is likely exogenous to the firms’

information environment. Our results hold in all these tests.

So far, we do not distinguish whether our results are driven by DRIs playing the

monitoring or advising roles (or a combination of both). In order to gain deeper insight on how

DRIs perform their functions, we perform a series of tests to differentiate between these two

roles of DRIs in improving management forecast accuracy. More specifically, we focus on

providing evidence on the advising role of DRIs, because prior studies have documented that

directors improve management forecast accuracy through monitoring (Ajinkya, Bhojraj, and

Sengupta 2005; Armstrong, Core, and Guay 2014; Karamanou and Vafeas 2005). Our tests are

based on the idea that, if we continue to find a positive association between DRIs and

management forecast accuracy in settings where DRIs are unlikely to monitor the CEOs, the

association is likely attributable to DRIs’ advising role. We identify three types of DRIs who are

unlikely to monitor the CEOs: 1) DRIs who are inside directors (predominantly CEOs

themselves); 2) DRIs hired by the current CEOs, because this type of directors tend to be

friendly to the CEOs and have been shown as poor monitors (Coles, Daniel, and Naveen 2014); 3)

DRIs with long joint tenure with the current CEOs, because this type of directors tend to have

reduced independence,4 and have been shown to be associated with higher CEO pay (Vafeas

2003). We find that DRIs are associated with more accurate management forecasts whether they

are likely to be monitors or not, suggesting that our findings are at least partly attributable to the

advising role played by DRIs.

In our final analyses, we examine whether DRIs are associated with other aspects of

firms’ information environment. Prior research shows that firms with independent, but

4 See “For Older Board Members, the Pressure to Move On,” “BlackRock Toughens Stance on Boards,” and “The 40-Year Club: America's Longest-Serving Directors” by Wall Street Journal (2013, 2014, 2015).

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presumably less informed, boards are more likely to issue forecasts or issue forecasts more

frequently (Ajinkya et al. 2005; Armstrong et al. 2014; Karamanou and Vafeas 2005), potentially

because “independent directors seek financial reporting systems and public information channels

that aid their monitoring activities” (Armstrong et al. 2014, p386). In contrast, we find that firms

with DRIs are not more likely to issue forecasts or issue forecasts more frequently, consistent

with DRIs being better informed and hence having less demand for additional disclosure from

the firm. Our results suggest that boards with more information are associated with higher

quality, but not necessarily greater quantity, of public disclosure. We also examine the effects of

DRIs on analyst behaviors, as prior studies document that analysts are sensitive to the

information environment of firms (Lang and Lundholm 1996). If DRIs are associated with

improvement in the information environment of the firms, we expect that analysts are more

likely to follow firms with DRIs, and when these analysts form forecasts, they are likely to place

more weight on public information (i.e., management forecasts) and less weight on their own

private information. Our results of higher analyst coverage and lower analyst dispersion

associated with DRIs are consistent with these expectations, suggesting that DRIs improve other

aspects of firms’ information environment as well.

Our study makes several contributions to the literature on management forecast accuracy.

First, to our knowledge, it is the first study to provide systematic evidence that information from

outside a firm—about related industries in particular—has important implications for

management forecast accuracy. As such, it complements the literature that examines how firms’

own information environment influences management forecast accuracy (Feng et al. 2009;

Jennings et al. 2014). Second, we shed new light on how managers collect information to form

management forecasts. In a related study, Ke, Li, Ling, and Zhang (2015) document that social

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connections help CEOs gather internal information from their subordinates. Our study

complements theirs by presenting evidence that DRIs could help managers gather external

information.

Our study also contributes to the literature that examines how boards affect firms’

information environment. Unlike prior research on corporate disclosure that has mainly focused

on the board’s monitoring role (e.g., Ajinkya et al. 2005; Armstrong et al. 2014; Karamanou and

Vafeas 2005), our paper fills a void in the literature by providing some initial evidence of the

advising role of the boards in management forecasts. We show that DRIs improve management

forecast accuracy even when they are unlikely to monitor, suggesting DRIs bringing valuable

information to the managers and performing their advising role.

Additionally, our study provides new insight on how boards affect firms’ disclosure

policy. While prior research has documented that more independent boards demand both a higher

quality and a greater quantity of public disclosure from managers (Ajinkya et al. 2005;

Armstrong et al. 2014), we show that boards with DRIs can actually supply information to the

managers and improve forecast accuracy without increasing the likelihood or frequency of

management forecasts.

Finally, our study joins the emerging research that documents the importance of directors

with industry expertise (e.g., Cohen, Hoitash, Krishnamoorthy, and Writing 2014; Dass et al.

2014). Closest to our study is Dass et al. (2014), which examines why firms choose DRIs and

shows that DRIs are associated with higher firm valuation and better performance. Our study

differs from theirs in two important ways. First, while Dass et al. (2014) conjecture that the

effects of DRIs on firm value are attributable to better information, they do not explicitly

examine it. Our study substantiates the mechanism through which DRIs may affect firm value by

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focusing directly on how DRIs affect a firm’s information environment, which is also of great

interest to accounting researchers.5 Second, as stated in Dass et al. (2014), they do not explicitly

distinguish between the advising and monitoring roles of DRIs (p. 1539). In contrast, our results

provide evidence that DRIs bring new information to the managers and play important advising

role in the management forecast setting, advancing our understanding of how corporate directors

perform their functions.

The rest of the paper is organized as follows. In Section 2, we review the literature and

develop our hypotheses. In Section 3, we discuss our sample and provide descriptive statistics. In

Section 4, we present the main results of our analyses, along with additional analyses that

examine alternative explanations for our results and address endogeneity concerns. In Section 5,

we provide evidence that differentiates between the board’s monitoring and advising roles. In

Section 6, we expand the scope of our paper by examining other information environment

variables. We conclude in Section 7.

II. LITERATURE REVIEW AND HYPOTHESIS DEVELOPMENT

The existing literature has identified various factors that affect properties of management

forecasts, such as frequency or accuracy. However, this literature has mainly focused on the

firms’ own characteristics. For example, Waymire (1985) finds that firms whose earnings are

more volatile issue earnings forecasts less frequently;6 Feng et al. (2009) and Jennings et al.

5 To the extent that shareholders select board composition to optimize firm value, any documented relations between board composition and firm value can be subject to endogeneity and hence hard to interpret (Hermalin and Weisbach 2003; Larcker and Rusticus 2007). In contrast, management forecast accuracy is unlikely to be a top priority when shareholders select the board. Therefore, our study uses a cleaner setting to examine the implications of DRIs. 6 Earnings volatility is by nature affected by both internal and external factors. However, prior research generally does not separately examine the components in earnings volatility (or other firm-level characteristics) that are due to internal versus external factors.

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(2014) document less accurate forecasts among firms reporting ineffective internal controls and

firms with greater organizational complexity, respectively.

However, a firm’s performance, and thus its management forecast accuracy, is affected

by not only its own activities, but also external forces, such as shocks to its upstream and

downstream industries (Hertzel, Li, Officer, and Rodgers 2008; Pandit, Wasley, and Zach 2011).

For example, if the upstream industries unexpectedly raise the prices for a firm’s inputs, then the

firm’s costs will increase; if the downstream industries unexpectedly reduce demand for the

firm’s outputs, then the firm’s sales will decrease. In fact, a survey of finance executives

conducted by PwC (CFO Publishing 2011) suggests that uncertainty in the external business

environment and difficulty in accessing and incorporating external information are the two most

important causes for management forecast inaccuracy. Therefore, information from related

industries could play an important role in helping managers anticipate future shocks and form

accurate earnings forecasts. Yet the current literature lacks an understanding of how firms obtain

such information and how the parties who possess such information influence management

forecast accuracy.

In this paper, we investigate whether and how a specific party that possesses external

information—DRIs—can help improve management forecast accuracy. Recent studies suggest

that directors with specialty, such as financial expertise and political connections, are sought for

advice by managers (Goldman, Rocholl, and So 2013; Güner, Malmendier, and Tate 2008). With

their connections to the firm’s related industries, DRIs can provide managers with timely

information or new perspectives that may be unavailable from other sources. For example, DRIs

can inform the managers about the ever-changing vendor supply or customer demand, or

economic shocks along the firm’s supply chain. Such information and accompanying advice

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from DRIs can help managers better anticipate and manage uncertainties in the upstream or

downstream industries, which is likely to result in more accurate forecasts.7

In addition to being valuable advisors, DRIs can also provide effective monitoring that

has been emphasized by the prior literature on the board of directors. This literature finds that

boards with a stronger incentive for monitoring—e.g., more independence—demand that

managers, who might otherwise withhold or manipulate information, disclose more and/or

higher-quality information (e.g., Ajinkya et al. 2005; Armstrong et al. 2014). Unlike this prior

literature, which emphasizes the incentives of the directors to monitor, our study focuses on the

information held by DRIs. Prior theoretical work on boards suggests that information, like

incentives, is an impetus for effective monitoring (e.g. Adams and Ferreira 2007; Harris and

Raviv 2008). Cohen et al. (2014) and Krishnan, Wen, and Zhao (2011) show that boards’

industry and legal expertise enhance their monitoring role in financial reporting. In a similar

spirit, we expect that the valuable information DRIs possess about the related industries

facilitates the monitoring activities of boards. Specifically, by helping the board understand the

firm’s supply-and-demand conditions, the DRIs can narrow the information gap between the

board and the managers and allow the board to more accurately assess the managers’ forecasts.

Meanwhile, when managers know the board has ready access to information about the related

industries, they may have fewer incentives and/or opportunities to withhold or manipulate

information presented to the board. Therefore, DRIs can also improve management forecast

accuracy through more effective monitoring.

7 Notice that this hypothesis does not rely on boards’ direct involvement in the forecast formation process; as long as DRIs communicate such external information to the managers in various capacities, for example, in planning corporate strategies, evaluating financial performance, or even providing important industry contacts, we would expect managers to incorporate such information into their forecasts and improve forecast accuracy.

