Bond Analysts’ Forecasts on Cash Flows and Earnings*

Robert G. Kim
Department of Accounting and Law
The State University of New York at Buffalo

Current Version: May 2, 2014

ABSTRACT
Using sell-side bond analysts’ forecasts, collected from their debt reports for the period 20012010, this study examines the determinants and properties of bond analysts’ forecasts on cash
flows and earnings. Initial evidence indicates that the probability to issue cash flow (earnings)
forecasts is greater (smaller) for bond analysts than for equity analysts, consistent with the notionthat cash flow (earnings) information is more (less) important to bond investors than for stock
investors. My analysis further shows that bond analysts are less optimistic in both cash flow andearnings forecasts than equity analysts, implying that bond analysts’ view on firm’s future
performance is driven by bond investors’ asymmetric demand for good news and bad news. In
addition, bond analysts’ cash flow (earnings) forecasts are more (less) accurate than equity
analysts’ cash flow (earnings) forecasts, which manifests bond investors’ stronger demand forreliable information on future cash flows than for earnings. Finally, my additional cross-sectional
analysis reveals that bond analysts are more likely to issue cash flow forecasts for firms withgreater volatility of cash flows and under more severe liquidity constraint. Overall, this study
enhances our understanding of the informational role bond analysts play in the bond market
through their forecasting activities.

Keywords: Bond Analysts; Equity Analysts; Cash Flow Forecasts; Earnings Forecasts; Forecast Bias andAccuracy
JEL Classifications: D84; G17; G24; G29; M41_________________*

This paper has benefited from the comments and suggestions of Sunhwa Choi, Bill Kross, Claudia Zhen Qi, Inho
Suk, Woongsun Yoo and seminar participants at the State University of New York at Buffalo, the University of
Massachusetts at Boston, the University of Massachusetts at Dartmouth, the University of New Hampshire, and the
University of Texas at Pen American. I thank Heeick Choi and Taegoo Kang for assistance with collecting andcoding bond analyst reports. All errors are my own.

1. Introduction
Since Amazon.com started to build up a dominant position in e-commerce, and enjoyedtriple-digit growth rates and expansion into new product lines, the market had positive
expectations about Amazon’s future profitability and growth. After hitting a peak of stock pricearound $106 on Dec. 10, 1999, Amazon's stock price appeared to trade at the range between $45
and $55 until mid-June of 2000. Some investors were starting to question about the future
profitability and growth. Nonetheless, there were still plenty of investors and equity analysts who
had faith in Amazon (Hof, Sparks, Neuborne, and Zellner, 2000).
On June 22, 2000, one bond analyst, Ravi Suria, hired by Lehman Brothers Inc., releaseda scathing report about Amazon's deteriorating credit situation. He argued that excessive debt
and poor inventory management would make Amazon's operating cash-flow situation worse themore it sells, consequently making it difficult for the company to meet its debt obligations by theend of the first quarter of 2001. 1 However, many equity analysts thought Suria was overly
pessimistic and continued to focus on Amazon’s potential earnings growth. 2 Eventually, Amazon
realized a huge amount of negative cash flows, and the bond analyst saved Lehman's clientsmillions of dollars when he cautioned them about the crushing wave of debt about to envelop thetelecom industry, giving them plenty of time to get out before the bubble burst. Investors who
followed Suria’s advice to put their money in the oil and energy sectors also got rich (Khan,
2001). As evident from this anecdotal evidence, bond analysts consider cash flow information
more important than earnings information, while equity analysts put more weight on earnings
information rather than cash flow information.
1

