The effects of financial derivatives on analyst coverage decisions

Hye Sun Chang† hschang@smu.edu.sg

Michael Donohoe*‡

mdonohoe@illinois.edu

Theodore Sougiannis* sougiani@illinois.edu

†Singapore Management University

60 Stamford Road Singapore 178900 

 

*University of Illinois at Urbana-Champaign 1206 S. Sixth Street, MC-706

Champaign, IL 61820

September 2017

Abstract

We investigate whether and how a firm’s use of derivatives influences analyst coverage decisions. Using a difference-in-differences design, we find that, relative to a matched control sample of non-users, analyst coverage for new derivatives users increases significantly after derivatives initiation. This increase in coverage is driven by analysts with less expertise, as reflected by career experience and All-Star status. We also find that the accuracy (dispersion) of earnings forecasts for new users decreases (increases) after derivatives initiation only when forecasts are issued by analysts with less expertise. Therefore, the evidence collectively suggests that, despite the notorious complexity of derivatives, analysts with less expertise begin covering new derivatives users, presumably to signal their talent, and, as a result, produce the less accurate and more dispersed earnings forecasts shown by prior research for these firms. Keywords: derivatives; economic complexity; reporting complexity; hedging; sell-side analysts;

earnings forecasts JEL Classification: G29; G32; M41 ‡Corresponding author. We appreciate helpful comments from Rashad Abdel-khalik, Andrew Bauer, Raluca Chiorean, Will Ciconte, Brooke Elliott, Simeon Ketterer, Marcus Kirk, Laura Li, Pete Lisowsky, Sean McGuire, Michael Mayberry, Mark Peecher, Jenny Tucker, Jim Vincent, participants at the 23rd Annual Conference of the Multinational Finance Society, the SMU/NUS/NTU Junior Faculty Research Conference, and workshop participants at the University of Illinois at Urbana-Champaign. Special thanks Mitchell Brown, Alex Menter, and Alexander Van Duch for research assistance. Donohoe and Sougiannis gratefully acknowledge financial support from the PricewaterhouseCoopers Faculty Fellowship and KPMG Professorship, respectively.

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The effects of financial derivatives on analyst coverage decisions

1. Introduction

Derivatives are an increasingly prevalent form of corporate risk management, with nearly

two-thirds of U.S. non-financial firms participating in the $710 trillion derivatives market (Bank

for International Settlements 2013). These instruments are notoriously complex, however, as their

value can be linked to virtually any underlying asset/liability, including other derivatives, to fulfill

diverse objectives. Expert groups claim that many firms using derivatives do not apply the requisite

accounting rules correctly or consistently, making it “next to impossible” for investors to assess a

firm’s derivatives activity from its financial reports (Kawaller 2004, 29). In such cases, investors

value the services of sell-side analysts, who simplify and convey complex information (Lawrence

et al. 2014). However, because recent research finds that analysts regularly misjudge the earnings

implications of firms’ derivatives activity (Chang et al. 2016), it is unclear whether and under what

circumstances the complexity of derivatives influences analysts’ decisions to cover a firm in the

first place. We fill this void in the literature by answering these two important questions.

Analyst coverage decisions are influenced by client demand for information, opportunities

to signal talent, and economic incentives (Ramnath et al. 2008; Brown et al. 2015). The complexity

of transactions and financial information resulting from a firm’s choice to use derivatives can affect

each of these factors, and thus, coverage decisions. Further, derivatives contracts, the markets in

which they trade, and requisite reporting are extraordinarily complex (Ryan 2007). The Financial

Accounting Standards Board (FASB) has issued a series of standards to help simplify derivatives

accounting, but some of these efforts have been criticized as the “poster child of complexity”

(Leone 2007) and a “labyrinth of processes and documentation” (Valladares 2014). Prior research

considers if firm-level complexities, such as the level of intangibles (Barth et al. 2001) and annual

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report readability (Lehavy et al. 2011), affect analyst coverage. However, empirical evidence as to

whether and when “shocks” to firm-level complexity resulting from a firm’s endogenous choice

to begin using sophisticated financial instruments influence analyst coverage is absent from the

literature. These questions are important given the interplay between financial reporting and the

information analysts provide to other market participants (Beyer et al. 2010; Chen et al. 2010).

Bhushan (1989) shows that the equilibrium number of analysts covering a firm lies at the

intersection of aggregate demand and supply curves for analyst services. Although the inherent

complexity of derivatives is very likely to increase investor demand for analyst services, the effects

on supply is an empirical question. On the one hand, analysts have financial incentives to cover

derivatives users. In particular, a firm’s use of derivatives could (1) result in more valuable advice

for analysts to sell to investors due to complex derivatives information (e.g., Lang and Lundholm

1996); (2) improve the predictability of firm performance (Zhang 2009), leading to promotions

(Hong et al. 2000); and (3) provide opportunities to earn higher trading commissions (Aretz and

Bartram 2010). Alternatively, the complexity of derivatives accounting (Ryan 2007), difficulty of

obtaining and analyzing derivatives information (Kawaller 2004), and potential for derivatives to

attract sharp criticism (Donohoe 2015a) could deter analyst coverage of derivatives users. Thus,

the overall effect depends on the competing demand and supply factors.

Using the focused setting of new derivatives users and difference-in-difference design with

a propensity score matched control sample, we find an increase in analyst coverage for firms that

begin using derivatives during 1998-2011. More specifically, analyst coverage for new derivatives

users increases by 10% after derivatives initiation relative to non-user control firms. This effect is

comparable to that of other major changes in firms’ financial reporting practices (Tan et al. 2011).

As such, these results suggest that analysts respond to the increased demand for their services due

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to the complexity of derivatives information in firms’ financial reports.

We next consider if the increase in analyst coverage for new derivatives users is driven by

analyst expertise. Analysts with more career experience as well as those attaining “All-Star” status

possess superior skill and knowledge (Ramnath et al. 2008). Such analysts could be more likely to

respond to the increased investor demand for analyst services due to the complexity of derivatives.

However, recent research finds that analysts (on average) produce less accurate and more dispersed

earnings forecasts for new derivatives users (Chang et al. 2016). Thus, it is possible that analysts

with more expertise might instead recognize and avoid such challenges, whereas analysts with less

expertise might view the complexity of derivatives as an opportunity to signal their talent.

Consistent with the latter, we find that the increase in analyst coverage for new derivatives users

is driven by analysts with less expertise, as reflected by experience (i.e., number of years issuing

earnings forecasts) and All-Star status by Institutional Investor Magazine (Bagnoli et al. 2008).

We also examine analyst coverage at a more granular level by creating transition matrices

that trace the proportion of analysts covering new derivatives users before derivatives initiation

and the proportion ceasing or initiating coverage of new users upon and after derivatives initiation,

all by analyst expertise. The matrices identify if the observed change in coverage is due to analysts

with less expertise initiating coverage or those with more expertise ceasing coverage, which can

ultimately affect a firm’s information environment. The results confirm that the overall net change

in coverage for new derivatives users is driven by analysts with less expertise initiating coverage

rather than analysts with more expertise ceasing coverage.

In another set of tests, we examine if analyst expertise explains the less accurate and more

dispersed earnings forecasts for new derivatives users shown by Chang et al. (2016). We find that

the accuracy (dispersion) of earnings forecasts for new derivatives users decreases (increases) after

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derivatives initiation only when forecasts are issued by analysts with low expertise. Therefore, the

evidence collectively indicates that, despite the renowned complexity of derivatives, analysts with

less expertise begin covering new derivatives users (likely to signal talent) and, in turn, produce

the less accurate and more dispersed earnings forecasts shown by Chang et al. (2016) for these

firms. Lastly, we perform structural break tests to validate that the complexity of derivatives drives

our results, as well as other analyses to mitigate other explanations such as forecast frequency,

ineffective management of risk, and firm complexity and size.

This study contributes to growing literatures on analysts (Ramnath et al. 2008; Beyer et al.

2010) and derivatives (Aretz and Bartram 2010) in numerous ways. First, anecdotal and empirical

evidence suggests that both practitioners (Holland and Glasgall 1994) and investors (Koonce et al.

2005) struggle to understand the risks and rewards of even mundane derivatives. We contribute by

examining how analysts respond to increased demand for their services due to the stark economic

and financial reporting complexity of derivatives. Second, recent research finds that, despite their

financial expertise, analysts misjudge the earnings implications of derivatives (Chang et al. 2016).

We show that the complexity of derivatives—a firm-level attribute—influences analysts’ decisions

to cover a firm in the first place. We also show that expertise—an analyst-level attribute—drives

coverage of new derivatives users and shapes analysts’ earnings forecast properties for such firms.

Finally, we answer the call for research on the interplay between financial reporting and analysts

(Beyer et al. 2010) by providing economic insight into how the institutional and regulatory setting

of derivatives interacts with market participants, and the relation between analysts and corporate

disclosures, which is critical for understanding firms’ information environments (Chen et al. 2010).

Section 2 provides a brief background, and Section 3 develops our hypothesis. Sample

selection and research design are described in Sections 4 and 5, respectively. Section 6 reports the

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main results, Section 7 discusses additional analyses, and Section 8 concludes.

2. Background and related literature

2.1 Analyst coverage

Sell-side analysts are important information intermediaries in capital markets. As financial

experts, they produce earnings and cash flow forecasts, stock recommendations, and other reports

that help investors make resource allocation decisions. As such, analysts can help improve market

efficiency (Healy and Palepu 2001; Barth and Hutton 2004) and enhance the visibility, liquidity,

and value of firms they cover (Merton 1987; Brennan and Subrahmanyam 1995).

The drivers of analyst coverage decisions often relate to the costs and benefits accruing to

analysts and their brokerages (Ramnath et al. 2008). For instance, analysts tend to cover profitable

firms (McNichols and O’Brien 1997) and those requiring less effort to evaluate (Bhushan 1989;

Lang and Lundholm 1996; Botosan and Harris 2000; De Franco et al. 2011). They are also more

likely to cover firms with higher trading volume as such firms generate larger commissions (Irvine

2004). However, a recent survey of 365 sell-side analysts suggests client demand for information

about a firm is the primary driver of coverage decisions, with little consideration given to financial

reporting attributes (e.g., predictability) that would enhance forecast accuracy (Brown et al. 2015).

Coverage decisions are also influenced by analyst-level factors, such as affiliation (Lin and

McNichols 1998), location (Tan et al. 2011), and personal traits (Tamura 2002). One well-studied

personal trait is analyst expertise, which has been measured by prior research in a number of ways,

including the Institutional Investor All-American Research Team, the Wall Street Journal All-Star

Analyst list, “celebrity” status based on media coverage, and career length (Ramnath et al. 2008).

Several studies find that more experienced analysts have more accurate earnings forecasts (Maines

et al. 1997; Mikhail et al. 1997; Clement 1999), and more accurate earnings forecasts increase the

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likelihood of being ranked as an All-Star analyst (Leone and Wu 2007). All-Star analysts are also

more likely to initiate coverage of firms that have a relationship with their brokerage, and cease

coverage of firms for which they produce less accurate earnings forecasts (Clarke et al. 2007).

Prior studies consider if information asymmetry and inherent uncertainty influence analyst

coverage. For instance, Barth et al. (2001) find that firms with higher levels of intangibles, which

require effort and expertise to evaluate, have higher analyst coverage. Tan et al. (2011) examine if

International Financial Reporting Standards (IFRS) reduce information gathering and processing

costs for analysts such that learning a new set of standards is not an impediment to covering foreign

firms. Lehavy et al. (2011) find greater coverage among firms with less readable annual reports.

However, prior research does not consider whether and how a substantial change in both

economic and financial reporting complexity influences analyst coverage decisions, overall or by

analyst expertise. Further, there is no empirical evidence on whether derivatives, an increasingly

common and infamously complex form of financial risk management, influences analyst coverage.

Although recent research finds a reduction in analyst earnings forecast accuracy after a firm begins

using derivatives (Chang et al. 2016), whether and how the complexity of derivatives influences

analysts’ decisions to cover a firm in the first place are open empirical questions that we consider.