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However, there are several reasons why firms with DRIs might not provide more accurate

management forecasts than those without DRIs. First, the board may only advise on major

corporate decisions, such as mergers and acquisitions or the firm’s strategic directions (Adams et

al. 2010; Hermalin and Weisbach 2003; Mace 1986). In these situations, the DRIs may not

provide managers with information relevant to the firm’s short- or medium-term earnings

forecasts. Second, the board might ineffectively monitor managers and simply rubber-stamp their

decisions (Jensen 1993; Mace 1986). Finally, although non-DRI directors may lack information

about the related industries, they may offer other types of information, such as macroeconomic

news or financial expertise, that help managers improve the accuracy of their earnings forecasts.

Thus, even though DRIs may provide advising and/or monitoring that leads to more

accurate management forecasts, the discussion above does not offer an unambiguous prediction

about the overall effect of DRIs on management forecast accuracy. Accordingly, we state our

first hypothesis in null form as follows:

H1: The proportion of directors from related industries on board is not associated with management forecast accuracy.

We next examine how the association between DRIs and management forecast accuracy

varies in the cross section. This association is likely to vary with the information uncertainty

faced by the firm. When a firm has greater uncertainty, information about its related industries

may be more valuable to managers (as they assess the supply and demand of the firm) and/or the

board (as it evaluates the firm’s disclosure quality). This may affect the association between

DRIs and management forecast accuracy.

We examine several aspects of uncertainty faced by firms, starting with uncertainty in the

related industries. When uncertainty in the related industries is high, information about a firm’s

supply and demand and input and output prices is especially valuable. In contrast, when

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uncertainty in the related industries is low, there is less need for new information in these areas.

As a result, we expect the positive effects of DRIs on forecast accuracy, if any, to be stronger

when there is higher uncertainty in the related industries.

Similar logic applies to firms’ own information uncertainty, as reflected by their innate

volatility and forecast horizons. Waymire (1985) shows that firms with higher innate volatility

are less likely to provide forecasts, due to higher inherent uncertainty. Baginski and Hassell

(1997) suggest that forecast horizon proxies for uncertainty and decreases the precision of

management forecasts. In firms with higher innate information uncertainty (as reflected by

greater innate volatility or longer forecast horizons), information provided by DRIs should be

more helpful for the board and/or the managers as they analyze the impacts of external economic

forces and anticipate industry conditions. We expect DRIs to play a more important role in

improving management forecast accuracy in these firms.

Finally, we consider adverse financial conditions as another aspect of uncertainty. Firms

facing economic difficulties are more vulnerable to supply or demand shocks, so information

from DRIs about upstream and downstream industries becomes more valuable to them. In

addition, firms operating under adverse financial conditions are less likely to obtain information

from alternative sources. For example, their actual suppliers and customers, fearing conflicts of

interest, may hesitate to disclose adverse information. DRIs, in contrast, have no such conflicts,

and, thus, can become more important as information sources. Therefore, we expect the

association between DRIs and forecast accuracy to be more pronounced when firms face

economic adversity.

Thus, the above discussion leads to the following hypotheses (in alternative form):

H2a: The association between the proportion of DRIs on the board and forecast accuracy is more positive when there is greater uncertainty in related industries.

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H2b: The association between the proportion of DRIs on the board and forecast accuracy is more positive for firms facing greater uncertainty of their own.

H2c: The association between the proportion of DRIs on the board and forecast accuracy

is more positive for firms with adverse financial conditions.

III. SAMPLE AND DESCRIPTIVE STATISTICS

Identifying Related Industries

We first identify related industries for each industry through supply-chain relationships

based on the Use table (producer value) from the Benchmark Input-Output (I-O) tables published

by the Bureau of Economic Analysis (BEA) for U.S. companies (Ahern and Hartford 2014;

Dass et al. 2014).8 The BEA detailed Use table provides the trade flows across about 400 I-O

industries that cover the whole economy. Because the detailed benchmark I-O tables are

published every five years, we identify the vertical relationships of I-O industries for years 2002-

2006 based on the 2002 table, and for 2007-2011 based on the 2007 table.

The following example illustrates how we identify two related industries. Suppose that

for two distinct I-O industries X and Y, the percentage output (input) of industry X that goes to

(comes from) industry Y is a% (b%). Industry Y is regarded as “related” to industry X via the

supply chain at the 5% level if the sum of a% and b% (which is called the vertical relatedness

coefficient, or “VRC”) exceeds 5%. Appendix A provides a detailed example of how we define

related industries for the coal mining industry based on the I-O table in 2007.

Appendix B provides descriptive statistics for related industries based on the I-O tables

provided by BEA in 2002 and 2007. Panel A presents the distribution of VRC for any pair of I-O

industries. The mean (median) VRC of any pair of industries is 0.62% (0.07%). The 5% VRC

cutoff that we use to define related industries falls at approximately the 97th percentile in the

8 The tables can be found at the BEA webpage: http://www.bea.gov/iTable/index_industry_io.cfm.

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distribution of VRC, suggesting that the cutoff we use to identify related industries represents an

economically significant relationship between two industries. Panel B shows the distribution of

the number of related industries based on our 5% cutoff. The mean (median) number of related

industries for each industry is 4 (4).9 Once we identify the related industries, we use the

concordance table by BEA to classify firms into I-O industries based on their historical North

American Industry Classification System (NAICS) codes provided by Compustat.10

Identifying DRIs

We obtain information on corporate directors from BoardEx, a database that provides

biographical information on corporate directors and executives.11 For each director on the board

of a firm in a given year, we identify all other firms (and their respective industries) for which

this director serves as either a director or an executive concurrently. A director is identified as a

DRI of the firm if those other firms belong to the firm’s related industries.

To construct our DRI index, we distinguish between directors who are executives in the

related industries and directors who are outside directors in the related industries.12 We expect

the former to be better informed about the firm’s related industries, so we assign a greater weight

to them. Specifically, we assign a weight of one if the director is an executive from a related

9 This number excludes exports and imports, as well as governmental and household consumption expenditures. 10 For the period before 1997, BEA provided the matching between I-O industries and 4-digit SIC codes. Afterwards, I-O industries are matched with NAICS code. Additionally, the definition of I-O industries itself has gone through changes over time due to the change in the landscape of the industries. For example, several apparel industries (such as men’s and women’s apparel manufacturing) in 2002 were combined into one industry labeled “Apparel manufacturing” in 2007. 11 BoardEx started its coverage in the late 1990s and covers approximately 4,000 firms a year during the most of our sample period. Thus, the data coverage is broader than the sample of firms covered in other corporate governance databases, such as Investor Responsibility Research Center database (which focuses mostly on S&P 1500 firms), and, therefore, allows us to capture the directors’ supply-chain relationships more completely. Moreover, the broader coverage of firms in BoardEx makes the implications of our study more generalizable. 12 It is rare that firms select directors from their actual customers or suppliers, probably because of potential conflict of interest. Based on the Compustat Segment files which provide information about major customers, only 0.5% of the directors in our sample come from actual suppliers/customers, although this number is likely understated because firms are not required to disclose information about their suppliers or smaller customers. Our results remain unchanged if we exclude firms with DRIs from actual suppliers or customers.

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industry, and a weight of 0.5 for each outside directorship in the related industries, and sum

across these positions for each director.13 The DRI index is then calculated as the sum of the

weighted numbers across all directors of the firm, deflated by the firm’s board size. An example

of how we construct DRI index for Arch Coal Inc. is provided in Appendix A.

Sample and Descriptive Statistics

Our sample period is from fiscal years 2002 to 2011. We start our sample in 2002

because we want to avoid the confounding impact of the enactment of Regulation Fair

Disclosure in 2000 (Heflin, Subramanyam, and Zhang 2003), as well as to utilize the 2002 and

2007 Benchmark Input-Output (I-O) tables. We acquire information on management forecasts

and actual earnings from a combination of First Call and I/B/E/S databases.14 We include annual

earnings forecasts issued during the fiscal year.15 Following the prior literature, we include both

point and range forecasts, and, for range forecasts, we use the midpoint of the value as the

forecasted number. We measure forecast accuracy (Accuracy) as the absolute value of the

difference between actual EPS and management-forecasted EPS scaled by the stock price at the

beginning of the fiscal year, multiplied by -1.

We merge the management forecast sample with the constructed DRI sample. We then

collect financial data from Compustat, stock price and return data from CRSP, data on analysts

from I/B/E/S, and data on institutional holdings from Thomson Financial. Our final sample

consists of 34,837 forecasts from 9,253 firm-years. The detailed process of sample selection is

described in Panel A of Table 1.

13 Our main results remain unchanged if we use the simple, non-weighted proportion of DRIs on board as our DRI index. 14 The management forecast data are from both First Call and I/B/E/S. Our results remain unchanged if we use First Call data only. 15 Thus we effectively exclude pre-announcements as they may have different information content than other management forecasts. Our inferences would be the same if we include only the first forecast during the year, include all pre-announcements, or take the average of all forecasts issued during the year.

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Panel B of Table 1 presents the descriptive statistics for the DRI measures. Over the

sample period, 20.19% of the sample firms have at least one DRI on board. The percentage was

19.11% in year 2002, and gradually increased to 23.11% in year 2011. Among the firms with

DRIs, the average value of DRI index is about 0.1, which is roughly equivalent to 2 of every 10

directors being outside directors from related industries, or 1 of 10 directors being an executive

from a related industry. Note that we may have under-identified DRIs because BoardEx does not

cover all public companies and some directors are affiliated with private firms whose industry

information is not available. However, this omission is likely to bias against finding results that

support our hypothesis, because some firms that do have DRIs may be classified as not having

them in our sample.

We control for variables that have been shown in prior studies to be associated with

management forecast properties. To control for firms’ information environment, we include firm

size (Size) and the number of analysts following the firm (Analyst) as control variables because

Lang and Lundholm (1996) document a positive association between disclosure, analyst

following, and firm size. We also include institutional ownership (Institution), as Ajinkya et al.

(2005) find that institutional ownership is positively associated with management forecast

accuracy. We control for litigation risk (Litigation) as Francis, Philbrick, and Schipper (1994)

suggest that litigation risk affects corporate disclosure.