In his bond report, Suria states “The fundamental problem lies in the fact that Amazon does not generate positive
net cash flow per unit of product it sells.”
2
Henry Blodget, a Merrill Lynch & Co. (equity) analyst, said that “I’m not at all concerned about the cash side.”
According to a JP Morgan & Co. (equity) analyst, Tom Wyman: “Their (Amazon’s) operating margin will be twicethat of brick-and-mortar retailers.”1This study investigates the determinants and properties of cash flow and earnings
forecasts issued by sell-side bond analysts. 3,4 Specifically, I first raise two questions: Between
bond analysts and equity analysts, which analysts are more likely to issue cash flow forecasts?
What about earnings forecasts? Further, I compare the bias and accuracy of equity and bondanalysts’ forecasts. Finally, I ask why some bond analysts issue cash flow forecasts while others
do not, and why some bond analysts are more likely to issue an earnings forecast together with acash flow forecast. This study attempts to answer these questions.
The informational role of sell-side debt analysts is important for several reasons. First,
both bond and stock markets are important to finance capital for firms. The amount ofoutstanding and newly issued debt is substantial compared to that of equity. For example, at theend of 2010, the amount of total U.S. outstanding debt was 343% of total GDP, while that ofoutstanding equity was 119% of total GDP. 5 Despite the significance of public debt markets,
there has been relatively less research on the corporate bond market, particularly on the role ofbond analysts in the bond market. Second, the recent credit market crises, in both debt/credit
derivative and housing markets, accelerated the criticism of the failure of credit rating agencies[hereafter ‘CRAs’] in providing relevant and reliable predictions on the credit risk of firms.
Many investors and regulators have casted doubts about the quality of credit rating reports issued
by CRAs. Although Cheng and Neamtiu (2009) find that CRAs have increased the information
quality of their ratings following recent regulatory changes, such as the Sarbanes and Oxley Act(2002), bond investors and bond analysts argue that the credit information provided by the CRAs

3

Throughout the paper, I use ‘debt analysts’, ‘bond analysts’, ‘sell-side debt analysts’, and ‘sell-side bond analysts’interchangeably. Also, ‘debt reports’ or ‘bond reports’ refer to analyst reports issued by sell-side bond analysts.
4
I focus on corporate bonds from debt research reports. Hence, governmental debt, macroeconomic or industry
research, and research conducted by credit rating agencies are excluded.
5
Roxburgh, Lund, and Piotrowski, 2011, available from (http://www.mckinsey.com/insights/global_capital_markets/
mapping_global_capital_markets_2011).
2

is not timely and accurate, which limits the usefulness of the credit information. 6 Under suchcircumstances, research on debt analysts has been recently called for by academics (Berger,
2011; Beyer, Cohen, Lys, and Walther, 2010; Kolasinski, 2009; Mangen, 2013). Beyer et al.
(2010) encourage researchers to investigate issues related to the development of debt markets
and their interactions with equity markets when they examine the valuation role of accounting
information. Berger (2011, page 216) also contends that “research should continue to build on
the recent work of Johnston et al. (2009) and De Franco et al. (2009) by further considering therole of debt analysts in the firm’s information environment.”
Several recent studies explore the role of bond analysts in the market. Johnston, Markov,
and Ramnath (2009) examine the determinants of bond analysts’ debt reports. They find thatbond analysts’ decisions to issue debt reports depend on costs and benefits of providing theinformation to the market. De Franco, Vasvari, and Wittenberg-Moerman (2009) and Gurun,
Johnston, and Markov (2011) examine the impact of debt research on both debt and equity
markets. De Franco et al. (2009) find that trading volume and abnormal returns change following
the provision of bond analysts’ recommendations in both debt and equity markets, suggestingthat both markets react to bond analysts’ recommendations. Gurun et al. (2011) conclude thatbond analysts’ debt reports enhance the bond market efficiency by both increasing the bond priceadjustment speed and increasing the total information available in the market. On the other hand,
De Franco, Vasvari, Vyas, and Wittenberg-Moerman (2014) find that the value of debt reports isdriven mainly by bond analysts’ unique view on debt-equity conflict events, such as M&A
activities, stock repurchase, or spin-off and that the bond market reaction is stronger than theequity market reaction when debt analysts’ view is different from equity analysts’ view.