2.2 Derivatives accounting and reporting

Firms routinely engage in risk management practices to insulate cash flow and earnings

from unfavorable changes in risk exposures, including interest rates, foreign exchange rates, and

commodity prices. While there are many techniques for managing risk, corporate use of derivatives

for this purpose is increasingly common (Bartram et al. 2009). A derivative is a contract or security

deriving its value based on its relation to something else, often referred to as the “underlying.” The

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underlying is often another financial instrument or economic good, but can be almost anything.1

Derivatives are an integral part of the global economy, with estimates of notional market size

above $710 trillion and derivatives usage by nearly 66% of U.S. non-financial firms (Bank for

International Settlements 2013).

As noted by Chang et al. (2016), complexity is the “state of being difficult to understand

and apply” (U.S. Securities and Exchange Commission 2008). In our context, complexity relates

to the difficulty in understanding the mapping of economic transactions and reporting standards

into financial statements (Peterson 2012). From an economic standpoint, derivatives are complex

because their value can be linked to virtually any underlying asset/liability, including other

derivatives, to fulfill a variety of objectives. Even mundane derivatives involve elaborate contracts

with ambiguous and evolving terminology. By virtue of such economic factors, financial reporting

for derivatives is also extraordinarily complex. Firms use considerable judgment to apply elaborate

accounting rules to sophisticated transactions with widely varying details (Ryan 2012).2 As a

result, many firms inaccurately and/or inconsistently account for derivatives in their financial

reports, complicating an assessment of these firms (Kawaller 2004). Along these lines, Chang et

al. (2016) show that the complexity of derivatives hinders the accuracy and increases the dispersion

of analysts’ earnings forecasts. We, however, focus on whether and under what circumstances the

inherent complexity of derivatives influences analysts’ decisions to cover a firm in the first place.

3. Hypothesis development

Theory suggests the equilibrium number of analysts covering a firm lies at the intersection

                                                            1 Derivatives generally fall into three categories: (1) options; (2) futures and forwards; and (3) swaps. Options involve the right, but not the obligation, to buy or sell the underlying at a set price within a specified period. A futures or forward contract involves an obligation to exchange the underlying at a future date for a specific price, and swaps are agreements to exchange a stream of payments based on some underlying over a predefined period. 2 See Appendix A of Chang et al. (2016) and Donohoe (2015a) for furthers details on derivatives accounting standards.

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of aggregate demand and supply curves for analyst services (Bhushan 1989). Numerous firm-level

factors, such as size and stock return volatility, influence the intersection of these curves (Ramnath

et al. 2008). Because the economic and reporting complexities of derivatives plague investors as

they assess firms’ financial statements (Kawaller 2004), analysts can use their financial expertise

to provide guidance and advice. Thus, the use of derivatives—a firm-level attribute—could affect

the aggregate demand and supply for analyst services.

From a demand perspective, firms provide information about derivatives in their financial

reports. While this information should aid market participants in assessing firm performance and

risk, it is not always helpful. In particular, the complex and ambiguous accounting and disclosure

practices for derivatives make it difficult for investors to evaluate the risk and reward implications

of even basic instruments. For instance, Koonce et al. (2005) provide experimental evidence that

the labels firms use to describe derivatives in their financial statements cause investors to assess

economically equivalent instruments as having different risk. But, as information intermediaries,

analysts can use their financial skills and knowledge to help investors navigate the complexity and

comprehend the economic effects of firms’ derivatives. Along these lines, research finds that the

demand for analyst information is increasing in analysts’ ability to both relay and process financial

information, implying that investors value analysts because they simplify and convey information

(Lawrence et al. 2014).3 Thus, all else equal, a firm’s use of derivatives increases the demand for

analyst services and the equilibrium number of analysts covering the firm.4

                                                            3 Chen et al. (2010) argue that financial analysts have superior information processing abilities because they have specialized training, experience, and knowledge about a firm/industry. For instance, analysts with more expertise can aid investors in understanding the meaning of certain accruals, while analysts with training in political economics can decipher the implications of international business issues, such as order backlogs from different countries. 4 The effective management of risk with derivatives can reduce noise in earnings and cash flows, potentially reducing the demand for analyst services. However, as investors struggle to comprehend even the most basic hedges and derivatives terminology (Koonce et al. 2005), the information processing role of analysts’ research is stronger and more long-lasting for firms with more complex information (Chen et al. 2010). Thus, on balance, an increase in demand for analyst services likely prevails.

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The effect of derivatives usage on the aggregate supply of analyst coverage is not as clear.

On the one hand, analysts have at least three financial incentives to cover derivatives users. First,

greater demand for analyst services due to the complexities of derivatives can translate into more

valuable advice for analysts to sell to investors. For instance, a recent survey finds that 72% of

analysts rate investor demand for firm-level information as the most important factor in deciding

whether to cover a firm (Brown et al. 2015). Second, when a firm effectively hedges (reduces) a

risk exposure, changes in the fair value of both the derivative and hedged item are recognized in

income together if the firm elects hedge accounting (FASB 1998). Thus, a reduction in earnings

volatility is a potential byproduct of derivatives usage (Zhang 2009), which can improve analysts’

earnings forecasts (Previts et al. 1994) and lead to career promotions (Hong et al. 2000). Third, by

reducing agency conflicts (Campbell and Kracaw 1990) and corporate taxes (Donohoe 2015b),

derivatives can improve performance metrics and firm value (Aretz and Bartram 2010). Because

these (and other) benefits are attractive to investors, a firm’s use of derivatives can provide analysts

with opportunities to earn higher trading commissions. Taken together, these financial incentives

could increase the aggregate supply of analyst coverage.

On the other hand, three issues could deter analysts from covering derivatives users. First,

evaluating financial reports is far more difficult when a firm uses derivatives (Kawaller 2004). At

a minimum, analysts must determine what risk exposures exist, how much of the exposures are

hedged, and how the hedges are managed.5 However, acquiring such information from firms’

financial reports is challenging because derivatives are reported in vastly different ways among

                                                            5 For example, in the case of an agricultural company, analysts must understand what factors affect inventory, the extent to which such factors are covered by contracts (e.g., futures), and how contract prices for the purchase, production, and sale of inventory are determined, monitored, and accounted for in the financial statements.

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the population of derivatives users (Kawaller 2004).6 Second, to accurately predict future earnings,

analysts must consider a firm’s ability to anticipate changes in its risk exposures and adjust hedge

coverage accordingly. But, assessing a static picture of derivatives positions (i.e., balance sheet)

and hedge results over a limited number of accounting periods (i.e., income statement) for this

purpose is rarely feasible. Indeed, Chang et al. (2016) find that the financial reporting complexity

of derivatives leads analysts to misjudge the effects of derivatives on earnings, resulting in less

accurate and more dispersed earnings forecasts. Third, derivatives have drawn sharp criticism from

experts, news media, and tax authorities since the mid-1990s (Donohoe 2015a). Because analysts

tend to cover firms for which they have favorable views (Hayes 1998), these negative perceptions

could deter analysts from covering derivatives users. A firm’s use of derivatives could therefore

decrease the aggregate supply of analyst coverage.

Overall, a firm’s use of derivatives will increase the demand for analyst services, but could

increase or decrease the supply of analyst coverage. Further, the effect of derivatives on analyst

coverage could vary across time as analysts’ decisions are dynamic by nature. Some analysts might

initiate coverage of derivatives users in response to investor demand, but later cease coverage if

earnings forecasts prove too difficult. Thus, we investigate the change in analyst coverage after a

firm begins using derivatives, both immediately and over time. Because analyst coverage depends

on the net effect of competing supply and demand forces, we test a non-directional hypothesis: 

H1: Analyst coverage does not change after a firm initiates derivatives.

4. Data and sample selection

Following Chang et al. (2016), we begin with Compustat observations for fiscal-years 1998

                                                            6 For example, although hedge accounting matches the value effects of hedge instruments with those of the underlying hedged asset/liabilities to the same accounting period, not all firms qualify and/or choose to practice this method. Even when hedge accounting is practiced, firms rarely disclose the unhedged portion of risk exposures, which is likely more relevant than knowing how much a derivative gained or lost (Chang et al. 2016, 588).

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to 2011 meeting four criteria: (1) publicly traded; (2) domestically incorporated; (3) non-financial,

non-utility industry; and (4) at least three years of consecutive data.7 We identify new derivatives

users by searching Form 10-Ks (extracted from the SEC’s EDGAR database) for keywords relating

to derivatives.8 Consistent with Guay (1999) and Donohoe (2015b), a firm is a New User if it does

not report a derivatives position when it first appears in the sample, but does report a position in a

later year. Firms enter the New User sample only when derivatives are first observed (after first

observing no usage). The resulting sample consists of 1,260 New Users during the sample window.

We also identify samples of derivatives users and non-users. A firm is a User if it reports

derivatives in year t and is not a new user, while firms reporting no derivatives are classified as

Non-Users. A New User firm can be a Non-User in an earlier period if it did not use derivatives

for at least two consecutive years, and enters the User sample after using derivatives for at least

two consecutive years.9 These samples consist of 17,987 Non-User and 15,584 User observations.

We then drop observations without necessary Compustat, CRSP, and I/B/E/S data to perform the

analyses that follow, resulting in 10,887 Non-User, 12,017 User, and 587 New User observations.

<INSERT TABLE 1 ABOUT HERE>

Table 1 reports characteristics of Non-Users, Users, and New Users. Panel A illustrates the

temporal distribution of each sample by derivatives reporting regime. Although the number of

Non-Users is somewhat stable, an increase in Users and New Users coincides with the enactment

                                                            7 Fiscal year 1998 is the first full financial reporting year after the phase-in of the EDGAR system. Financial (utility) firms have two-digit SIC codes 60-69 (49). We remove these firms as they are more likely to use derivatives primarily for trading purposes or act as a derivatives dealer, both of which involve different financial reporting requirements. 8 We discover a large majority of derivatives users by searching for the word, “derivative.” Other keywords include: “hedge”, “forward contract”, “futures contract”, “option”, “swap”, and “notional”. 9 To illustrate, consider a firm that did not use derivatives until 2006. From 1998 to 2005, observations for this firm are classified in the Non-User sample. In 2006, the observation is classified in the New User sample. If the firm does not continue to use derivatives in 2007, the observation for 2007 is classified in the Non-User sample. If, however, the firm continues to use derivatives in 2007, the observation for 2007 is classified in the User sample. Thus, New User designation only occurs the first time that derivatives usage is observed (after initially observing no usage). A small number of firms stop and later restart using derivatives; however, omitting them does not influence our results.

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of SFAS Nos. 133/138 (2,602 Users; 228 New Users) and 149 (3,448 Users; 123 New Users),

which greatly altered derivatives accounting. Similarly, an increase in New Users (120) occurs

after the enhanced disclosures of SFAS No. 161. These trends are consistent with evidence that

accounting standards, namely SFAS Nos. 133/138, increased derivatives usage (Abdel-khalik and

Chen 2015). Nevertheless, we take steps to mitigate the possibility that changes in accounting

practices made existing Users appear to be New Users.10 We also examine the effects of these

standards on analysts’ coverage decisions in Section 7.1. Panel B reports the industry distribution

of each sample. Overall, firms in the manufacturing and business equipment industries comprise a

large portion of each group. Thus, where applicable, we control for industry and year fixed-effects.