We control for firms’ performance following Ajinkya et al. (2005), Bamber and Cheon

(1998), and Hayn (1995), by including return on asset (ROA), book-to-market ratio (BTM),

incidence of loss (Loss), and current year’s earnings increase indicator (News). To control for

firms’ uncertain environment, we include the following variables: beta (Beta), earnings volatility

(EarnVol), return volatility (RetVol), analyst forecast dispersion (Dispersion), number of

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segments (Segment), and horizon of the forecast (Horizon). We expect that higher uncertainty is

associated with lower forecast accuracy. Finally, we include board characteristics, namely, board

size (Board size) and board independence (Board indep), because Karamanou and Vafeas (2005)

show that firms with more effective board structures have more precise management forecasts.

Detailed variable definitions can be found in Appendix C.

Panel C of Table 1 provides descriptive statistics of the variables we use for firms with

and without DRIs. To reduce the impact of outliers, we winsorize all continuous variables at 1%

and 99%. Management forecast accuracy is higher for firms with DRIs than for firms without

DRIs, with means (medians) of -0.0089 (-0.0036) and -0.0109 (-0.0040), respectively. The

differences are statistically significant at the 1% level. Therefore, compared to firms without

DRIs, firms with DRIs have significantly more accurate forecasts on average. This result

provides preliminary support to our conjecture that DRIs bring relevant information to the firm,

and hence increase management forecast accuracy.

Panel C also shows that firms with DRIs tend to be larger, less risky, and less profitable,

and have more analysts following them, less institutional ownership, more segments, larger

boards, and more independent boards, compared with firms without DRIs in our sample. In our

empirical analyses below, we employ several research designs to mitigate the effects of these

differences, including directly controlling for these variables and propensity score matching.

IV. EMPIRICAL RESULTS

Testing Hypothesis 1

To test our first hypothesis on the association between the proportion of DRIs on the

board and management forecast accuracy, we estimate the following model:

18

∙ ∑ ∙ (1)

Table 2 presents the coefficient estimates of Model (1). In all of our specifications, we

cluster the standard errors at both the firm and year levels. Column (1) does not include year or

industry fixed effects, Column (2) includes only year fixed effects, and Column (3) includes both.

The coefficients on DRI index are significantly positive in all three columns. When we include

both year and industry fixed effects, as shown in Column (3), the coefficient is 0.0095. To put

this number into perspective, on a board with 10 directors, having one more DRI who holds an

outside directorship in the related industries is associated with 0.0005, or 4.5% (12.3%) increase

relative to the mean (median) forecast accuracy in our full sample; having one more DRI who is

an executive from the related industry is associated with 0.0010, or 9.1% (24.6%) increase

relative to the mean (median) forecast accuracy in our full sample.16

The coefficients on control variables are mostly consistent with prior literature. Analyst,

Institution, and Litigation load significantly positive in all three specifications, while the

coefficients on Size are only marginally negative in one specification. ROA is not significantly

associated with forecast accuracy in any of the specifications, and BTM is negatively correlated

with forecast accuracy in all specifications. The coefficients on Loss are significantly negative,

while those on News are not significant. EarnVol, RetVol, Dispersion, and Horizon are

significantly negatively associated with Accuracy, which suggests that risk and uncertainty

generally reduce forecast accuracy. Finally, Board size is significantly positively correlated with

forecast accuracy in all specifications, while the coefficient on Board indep is insignificant in all

specifications. The adjusted R-squared of these regressions generally ranges between 24% and

16 We use 0.05 (0.5/10) multiplied by the coefficient 0.0095 to obtain 0.0005, and use 0.1 (1/10) multiplied by the coefficient 0.0095 to obtain 0.0010.

19

30%, which is in line with prior research (Feng et al. 2009; Karamanou and Vafeas 2005; Ke et

al. 2015).17

Testing Hypothesis 2

Hypothesis 2a predicts that the association between DRI index and forecast accuracy is

stronger when there is greater uncertainty in the related industries. We use the return volatility of

the related industries to measure their uncertainty (Zhang 2006). The return volatility is

calculated as the standard deviation of the daily returns of the related industries during the

forecast year.18 To test this hypothesis, we include a dummy variable that equals to one if the

return volatility of the firm’s related industries is greater than the sample median and zero

otherwise, and an interaction term of this dummy variable and DRI index in our main regression.

Control variables are the same as in Table 2. The regression takes the following form:

∙ ∙ ∙ ∙

∑ ∙ (2)

Panel A of Table 3 presents the results with control variables suppressed for brevity. Consistent

with Hypothesis 2a, the coefficient on the interaction term is significantly positive, suggesting

that DRIs play a more important role for firms with greater uncertainty in the related industries.

The coefficient on the dummy variable for high related industry uncertainty is significantly

negative, indicating that management forecasts are indeed less accurate when there is higher

uncertainty in the related industries.

17 Our results remain qualitatively unchanged when we control for internal control weakness (Feng et al. 2009) for a subsample in which internal control weakness data are available (after 2004). 18 If the firm has only one related industry, the returns of the related industry are the value-weighted returns of all the firms that belong to this industry. When the firm has multiple related industries, we first calculate the returns for each related industry as described before, then use the simple average of the returns of all the related industries as the returns of the related industries for the firm.

20

To test Hypothesis 2b, we use three proxies, forecast horizon, return volatility, and

earnings volatility, to capture uncertainty about the firm’s future prospects. For each proxy, we

create a dummy variable that equals to one if the proxy is greater than the sample median and

zero otherwise. We run three separate regressions with each of the three dummy variables and its

interaction term with DRI index included as in Model (2) above. Panel B of Table 3 provides the

regression results. Consistent with Hypothesis 2b, we find that the coefficients on the interaction

terms are significantly positive in all three regressions, suggesting that DRIs play a more

important role for firms with higher uncertainty of their own.

For Hypothesis 2c, we use two variables, loss and earnings decrease, to proxy for adverse

financial conditions. We create two dummy variables correspondingly: a dummy equals to one if

the firm reports a loss in the current period, and zero otherwise; the other dummy equals to one if

the firm reports an earnings decrease in the current period, and zero otherwise. We run

regressions of Model (2) corresponding to these two proxies. As evident from Panel C of Table 3,

the coefficients on the interaction terms are both significantly positive. These results are

consistent with Hypothesis 2c and suggest that DRIs play a more important role for firms in

adverse financial conditions.

To summarize, consistent with our hypothesis that DRIs provide firms with information

relevant to management forecasts, we document a significantly positive association between DRI

index and management forecast accuracy. This positive association is stronger when the related

industries or the firms themselves have higher uncertainty or when these firms operate under

adverse financial conditions. These cross-sectional findings underscore the importance of DRIs

in improving management forecast accuracy.

21

Controlling For the Overall Connectedness and the Ability of the Board

We also investigate whether the positive association between DRI measures and forecast

accuracy is driven by the overall connectedness of the board. If boards that have DRIs also tend

to choose well-connected, better-informed directors, our results may be attributable to boards

that are better-informed in general, rather than to DRIs per se. To address this concern, we

include an additional variable to proxy for the board’s overall connectedness in our main

regression: the average outside directorships (not limited to the related industries) per director for

each board (Average directorship). Moreover, to the extent that the more capable directors are

likely to be in higher demand as directors (and thus hold more directorships), Average

directorship could also capture the ability of the board.

Columns (1)-(3) of Table 4 present the regression results after we include this additional

control variable. The coefficients on DRI index remain significantly positive, and of similar

magnitudes as in our main regressions. The coefficients on Average directorship are statistically

insignificant. These results alleviate our concern that our main results are driven by the overall

connectedness or the ability of the boards.

Controlling for Endogenous Director-Firm Matching

One important concern is that our main independent variable, DRI index, is firms’

endogenous choice. If there are correlated omitted variables that affect both the choice of DRIs

on the board and the firm’s management forecast accuracy, then the documented association

between DRI index and forecast accuracy in Table 2 is biased and possibly spurious. To address

this concern, we conduct a battery of tests: the propensity-score-matching technique, Heckman

22

treatment effect models, two-stage least squares regressions with instrumental variable, and a

subsample of firms that experienced change in DRI index but no change in board composition.19

Propensity-Score Matching

Panel C of Table 1 suggests that firms with and without DRIs are significantly different

in various dimensions. Although we control for these variables in our regression analyses in

Table 2, these regressions might not be fully effective as controls, especially if the firm

characteristics are not linearly associated with forecast accuracy. Therefore, we use propensity-

score-matching technique to more effectively control for observable confounding factors.

In applying the propensity-score-matching technique, we first use a logistic regression

model to estimate the conditional odds of having DRIs on the board. We use DRI dummy as the

dependent variable and all the control variables in our main analysis as the independent

variables.20 Panel A of Table 5 presents the Fama-MacBeth statistics of annual logistic

regressions used to derive the propensity scores. The coefficients on Size, Beta, EarnVol, Board

size, and Board indep are significantly positive, while the coefficients on Litigation, ROA, BTM,

and RetVol are significantly negative. These results are largely consistent with the descriptive

statistics shown in Table 1 and suggest that our control variables are potentially important

confounding factors.

Using propensity scores derived from the logistic regressions, we match each firm-year-

forecast observation with DRIs to an observation without DRIs in the same year that has the

closest odds of having DRIs. The procedure is as follows: First, for each observation with DRIs,

we find observations without DRIs in the same year, within 10% difference of firm size and

19 Another possible exogenous change setting to explore is DRIs’ sudden deaths. However, we are only able to identify about 20 cases of “sudden deaths” of DRIs during our sample period. This extremely small number of observations does not provide enough power for statistical tests. 20 Note that our goal here is not to estimate a determinant model for having DRIs on the board. We seek to identify firm characteristics that are associated with both the likelihood of having DRIs and forecast accuracy (Stuart 2010).

23

within 1% difference of the calculated propensity score. Next, within these observations without

DRIs, we find the one whose forecast horizon is closest to that of the observation with DRIs.

Finally, if there are still multiple observations without DRIs corresponding to the one

observation with DRIs, we pick the observation without DRIs that has the closest propensity

score of having DRIs. This matching procedure yields 2,103 pairs of forecasts from firms with

and without DRIs for our sample period. As shown in Panel B of Table 5, all the control

variables except News, EarnVol, RetVol, and Dispersion now become insignificantly different

between the two samples, suggesting reasonable covariate balance overall. More importantly,

Accuracy remains significantly different between the matched samples with and without DRIs.