6“Investing solely or mainly on the basis of rating-agency bands has become an almost useless strategy” (Peter,
2002).
3

Many bond analysts provide forecasts on cash flows and earnings in their debt reports.
Malmendier and Shanthikumar (2007) document that recommendations have the most impact on
the trading behavior of small traders, but that (equity) analyst earnings forecasts affect thetrading behavior of large traders. Consistent with this, Mikhail, Walther, and Willis (2007) findthat, while small investors are able to incorporate the information in a coarse and easilyinterpreted signal (i.e., the recommendation revision), they appear unable or uninterested in
extracting the information in the more complex signal (i.e., the earnings forecast revision). Since
most bond investors are institutional investors (i.e., large investors), information on cash flow
and earnings forecasts, compared to bond recommendations, is particularly important for bondinvestors. 7 Despite the importance of the informational role of bond analysts’ cash flow andearnings forecasts in the bond market, prior literature has paid little attention to forecasts issued
by bond analysts. My study fills this gap by examining the properties of bond analysts’ forecasts
on the firm’s future performance.
Sell-side bond analysts’ forecasts are manually-collected from debt reports provided by
Investext for the period 2001-2010. My first findings show that the likelihood to issue cash flow
forecasts is greater for bond analysts than for equity analysts, whereas the probability to issue
earnings forecasts is greater for equity analysts than for bond analysts, implying that cash flow(earnings) information is more (less) important to bond investors than to stock investors andhence to bond analysts than to equity analysts. Next, I compare the bias and accuracy of theearnings and cash flow forecasts for both bond analysts and equity analysts. The results showthat bond analysts are less optimistic in both cash flow and earnings forecasts than equity
analysts, suggesting that bond analysts have a more conservative view on company’s future
7This is also consistent with Malmendier and Shanthikumar (2014)’s argument that the upward bias in (equity)
analysts’ recommendations are likely to be corrected by large investors where (equity) analysts, who are fear of
tarnishing reputations with large investors, are likely to provide relatively unbiased earnings forecasts.
4

performance than equity analyst with respect to both earnings and cash flow forecasts, which
may be driven by bond investors’ asymmetric demand for good news and bad news. In addition,bond analysts’ cash flow (earnings) forecasts are more (less) accurate than equity analysts’ cash
flow (earnings) forecasts, which again corroborates my first finding of the greater importance of
cash flows (earnings) to bond (equity) investors and analysts. Finally, I examine the determinantsof bond analysts’ forecasts on cash flows and earnings. I find that bond analysts are more likely
to issue cash flow forecasts for firms with greater volatility of cash flows and under more severe
liquidity constraint. These results suggest that bond analysts are striving to meet bond investors’
demand for future cash flows information, especially when the uncertainty and constraint on cash
flows are severe. I also find that bond analysts are more likely to provide earnings forecasts
along with cash flow forecasts for firms with greater accruals volatility and with lower earnings.
This study contributes to the literature in two important ways. First, my study
contributes to the recent growing literature that examines the informational role of bond analysts.
Despite that majority of bond traders are institutional investors, bond analysts’ cash flow andearnings forecasts, one of the most important components in bond analysts’ reports, have been
neglected by academic researchers. To my knowledge, my study is the first to document bondanalysts’ efforts on forecasting firms’ future cash flows and earnings. Second, this paper also
supplements the literature on the properties of equity analysts’ forecasts. My findings suggestthat the properties of bond analysts’ forecasts are quite different from those of equity analysts’
forecasts. In particular, as regards to the debate on the quality of analysts’ cash flows forecasts
literature (e.g., Givoly, Hayn, and Lehavy, 2009; Call, Chen, and Tong, 2013), my study
provides evidence supportive of Givoly et al. (2009), who argue the quality of equity analysts’
cash flow forecasts is limited.

5

The paper proceeds as follows. The next section discusses the institutional context of thebond market and bond analysts’ activities, reviews the related literature, and develops my
hypotheses. Section 3 explains the research design and Section 4 describes the nature of mysample and discusses summary statistics. Section 5 reports the main empirical results. Finally,
Section 6 summarizes the findings of the paper and concludes with the discussion on theireconomic implications.