5. Research design

We use the focused setting of New Users and a difference-in-differences design to test our

hypothesis. This approach offers four distinct advantages (Chang et al. 2016). First, it captures the

dynamic, multi-period setting in which analysts make decisions by evaluating changes in coverage

before and after derivatives initiation. Second, it accounts for variation in an outcome (coverage)

that is not the result of treatment exposure (derivatives initiation) by comparing the treatment

group to an untreated control group (Roberts and Whited 2013). Third, by examining the effects

of derivatives initiation, it overcomes many methodological limitations, including the possibility

of correlated omitted variables bias (Skinner 1996). Finally, it mitigates concerns that unobserved

factors drive the relation between derivatives and analyst coverage.11

                                                            10 In particular, we confirmed the initiation year for each New User by searching the entirety of Form 10-K in prior years for any evidence of derivatives usage. We also test our hypothesis (unreported) after excluding New Users in the enactment year for each standard (2001; 2003; 2008). Inferences remain the same. 11 Difference-in-differences assumes that in the absence of the treatment, average outcomes for treatment and control groups would have followed parallel paths over time. A direct test of this assumption is not possible because one cannot observe the absence of a treatment once it has occurred. However, placebo tests mitigate concerns that the parallel paths assumption is violated by falsely assuming that treatment occurs in a prior period. We conduct two (unreported) placebo tests by falsely assuming that derivatives initiation occurs either one or two years prior to

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5.1 Untreated control group

We use propensity score matching to identify a control group of Non-Users and account

for the endogeneity in a firm’s decision to use derivatives. This nonparametric matching technique

facilitates causal inference in non-experimental settings by constructing a control group that is

similar to a treatment group (Rosenbaum and Rubin 1983). Because a firm can be a Non-User in

one year and a User in another year (Section 4), we limit potential control firms to those that do

not use derivatives at any point during the sample period (“pure” Non-Users). We then estimate

the propensity of derivatives initiation using the following probit regression model:

0 1 1 1 1Pr ,x y z k tit x it y it z it k it t it it

x y z k t

INIT RMI ACI CTRL IND YR (1)

where INIT equals 1 for New Users (i.e., initiation) and 0 for “pure” Non-User observations.

Following Donohoe (2015b), RMI is a vector of risk management incentives that explain

derivatives usage. It includes exposures to interest rate (IRISK), foreign exchange rate (FRISK),

and commodity price (CRISK) risks as surveys reveal these are the risks most often managed with

derivatives (Bodnar et al. 2003). By insulating firm value and cash flow from unfavorable changes

in risk exposures, derivatives can thwart financial distress (Mayers and Smith 1982), harmonize

financing and investment goals (Froot et al. 1993), and reduce agency conflicts (Smith and Stulz

1985). We include financial distress likelihood (ALTZ), underinvestment likelihood (USCORE),

and the sensitivity of executive compensation to firm value (ECSENS) to capture these incentives.

We also include the cash ETR (CETR) to reflect the tax planning features of derivatives (Donohoe

2015b). As derivatives substitutes, we control for convertible debt (CDEBT), preferred stock

(PSTOCK), and abnormal accruals (ABACC).12 Lastly, the volatility in cash flow (CFV) and

                                                            initiation. We find insignificant coefficients for the difference-in-differences estimator, suggesting that the parallel paths assumption holds and our reported results are associated with derivatives initiation. 12 Convertible debt includes an embedded option on firm assets, which reduces the sensitivity of equity value to changes in firm value. Preferred stock reduces the probability of financial distress by paying periodic dividends as

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earnings (EV) reflect other basic incentives for derivatives usage (Zhang 2009). By including RMI,

the absence of derivatives among potential Non-User control firms reflects a choice not to use

them, rather than no incentives to do so. All variables are defined in the Appendix.

ACI is a vector of analyst coverage incentives.13 Consistent with prior studies (Lang and

Lundholm 1996; Barth et al. 2001; Tan et al. 2011), we include market value of equity (SIZE),

intangibles (INTANG), stock return volatility (RETVOL), and the market-to-book (MB) ratio. We

also include stock and debt issuances (ISSUE), share turnover (TURNO), and annual stock returns

(ANRET) to capture analysts’ tendency to cover firms that have more predictable performance and

provide opportunities to earn higher commissions (Hayes 1998; Lang et al. 2004). CTRL is a vector

of controls that likely influence derivatives usage and analysts’ coverage decisions. We include

geographic (GSC) and industry (ISC) sales concentration (Bushman et al. 2004) because firm-level

complexity, apart from that relating to derivatives, can influence analysts’ coverage decisions.14

We also control for profitability (ROA), foreign activity (FRGN), and mergers/acquisitions (M&A).

Finally, industry (IND) and year (YR) fixed-effects account for variation in the decision to initiate

derivatives across industries and time, respectively.15

RMI, ACI, and CTRL are lagged (t−1) to avoid simultaneity with analyst coverage in the

hypothesis tests. The predicted probabilities from Eq. (1) are the propensity scores. We match each

New User to only one “pure” Non-User as of the year before initiation by nearest propensity score,

                                                            opposed to interest. These alternatives reduce the incentive to hedge with derivatives (Nance et al. 1993). Similarly, derivatives and accruals can serve as partial substitutes for smoothing earnings (Barton 2001). 13 Because matching models do not require exclusion restrictions, the general rule is to include a comprehensive list of covariates when estimating propensity scores (Rubin 2009). Thus, while ACI is not directly related to derivatives usage, we include this vector in Eq. (1) to identify an untreated control group of Non-Users that is matched on as many relevant characteristics as possible. Nevertheless, excluding ACI from Eq. (1) yields similar inferences. 14 GSC and ISC are revenue-based Herfindahl-Hirschman indices, where smaller values indicate less geographic and industry sales concentration, respectively, and thus more complexity (see Bushman et al. 2004). The inclusion of only one of these variables in the model at a time does not influence the results. 15 In unreported tests, we replace year fixed-effects with indicators for the effective dates of derivatives accounting standards enacted during the sample period (SFAS Nos. 133/138, 149, 155, and 161). Inferences remain the same.

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within common support, without replacement, using a caliper distance of 0.01.

5.2 Difference-in-differences specification

We compare analyst coverage of the 587 New Users to that of the 587 Non-User control

firms (identified with Eq. [1]) with the following negative binomial regression:

0 1 2 3

,

yit i it i it y it

yz k t

z it k it t it itz k t

COV NEWUSER POST NEWUSER POST ACI

+ CTRL IND YR

(2)

where COV is the number of analysts covering firm i in year t.16 NEWUSER equals 1 for New User

observations and 0 for control firm observations. POST is coded 1 for post-treatment periods (i.e.,

after initiation) for New Users and corresponding control firms (0 otherwise). The coefficient for

NEWUSER (Ψ1) captures the difference in analyst coverage between New Users and control firms

before derivatives initiation, and the coefficient for POST (Ψ2) reflects the change in analyst

coverage among control firms between pre- and post-initiation periods. Thus, the coefficient for

NEWUSER×POST (Ψ3) captures the effect of initiation on analyst coverage for New Users relative

to Non-User control firms (test of H1). ACI and CTRL are as described above. Industry (IND) and

year (YR) fixed-effects control for variation in coverage across industries and time, respectively,

and firm clustered standard errors mitigate concerns about cross-correlated residuals.17

6. Main results

6.1 Descriptive statistics and univariate tests

Table 2 reports descriptive statistics for the dependent variable, RMI, ACI, and CTRL, along

                                                            16 Negative binomial regression is common in tests of analyst coverage as it is preferred to OLS when the dependent variable ranges among nonnegative integers. The benchmark model for such count data is the Poisson (Cameron and Trivedi 2009); however, it makes two strict assumptions: (1) equal mean and variance; and (2) independence. The negative binomial distribution relaxes these assumptions and is used when count data are over-dispersed (i.e., variance exceeds the mean). For our data, diagnostic tests reveal considerable over-dispersion. 17 Following McInnis and Collins (2011) and Donohoe (2015b), we construct pseudo pre/post-initiation periods for control firms based on when the matched New User initiates. Note that we omit the vector RMI from Eq. (2) as prior research suggests that risk management incentives are more apt to explain derivatives initiation than analyst coverage (Aretz and Bartram 2010). However, in unreported tests we include RMI and find similar results.

16  

with t-statistics for mean tests of differences between Non-Users and that of Users and New Users.

Mean tests of COV reveal that analyst coverage for Users and New Users is greater than that for

Non-Users. Mean tests for RMI, ACI, and CTRL indicate that Non-Users differ from Users and

New Users across many dimensions, such as interest rate risk (IRISK), distress (ALTZ), intangibles

(INTANG), stock turnover (TURNO), and profitability (ROA). Users and New Users are also larger

(SIZE) and more complex (GSC, ISC) than Non-Users. Overall, the disparity across the three

samples indicates that multivariate analyses with a matched sample will provide for more robust

tests of the link between derivatives and analyst coverage.18

6.2 Untreated control group

We examine Pearson correlations (unreported) for the variables used to identify the control

group of Non-Users (Eq. [1]). Notably, derivatives initiation (INIT) is significantly correlated with

GSC (−0.035), ISC (−0.085), and SIZE (0.104). Thus, in Section 7.4, we examine if these other

types of complexity or size drive our results. Other correlations are consistent with prior studies,

and Variance Inflation Factors (unreported) indicate no multicollinearity issues in any of our tests.

<INSERT TABLES 2–3 ABOUT HERE>

Table 3 reports the covariate balance between New Users and Non-User control firms

identified with Eq. (1). If covariates are balanced, then differences in analyst coverage can be

attributed to derivatives initiation rather than other firm attributes. Reported amounts are p-values

from tests of differences in means (t-test), medians (Wilcoxon rank-sum test), and distributions

(Kolmogorov-Smirnov test) of RMI, ACI, and CTRL between the two samples. Propensity score

matching does not require matched firms to be identical across all covariates (Caliendo and

Kopeinig 2008). Of the 24 variables, only three (FRISK, ECSENS, ROA) are statistically dissimilar

                                                            18 We use the sample of Users in a validation test in Section 7.1. Unreported tests of differences between Non-Users and potential non-user control firms (“pure” Non-Users) indicate these two samples are similar along most dimensions.

17  

at a 90 percent confidence level. When all covariates are considered jointly, Hotelling’s T2-test (p

=0.998) reveals that New Users and control firms are not different from one another. In sum, these

results suggest the matching process was successful in balancing the control variables.19

6.3 Hypothesis tests

We begin our tests with Figure 1, which plots the number of analysts covering a balanced

sample of 549 New User observations relative to derivatives initiation year (t−1 to t+1). The

positive slope offers initial evidence that analyst coverage increases after derivatives initiation. In

addition, the quick reaction of analysts upon initiation (from t−1 to t) is similar to their response

to other substantial within-firm financial reporting changes (Botosan and Harris 2000).

<INSERT TABLE 4 & FIGURE 1 ABOUT HERE>

Table 4 reports estimates of Eq. (2). To examine the immediate effects of derivatives

initiation, the tests in column (1) use data for the year before (t−1) and upon (t) initiation for the

sample of 587 New Users and 587 matched control firms (2,348 firm-years). To assess the overall

effects, column (2) reports results based on all data before and after initiation (9,826 firm-years).

In column (1), the insignificant coefficient for NEWUSER indicates that analyst coverage of New

Users is similar to that of control firms before initiation in the immediate sample window, while

the negative coefficient for POST (−0.045) reveals a reduction in analyst coverage of control firms.

Across all sample years (column (2)), however, more analysts cover New Users than control firms

                                                            19 Our primary tests are susceptible to “hidden” bias if there are correlated omitted variables that are unbalanced across treatment and control groups. Although it is not possible to test if there are no unobserved variables that influence treatment selection, there are tests for assessing the sensitivity of matched-pair results to such hidden bias (Rosenbaum 2007). The central issue of these tests is how strongly an unmeasured variable must influence the selection process to undermine the matched-pair analysis (Becker and Caliendo 2007). Using the Mantel and Haenszel (1959) test statistic for binary outcomes, we find the results are insensitive to a bias that would affect the odds of derivatives initiation by a factor greater than 10. Although no strict benchmarks exist for gauging sensitivity (Armstrong et al. 2012), by comparison, if control firms were matched only by industry the results would be sensitive to a bias that less than doubled the odds of derivatives initiation. Thus, the results are robust to significant correlated omitted variables bias, largely because of the expansive controls included in the propensity score matching model (Eq. [1]). Nevertheless, as an additional robustness check, we use entropy balancing (Hainmueller 2012). Inferences remain the same.

18  

before initiation (i.e., NEWUSER is positive and significant), with no significant change in control

firm coverage (i.e., POST is insignificant).