We also use regressions to test the association between DRI dummy and management

forecast accuracy with this propensity-score-matched sample. Panel C of Table 5 presents the

results. In Column (1), we regress Accuracy on DRI dummy only. In Column (2), we add the

control variables that are still significantly different between the samples with and without DRIs,

namely, News, EarnVol, RetVol, and Dispersion. In both regressions, we find that the

coefficients on DRI dummy continue to be significantly positive. Therefore, our results are robust

to the propensity-score-matching method.

Heckman Treatment Effect Model

Next, we use Heckman treatment effect model (Lennox, Francis, and Wang 2012), in

which the first stage is the firm’s decision to have DRIs on board. Following Dass et al. (2014),

we include the following determinants: supply of DRIs (Ln(SupplyPerSeat)), R&D expense

(R&D), the informativeness of the firm’s stock price (Price info), the extent to which the firm’s

stock price is explained by industry effects (Homogeneity), the correlation of the firm’s industry

and its related industries’ stock returns (Industry correlation), the firm’s industry market share

24

(Market share), the concentration (HHI) and the extent of the vertical integration (Integrated) of

the industry the firm belongs to, firm size (Size), leverage (Leverage), number of segments

(Segment), board size (Board size), board independence (Board indep), and CEO-Chairman

duality (CEO-Chair).

Panel A of Table 6 presents the first-stage results. We find significantly positive

coefficients on Ln(SupplyPerSeat), R&D, Industry correlation, Integrated, Size, Board indep,

and CEO-Chair, and significantly negative coefficients on Homogeneity, Market share, HHI,

and Leverage. These results are largely consistent with those in Dass et al. (2014). In the second

stage, we regress Accuracy on DRI dummy and the control variables, as well as the Inverse Mills

ratio to control for possible selection bias. Panel B of Table 6 presents the second-stage

regression results. We find that the coefficients on DRI dummy are significantly positive at 1%

level in all specifications.

Instrumental Variable

We next use an instrumental variable approach to identify exogenous variation in DRI

index and examine whether it is significantly associated with management forecast accuracy.

Following Dass et al. (2014), we use Ln(SupplyPerSeat)—the natural logarithm of the ratio of

the total number of director seats in the firm’s related industries to the total number of director’s

seats in the firm’s industry—as our instrument. The instrument is likely to be positively

correlated with DRI index because the greater availability of DRIs increases the chance of having

one on board. Yet there is no particular reason why the greater DRI availability would directly

affect firms’ management forecast accuracy. Therefore, we expect that the instrument affects the

second-stage forecast accuracy variable only through its effect on the DRI index, thus satisfying

the exclusion restriction.

25

We estimate the 2SLS models using Ln(SupplyPerSeat) as the instrument. In the first

stage, we regress DRI index on the instrument and control variables. In the second stage, we

regress Accuracy on the predicted value of DRI index from the first stage, controlling for other

known determinants of Accuracy. Table 7 shows the second-stage regression results. As evident

from the table, the coefficients on the DRI index in all three regressions are significantly positive,

whether or not we include industry and year fixed effects. The Kleibergen-Paap F-stats are

statistically significant, suggesting that the instrument satisfies the relevance condition.

Therefore, our results are robust to the use of the instrumental variable.

Pseudo-Exogenous Shock Setting

Finally, we exploit a pseudo-exogenous shock setting: a subsample of firms that have the

exact same boards in two adjacent years but experience changes in DRI index due to directors

getting on or off the boards of firms in related industries. Factors that drive a director to join or

leave a firm in the related industries are likely exogenous to the firm. We include the year before

and the year of the change and rerun our main analyses, with a total of 1,308 observations. Table

8 presents our results based on this subsample. We find that the association between DRI index

and Accuracy remains significantly positive.

Overall, in this subsection, we conduct a series of tests to alleviate concerns about

endogeneity. Taken together, our results are robust to these tests, which lends further support to

our hypothesis that DRIs bring forecast-relevant information to boards/managers and thereby

contribute to more accurate management forecasts.

Alternative Measures of DRIs

To test the robustness of our results to the definition of DRIs, we construct alternative

measures of DRIs. First, we use 1% and 10% VRC thresholds (as opposed on 5% in our

26

definition of DRI index) to construct DRI index_1% and DRI index_10%, respectively. Second,

we identify the related industries (at the 5% VRC threshold) for all the business segments (as

opposed to just the primary industry) of a firm, and denote this alternative measure as Segment

DRI. Third, we construct a measure, Outside DRI, which is the ratio of DRIs who are outside

directors to all outside directors on the board. Fourth, we include directors who are current

directors and/or executives in other firms of the firm’s own I-O industry. We define the

proportion of both DRIs from the firm’s own industry and DRIs from the firm’s related

industries at 5% VRC threshold as Broad DRI. Fifth, we define another type of DRI measure,

denoted Breadth, to capture the extent of directors’ expertise in related industries. This measure

is calculated as the sum of the vertical relatedness coefficients (VRCs) of all the unique related

industries represented by the DRIs (identified at 5% threshold) on the firm’s board. Finally, we

define another DRI measure based on an alternative classification of related industries, namely,

the industries represented by the union of the actual supplier and customer firms for all firms in

the given firm’s own industry. Directors affiliated with the above-defined related industries are

identified as DRIs, and their proportion on the board is denoted by Union DRI.

As evidenced by Table 9, with the exception of Segment DRI, our main results are robust

to the above alternative measures of DRIs. These results lend further support to our premise that

directors with access to information in the firms’ related industries, however they are defined,

help improve management forecast accuracy.

V. DRIS’ MONITORING VERSUS ADVISING ROLES

Prior research on corporate disclosures has mainly focused on the monitoring roles of

directors (e.g., Ajinkya et al. 2005; Karamanou and Vafeas 2005). However, as discussed in

27

Section 2, the monitoring role of DRIs, the advising role of DRIs, or a combination of both could

contribute to the results in Section 4. In this section, we conduct several tests in an attempt to

parse out these advising and monitoring roles of DRIs, particularly seeking to provide some

evidence on the advising role which has received less attention in the prior literature.

Broadly speaking, directors’ functions can be categorized as monitoring and advising.

Directors serve as monitors when they guard against managers’ self-serving behaviors, and as

advisors when they help managers make good strategic or operational decisions (Adams and

Ferreira 2007; Adams et al. 2010). While directors can certainly play both roles at the same time,

prior research (e.g., Coles, Daniel, and Naveen 2008; Field, Lowry, and Mkrtchyan 2013; Linck,

Netter, and Yang 2008) generally assumes that they are more likely to serve as advisors (if at all)

when they lack the incentives to monitor. Therefore, if DRIs are associated with more accurate

management forecasts in settings where DRIs are unlikely to monitor the CEOs, the association

is likely due to DRIs serving as advisors.

We first examine whether DRIs who are insiders of the firms are associated with more

accurate management forecasts. Raheja (2005) argues that insiders are poor monitors because

they lack independence from the CEO.21 Inside DRIs are even less likely to monitor the CEOs in

our setting because 97 percent of inside DRIs are CEOs themselves. Column 1 of Table 10

shows that both outside and inside DRIs are significantly associated with more accurate

management forecasts. Because inside DRIs are unlikely to monitor themselves, the significant

coefficient on inside DRIs provides initial evidence that DRIs bring new information to the

managers and serve as effective advisors. On the other hand, the significant coefficient on

21 Linck et al. (2008) provides empirical evidence consistent with this argument.

28

outside DRIs can be attributable to DRIs using the information about related industries in either

their monitoring or advising role or both.22

We next examine whether DRIs hired by current CEOs are associated with more accurate

management forecasts. Coles et al. (2014) argue that directors hired by the current CEO are

likely to be faithful to the CEO, and thus are ineffective monitors. Consistent with the argument,

they find that these “captured” directors are associated with lower CEO turnover following poor

performance, higher CEO pay and lower sensitivity of pay to performance, and higher likelihood

of suboptimal investment. After separating DRIs into those hired by the current CEOs and those

not, we find that both groups of DRIs are associated with more accurate forecasts (Column 2 of

Table 10), lending further support to the existence of advising role played by DRIs.

Lastly, we examine DRIs who have long joint tenure with the CEOs. Recently, activist

investors have frequently taken issue with long-serving directors, arguing that these directors

have grown too cozy with management and lost their independence. Consistent with this

argument, Vafeas (2003) finds that long director tenure is associated with higher CEO pay.

Therefore, we separate the DRIs based on their joint tenure with the current CEOs, and find that

only the DRIs with relatively long joint tenure with the current CEOs are associated with more

accurate management forecasts (Column 3 of Table 10), again suggesting the existence of the

advising role played by DRIs.23

22 To alleviate the concern that earnings management or expectation management associated with weak monitoring is driving our results (Klein 2002), we exclude observations whose realized EPS meets or beats the forecasted EPS by no greater than five cents throughout Table 10, as these observations are susceptible to earnings/expectation management. Our inference remains unchanged if we use the full sample in these tests. 23 We also investigate whether DRIs’ association with management forecast accuracy varies with the boards’ (instead of DRIs’) likelihood to monitor the CEOs. We expect that boards with dual CEO and chairman role (Adams et al. 2010), lower proportion of independent directors (Ajinkya et al. 2005), lower G-index (Gompers, Ishii, and Metrick 2003), higher proportion of directors hired by the current CEOs (Coles et al. 2014), or longer joint tenure with the current CEOs (Vafeas 2003), are less likely to monitor the CEOs. Untabulated results show that DRIs are positively significantly associated with management forecast accuracy, regardless of the boards’ likelihood to monitor.

29

To summarize, we find that DRIs are associated with more accurate management

forecasts even when they are unlikely to play the monitoring role, providing strong support for

the existence of the advising role by DRIs. Meanwhile, the positive association between DRIs

and forecast accuracy when DRIs have strong incentives to monitor suggests that DRIs are also

likely to incrementally contribute to the monitoring role, although we cannot rule out the

possibility that these results are also attributable to DRIs’ advising activities.

VI. ADDITIONAL ANALYSES

In this section, we examine whether DRIs affect other aspects of firms’ information

environment in addition to management forecast accuracy. Specifically, we examine other

properties of management forecasts and analyst behaviors.