2. Institutional Background, Literature Review, and Hypothesis Development
2.1. Institutional background: Role of bond analysts
There are several differences between bond and equity markets. First, the bond market,
especially the U.S. debt market, is less transparent and less liquid compared to the stock market(e.g., Kwan, 1996; Hong and Warga, 2000; Hotchkiss and Ronen, 2002; Downing, Underwood,and Xing, 2009; De Franco et al., 2009). Second, most of the bond investors are institutional
investors, thus the degree of sophistication of investors is relatively high. Lastly, because
bondholders receive less value when firms’ total value of assets become less than the total
amount of debt, nor do they fully receive the benefit when firms earn large profits or earnings,
bond investors have an asymmetric demand for information about firm’s prospect and future
performance. Consistent with this, Easton, Monahan, and Vasvari (2009) examine theassociation between bond returns and news proxied by earnings surprises at the earnings
announcement. While they find significant bond market reactions to both good news and bad
news, the magnitude of reactions is greater for bad news than for good news.
Debt analysts play pervasive and significant roles in the capital market. Similar to equity
analysts, debt analysts issue not only buy/hold/sell recommendations on the debt security but

6also forecasts on future performance (e.g., earnings and cash flows). 8 Further, both debt analysts
and equity analysts are subject to Regulation Fair Disclosure (“Reg. FD”) and Regulation
Analyst Certification (“Reg. AC”). However, since they are analyzing different securities, theydiffer in many aspects. First, debt analysts are more likely to cover risker firms compared tostock analysts (Johnston et al., 2009; De Franco et al., 2009) whereas stock analysts are more
likely to follow firms which have greater growth potential and visibility (Bhushan, 1989).
Second, debt analysts also provide an analysis of potential impact of debt-specific risk events,
including stock repurchases, divestments of assets, spin-offs, leverage buyouts, and debt-fundedacquisitions (De Franco et al., 2014). Since the benefits and risks facing these events aredifferent for bondholders and shareholders, debt analysts’ interests in these events drive uniquecharacteristics of debt reports. 9
2.2. Literature review
Johnston et al. (2009) find that 1) bond analysts tend to cover firms with a higher
probability of financial distress, 2) debt analysts’ coverage increases with the total amount ofdebt issued, 3) firms with a higher market-to-book ratio are less covered by bond analysts, 4)
firms with greater leverage receives more debt research, and 5) the stock market reacts strongly
when debt analysts issue negative news but does not react when they issue positive news. De
Franco et al. (2009) examine characteristics of debt analysts’ bond recommendations and market
consequences of their recommendations in both equity and bond markets. First, they find that the
distribution of recommendations is positively skewed (i.e. more buy recommendations than sell
8

Bond analysts, as well as equity analysts, attend to conference calls and All-America Institutional Investor selects
the best bond analysts across industries each year (Kandler, 2001; Ronan, 2006). For more detailed information
about debt analysts’ activities, please see Kandler (2001), Peter (2002), and Ronan (2006).
9
While credit rating agencies also provide company-level and debt-level research reports, unlike credit rating
agencies debt analysts identify undervalued/overvalued debt and forecast a firm’s upcoming credit rating change andfuture profitability. Consistent with this, Johnston et al. (2009) and DeFranco et al. (2009) provide evidence that debt
analysts issue more timely reports than CRAs.
7

recommendations), similar to that of equity analysts’ recommendations. Second, the bond market
reaction, measured by both volume and price change, to bond recommendations is economicallyand statistically significant. Third, the market reaction to bond recommendations leads credit
rating agencies reports. In conclusion, they provide evidence that bond analysts’
recommendations provide incremental information to the market, thus enhancing the information
efficiency of the corporate bond market. Gurun et al. (2011) corroborates De Franco et al.
(2009)’s results. They find that debt analysts’ research lessens the lead-lag relationship between
equity market and bond market documented by previous studies (Kwan, 1996; Hotchkiss andRonen, 2002; Downing, Underwood, and Xing, 2009). Furthermore, they also find an increase in
information efficiency driven by bond analysts in the bond market and that the increase is more
likely due to bond analysts’ efforts to increase the speed of price adjustment than to expand thetotal information set to the market. De Franco et al. (2014) provide a more in-depth investigation
into the informational role of bond analysts. Using a computational linguistic program to code
debt analysts’ tones on debt-specific events, they find that the bond market reaction to debt
analysts’ tones in their reports increases with debt analysts’ negative discussions of conflict
events.
A large body of studies has examined the determinants and properties of equity analysts’
forecasts. Bhushan (1989) finds that equity analysts follow larger firms, firms with larger stock
return variability, firms that have fewer lines of business, and firms whose returns are morecorrelated to the market return. DeFond and Hung (2003) find that equity analysts’ cash flow
forecasts are more demanded by equity investors for firms with 1) larger accruals, 2) moreheterogeneous accounting choices, 3) higher past earnings volatility, 4) higher capital intensity,and 5) poorer financial health. The quality of equity analysts’ earnings forecasts has been8