As tests of H1, the coefficients for NEWUSER×POST are positive and significant in both

columns (0.105; 0.091). These results indicate that, relative to control firms, New Users realize an

increase in analyst coverage after derivatives initiation, both immediately and overall. The increase

in coverage is consistent with analysts supplying more of their services in response to heightened

client demand for information about firms initiating complex derivatives programs (e.g., Brown et

al. 2015).20 The coefficients for other variables are consistent with prior research. For example,

the positive coefficients for SIZE and TURNO are consistent with analysts covering large firms

with higher share turnover, respectively, to obtain larger commissions (Irvine 2004). These (and

other) coefficients are statistically significant despite covariate balance with control firms (Table

3) as changes in these factors in non-match years can influence analyst coverage decisions.21

To gauge the economic effects of derivatives initiation on analyst coverage, we estimate

the percentage change in COV for New Users from the pre-initiation period to the post-initiation

period using all available data. Following Tan et al. (2011), we estimate the percentage change in

COV for New Users after initiation by computing the marginal effect of POST on COV for New

Users. The marginal effect is the partial derivative of Eq. (2) with respect to POST (NEWUSER

equals 1 and all independent variables are held at the sample mean for New Users). It indicates

                                                            20 It is not possible to further disentangle the supply and demand effects using publicly available data because observed analyst coverage depends on the net effect of these competing factors. Such distinctions are not important for our tests of H1 because either an increase in supply or demand (or both) leads to an observed increase in analyst coverage. 21 We verify that our results are robust to using strict one-to-one matching and a changes specification of differences-in-differences (Roberts and Whited 2013). That is, we regress the change in COV between the pre- and post-initiation period for New Users and control firm observations on NEWUSER and changes in ACI and CTRL. Inferences remain the same. To ensure that the results are not driven by changes in analyst coverage among control firms, we also examine whether changes in analyst coverage after initiation are statistically significantly among only New Users. We estimate Eq. (2) using the time-series of New Users, such that POST is the variable of interest. The (unreported) coefficients for POST are positive and significant, indicating that the observed increase in COV after initiation is statistically significant (p-value<0.01) among only New Users.

19  

how COV changes as POST changes from 0 to 1, holding other variables constant. As a benchmark,

we compute estimates of COV in the pre-initiation period, where POST=0 and NEWUSER=1. The

ratio of the marginal effect of POST to its pre-initiation value estimates the relative percentage

change in COV for New Users after initiation. These (unreported) ratios are based on the most

conservative results in Table 4 and, thus, reflect a lower bound estimate of economic significance.

The ratios indicate that, relative to Non-User control firms, New Users experience a 10%

increase in analyst coverage (on average) after derivatives initiation. This estimate is similar to the

effects of other changes in firms’ financial reporting practices on analyst coverage. For instance,

Tan et al. (2011) find a 20% increase in analyst coverage for firms adopting IFRS. Although the

average effect of derivatives is smaller than the effect of adopting an entirely different set of

reporting standards, it is nonetheless material, suggesting that analysts do indeed respond to the

increased demand for their services due to the complexity of derivatives. However, which types of

analysts respond (or do not respond) to such demand is an open question that we address next.

6.4 Analyst expertise

6.4.1 Difference-in-differences tests

The tests in Table 4 examine whether and how the complexity of derivatives influences the

coverage decisions of analysts. We next evaluate if the observed increase in analyst coverage for

New Users after derivatives initiation is driven by analyst expertise. Analysts with more expertise

(e.g., career experience) produce more accurate earnings forecasts, often leading to media attention

and All-Star status (Ramnath et al. 2008). Because these analysts possess superior skill and

knowledge, they could be more likely than other analysts to respond to the increased demand for

analyst services due to the complexity of derivatives. However, recent research finds that analysts

(on average) produce less accurate earnings forecasts for new derivatives users (Chang et al. 2016).

20  

As such, analysts with more expertise might instead recognize and avoid such challenges, while

analysts with less expertise might view the complexity of derivatives as an opportunity to signal

their talent and obtain media attention as well as career promotions. Because analyst expertise is

difficult to measure, we conduct several tests of whether the increase in coverage for New Users

differs as aspects of expertise vary. Corroborating evidence across these tests will provide stronger

inferences about the link between analyst coverage and the complexity of derivatives.

We first examine if the increase in coverage for New Users after initiation is driven by

analyst career experience. Figure 2 plots the number of analysts covering New Users relative to

initiation year (t−1 to t+1) using a balanced sample. We group analysts based on career experience,

which we define as the number of years (since 1980) that analyst j has issued forecasts for any

firm in I/B/E/S. Analysts in the top quintile of career experience are designated as high experience

(HIEXP); low experience (LOEXP) otherwise. The number of high experience analysts (solid line)

covering New Users remains stable before and after initiation (t), while the number of low

experience analysts (dashed line) increases substantially upon (t) and after initiation (t+1). This

pattern suggests that the increase in coverage after derivatives initiation is likely driven by analysts

with low career experience.

<INSERT TABLE 5 & FIGURES 2-3 ABOUT HERE>

Panel A of Table 5 reports estimates of Eq. (2), where the dependent variable is the number

of high (COV_HIEXP) and low (COV_LOEXP) career experience analysts covering firm i in year

t in columns (1) and (2), respectively, using data for the year immediately before (t−1) and upon

(t) initiation.22 The coefficient for NEWUSER×POST captures the effect of derivatives initiation

                                                            22 The sum of observations in each column do not match those in Table 4 because the dependent variable is the number of analysts with high or low career experience and our tests use firm-year data. For example, assume a New User is covered by two high-experience analysts and four low-experience analysts. The dependent variable in column (1) reflects the two high-experience analysts covering the New User, while the dependent variable in column (2) reflects

21  

on the number of high (column (1)) versus low (column (2)) career experience analysts covering

New Users relative to Non-User control firms. In column (1), this coefficient is insignificant,

indicating no change in coverage for New Users from high career experience analysts after

initiation. However, the positive and significant coefficient for NEWUSER×POST (0.094) in

column (2) suggests that the increase in analyst coverage for New Users after initiation (Table 4)

is driven by analysts with low career experience. A Wald χ2-test (3.35) confirms that the interaction

coefficient in column (2) is different (p-value<0.03) from the coefficient in column (1).23

We next consider whether All-Star analysts drive the increase in analyst coverage for New

Users after derivatives initiation. All-Star analysts are selected annually by Institutional Investor

Magazine based on solicited input from buy-side managers (i.e., chief investment officers of large

money management institutions, directors of research, select analysts, and portfolio managers).24

Prior research finds that the experience level of these analysts is greater than that of less talented

analysts as the latter tend to fade away over time (Leone and Wu 2007). In our sample, All-Stars

have 6.75 years of mean career experience versus 4.75 years for non-All-Stars (unreported). The

All-Star (STAR) and high career experience (HIEXP) analyst subsamples are only moderately

correlated (0.09), however, indicating that All-Star status reflects unique analyst attributes not

otherwise captured by career experience alone.

Figure 3 plots the number of analysts covering New Users relative to initiation year (t−1 to

t+1) using a balanced sample, where analysts are grouped by All-Star (STAR) or non-All-Star

                                                            the four low-experience analysts for the same New User. Because some firms are covered by both high and low experience analysts, the sample sizes do not necessarily sum to the 2,348 observations in Table 4. 23 These results are robust to expertise definitions based on quartiles and terciles. We report quintiles as they reflect the most conservative results. 24 In particular, survey participants vote for the analysts they believe have been most helpful. Analysts are ranked by numerical scores created from the votes and weighted by the size of the voter’s institution (Bagnoli et al. 2008). Some important performance factors include industry knowledge, written reports, stock picks, earnings estimates, timely communication with investors, and responsiveness to investor reports (Leone and Wu 2007). The results are published annually in the October issue of the magazine.

22  

(NONSTAR) status.25 The solid line depicting the number of All-Star analysts covering New Users

is flat before and after initiation (t), whereas the dashed line for non-All-Star analysts markedly

increases upon (t) and after (t+1) initiation. This pattern suggests that the increase in coverage after

derivatives initiation is likely driven by non-All-Star analysts.

Panel B of Table 5 reports estimates of Eq. (2), where the dependent variable is the number

of All-Star (COV_STAR) and non-All-Star (COV_NONSTAR) analysts covering firm i in year t in

columns (1) and (2), respectively, using data for the year immediately before (t−1) and upon (t)

initiation. The insignificant coefficient for NEWUSER×POST in column (1) indicates no change

in coverage for New Users from All-Star analysts after derivatives initiation. However, the positive

and significant interaction coefficient (0.087) in column (2) suggests the increase in coverage for

New Users after initiation is driven by analysts other than All-Stars. As before, the interaction

coefficient in column (2) is statistically different (p<0.02) from that in column (2).26

6.4.2 Transition tests

Table 5 reveals that the increase in analyst coverage after derivatives initiation is driven by

analysts with less expertise. However, because the difference-in-differences estimator (ψ3) reflects

the average treatment effect, the tests do not reveal if the observed change in coverage results from

analysts with less expertise initiating coverage or those with more expertise ceasing coverage of

                                                            25 I/B/E/S (detail file) includes a unique identifier for each analyst, but does not provide personal details (i.e., names). The Institutional Investor All-American Research Team file includes analyst names, but not the identifier. We use the Broker Code Key (I/B/E/S), which includes both identifiers and names, to merge the two files. However, because I/B/E/S does not provide the Broker Code Key after 2006, all subsequent observations are matched by hand. 26 It is possible that the insignificant coefficients for NEWUSER×POST in column (1) of Panels A and B of Table 5 are because our tests do not possess sufficient statistical power to detect an effect across the analyst expertise partitions. To mitigate this possibility, we conduct a post hoc power analysis, which computes achieved statistical power (1−β) as a function of significance level (α), sample size (n), and effect size (Faul et al. 2007). Following Cohen (1988), we define effect size as the incremental adjusted-R2 obtained when NEWUSER×POST is included in Eq. (2) (relative to a model with only other covariates). Using a statistical significance level of 0.10, sample sizes for tests in Table 5, and incremental adjusted-R2 (unreported), we find statistical power in excess of conventional levels (0.80). Thus, lack of statistical power is not an alternative explanation for these results.

23  

New Users, which can influence the information environment.27 Thus, to ensure that less expertise

does indeed drive the results, we examine analyst coverage at a more granular level. In particular,

we create transition matrices that trace the proportion of analysts covering New Users before

derivatives initiation and the proportion ceasing or initiating coverage of New Users upon and after

derivatives initiation, across differing levels of expertise. While we expect the inferences will be

similar to those in Table 5, the matrices allow us to more closely examine the characteristics of

analysts responding to the increase in demand for their services.

Panels A and B of Table 6 trace the change in analyst coverage after derivatives initiation

by career experience and All-Star status, respectively. To construct the matrices, we sort analysts

covering a balanced sample of New Users from t−2 to t+1, where t is derivatives initiation, into

quintiles based on level of career experience and All-Star status ranking (as defined in Section

6.4.1). The balanced sample (i.e., requiring observations for each year of the t−2 to t+1 window)

helps ensure our tests focus on changes in coverage that are not due to variation in the number of

New Users across time. We then count the number of analysts ceasing or initiating coverage upon

(t) and one year after (t+1) derivatives initiation.28

<INSERT TABLE 6 ABOUT HERE>

In Panel A, for the two years before derivatives initiation (t−2 and t−1), 25.15% of analysts

covering the balanced sample of New Users are in the lowest quintile of career experience, relative

to 17%-19% in each of the other quintiles. Upon initiation (t), 670 analysts cease coverage of New

Users while 1,713 analysts begin covering these firms, resulting in a net coverage increase of 1,043

                                                            27 For example, among our sample firms, Alexion Pharmaceuticals experienced a large net increase in analyst coverage after derivatives initiation (from 12 to 17 analysts). Specifically, seven new analysts began covering the firm, and two analysts ceased coverage, after the firm initiated a derivatives program. None of the seven new analysts were in the high career experience (HI_EXP) or All-Star (STAR) groups. 28 We verify that analysts ceasing (initiating) coverage in t or t+1 did (did not) cover a New User firm in t−2 or t−1.