Likelihood and Frequency of Management Forecasts

Prior studies (e.g., Armstrong et al. 2014) argue that more independent boards, who are

presumably less informed, demand that managers release more information to the public,

because the less informed directors can leverage outside forces such as regulators, analysts, and

institutional investors to more effectively monitor the managers. Consistently, the literature finds

that more independent boards are associated with a higher likelihood and a greater frequency of

management forecast issuance. Therefore, if DRIs are playing this traditional monitoring role,

we expect that they are associated with a higher likelihood and a greater frequency of forecast

issuance. Alternatively, as discussed in Section 2, DRIs can contribute to the board’s monitoring

role by providing information on related industries to the board, and the better-informed board

can then more effectively evaluate the accuracy of earnings forecasts provided by the managers

30

without demanding more public disclosures from the managers. If this is the case, we may not

observe a positive association between DRIs and the likelihood or frequency of forecast issuance.

If DRIs mainly engage in advising, as opposed to monitoring, activities by providing

information to the managers, then the managers in firms with DRIs may be better informed about

future prospects and hence more likely to issue forecasts. However, firms often follow a pre-

established forecast policy, which is generally sticky over time (Brochet, Faurel, and McVay

2011). Unless the board requests a public forecast, the managers are likely to adhere to these

policies. Thus, firms with DRIs who act mainly as advisors do not necessarily have a greater

likelihood of issuing forecasts. As for the forecast frequency, the advising role of DRIs does not

provide an unambiguous prediction either. On the one hand, timely information from DRIs could

prompt the managers to issue more updates; on the other hand, information from DRIs could lead

to more accurate forecasts, which require fewer revisions or updates later.

In Table 11 Panel A, we find that DRIs are not associated with the likelihood or the

frequency of issuing forecasts. These results are consistent with the idea that a better informed

board with DRIs affects the disclosure policy of the firms through a subtly different mechanism

than the mechanism implemented by an independent board, which has been the focus in prior

research. DRIs bring new information to the board or managers, leading managers to disclose

information of higher quality, but not necessarily in greater amount, to the shareholders and the

public.

Analyst Behaviors

We next examine whether DRIs influence analysts’ behaviors. Previous studies have

documented that analysts are sensitive to the information environment of the firms they follow

(Armstrong et al. 2014; Lang and Lundholm 1996). If DRIs improves firms’ information

31

environment (whether through monitoring or advising), we would expect that they affect the

behaviors of analysts following these firms as well.

If analysts recognize that firms with DRIs provide more accurate management forecasts

and are more transparent, they are more likely to follow these firms. Therefore, we predict that

DRIs are associated with more analyst coverage. Moreover, when analysts form their forecasts,

they are likely to place more weight on the public information provided by the firms with DRIs

and less weight on their own private information. Therefore, we predict that DRIs are associated

with lower dispersion of analyst forecasts issued immediately following management forecasts.

The results are presented in Panel B of Table 11. Consistent with our prediction, we find that

DRI index is associated with more analyst coverage and lower forecast dispersion. These results

suggest that DRIs help improve firms’ overall information environment.

 

VII. CONCLUSION

We investigate whether directors from the firm’s related industries help improve

management forecast accuracy by supplying relevant information to either the boards as

monitors or the managers as advisors. We find that more DRIs are associated with higher

management forecast accuracy, and that the association is stronger for firms that have related

industries with high uncertainty, that have greater uncertainty themselves, and that are under

more adverse financial conditions. Further analyses suggest that our findings are at least partly

attributable to the advising roles played by the DRIs, and that DRIs are associated with

improvement in other aspects of firms’ information environment such as analyst following and

analyst forecast dispersion.

32

Our study highlights the importance of the external information environment in

determining firms’ management forecast accuracy and identifies DRIs as one channel through

which firms can obtain outside information. It also extends the literature on how directors

improve management forecast practices. While prior literature emphasizes that independent

boards monitor managers more effectively and help improve firms’ information environment, we

complement this literature by providing evidence that the information directors possess is also

crucial to their functions, which has been emphasized in the theoretical literature but received

less attention empirically.

It is important to note that one should not interpret our findings as suggesting that every

company should have DRIs on the board. Even though DRIs could bring useful information, the

size of the board is limited. Firms must balance the benefits brought by other types of directors,

such as financial experts (Güner et al. 2008) and directors with political connections (Goldman et

al. 2013), with the informational benefits brought by DRIs. As argued in the literature on board

of directors, one size does not fit all (Coles et al. 2008).

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Appendix A – An example of related industries and DRI index construction for Arch Coal

Corporation (in the coal mining industry)

Identifying related industries

The table below provides the top 10 related industries for the coal mining industry (I-O code 212100) based on the I-O table provided by the Bureau of Economic Analysis in 2007, with the column “% Total” representing the VRC of each industry to the coal mining industry. The top three related industries, Iron and steel mills and ferroalloy manufacturing, electric power generation, transmission and distribution, and state and local government electric utilities, are downstream industries with 25.4%, 17.7%, and 12.0% of coal mining’s output going into them respectively. The next four related industries, petroleum refineries, rail transportation, wholesale trade, and mining and oil and gas field machinery manufacturing, are upstream industries with 10.7%, 10.3%, 6.1%, and 5.8% of coal mining’s input coming from them respectively. Overall, the above mentioned seven industries exceed the threshold of 5% VRC and become “related industries” for coal mining by our definition.

Related IO

Industry name % Output

% Input

% Total

331110 Iron and steel mills and ferroalloy manufacturing 25.4% 0.5% 25.9% 221100 Electric power generation, transmission, and distribution 17.7% 1.7% 19.4% S00202 State and local government electric utilities 12.0% 0.0% 12.0% 324110 Petroleum refineries 1.0% 10.7% 11.6% 482000 Rail transportation 0.0% 10.3% 10.3% 420000 Wholesale trade 0.1% 6.1% 6.2% 333130 Mining and oil and gas field machinery manufacturing 0.0% 5.8% 5.8% 230301 Nonresidential maintenance and repair 0.0% 3.1% 3.1% 21311A Other support activities for mining 0.0% 2.6% 2.7% 532400 Commercial and industrial machinery and equipment rental

and leasing 0.0% 2.6% 2.6%

Identifying DRIs and calculating DRI index

In 2010, Arch Coal, a firm in the coal mining industry, had 13 directors on the board with two DRIs, outside director Wesley Taylor and inside director Steven Leer. Wesley Taylor held a directorship at FirstEnergy Corp from the electric power generation, transmission and distribution industry, a downstream industry of coal mining. Steven Leer, the CEO and chairman of the board, held a directorship at Norfolk Southern Corp from the rail transportation industry, an upstream industry of coal mining. (Steven Leer was also an outside director at USG Corp from the lime and gypsum product manufacturing, which is not a related industry of coal mining.) Therefore, the DRI index for Arch Coal in 2010 is (0.5+0.5)/13, which equals 0.077.

34

Appendix B – Summary statistics for related industries

Panel A. Distribution of vertical relatedness coefficient (VRC) for any pair of I-O industries

No. of Pairs Mean 10% 25% 50% 75% 90% 95% 99%167,452 0.62% 0.00% 0.01% 0.07% 0.31% 1.17% 2.68% 11.25%

Panel B. Distribution of the number of related industries based on 5% cutoff of VRC

No. of Related Industries Mean 10% 25% 50% 75% 90% 4.0 1 2 4 6 11

Appendix B provides descriptive statistics for related industries based on the I-O tables provided by BEA in 2002 and 2007. Panel A presents the distribution of vertical relatedness coefficient (VRC) for any pair of I-O industries. Panel B shows the distribution of the number of related industries based on the 5% cut-off of VRC for each industry.

35

Appendix C – Variable definitions

Variable Definition Accuracy Absolute value of the difference between management forecasted EPS and

actual EPS scaled by the stock price at the beginning of the fiscal year, multiplied by -1.

Analyst Number of analysts following the firm at the beginning of the fiscal year. Average directorship

Total number of outside directorships held by all directors on the board, divided by the number of directors.

Beta Equity beta for the previous fiscal year calculated using daily returns. Board indep Percentage of outside directors on the board. Board size Number of board members. Breadth Sum of the vertical relatedness coefficients (VRCs) of all unique related

industries represented by the DRIs (identified at 5% threshold) on the board. Broad DRI Proportion of all directors who are current directors and/or executives from

industries that are vertically related at 5% threshold to the primary industry of the firm or the same I-O industry as the firm itself, with weights assigned in the same way as in the definition of DRI index.

BTM Book value of equity scaled by market value at the beginning of the fiscal year. CEO-Chair One if the CEO is also the Chairman of the board, and zero otherwise. Dispersion Standard deviation of analyst forecasts issued at the beginning of the fiscal year

scaled by the firm’s stock price. DRI dummy One if the firm has at least one DRI on board, and zero otherwise. DRI index Proportion of directors who are current directors (with a weight of 0.5) and/or

executives (with a weight of 1) from industries that are vertically related at 5% threshold to the industry of the firm. It is calculated as the sum of the weighted numbers of directorship divided by the total number of directors on board.

DRI index_1% (DRI index_10%)

Proportion of directors who are current directors and/or executives from industries that are vertically related at 1% (10%) threshold to the primary industry of the firm, with weights assigned in the same way as in the definition of DRI index.

EarnVol Standard deviation of annual earnings scaled by total assets over past five years.Frequency Number of management forecasts issued by the firm during the fiscal year. HHI Sum of square of the market shares of firms in a given I-O industry. Homogeneity The average partial correlation coefficient between monthly stock returns of all

firms in the same I-O industry and monthly industry returns calculated at the I-O level. We use rolling three-year windows and require that firms have at least two years of monthly returns.

Horizon The number of days between the fiscal year-end date and the forecast release date, divided by 365.

Industry correlation

Correlation over rolling three-year windows in monthly returns between firm’s industry and all its vertically related industries at the 5% thresholds.

Inside DRI Proportion of all inside directors who are either current directors and/or executives from industries that are vertically related at 5% thresholds to the primary industry of the firm, with weights assigned in the same way as in the

36

definition of DRI index. Inside average directorship

Total number of outside directorships held by all inside directors on the board, divided by the number of inside directors.

Institution Percentage of the company’s common stock held by institutions. Integrated Proportion of firms in the I-O industry that have at least one secondary segment

that is vertically related to its primary segment in a given year at the 5% vertical-relatedness threshold.

Leverage Ratio of total debt to total assets. Litigation One for all firms in the biotechnology (2833-2836 and 8731-8734), computers

(3570-3577 and 7370-7374), electronics (3600-3674), and retail (5200-5961) industries, and zero otherwise.

Loss One if the firm reported loss in the current period, and zero otherwise. Market share Ratio of firm sales to the sales of the I-O industry the firm belongs to. News One if the current-period EPS is greater than or equal to the previous-period

EPS, and zero otherwise. Occurrence One if the firm make at least one annual earnings forecast during the current

fiscal year, and zero otherwise. Outside DRI Proportion of outside directors who are either current directors and/or

executives from industries that are vertically related at 5% thresholds to the primary industry of the firm, with weights assigned in the same way as in the definition of DRI index.

Outside average directorship

Total number of outside directorships held by all outside directors on the board, divided by the number of outside directors.

Price info One minus the R2 from the regression of the firm’s monthly returns on the market returns and the returns of the industry the firms belong to. We use rolling three-year windows and require that firms have at least two years of monthly returns.

RetVol Standard deviation of monthly returns over the previous fiscal year. R&D R&D expense scaled by sales. ROA Earnings before extraordinary items scaled by lagged total assets. Segment The total number of operating segments. Segment DRI Proportion of the board that consists of current directors and/or executives from

industries that are vertically related at 5% threshold to any segment of the firm, with weights assigned in the same way as in the definition of DRI index.

Size Natural logarithm of total assets for the firm at the beginning of the year. SupplyPerSeat Total number of directors in the related industries, divided by the total number

of board seats available in the industry of the firm. Union DRI Proportion of all directors who are current directors and/or executives in the

union of industries represented by the actual suppliers and customers of any firm within the same I-O industry of the firm, with weights assigned in the same way as in the definition of DRI index.

VRC (Vertical relatedness coefficient)

Suppose for two distinct I-O industries X and Y, the percentage output (input) of industry X that goes to (comes from) industry Y is a% (b%). For industry X, a%+b% is the VRC of Y.

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Table 1. Sample selection and descriptive statistics Panel A. Sample selection

Selection criteria Unique firmsFirm-years Guidance

Annual guidance sample, 2002 - 2011 2,934 12,889 47,686 Excluding forecasts made before the beginning of the fiscal year -67 -854 -4,060 Excluding firms not covered by BoardEx -466 -1,316 -3,437 Excluding firms with missing control variables -345 -1,367 -4,449 Excluding earnings preannouncement -39 -99 -903Final Sample 2,017 9,253 34,837 Panel B. Summary statistics for DRIs

Year N % of firms with DRIs

DRI index for firms with DRIs Mean Median STD

2002 675 19.11% 0.123 0.071 0.113 2003 966 18.01% 0.121 0.083 0.104 2004 1,118 19.14% 0.116 0.083 0.093 2005 1,066 19.51% 0.117 0.083 0.092 2006 1,105 18.82% 0.116 0.083 0.094 2007 1,058 19.28% 0.103 0.077 0.080 2008 950 22.42% 0.098 0.077 0.063 2009 761 21.68% 0.092 0.071 0.063 2010 775 22.32% 0.087 0.071 0.047 2011 779 23.11% 0.089 0.071 0.052 Total 9,253 20.19% 0.106 0.077 0.083

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Panel C. Descriptive statistics for the sample No DRIs With DRIs N Mean Median STD N Mean Median STD Accuracy 27,577 -0.0109 -0.0040 0.0186 7,260 -0.0089 *** -0.0036 *** 0.0158DRI index 27,577 0.000 0.000 0.000 7,260 0.102 *** 0.071 *** 0.078Size 27,577 7.358 7.247 1.790 7,260 8.141 *** 8.189 *** 1.694Analyst 27,577 9.780 8.000 6.598 7,260 11.209 *** 10.000 *** 6.429Institution 27,577 0.530 0.542 0.163 7,260 0.524 ** 0.531 *** 0.152Litigation 27,577 0.346 0.000 0.476 7,260 0.233 *** 0.000 *** 0.423ROA 27,577 0.065 0.062 0.086 7,260 0.060 *** 0.054 *** 0.080BTM 27,577 0.486 0.413 0.324 7,260 0.477 ** 0.430 *** 0.301Loss 27,577 0.100 0.000 0.300 7,260 0.089 *** 0.000 *** 0.285News 27,577 0.665 1.000 0.472 7,260 0.661 1.000 0.473Beta 27,577 1.002 0.967 0.438 7,260 0.992 0.945 *** 0.424EarnVol 27,577 0.046 0.026 0.053 7,260 0.041 *** 0.023 *** 0.051RetVol 27,577 0.098 0.087 0.050 7,260 0.089 *** 0.078 *** 0.050Dispersion 27,577 0.003 0.001 0.003 7,260 0.003 *** 0.002 *** 0.003Segment 27,577 2.549 2.000 1.675 7,260 2.930 *** 3.000 *** 1.662Horizon 27,577 0.535 0.537 0.272 7,260 0.537 0.545 0.272Board size 27,577 9.090 9.000 2.215 7,260 9.920 *** 10.000 *** 2.208Board indep 27,577 0.784 0.818 0.118 7,260 0.812 *** 0.857 *** 0.112

This table presents the selection criteria of our sample and its descriptive statistics. Panel A presents our sample selection rules. Panel B reports summary statistics of DRI index by firm-year, including the percentage of firms with DRIs each year, as well as the means, medians and standard deviations of DRI indices for firms with DRIs for each year. Panel C presents summary statistics for variables used in our main analysis for firms with and without DRIs separately. Definitions of all variables are reported in Appendix C. Differences significant at the 10%, 5%, and 1% levels are denoted *, **, and ***, respectively, based on t-tests for means and Wilcoxon tests for medians.

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Table 2. Regression of management forecast accuracy on DRI index (1) (2) (3) DRI index 0.0098 *** 0.0096 *** 0.0095 ** [2.97] [3.03] [2.46] Size -0.0002 -0.0004 * -0.0002 [0.66] [1.70] [0.74] Analyst 0.0002 *** 0.0002 *** 0.0002 *** [3.92] [3.76] [3.74] Institution 0.0051 ** 0.0040 ** 0.0051 ** [2.19] [2.14] [2.50] Litigation 0.0014 ** 0.0014 ** 0.0026 *** [2.06] [2.03] [2.73] ROA -0.0030 -0.0048 -0.0005 [0.66] [1.10] [0.09] BTM -0.0060 *** -0.0069 *** -0.0068 *** [4.79] [5.48] [5.30] Loss -0.0122 *** -0.0123 *** -0.0121 *** [11.10] [10.50] [9.57] News 0.0006 0.0008 0.0007 [0.85] [1.18] [1.08] Beta 0.0001 0.0010 0.0014 ** [0.14] [1.48] [2.40] EarnVol -0.0140 ** -0.0112 * -0.0211 *** [2.27] [1.90] [3.78] RetVol -0.0572 *** -0.0791 *** -0.0738 *** [7.72] [14.71] [14.31] Dispersion -0.7892 *** -0.7831 *** -0.7107 *** [5.02] [6.13] [5.34] Segment -0.0001 -0.0001 -0.0001 [0.57] [0.51] [0.74] Horizon -0.0116 *** -0.0116 *** -0.0116 *** [13.82] [14.48] [14.17] Board size 0.0002 * 0.0002 * 0.0002 * [1.87] [1.70] [1.74] Board indep 0.0000 -0.0014 -0.0034 [0.00] [0.75] [1.62] Year fixed effects No Yes Yes Industry fixed effects No No Yes N 34,837 34,837 34,837 Adj. R2 24.0% 25.4% 30.3%

This table presents results from OLS regression of management forecast accuracy on DRI index. Accuracy is the absolute value of the difference between management forecasted EPS and actual EPS scaled by the stock price at the beginning of the fiscal year, multiplied by -1. DRI index is the weighted proportion of DRIs on boards. The definitions of all other independent variables are reported in Appendix C. Standard errors are clustered at both the firm and year levels, and t-statistics are reported in the brackets. Year and industry fixed effects are included where indicated. Significance at the 10%, 5%, and 1% levels are denoted *, **, and ***, respectively.

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Table 3. Regression of management forecast accuracy on DRI index: cross-sectional analyses

Panel A.

Panel B.

Panel C.

Related industry uncertainty

Firm’s own information uncertainty Adversity

Related industries’ return volatilities

Forecast Firms’ own

return volatility Firms’ earnings

volatility Loss

Earnings

horizon decrease

(1) (2) (3) (4) (5) (6) DRI index 0.0041 0.0056 0.0038 0.0008 0.0059 * 0.0046

[1.20] [1.53] [1.17] [0.19] [1.78] [1.15] Dummy -0.0012 *** -0.0055 *** -0.0026 *** -0.0021 *** -0.0125 *** -0.0010

[2.86] [15.38] [4.58] [4.79] [9.98] [1.47] DRI index*Dummy 0.0111 ** 0.0080 *** 0.0100 * 0.0141 ** 0.0189 * 0.0126 *** [2.43] [3.28] [1.77] [2.40] [1.92] [2.98] Control variables Yes Yes Yes Yes Yes Yes Year fixed effects Yes Yes Yes Yes Yes Yes Industry fixed effects Yes Yes Yes Yes Yes Yes N 28,252 34,837 34,837 34,837 34,837 34,837 Adj. R2 31.1% 29.4% 28.7% 30.3% 30.3% 30.3% This table presents cross-sectional analyses based on OLS regressions of management forecast accuracy on DRI index. Accuracy is the absolute value of the difference between management forecasted EPS and actual EPS scaled by the stock price at the beginning of the fiscal year, multiplied by -1. DRI index is the weighted proportion of DRIs on boards. Panel A reports regression results conditional on the uncertainty in related industries, with dummy equal to one if the return volatilities of the firm’s related industries are greater than the sample median and zero otherwise. Panel B reports regression results conditional on the firm’s own information uncertainty. In Columns (2), (3) and (4), the dummy variable equals to one if forecast horizon, return volatility, or earnings volatility, respectively, is greater than the sample median and zero otherwise. Panel C contains regression results conditional on adverse environment. In Columns (5) and (6), the dummy variable equals to one if the firm has a loss or earnings decrease, respectively, and zero otherwise. Control variables are the same as in Table 2, and their definitions are reported in Appendix C. Standard errors are clustered at both the firm and year levels, and t-statistics are reported in the brackets. Both year and industry fixed effects are included in the regressions. Significance at the 10%, 5%, and 1% levels are denoted *, **, and ***, respectively.