24  

analysts. Similarly, one year later (t+1), the number of analysts beginning coverage of New Users

exceeds the number of analysts ceasing coverage by 374. However, in both post-initiation periods,

the largest proportion of analysts ceasing and initiating coverage are those with the lowest amount

of career experience. For example, upon derivatives initiation, 32.09% (38.59%) of the 670 (1,713)

analysts ceasing (initiating) coverage are in the lowest quintile of career experience, compared to

17.16% (12.38%) in the highest quintile. A similar pattern exists in Panel B, where analysts ceasing

and initiating coverage are predominately not labeled as All-Stars. Thus, while analysts in all levels

of expertise both cease and initiate coverage of New Users after derivatives initiation, the overall

net increase in coverage is driven by analysts with less expertise initiating coverage.

Panels C and D of Table 6 trace the change in coverage after derivatives initiation by career

experience and All-Star status, respectively, for analysts that already cover New User firms before

they begin using derivatives. These matrices consider how the proportion of analysts with prior

knowledge of New Users (at the firm-level) changes after derivatives are introduced. To construct

these matrices, we trace analysts already covering New Users upon derivatives initiation (i.e., not

new analysts) back two years before derivatives initiation (t−2 and t−1) and track these analysts

one year after derivatives initiation (t+1). In Panel C, the career experience of analysts that already

cover New Users before initiation (t−2 and t−1) is fairly diverse, but slightly skewed towards more

experience. This diversity in experience persists upon (t) and after (t+1) initiation, even though the

proportion of analysts in the lowest quintile of experience becomes much smaller in t+1 (14.13%).

Panel D reveals only minor changes in the ratio of All-Star analysts already covering New Users

upon and after initiation. Collectively, these matrices show that the analysts already covering New

Users before derivatives initiation have diverse levels of expertise before and after initiation. These

findings confirm the results in Panels A and B that the overall net increase in coverage is driven

25  

by new analysts with less expertise initiating coverage rather than the expertise of existing analysts.

Finally, Panel E traces the career outcomes of analysts ceasing coverage of New Users. In

particular, we classify these analysts into four groups upon (t) and after (t+1) derivatives initiation:

(1) promotion (moving to a larger brokerage; defined based on a quintile rank of all brokerages in

I/B/E/S by number of analysts employed); demotion (moving to a smaller brokerage); (3) stay (no

change in brokerage size quintile); and (4) missing (analyst no longer appears in I/B/E/S). For both

t and t+1, about half of the analysts that cease coverage of New Users remain at the same brokerage

or one of similar size. A large proportion of analysts also disappear from the dataset after ceasing

coverage. While the inferences we can draw from these trends are limited, ceasing coverage of

New Users does not seemingly lead to positive or negative career outcomes in the short-term.

6.4.3 Analyst earnings forecast properties

Tables 5 and 6 indicate that the increase in analyst coverage of New Users after derivatives

initiation is not driven by analysts with more expertise, as reflected by career experience and All-

Star status. Instead, analysts with less expertise largely begin covering New Users, likely because

they believe the complexity of derivatives provides an opportunity to signal their talent, issue more

valuable reports, and earn higher commissions. These results complement Chang et al. (2016) by

providing an explanation for the earnings forecast properties they find for New Users. That is, a

large number of low expertise analysts begin covering New Users and, in turn, produce weaker

and more dispersed earnings forecasts for these firms.

To confirm this explanation, we perform difference-in-difference tests of the accuracy and

dispersion of analysts’ earnings forecasts for New Users conditional on expertise level. Using the

I/B/E/S detail file, we measure career experience consistent with the earlier tests, and then calculate

each individual analysts’ mean earnings forecast accuracy (AEFA) and dispersion (AEFD) by firm-

26  

year and expertise group.29 We merge this data with the sample used to test the immediate effects

of derivatives initiation (Table 4). Finally, we calculate all of the covariates in Eq. (2) of Chang et

al. (2016), resulting in 1,271 and 1,892 analyst-firm-year observations in high and low experience

groups, respectively. Variables are defined in the Appendix.

<INSERT TABLE 7 ABOUT HERE>

Panel A (B) of Table 7 reports the results, where the dependent variable is earnings forecast

accuracy (dispersion) for high and low career experience analysts in columns (1) and (2),

respectively. In Panel A, the coefficient for NEWUSER×POST is insignificant in column (1),

indicating no change in forecast accuracy for New Users by high career experience analysts after

derivatives initiation. In contrast, the negative and significant coefficient for NEWUSER×POST

(−0.512) in column (2) reveals that, relative to control firms, the accuracy of analysts’ earnings

forecasts for New Users declines after derivatives initiation for the low experience group. In Panel

B, the coefficient for NEWUSER×POST (0.083) is positive and significant in only column (2),

suggesting analysts’ earnings forecasts for New Users are more dispersed for the low experience

group after derivatives initiation. A Wald χ2-test in each panel confirms that the coefficient for

NEWUSER×POST in column (2) is statistically different (p<0.05) from that in column (1).30 Thus,

the increase in low expertise analysts initiating coverage of New Users explains the less accurate

and more dispersed earnings forecasts shown by Chang et al. (2016) for these firms.

7. Other tests

7.1 Validation test

To validate that the complexity of derivatives does indeed influence analyst coverage, we

test the structural stability of analyst coverage over specific periods of time in the broader setting

                                                            29 Chang et al. (2016) use the consensus forecast as they do not consider analyst-level characteristics in their tests. 30 Results for tests of All-Star status (untabulated) are similar.

27  

of Users. In this sense, structural stability is a statement about parameters in the context of an

econometric model. An assumption of stationarity implies model parameters (mean and variance)

are constant over time, and a structural break occurs if at least one parameter changes. Structural

breaks can occur, for example, as a result of regulatory regime changes; that is, when one mix of

reporting standards is replaced by another (Donohoe and McGill 2011; Chang et al. 2016).31

Extensive changes in reporting rules for derivatives during the sample period are likely to

influence analyst coverage. The application of fair value-based accounting under SFAS No. 133

after 2000, and its subsequent repairs/amendments by SFAS Nos. 138, 149, and 155 substantially

altered the financial reports of derivatives users. Although these standards enhanced and clarified

derivatives accounting, they permitted two distinct and basically inconsistent approaches to hedge

accounting (see Appendix A of Chang et al. (2016)). Further, SFAS No. 161, effective after 2008,

required enhanced disclosures to address concerns that SFAS No. 133 did not provide adequate

detail about how derivatives usage affects financial position, performance, and cash flow. If

analyst coverage is indeed influenced by the complexity of derivatives, we expect to see changes

in analyst coverage as the reporting practices of derivatives change.

Similar to Chang et al. (2016), we alter Eq. (2) in three ways. First, we replace NEWUSER

and POST with an indicator variable, USER, identifying the User and Non-User samples. We then

add four regime indicators equal to 1 for observations after fiscal-years June 2000 (SFAS133/138),

June 2003 (SFAS149), September 2006 (SFAS155), and November 2008 (SFAS161), respectively

(0 otherwise).32 Finally, we interact each indicator with USER such that the interaction coefficients

                                                            31 We focus on Users because the difference-in-difference design is not feasible for regime-level tests (Chang et al. 2016). By excluding New Users, structural break tests focus on the effects of ongoing derivatives usage and alleviate the potential that firm-level changes at initiation (e.g., risk exposures) drive the results. 32 Although SFAS No. 133 had an original effective date of June 1999, the FASB delayed implementation by one year. In addition, SFAS No. 138, effective June 2000, amended several aspects of SFAS No. 133. Based on their close proximity, we use only one regime indicator (SFAS133/138) to reflect these two standards.

28  

capture structural breaks in analyst coverage among Users in a given reporting regime. Thus, these

tests focus on the financial reporting complexities of derivatives.33

We find a positive and significant coefficient for USER (untabulated), suggesting greater

analyst coverage for Users relative to Non-Users. The interaction coefficients also indicate that

analyst coverage for Users increases after SFAS Nos. 133/138, but declines after SFAS Nos. 149

and 155. Together, these results help validate the main results by providing further evidence that

analyst coverage is related to changes in the reporting practices of derivatives. Note, however, that

we do not infer causality between the regulatory events and coverage. Instead, these results show

a statistical relation between trends in coverage and periods of time in which the FASB was

actively changing derivatives reporting.

7.2 Forecast frequency

Table 5 suggests that analysts respond to the increased demand for their services due to the

complexity of derivatives by initiating coverage for these firms. Another potential response to such

heightened demand is for analysts to issue forecasts for the earnings of New Users more frequently.

To investigate this possibility, we estimate Eq. (2) after replacing the dependent variable with the

number of annual earnings forecasts issued by each analyst covering a firm (FREQ). We use data

for the year before (t−1) and upon (t) initiation to hold analyst coverage constant across the pre-

and post-initiation periods. We find an insignificant coefficient (unreported) for NEWUSER×

POST (p>0.34), suggesting that forecast frequency for New Users does not change after initiation.

7.3 Ineffective management of financial risk

Corporate use of derivatives is often motivated by their ability to hedge (reduce) the risk

                                                            33 Structural break analysis accounts for changes in both intercept and slope. In our model, the coefficients for the interactions (regime indicators) capture shifts in slope (intercept). We focus on interaction coefficients as they are indicative of a parameter change among derivatives users in a given reporting regime (Donohoe and McGill 2011). We are primarily interested in whether model parameters are non-stationary over time.

29  

of unfavorable changes in interest rates, foreign exchange rates, and commodity prices (Guay and

Kothari 2003). However, some firms fail to hold effective hedges or intentionally speculate (e.g.,

Hentschel and Kothari 2001). As noted by Chang et al. (2016), these distinctions are important for

analysts as many of the key benefits of derivatives, such as less volatile earnings and cash flow,

only result from effective hedging (see Section 2). Thus, we consider whether analyst coverage is

influenced by how successfully firms use derivatives to manage risk exposures. On the one hand,

analysts may prefer to cover effective hedgers as their earnings are better insulated from exposures

to risk. On the other hand, analysts’ reports are potentially more valuable to investors when firms

fail to hold effective hedges or speculate in the derivatives market.

We classify New Users as effective or speculative/ineffective hedgers following Zhang

(2009).34 We estimate three models quantifying how New Users’ exposures to IRISK, FRISK, and

CRISK relate to firm features prior to derivatives usage. Using the resulting coefficients, we

compute New Users’ expected risk exposures after using derivatives. We label a New User as an

effective hedger if at least two risk exposures are less than expected after initiation. Of the 587

New Users, 499 are classified as an effective hedger; speculative/ineffective otherwise.

We then estimate Eq. (2) with two modifications for the overall effects of initiation. First,

we replace NEWUSER with (1) EH, which is equal to 1 for effective hedgers (0 otherwise), and

(2) SPIN, which is equal to 1 for speculative/ineffective hedgers (0 otherwise). Second, we interact

these two variables with POST. Consistent with our main results, the (unreported) results indicate

an increase in analyst coverage after initiation for both effective and speculative/ineffective

                                                            34 Zhang (2009) introduces a procedure for identifying firms that reduce specific risk exposures after holding all of their derivatives positions. We use this procedure because (1) it provides a reasonable ex post assessment of hedge effectiveness as few firms disclose parameters that help discern the extent to which a risk exposure is hedge (Kawaller 2004), and (2) by focusing on outcomes, it is not confounded by a firm’s discretion in hedge designation or choice to practice hedge accounting. See Zhang (2009) for details. Following Donohoe (2015b), we make two modifications to quantify factors of FRISK: (1) we use foreign income (pifo in Compustat) as an explanatory variable rather than foreign sales; and (2) due to data availability, we omit the sum of industry imports and exports as an explanatory variable.

30  

hedgers. However, the increases in coverage are not statistically different between the two groups

(p>0.98), suggesting that, on average, analysts have no preference for covering firms whose

earnings are potentially better insulated from risk exposures.