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Table 4. Regression of management forecast accuracy on DRI index: controlling for the overall connectedness of board of directors

(1) (2) (3) DRI index 0.0096 *** 0.0090 *** 0.0095 ** [2.89] [2.81] [2.50] Average directorship 0.0001 0.0003 0.0000 [0.30] [0.80] [0.04] Control variables Yes Yes Yes Year fixed effects No Yes Yes Industry fixed effects No No Yes N 34,837 34,837 34,837

Adj. R2 24.0% 25.4% 30.3% This table presents results from OLS regression of management forecast accuracy on DRI index, controlling for the overall connectedness of board of directors. Accuracy is the absolute value of the difference between management forecasted EPS and actual EPS scaled by the stock price at the beginning of the fiscal year, multiplied by -1. DRI index is the weighted proportion of DRIs on boards. Average directorship is the total number of outside directorships held by all directors on the board, divided by the number of directors. All other control variables are the same as in Table 2, and their definitions are reported in Appendix C. Standard errors are clustered at both the firm and year levels, and t-statistics are reported in the brackets. Year and industry fixed effects are included where indicated. Significance at the 10%, 5%, and 1% levels are denoted *, **, and ***, respectively.

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Table 5. Propensity-score matching Panel A. Propensity-score derivation

Dependent variable= DRI dummy Size 0.163 [4.50] *** Analyst -0.001 [0.09] Institution -0.266 [1.26] Litigation -0.499 [4.02] *** ROA -0.887 [1.85] * BTM -0.397 [2.16] * Loss -0.032 [0.18] News -0.059 [0.74] Beta 0.264 [2.56] ** EarnVol 1.644 [2.56] ** RetVol -2.664 [2.12] * Dispersion 14.811 [1.46] Segment -0.115 [0.72]

Board size 0.052 [3.40] *** Board indep 1.963 [4.30] ***

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Panel B. Matched samples with and without DRIs

N Connected firms Control sample

Diff. in means Mean Median STD Mean Median STDAccuracy 4,206 -0.0094 -0.0039 0.0163 -0.0123 -0.0045 0.0207 0.0029 *** Matching variables Size 4,206 7.615 7.614 1.415 7.650 7.715 1.386 -0.035 Analyst 4,206 10.023 9.000 5.818 9.994 9.000 5.963 0.029 Institution 4,206 0.553 0.559 0.151 0.553 0.562 0.152 0.000 Litigation 4,206 0.259 0.000 0.438 0.245 0.000 0.430 0.014 ROA 4,206 0.067 0.062 0.079 0.071 0.065 0.080 -0.004 BTM 4,206 0.458 0.417 0.274 0.463 0.417 0.307 -0.004 Loss 4,206 0.084 0.000 0.278 0.088 0.000 0.284 -0.004 News 4,206 0.697 1.000 0.460 0.664 1.000 0.472 0.032 ** Beta 4,206 1.012 0.961 0.439 1.027 0.995 0.403 -0.015 EarnVol 4,206 0.043 0.025 0.053 0.037 0.022 0.041 0.006 ***RetVol 4,206 0.092 0.082 0.049 0.088 0.081 0.042 0.004 ** Dispersion 4,206 0.002 0.001 0.003 0.003 0.002 0.003 0.000 ***Segment 4,206 2.719 2.500 1.605 2.753 2.000 1.647 -0.034 Horizon 4,206 0.540 0.566 0.274 0.547 0.618 0.265 -0.007 Board size 4,206 9.377 9.000 1.943 9.298 9.000 2.059 0.078 Board indep 4,206 0.805 0.833 0.113 0.807 0.833 0.105 -0.002

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Panel C. Regression results Dependent variable = Accuracy

(1) (2) DRI dummy 0.0032 ** 0.0037 ** [2.55] [2.56] News 0.0017 * [1.71] EarnVol -0.0171 [1.38] RetVol -0.0686 ** [2.36] Dispersion -0.9784 *** [3.36] Year fixed effects Yes Yes Industry fixed effects Yes Yes N 4,206 4,206

Adj. R2 24.9% 29.4% This table presents the results of the propensity-score-matching method. Panel A provides the Fama- MacBeth statistics of annual logistic regressions from which we derive propensity scores. The dependent variable is the indicator variable DRI dummy, which equals one if the firm has at least one DRI on the board, and zero otherwise. The definitions of all variables are reported in Appendix C. Panel B presents the matched samples with and without DRIs. We match observations based on the propensity score from the regression in Panel A. Panel C presents the regression results based on the matched sample from Panel B. Standard errors are clustered at both the firm and year levels, and t-statistics are reported in the brackets. Year and industry fixed effects are included where indicated. Significance at the 10%, 5%, and 1% levels are denoted *, **, and ***, respectively.

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Table 6. Heckman treatment effect model Panel A. First-stage regression: determinants to hire DRIs

   Dependent variable = DRI dummy    (1) (2) (3)

Ln(SupplyPerSeat) 0.4803 *** 0.4811 *** 0.4996 *** [22.95] [22.98] [21.79] R&D 0.1090 *** 0.1084 *** 0.0171 [2.67] [2.65] [0.31] Price info -0.1146 -0.1476 -0.2229 ** [1.11] [1.33] [1.97] Homogeneity -0.9267 *** -0.9486 *** -1.0651 *** [5.51] [5.58] [5.43] Industry correlation 0.4486 ** 0.4429 ** 0.6728 *** [2.51] [2.46] [3.31] Market share -0.5244 *** -0.5329 *** -0.4014 *** [3.74] [3.79] [2.75] HHI -1.1309 *** -1.1168 *** -1.0051 *** [7.35] [7.24] [6.30] Integerated 1.2958 *** 1.2975 *** 1.0451 *** [10.30] [10.29] [7.63] Size 0.1660 *** 0.1680 *** 0.1545 *** [10.44] [10.52] [9.46] Leverage -0.1776 ** -0.1829 ** -0.1369 [1.99] [2.04] [1.41] Segment -0.0056 -0.0053 -0.0127 [0.47] [0.44] [1.02] Board size -0.0416 -0.0451 -0.0301 [1.13] [1.21] [0.80] Board indep 0.0333 *** 0.0327 *** 0.0376 *** [3.50] [3.41] [3.86] CEO-Chair 1.4324 *** 1.4692 *** 1.4305 *** [5.88] [5.93] [5.64] Year fixed effects No Yes Yes Industry fixed effects No No Yes N 7,057 7,057 7,057

Adj. R2 15.8% 15.8% 17.2%

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Panel B. Second-stage regression Dependent variable = Accuracy (1) (2) (3) DRI dummy 0.0056 *** 0.0052 *** 0.0041 *** [4.36] [4.25] [3.03] Size -0.0001 -0.0004 -0.0003 [0.41] [1.12] [0.88] Analyst 0.0002 *** 0.0002 *** 0.0002 *** [3.15] [3.00] [2.91] Institution 0.0058 ** 0.0049 *** 0.0052 *** [2.47] [2.65] [2.98] Litigation 0.0012 ** 0.0012 ** 0.0005 [2.01] [2.08] [0.65] ROA -0.0021 -0.0043 -0.0018 [0.44] [0.91] [0.36] BTM -0.0051 *** -0.0062 *** -0.0062 *** [4.82] [5.65] [5.78] Losses -0.0140 *** -0.0139 *** -0.0137 *** [8.91] [8.34] [8.34] News 0.0001 0.0002 0.0001 [0.12] [0.46] [0.20] Beta 0.0008 0.0017 * 0.0023 ** [0.79] [1.94] [2.57] EarnVol -0.0033 -0.0008 -0.0080 [0.55] [0.13] [1.44] RetVol -0.0617 *** -0.0829 *** -0.0776 *** [8.24] [13.00] [12.02] Dispersion -0.6967 *** -0.6967 *** -0.6577 *** [4.84] [5.75] [5.14] Segment -0.0001 -0.0001 -0.0001 [0.35] [0.25] [0.33] Horizon -0.0114 *** -0.0114 *** -0.0115 *** [12.42] [12.85] [12.97] Board size 0.0002 0.0002 0.0003 ** [1.56] [1.45] [2.11] Board indep. -0.0041 -0.0054 *** -0.0067 *** [1.61] [2.61] [2.95] Inverse Mills ratio -0.0029 *** -0.0027 *** -0.0021 ** [3.76] [3.68] [2.51] Year fixed effects No Yes Yes Industry fixed effects No No Yes N 25,640 25,640 25,640

Adj. R2 24.7% 26.1% 27.0%

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This table presents the results that correct for potential bias due to firms self-selecting DRIs on their boards. Panel A provides the first stage Probit regression with the dependent variable being DRI dummy. Panel B presents the second stage regression where the dependent variable is Accuracy. The Inverse Mills ratios are calculated from the regressions in the corresponding columns of Panel A. The definitions of all the independent variables from both stages are reported in Appendix C. Standard errors are clustered at both the firm and year levels, and t-statistics are reported in the brackets. Year and industry fixed effects are included where indicated. Significance at the 10%, 5%, and 1% levels are denoted *, **, and ***, respectively.