We also examine whether the increase in coverage for effective and speculative/ineffective

hedgers differs by analyst expertise. For these tests, the dependent variable in the modified Eq. (2)

is the number of low career experience (COV_LOEXP) and non-All-Star (COV_NONSTAR)

analysts covering a firm. In both cases, only the coefficients for NEW_EH×POST are positive and

significant, indicating that analysts with less expertise tend to cover effective hedgers (rather than

speculative/ineffective hedgers) after initiation. One explanation is that the earnings of effective

hedgers are viewed as easier to forecast as such firms are likely insulated from risk exposures.

7.4 Firm complexity and size

The use of a difference-in-differences design with an untreated control group and numerous

covariates alleviates many alternative explanations. However, we conduct two additional tests to

further mitigate the concern that other types of complexity and/or firm size drive the main results.

First, we estimate Eq. (2) with two modifications. We replace NEWUSER with (1) an indicator

variable equal to 1 for New Users in the lowest decile of either GSC or ISC, the two complexity

measures in our model (0 otherwise), and (2) an indicator variable equal to 1 for New Users not in

the lowest decile of either measure (0 otherwise). We then interact these variables with POST.

Second, we partition New Users by the highest decile of SIZE in the same manner. For complexity,

we find that the effects of initiation on analyst coverage are not significant for geographically

complex firms (GSC). Further, while the effects are significant for industry sales concentration

(ISC) overall, they are no different among low versus high ISC partitions. For size, we also find

no difference among the partitions. Together, these (unreported) results indicate that the effects of

31  

derivatives initiation on analyst coverage are not likely driven by firm complexity or size. 

8. Conclusion

We examine whether and how the complexity of derivatives influences analysts’ decisions

to cover a firm. Using the focused setting of new derivatives users and a difference-in-differences

design, we find that analyst coverage increases among firms that begin using derivatives during

1998-2011. This increase in coverage is driven by analysts with low expertise, as reflected by both

career experience and All-Star status. We also find that the accuracy (dispersion) of earnings

forecasts for new derivatives users decrease (increases) after derivatives initiation only when the

forecasts are issued by analysts with less expertise. Therefore, the evidence collectively suggests

that, despite the renowned complexity of derivatives, analysts with less expertise begin covering

new derivatives users, and subsequently produce the less accurate and more dispersed earnings

forecasts shown by prior research for these firms (Chang et al. 2016).

We note three key caveats. First, a firm’s decision to use derivatives is determined, in part,

by unobservable factors, making it difficult to eliminate all alternative explanations. However, we

mitigate many of the major concerns by (1) using propensity score matching and a difference-in-

differences design; (2) validating our findings in the broader setting of derivatives users; and (3)

evaluating if ineffective risk management, firm complexity, or size drive the results. Second, we

note that our structural break tests among derivatives users preclude any inferences other than a

statistical relation. Finally, the effects of derivatives on analyst coverage could vary depending on

whether a firm hedges a majority or a small portion of its risk exposures (Ryan 2012). While

current disclosure practices limit the ability of researchers to obtain such information, we believe

this is an issue that should be addressed by future research.

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Appendix Variable definitionsa

Dependent variables

COV Analyst coverage, defined as the total number of analysts covering firm i in year t (obtained from I/B/E/S detail file).

COV_HIEXP Analyst coverage from high career experience analysts, defined as the total number of analysts covering firm i in year t that are in the top quintile of the number of years (since 1980) issuing forecasts for any firm in I/B/E/S (obtained from I/B/E/S detail file).

COV_LOEXP Analyst coverage from high career experience analysts, defined as the total number of analysts covering firm i in year t that are not in the top quintile of the number of years (since 1980) issuing forecasts for any firm in I/B/E/S (obtained from I/B/E/S detail file).

COV_STAR Analyst coverage from All-Star analysts, defined as the total number of analysts covering firm i in year t that are designated as All-Stars by Institutional Investor Magazine (see Leone and Wu 2007).

COV_NOSTAR Analyst coverage from non-All-Star analysts, defined as the total number of analysts

covering firm i in year t that are not designated as All-Stars by Institutional Investor Magazine (see Leone and Wu 2007).

AEFA_HIEXP Analyst earnings forecast accuracy for high career experience analysts, defined as the absolute value of the difference between the last earnings forecast issued by analyst j and actual earnings scaled by stock price of year t for firm i. We multiply the result by −100 such that greater values indicate more accurate forecasts, and then compute the mean of individual analyst forecast accuracy by firm-year and expertise group. See Tan et al. (2011). High career experience analysts are those in the top quintile of the number of years (since 1980) issuing forecasts for any firm in I/B/E/S (obtained from I/B/E/S detail file).

AEFA_LOEXP Analyst earnings forecast accuracy for low career experience analysts, where accuracy is defined above. Low career experience analysts are those not in the top quintile of the number of years (since 1980) issuing forecasts for any firm in I/B/E/S (obtained from I/B/E/S detail file).

AEFD_HIEXP Analyst earnings forecast dispersion for high career experience analysts, defined as the absolute value of the difference between the last earnings forecast issued by analyst j and the average of forecasts made by all other analysts except analyst j covering firm i in year t. We scale the absolute value by stock price at the end of year t for firm i and multiply the result by 100. We then take the mean of individual analyst forecast deviation from the consensus forecast by firm-year and expertise group. See Hong et al. (2000). High career experience analysts are those in the top quintile of the number of years (since 1980) issuing forecasts for any firm in I/B/E/S (obtained from I/B/E/S detail file).

AEFD_LOEXP Analyst earnings forecast dispersion for low career experience analysts, where dispersion is defined above. Low career experience analysts are those not in the top quintile of the number of years (since 1980) issuing forecasts for any firm in I/B/E/S (obtained from I/B/E/S detail file).

Variables of interest

NEWUSER Indicator variable equal to 1 for all New User firm observations and 0 for all matched

37  

control firm observations.

POST Indicator variable equal to 1 for both New User and matched control firm observations in periods after derivatives initiation; 0 otherwise.

Risk management incentives (RMI)

IRISK Interest rate risk exposures, defined as the absolute value of the estimated coefficient from a regression of firms’ monthly holding period stock returns on the monthly percentage change in the London Interbank Offered Rate (LIBOR) for 36 months prior to fiscal-year end. See Guay (1999), Zhang (2009), and Donohoe (2015b).

FRISK Foreign currency exchange rate risk exposures, defined as the absolute value of the estimated coefficient from a regression of firms’ monthly holding period stock returns on the monthly percentage change in the Federal Reserve Board trade-weighted U.S. dollar index for 36 months prior to fiscal-year end. See Guay (1999), Zhang (2009), and Donohoe (2015b).

CRISK Commodity price risk exposures, defined as the absolute value of the estimated coefficient from a regression of firms’ monthly holding period stock returns on the monthly percentage change in the Producer Price Index for 36 months prior to fiscal-year end. See Guay (1999), Zhang (2009), and Donohoe (2015b).

ALTZ Likelihood of entering financial distress, defined as the modified Altman-Z score based on parameter weights reported by Shumway (2001).

USCORE Likelihood of underinvestment, defined by first ranking cash flow from operations (oancf), debt-to-assets ratio (lt/at), and scores from a factor analysis of four growth opportunity measures (prior investment activity, geometric growth in market value of assets, market-to-book ratio, and research and development into deciles by year and industry. Decile ranks for debt-to-asset ratios and growth opportunity factor scores are then added to the reverse decile rank for cash flow from operations, with the result scaled by 30 (total possible points). See Donohoe (2015b).

ECSENS Sensitivity of executive compensation to firm value, defined by first computing the dollar change in value of CEO stock and option holdings that would result from a one percentage point increase in the stock price of the firm (0.01×prcc_f×[shrown_tot+ opt_unex_exer_num]). The result is then normalized by the sum of CEO salary and bonus (salary+bonus) to capture the share of total CEO compensation that would result from a one percentage point increase in firm value. Compensation data obtained from Execucomp. See Bergstresser and Philippon (2006).

CETR Cash effective tax rate (3-year), defined as the three-year sum (t to t+2) of worldwide cash taxes paid (txpd) divided by the three-year sum (t to t+2) of pre-tax book income (pi) less special items (spi). ETRs are reset to 1 (0) if greater (less) than 1 (0). See Dyreng et al. (2008).

CDEBT Convertible debt, defined as convertible debt (dcvt) divided by lagged total assets (at).

PSTOCK Preferred stock, defined as preferred stock (pstk) divided by lagged total assets (at).

ABACC Abnormal accruals, based on the performance-matched modified Jones model.

CFV Cash flow volatility, defined as the standard deviation of quarterly operating cash flows (oancfy, adjusted to reflect quarterly data) during the most recent two years.

EV Earnings volatility, defined as the standard deviation of quarterly earnings before

38  

extraordinary items (ibq) during the most recent two years.

Analyst coverage incentives (ACI)

SIZE Log of equity market value (prcc_f×csho) at beginning of year t.

INTANG Ratio of intangible assets (intan) to total assets (at) at beginning of year t.

RETVOL Return volatility, defined as the standard deviation of monthly stock returns for firm i at year t−1. Ranked into deciles, where decile ranks are transformed by dividing the rank by 9 and subtracting 0.5 such that values range from −0.50 to 0.50.

MB Market to book ratio, defined as equity market value (prcc_f×csho) divided by book value of equity (at−lt−pstkl+txditc+dcvt) at beginning of year t.

ISSUE Indicator variable equal to 1 if firm issues equity or debt greater than 5 percent of total assets in year t; 0 otherwise.

TURNO Stock turnover, defined as the number of shares traded in year t, divided by the average number of shares outstanding in year t.

ANRET Annual stock return for firm i in year t−1, adjusted for contemporaneous annual market return.

Control variables (CTRL)

GSC Geographic sales concentration, defined as the sum of squares of (firm sales in each geographic segment / total firm sales) (obtained from the Compustat Segments File).

ISC Industry sales concentration, defined as the sum of squares of (firm sales in each industry segment / total firm sales) (obtained from the Compustat Segments File).

ROA Return on assets, defined as income before extraordinary items (ib) divided by total assets (at) at beginning of year t.

FRGN Indicator variable equal to 1 if foreign income or loss (pifo) is not equal to 0; 0 otherwise.

M&A Indicator variable equal to 1 if cash flow from mergers and acquisitions (aqc) is not equal to 0; 0 otherwise.

Other variables

INIT Indicator variable equal to 1 for New Users and 0 for “pure” Non-Users observations (i.e., firms that do not use derivatives at any point during the sample period).

USER Indicator variable equal to 1 if the firm reports a position in derivatives in both fiscal years t and t−1 (i.e., a User, but not a New User); 0 if the firm does not report a position in derivatives. This variable identifies the User (=1) and Non-User (=0) samples.

SURPRISE Earnings surprise, defined as the absolute value of the difference between earnings per share in year t and year t−1, divided by stock price at the beginning of year t.

SFAS133/138 Indicator variable equal to 1 for observations after fiscal-year June 2000; 0 otherwise.

SFAS149 Indicator variable equal to 1 for observations after fiscal-year June 2003; 0 otherwise.

SFAS155 Indicator variable equal to 1 for observations after fiscal-year September 2006; 0 otherwise.

SFAS161 Indicator variable equal to 1 for observations after fiscal-year November 2008; 0 otherwise.

39  

FREQ Forecast frequency, defined as the number of annual earnings forecasts issued by each analyst covering firm i in year t (obtained from the I/B/E/S detail file).

EH Indicator variable equal to 1 if the firm effectively hedges (reduces) its exposure to at least two risks (interest rate (IRISK), foreign exchange rate (FRISK), or commodity price (CRISK) risks) relative to expectations after derivatives initiation; 0 otherwise. We define risk exposure reductions in the bottom decile as immaterial. See Zhang (2009) for details.

aCompustat mnemonics in parentheses.

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Table 1 Characteristics of Non-Users, Users, and New Users

Panel A: Temporal distribution of sample observations by derivatives reporting regimea

1/1/98 to 6/15/00 to 6/30/03 to 9/15/06 to 11/15/08 to Total 6/14/00 6/29/03 9/14/06 11/14/08 12/31/11

Non-Users 1,327 3,068 2,866 1,673 1,953 10,887 Users 765 2,602 3,448 2,310 2,892 12,017 New Users 48 228 123 68 120 587 Total 2,140 5,898 6,437 4,051 4,965 23,491

Panel B: Industry distribution of sample observations

Non-Users Users New Users Industry groupb Obs. % Obs. % Obs. %

Consumer Non-Durables 483 4 925 8 29 5 Consumer Durables 336 3 430 4 27 5 Manufacturing 1,120 10 2,781 23 101 17 Energy & Extraction 215 2 793 7 15 3 Chemicals & Allied Products 145 1 538 4 10 2 Business Equipment 3,211 29 2,307 19 176 30 Telecommunications 236 2 435 4 21 4 Wholesale & Retail 1,524 14 1,286 11 79 13 Healthcare 2,028 19 946 8 59 10 Constr., Transport. & Services 1,589 15 1,576 13 70 12 Total 10,887 12,017 587

This table presents characteristics of Non-Users, Users, and New Users. Panel A illustrates the temporal distribution of sample observations, and Panel B reports industry distributions. A firm is a New User if it did not report a position in derivatives when it first appears in the sample, but did in a subsequent year. Firms enter the New User sample only when derivatives usage is first observed after initially observing no usage (see footnote 9 for an example). A firm is a User if it reports a position in derivatives at the end of both fiscal years t and t−1 (i.e., uses derivatives, but is not a new user), and a Non-User if it reports no position in derivatives at fiscal year-end. aConsistent with prior studies (Guay 1999; Donohoe 2015b), the sum of Users and New Users in a given year does not necessarily equal the amount of Users in the subsequent year because a firm can enter the sample for the first time as a User, but not a New User. bFama-French industry groups are available at: http://mba.tuck.dartmouth.edu/pages/Faculty/ken.french/datalibrary.html.

SFAS Nos. 133/138

SFAS No. 149

SFAS No. 155

SFAS No. 161

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Table 2 Descriptive statistics

(1) (2) (3) Non-Users Users New Users Mean Median Mean Median t-stat [(1)−(2)] Mean Median t-stat [(1)−(3)]

Dependent variable COV 7.429 5.000 10.987 9.000 −33.68 8.828 7.000 −4.96

Risk management incentives (RMI) IRISK 0.004 0.002 0.002 0.001 23.59 0.004 0.002 1.97 FRISK 0.019 0.011 0.014 0.008 21.74 0.018 0.012 1.07 CRISK 0.021 0.011 0.014 0.008 21.39 0.026 0.015 −3.75 ALTZ 5.912 4.060 3.403 2.675 34.62 3.955 2.959 6.76 USCORE 0.546 0.533 0.527 0.533 9.53 0.547 0.533 −0.23 ECSENS 0.060 0.000 0.113 0.056 −28.52 0.073 0.020 −2.49 CETR 0.174 0.096 0.207 0.185 −11.81 0.204 0.164 −3.38 CDEBT 0.028 0.000 0.027 0.000 0.78 0.047 0.000 −4.83 PSTOCK 0.004 0.000 0.003 0.000 2.80 0.005 0.000 −0.48 ABACC −0.012 −0.005 −0.015 −0.008 1.60 −0.017 −0.011 0.73 CFV 0.038 0.028 0.027 0.020 26.81 0.033 0.024 3.87 EV 0.033 0.015 0.019 0.009 17.91 0.023 0.011 3.19

Analyst coverage incentives (ACI) SIZE 5.802 5.711 7.121 7.044 −59.85 6.361 6.295 −8.32 INTANG 0.124 0.050 0.173 0.111 −21.18 0.178 0.113 −7.85 RETVOL 0.117 0.167 −0.038 −0.056 39.74 0.069 0.056 3.98 MB 3.085 2.065 2.331 1.684 16.69 2.837 1.999 1.55 ISSUE 0.068 0.000 0.104 0.000 −9.67 0.063 0.000 0.47 TURNO 2.092 1.510 2.163 1.684 −3.01 2.320 1.721 −2.90 ANRET 0.139 −0.007 0.104 0.000 2.87 0.139 −0.025 0.00

Control variables (CTRL) GSC 0.768 0.890 0.643 0.595 32.51 0.721 0.759 4.23 ISC 0.813 1.000 0.692 0.687 32.38 0.732 0.844 7.56 ROA −0.026 0.035 0.028 0.042 −24.38 0.016 0.040 −4.84 FRGN 0.475 0.000 0.692 1.000 −34.09 0.650 1.000 −8.36 M&A 0.398 0.000 0.578 1.000 −27.69 0.537 1.000 −6.75

Obs. 10,887 12,017 587 This table reports descriptive statistics, along with t-statistics for mean tests of differences between Non-Users and that of Users and New Users (assuming unequal variance). Continuous variables are winsorized at 1st and 99th percentiles. Bold t-statistics denote statistical significance of at least 0.10 (two-tailed). Variables are defined in the Appendix.

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Table 3 Covariate balance

Mean Difference Median Difference Distributional Difference p-value p-value p-value Risk management incentives (RMI) IRISK 0.309 0.588 0.102 FRISK 0.915 0.120 0.058* CRISK 0.233 0.517 0.101 ALTZ 0.762 0.960 0.214 USCORE 0.961 0.989 1.000 ECSENS 0.663 0.000*** 0.000*** CETR 0.855 0.564 0.225 CDEBT 0.677 0.574 1.000 PSTOCK 0.828 0.623 1.000 ABACC 0.408 0.507 0.535 CFV 0.995 0.848 0.973 EV 0.781 0.813 0.731

Analyst coverage incentives (ACI) SIZE 0.564 0.771 0.108 INTANG 0.509 0.625 0.842 RETVOL 0.489 0.634 0.969 MB 0.970 0.682 0.837 ISSUE 0.295 0.295 1.000 TURNO 0.397 0.384 0.337 ANRET 0.610 0.123 0.159

Control variables (CTRL) GSC 0.734 0.623 0.845 ISC 0.579 0.509 0.602 ROA 0.873 0.772 0.076* FRGN 0.908 0.908 1.000 M&A 0.697 0.697 1.000 Hotelling’s T2 0.998

This table reports the covariate balance between the 587 New Users and 587 propensity score matched control firms in the match year. Reported values are p-values for tests of differences in means (t-tests), medians (Wilcoxon rank-sum test), and distributions (Kolmogorov-Smirnov homogeneous distributions test) of matching variables (Eq. [1]). Hotelling’s T2 test is the multivariate equivalent of the two-sample t-test and considers whether the vector of all variable means differ between the two groups. *, **, and *** denote statistical significance levels of 0.10, 0.05, and 0.01, respectively (two-tailed). Variables are defined in the Appendix.

43  

Table 4 Difference-in-differences tests of analyst coverage

(1) (2) COV COV Immediate Effects Overall Effects Coeff. RSE Coeff. RSE

Intercept −0.750 *** 0.147 −0.795 *** 0.118 NEWUSER 0.016 0.036 0.076 ** 0.032 POST −0.045 ** 0.026 −0.040 0.041 NEWUSER×POST 0.105 *** 0.026 0.091 ** 0.044

Analyst coverage incentives (ACI) SIZE 0.359 *** 0.012 0.319 *** 0.011 INTANG 0.352 *** 0.087 0.342 *** 0.068 RETVOL 0.357 *** 0.060 0.164 *** 0.033 MB 0.000 0.004 0.004 0.003 ISSUE −0.024 0.049 −0.047 0.040 TURNO 0.122 *** 0.010 0.134 *** 0.007 ANRET −0.183 *** 0.019 −0.164 *** 0.011

Control variables (CTRL) GSC 0.110 0.074 0.146 *** 0.055 ISC 0.221 *** 0.058 0.200 *** 0.047 ROA 0.098 0.104 0.076 0.060 FRGN −0.030 0.040 −0.008 0.030 M&A 0.053 * 0.028 0.023 0.018

Industry Included Included Year Included Included Pseduo R2 0.14 0.19 Wald χ2 (model) 2,394.24*** 3,340.25*** Observations 2,348 9,826

This table reports tests of whether the complexity of derivatives initiation influences analyst coverage (Eq. [2]). NEWUSER equals 1 for New User firm observations and 0 for matched control firm observations. POST equals 1 for periods after derivatives initiation for New Users and corresponding control firms (0 otherwise). The coefficients for NEWUSER, POST, and NEWUSER×POST reflect (a) differences in analyst coverage between New Users and control firms during the pre-initiation period, (b) the change in analyst coverage among control firms between pre- and post-initiation periods, and (c) the difference-in-differences estimator of the effect of derivatives initiation on analyst coverage for New Users relative to control firms, respectively. To assess the immediate effects of initiation, column (1) uses data for the year immediately before (t−1) and upon (t) initiation for the sample of 587 New Users and 587 matched control firms (2,348 firm-years). Column (2) reports results based on all available data before and after initiation (9,826 firm-years) to assess the overall effects of initiation. *, **, and *** denote statistical significance levels of 0.10, 0.05, and 0.01, respectively (two-tailed). Robust standard errors (RSE) are clustered by firm (Petersen 2009). Variables are defined in the Appendix.

44  

Table 5 Difference-in-differences tests of analyst coverage by analyst expertise Panel A: High versus low career experience (1) (2)

COV_HIEXP COV_LOEXP High Career Experience Low Career Experience Coeff. RSE Coeff. RSE

Intercept −0.543 *** 0.182 −0.747 *** 0.144 NEWUSER −0.019 0.049 −0.031 0.036 POST 0.049 0.032 −0.028 0.022 NEWUSER×POST Ψ3 0.000 0.045 0.094 *** 0.028

Analyst coverage incentives (ACI) SIZE 0.257 *** 0.014 0.336 *** 0.012 INTANG −0.135 0.110 0.335 *** 0.083 RETVOL −0.033 0.067 0.380 *** 0.058 MB −0.006 0.005 0.003 0.004 ISSUE 0.017 0.062 −0.033 0.050 TURNO 0.038 *** 0.012 0.121 *** 0.009 ANRET −0.162 *** 0.031 −0.160 *** 0.020

Control variables (CTRL) GSC −0.043 0.096 0.042 0.072 ISC −0.072 0.071 0.253 *** 0.058 ROA 0.086 0.144 −0.005 0.095 FRGN −0.020 0.053 −0.022 0.039 M&A −0.035 0.038 0.061 ** 0.027

Industry Included Included Year Included Included Pseduo R2 0.12 0.16 Wald χ2 (model) 830.83*** 2,385.96*** Observations 1,487 2,214 Wald χ2: Ψ3 (1) = Ψ3 (2) 3.35**

45  

Panel B: All-Star analysts versus non-All-Star analysts (1) (2)

COV_STAR COV_NOSTAR All-Star Analysts Other Analysts Coeff. RSE Coeff. RSE

Intercept 0.609 *** 0.228 −0.609 *** 0.135 NEWUSER −0.005 0.047 −0.012 0.034 POST 0.080 ** 0.039 −0.030 0.021 NEWUSER×POST Ψ3 −0.039 0.059 0.087 *** 0.026

Analyst coverage incentives (ACI) SIZE 0.249 *** 0.018 0.351 *** 0.011 INTANG −0.027 0.138 0.306 *** 0.081 RETVOL 0.219 *** 0.075 0.318 *** 0.056 MB 0.003 0.005 −0.000 0.004 ISSUE −0.030 0.057 −0.028 0.046 TURNO 0.080 *** 0.016 0.120 *** 0.009 ANRET −0.105 *** 0.038 −0.172 *** 0.018

Control variables (CTRL) GSC −0.077 0.122 0.074 0.071 ISC 0.103 0.082 0.210 *** 0.054 ROA 0.201 0.175 0.031 0.093 FRGN −0.009 0.057 −0.016 0.038 M&A −0.002 0.042 0.051 ** 0.026

Industry Included Included Year Included Included Pseduo R2 0.14 0.16 Wald χ2 (model) 807.15*** 2,852.57*** Observations 386 2,271 Wald χ2: Ψ3 (1) = Ψ3 (2) 4.75**

This table reports tests of whether the immediate effects of the complexity of derivatives initiation on analyst coverage are driven by analyst expertise (Eq. [2]). In Panel A, the dependent variable is the number of high (COV_HIEXP) and low (COV_LOEXP) career experience analysts covering firm i in year t in columns (1) and (2), respectively. We define career experience as the number of years (since 1980) that analyst j has issued forecasts for any firm in I/B/E/S. Analysts in the top quintile of career experience are designated as high experience; low otherwise. In Panel B, the dependent variable is the number of All-Star (COV_STAR) and non-All-Star (COV_NONSTAR) analysts covering firm i in year t in columns (1) and (2), respectively. All-Star analysts are selected annually by Institutional Investors Magazine. *, **, and *** denote statistical significance levels of 0.10, 0.05, and 0.01, respectively (two-tailed). Robust standard errors (RSE) are clustered by firm (Petersen 2009). Variables are defined in the Appendix.