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Table 7. 2SLS regression using instrumental variable Dependent variable = Accuracy

Second Stage (1) (2) (3)

DRI index 0.0415 * 0.0359 * 0.0471 **

[1.85] [1.66] [2.06]

Size 0.0005 * 0.0003 0.0002

[1.75] [1.16] [0.52]

Analyst 0.0001 ** 0.0001 0.0001

[1.99] [1.57] [1.57]

Institution 0.0052 *** 0.0043 ** 0.0049 ***

[2.89] [2.33] [2.72]

Litigation 0.0008 0.0006 0.0001

[1.13] [0.83] [0.07]

ROA -0.0028 -0.0058 -0.0040

[0.52] [1.10] [0.76]

BTM -0.0060 *** -0.0074 *** -0.0074 ***

[5.04] [6.19] [6.19]

Loss -0.0138 *** -0.0137 *** -0.0135 ***

[9.42] [9.53] [9.33]

News 0.0000 0.0002 0.0001

[0.07] [0.42] [0.18]

Beta 0.0004 0.0014 ** 0.0017 **

[0.62] [2.19] [2.52]

EarnVol -0.0147 *** -0.0120 ** -0.0143 **

[2.62] [2.18] [2.53]

RetVol -0.0562 *** -0.0754 *** -0.0720 ***

[8.00] [9.65] [9.03]

Dispersion -0.6538 *** -0.6336 *** -0.6127 ***

[6.98] [6.63] [6.14]

Segment 0.0000 0.0000 0.0000

[0.13] [0.22] [0.31]

Horizon -0.0114 *** -0.0114 *** -0.0115 ***

[26.78] [27.14] [27.07]

Board size 0.0003 * 0.0003 * 0.0003 **

[1.93] [1.83] [2.29]

Board indep -0.0020 -0.0032 -0.0047 *

[0.92] [1.34] [1.82]

CEO-Chair -0.0006 -0.0004 -0.0003

[1.12] [0.89] [0.67]

Leverage -0.0104 *** -0.0108 *** -0.0102 ***

[5.93] [6.32] [6.07]

52

R&D 0.0036 0.0019 -0.0023

[0.56] [0.31] [0.33]

Price info -0.0036 *** -0.0022 * -0.0020

[3.05] [1.76] [1.50]

Industry correlation -0.0051 * -0.0043 -0.0033

[1.87] [1.56] [1.25]

Homogeneity -0.0012 -0.0010 0.0011

[0.44] [0.38] [0.37]

Market share 0.0014 0.0019 0.0027

[0.69] [0.94] [1.27]

HHI -0.0019 -0.0030 -0.0012

[0.84] [1.37] [0.49]

Integrated -0.0001 -0.0005 -0.0028

[0.05] [0.18] [0.94]

Year fixed effects No Yes Yes Industry fixed effects No No Yes N 26,636 26,636 26,636

Adj. R2 24.4% 26.2% 26.0%

Kleibergen-Paap F-Stat 83.78 *** 85.77 *** 77.30 ***

This table presents results from the second stage of 2SLS regression of management forecast accuracy on DRI index, using Ln(SupplyPerSeat) as an instrumental variable. Ln(SupplyPerSeat) is defined as the natural logarithm of the ratio of total number of director seats in the related industries to that of the firm’s own industry. Accuracy is the absolute value of the difference between management forecasted EPS and actual EPS scaled by the stock price at the beginning of the fiscal year, multiplied by -1. DRI index is the weighted proportion of DRIs on boards. The definitions of all the control variables are reported in Appendix C. Standard errors are clustered at both the firm and year levels, and t-statistics are reported in the brackets. Year and industry fixed effects are included where indicated. Significance at the 10%, 5%, and 1% levels are denoted *, **, and ***, respectively.

53

Table 8. Regression of management forecast accuracy on DRI index: pseudo exogenous shock

(1) (2) (3) DRI index 0.0103 * 0.0166 *** 0.0150 * [1.84] [2.60] [1.93] Control variables Yes Yes Yes Year fixed effects No Yes Yes Industry fixed effects No No Yes N 1,308 1,308 1,308

Adj. R2 17.7% 22.6% 39.6% This table presents results from OLS regression of management forecast accuracy on DRI index for a subsample of firms whose boards remain unchanged and DRI index changes in two adjacent years only because directors get on or off the boards of related industry firms. The sample includes year t-1 and year t around the change of DRI index in year t. Accuracy is the absolute value of the difference between management forecasted EPS and actual EPS scaled by the stock price at the beginning of the fiscal year, multiplied by -1. DRI index is the weighted proportion of DRIs on boards. All the control variables are the same as in Table 2, and their definitions are reported in Appendix C. Standard errors are clustered at both the firm and year levels, and t-statistics are reported in the brackets. Year and industry fixed effects are included where indicated. Significance at the 10%, 5%, and 1% levels are denoted *, **, and ***, respectively.

54

Table 9. Regression of management forecast accuracy on alternative measures of DRI index

DRI

index_1% DRI

index_10% Segment DRI Outside DRI Broad DRI Breadth Union DRI (1) (2) (3) (4) (5) (6) (7) DRI index 0.0036 ** 0.0143 *** 0.0038 0.0089 ** 0.0069 *** 0.0035 * 0.0038 *** [1.97] [3.14] [1.18] [2.03] [3.97] [1.95] [3.09] Control variables Yes Yes Yes Yes Yes Yes Yes Year fixed effects Yes Yes Yes Yes Yes Yes Yes Industry fixed effects Yes Yes Yes Yes Yes Yes Yes N 34,837 34,837 34,837 34,837 34,837 34,837 34,837

Adj. R2 30.2% 30.3% 30.2% 30.3% 30.3% 30.2% 30.3% This table presents results from OLS regressions of management forecast accuracy on seven alternative measures of DRI index. Accuracy is the absolute value of the difference between management forecasted EPS and actual EPS scaled by the stock price at the beginning of the fiscal year, multiplied by -1. In Columns (1) and (2), DRI index_1% and DRI index_10% are the weighted proportion of the board that consists of current directors and/or executives from industries that are vertically related at 1% and 10% thresholds to the primary industry of the firm, respectively. In Column (3), Segment DRI is the weighted proportion of the board that consists of current directors and/or executives from industries that are vertically related at 5% threshold to the primary industry of any segment of the firm. In Column (4), Outside DRI is the weighted proportion of all outside directors who are either current directors and/or executives from industries that are vertically related at 5% thresholds to the primary industry of the firm. In Column (5), Broad DRI is the weighted proportion of all directors who are current directors and/or executives from industries that are vertically related at 5% threshold to the primary industry of the firm, or from the same I-O industry as the firm itself. In Column (6), Breadth is the sum of the “vertical relatedness coefficients (VRCs)” of all the unique related industries represented by the DRIs (identified at 5% threshold) on the firm’s board. In Column (7), Union DRI is the weighted proportion of all directors who are current directors and/or executives in the industries represented by the actual suppliers and customers of any firm within the same I-O industry of the firm. All the control variables are the same as in Table 2, and their definitions are reported in Appendix C. Standard errors are clustered at both the firm and year levels, and t-statistics are reported in the brackets. Both year and industry fixed effects are included in the regressions. Significance at the 10%, 5%, and 1% levels are denoted *, **, and ***, respectively. 2

55

Table 10. Regression of management forecast accuracy on DRI index: differentiating monitoring versus advising roles

Outside DRI DRI not hired by

CEO Short joint tenure with

CEO

(1) (2) (3) Strong monitor incentive dummy -0.0011 0.0014

[0.76] [1.17]

DRI index * Weak monitor incentive dummy 0.0145 * 0.0138 **

[1.92] [2.45]

DRI index* Strong monitor incentive dummy 0.0181 * 0.0056

[1.89] [0.53]

Inside DRI (Weak monitor) 0.0325 *** [2.90] Outside DRI (Strong monitor) 0.0117 ** [2.28] Inside average directorship -0.0002 [0.50] Outside average directorship 0.0000 [0.08] Control variables Yes Yes YesYear fixed effects Yes Yes YesIndustry fixed effects Yes Yes YesN 27,310 27,310 27,310

Adj. R2 32.40% 32.40% 32.40%

This table presents cross-sectional analyses examining the effect of DRIs on management forecast accuracy based on whether DRIs are likely to monitor the CEOs or not. Accuracy is the absolute value of the difference between management forecasted EPS and actual EPS scaled by the stock price at the beginning of the fiscal year, multiplied by -1. DRI index is the weighted proportion of DRIs on boards. In Column 1, Inside (Outside) DRI is the proportion of all inside (outside) DRIs, who are less (more) likely to monitor the CEOs. Inside (Outside) average directorship is the total number of outside directorships held by all inside (outside) directors on the board, divided by the number of inside (outside) directors. In Column 2, Strong (Weak) monitor incentive dummy equals one if any one of DRIs is not hired (all DRIs are hired) by the CEO, and zero otherwise. In Column 3, Strong (Weak) monitor incentive dummy equals one if the joint tenure of DRIs and the CEO is (not) shorter than the sample median, and zero otherwise. To mitigate the concern on potential earnings management due to weak monitoring, we exclude observations where the realized EPS just meets or beats the forecasted EPS by no greater than five cents. All other control variables are the same as in Table 2, and their definitions are reported in Appendix C. Standard errors are clustered at both the firm and year levels, and t-statistics are reported in the brackets. Both year and industry fixed effects are included in the regressions. Significance at the 10%, 5%, and 1% levels are denoted *, **, and ***, respectively.

56

Table 11. DRIs and other aspects of information environment

Panel A. Guidance Panel B. Analyst Occurrence Frequency Coverage Forecast dispersion (1) (2) (3) (4)

DRI index -0.1774 -0.3278 0.2485 ** -0.0016 ** [0.41] [0.63] [2.28] [2.38]

Control variables Yes Yes Yes Yes Year fixed effects Yes Yes Yes Yes Industry fixed effects Yes Yes Yes Yes N 25,747 9,253 9,253 30,746

Adj. R2 28.4% 19.4% 82.3% 19.9% This table presents results from Logit or OLS regressions of management forecast occurrence, management forecast frequency, analyst coverage, and analyst forecast dispersion, on DRI index. DRI index is the weighted proportion of DRIs on boards. Occurrence equals one if the firm issues at least one forecast in the current fiscal year, and zero otherwise. Frequency is the number of guidance issued by the firm during the fiscal year. Coverage is the number of analysts issuing forecasts for the firm during the fiscal year. Forecast dispersion is the standard deviation of analyst forecasts issued within two weeks after management forecasts are issued, scaled by pre-guidance price. The control variables include Size, Analyst t-1,Institution, Litigation, ROA, BTM, Loss, News, Beta, EarnVol, RetVol, Dispersion t-1, Segment, Board size, and Board indep, and their definitions are reported in Appendix C. Standard errors are clustered at both the firm and year levels, and t-statistics are reported in the brackets. Both year and industry fixed effects are included in the regressions. Significance at the 10%, 5%, and 1% levels are denoted *, **, and ***, respectively.