46  

Table 6 Transition tests of analyst coverage Panel A: Transition upon derivatives initiation (t) by career experience

Number of analysts covering New Users

t−2 and t−1 %

Car

eer

Exp

erie

nce 1 (Low) 2,069 25.15

2 1,412 17.17 3 1,633 19.85 4 1,573 19.12 5 (High) 1,539 18.71

Total 8,226 100.00 Panel B: Transition upon derivatives initiation (t) by All-Star status

Number of analysts covering New Users

t−2 and t−1 %

All

-Sta

r

NONSTAR 7,777 94.54 STAR by ranking: Rank 1 143 1.74 Rank 2 153 1.86 Rank 3 153 1.86

Total 8,226 100.00

Number of analysts ceasing coverage of New Users

t % t+1 %

Car

eer

Exp

erie

nce 1 (Low) 215 32.09 328 29.66

2 120 17.91 206 18.63 3 106 15.82 195 17.63 4 114 17.01 201 18.17 5 (High) 115 17.16 176 15.91

Total 670 100.00 1,106 100.00

Number of analysts initiating coverage of New Users

t % t+1 %

Car

eer

Exp

erie

nce 1 (Low) 661 38.59 619 41.82

2 305 17.81 271 18.31 3 278 16.23 230 15.54 4 257 15.00 196 13.24 5 (High) 212 12.38 164 11.08

Total 1,713 100.00 1,480 100.00

Number of analysts ceasing coverage of New Users

t % t+1 %

All

-Sta

r

NONSTAR 647 96.57 1,065 96.29 STAR by ranking: Rank 1 8 1.19 195 1.36 Rank 2 4 0.60 201 0.90 Rank 3 11 1.64 176 1.45

Total 670 100.00 1,106 100.00

Number of analysts initiating coverage of New Users

t % t+1 %

All

-Sta

r

NONSTAR 1,637 95.56 2,595 95.76 STAR by ranking: Rank 1 34 1.98 38 1.40 Rank 2 17 0.99 47 1.73 Rank 3 25 1.46 30 1.11

Total 1,713 100.00 2,710 100.00

47  

Panel C: Transition upon derivatives initiation (t) among analysts already covering New Users by career experience

Panel D: Transition upon derivatives initiation (t) among analysts already covering Users by All-Star status

Number of analysts covering New User

firms before derivatives initiation t−2 and t−1 %

All

-Sta

r

NONSTAR 4,932 93.04 STAR by ranking: Rank 1 122 2.30 Rank 2 117 2.21 Rank 3 130 2.45

Total 5,301 100.00 Panel E: Transition upon derivatives initiation (t) among analysts ceasing coverage of New Users by outcome

Note: This table reports transition matrices that trace the proportion of analysts ceasing or initiation coverage of New Users upon and after derivatives initiation, across differing levels of expertise. Panels A and B trace the change in analyst coverage after derivatives initiation by career experience and All-Star status, respectively. To construct the matrices, we sort analysts covering a balanced sample of New Users from t−2 to t+1, where t is derivatives initiation, into quintiles based on level of career experience and All-Star status ranking (as defined in Section 6.4.1). The balanced sample (i.e., requiring observations for each year of the t−2 to t+1 window) helps ensure our tests focus on changes in coverage that are not due to variation in the number of New Users across time. We then count the number of analysts ceasing or initiating coverage upon (t) and one year after (t+1) derivatives initiation. Panels C and D trace the change in coverage after derivatives initiation by career experience and All-Star status, respectively, for analysts that already cover New Users before they initiate a derivatives program. These matrices consider how the proportion of analysts with prior knowledge of New Users (at the firm-level) changes after derivatives are introduced. To construct these matrices, we trace analysts already covering New Users upon derivatives initiation (i.e., not new analysts) back two years before derivatives initiation (t−2 and t−1) and track these analysts one year after derivatives initiation (t+1). Panel E traces the career outcomes of analysts ceasing coverage of New Users. We classify these analysts into four groups upon (t) and after (t+1) derivatives initiation: (1) promotion (moving to a larger brokerage; defined based on a quintile rank of all brokerages in I/B/E/S by number of analysts employed); demotion (moving to a smaller brokerage); (3) stay (no change in brokerage size quintile; and (4) missing (analyst no longer appears in I/B/E/S).

Number of analysts covering New Users after derivatives initiation

t % t+1 %

Car

eer

Exp

erie

nce 1 (Low) 1,007 19.00 501 14.13

2 919 17.34 602 16.98 3 1,100 20.75 788 22.23 4 1,190 22.45 867 24.46 5 (High) 1,085 20.47 787 22.20

Total 5,301 100.00 3,545 100.00

Number of analysts covering New User

firms before derivatives initiation t−2 and t−1 %

Car

eer

Exp

erie

nce 1 (Low) 1,030 19.43

2 893 16.85 3 1,118 21.09 4 1,188 22.41 5 (High) 1,072 20.22

Total 5,301 100.00

Number of analysts covering New Users after derivatives initiation

t % t+1 %

All

-Sta

r

NONSTAR 4,928 92.96 3,263 92.05 STAR by ranking: Rank 1 126 2.38 99 2.79 Rank 2 120 2.26 93 2.62 Rank 3 127 2.40 90 2.54

Total 5,301 100.00 3,545 100.00

Number of analysts ceasing coverage of New Users by career outcome t % t+1 % Promotion (to large brokerage) 8 1.19 16 1.45 Demotion (to small brokerage) 37 5.52 71 6.42 Stay (same brokerage) 318 47.46 583 52.71 Missing (disappear from I/B/E/S) 307 45.82 436 39.42 Total 670 100.00 1,106 100.00

48  

Table 7 Difference-in-differences tests of analyst earnings forecast properties by analyst expertise Panel A: Analyst earnings forecast accuracy (AEFA)

(1) (2) AEFA_HIEXP AEFA_LOEXP High Career Experience Low Career Experience Coeff. RSE Coeff. RSE

Intercept −1.023 *** 0.164 −2.867 *** 0.415 NEWUSER 0.020 0.035 0.035 0.086 POST −0.018 0.022 0.096 * 0.053 NEWUSER×POST βa 0.000 0.033 −0.152 ** 0.072

Analyst coverage incentives (ACI) COV −0.001 0.003 0.011 0.007 SIZE 0.079 *** 0.016 0.160 *** 0.039 INTANG 0.281 *** 0.082 0.484 ** 0.218 RETVOL −0.019 *** 0.005 −0.068 *** 0.014 MB 0.005 0.004 0.020 * 0.012 ISSUE 0.029 0.047 0.108 0.108 TURNO 0.001 0.009 −0.041 0.025 ANRET −0.057 ** 0.024 −0.152 *** 0.054 SURPRISE −0.026 *** 0.002 −0.065 *** 0.006

Control variables (CTRL) GSC 0.048 0.070 −0.186 0.167 ISC −0.025 0.052 −0.035 0.141 ROA 0.003 ** 0.001 0.019 *** 0.003 FRGN −0.016 0.035 0.158 * 0.090 M&A −0.010 0.026 0.101 0.066

Industry Included Included Year Included Included Adjusted R2 0.37 0.39 Observations 1,271 1,892 Wald χ2: βa (1) = βa (2) 4.85**

49  

Panel B: Analyst earnings forecast dispersion (AEFD)

(1) (2) AEFD_HIEXP AEFD_LOEXP High Career Experience Low Career Experience Coeff. RSE Coeff. RSE

Intercept 0.731 *** 0.110 1.673 *** 0.242 NEWUSER −0.003 0.025 −0.016 0.052 POST −0.000 0.017 −0.038 0.032 NEWUSER×POST βb 0.015 0.024 0.083 ** 0.043

Analyst coverage incentives (ACI) AFOL 0.000 0.002 −0.009 * 0.005 SIZE −0.055 *** 0.011 −0.074 *** 0.023 INTANG −0.194 *** 0.061 −0.389 *** 0.133 RETVOL 0.017 *** 0.004 0.054 *** 0.009 MB −0.006 ** 0.003 −0.020 *** 0.007 ISSUE −0.021 0.034 −0.098 0.064 TURNO −0.002 0.007 0.023 0.016 ANRET 0.026 0.017 0.055 0.036 SURPRISE 0.017 *** 0.002 0.039 *** 0.004

Control variables (CTRL) GSC −0.013 0.051 0.096 0.107 ISC 0.016 0.038 0.039 0.088 ROA −0.002 ** 0.001 −0.012 *** 0.002 FRGN 0.024 0.026 −0.097 * 0.055 M&A −0.022 0.019 −0.118 *** 0.042

Industry Included Included Year Included Included Adjusted R2 0.38 0.40 Observations 1,271 1,892 Wald χ2: βb (1) = βb (2) 2.87**

Note: This table reports tests of the accuracy (Panel A) and dispersion (Panel B) of analysts’ earnings forecasts for New Users by career experience. NEWUSER equals 1 for New User firm observations and 0 for matched control firm observations. POST equals 1 for periods after derivatives initiation for New Users and corresponding control firms (0 otherwise). The coefficients for NEWUSER, POST, and NEWUSER×POST reflect (a) differences in analyst earnings forecast properties between New Users and control firms during the pre-initiation period, (b) the change in analyst earnings forecast properties among control firms between pre- and post-initiation periods, and (c) the difference-in-differences estimator of the effect of derivatives initiation on analyst earnings forecast properties for New Users relative to control firms, respectively. To assess the immediate effects of initiation, these tests use data for the year immediately before (t−1) and upon (t) initiation for both New Users and matched control firms. *, **, and *** denote statistical significance levels of 0.10, 0.05, 0.01, respectively (two-tailed). Robust standard errors (RSE) are clustered by firm (Petersen 2009). Variables are defined in the Appendix.  

50  

Figure 1 Analyst coverage relative to derivatives initiation

Note: This figure plots the number of analysts covering New Users relative to the year of derivatives initiation (t−1 to t+1) for a balanced sample of 549 New Users. We restrict the sample to t−1 to t+1 to highlight the immediate effects of derivatives initiation on analyst coverage and to minimize data attrition.

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naly

sts

cove

ring

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rs

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51  

Figure 2 Analyst coverage by analyst career experience

Note: This figure plots the number of analysts covering New Users relative to the year of derivatives initiation (t−1 to t+1) for a balanced sample of 549 New Users. We group analysts based on career experience, defined as the number of years (since 1980) that analyst j has issued forecasts for any firm in I/B/E/S. Analysts in the top quintile of career experience are designed as high experience (HIEXP), low experience (LOEXP) otherwise. We restrict the sample to t−1 to t+1 to highlight the immediate effects of derivatives initiation on analyst coverage and to minimize data attrition.

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er e

xper

ienc

e an

alys

ts

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HIEXP LOEXP

52  

Figure 3 Analyst coverage by All-Star status

Note: This figure plots the number of analysts covering New Users relative to the year of derivatives initiation (t−1 to t+1) for a balanced sample of 549 New Users. We group analysts by All-Star (STAR) or non-All-Star (NOSTAR) status. All-Star analysts are selected annually by Institutional Investor Magazine based on solicited input from buy-side managers (i.e., chief investment officers of large money management institutions, directors of research, select analysts, and portfolio managers). We restrict the sample to t−1 to t+1 to highlight the immediate effects of derivatives initiation on analyst coverage and to minimize data attrition.

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