extensively studied during the late 1970s and 1980s (for example, see Brown and Rozeff, 1978;Fried and Givoly, 1982; Brown, Griffin, Hagerman, and Zmijewski, 1987). Givoly, Hayn, andLehavy (2009) document that the quality of equity analysts’ cash flow forecasts does notsubstantially dominate the quality of time-series cash flows forecast models, arguing thatanalysts simply add depreciation and amortization to their earnings forecasts to derive their cash
flow forecasts. In contrast, others believe that equity analysts’ cash flow forecasts are not naïve
extensions from their earnings forecasts (Call, 2008; Call, Chen, and Tong, 2009, 2013; McInnis
and Collins, 2011). For example, Call et al. (2013) manually collect samples of full-text equity
analysts’ reports and find that they, indeed, explicitly forecasts working capitals in order to form
their cash flow forecasts.
Similar to equity analysts, bond analysts provide forecasts on firm’s future operating
performance, such as earnings, operating income (EBIT), earnings before interest, tax, anddepreciation and amortization (EBITDA), operating cash flows, or free cash flows. However,prior studies have paid little attention to bond analysts’ forecast behaviors despite the fact thatmost bond investors are institutional investors for whom cash flow and earnings forecasts are
particularly important information sources. Further, although bond analysts’ incentives to issue
cash flow and earnings forecasts are expected to be different from those of equity analysts, priorstudies have not compared forecast properties between bond analysts’ and equity analysts’ cash
flow or earnings forecasts. In the following section, I develop my hypotheses to examine theseissues.
2.3. Hypothesis development
Although both cash flow and earnings information is important to bond investors, thestability of future cash flow patterns is the first-order concern for bond investors because, unlike

9equities, the payment of debt securities (i.e., principal and interests) is fixed and their maturity is
finite. While earnings information is also important to bondholders for contracting purposes(Watts and Zimmerman, 1986; DeFond and Jiambalvo, 1994), bondholders’ wealth is moresensitive to cash flow information than to earnings information. I therefore posit that bondinvestors are more likely to demand cash flow information than for earnings information. 10
Meanwhile, earnings information is the most important source to equity investors for both
valuation (Ball and Brown, 1968; Dechow, 1994; Ohlson, 1995) and contracting purposes (Watts
and Zimmerman, 1986, Sloan, 1993) although cash flow information is incrementally valuable
information to stock investors (DeFond and Hung, 2003). Consequently, earnings are more likely
to be the primary interest to equity analysts. 11
Therefore, the relative emphasis on cash flows and earnings is likely to differ between
bond analysts and equity analysts. Specifically, I argue that cash flows are the primary concernof bond analysts whereas earnings are the main interest to equity analysts. Based on thesearguments, I advance the following hypothesis:
H1a: Ceteris Paribus, bond analysts are more likely to issue cash flow forecasts than equity
analysts.
H1b: Ceteris Paribus, equity analysts are more likely to issue earnings forecasts than bondanalysts.

A vast majority of prior research documents that equity analysts are optimistically biased.
Analysts have incentives to issue optimistic forecasts in order (1) to win lucrative investment
10

Consistent with this argument, McEnroe (1996) reports survey results that accounting professionals, such as
financial analysts, investment advisors, accounting professors and accountants, view debt investors as the main
beneficiaries of cash flow information. Edmonds et al. (2011) suggest that cash flow forecasts are important forbondholders.
11
I randomly selected 50 full-text reports in Investext and find that almost every equity report contains an earnings
forecast whereas about 60% of equity reports include cash flow forecasts, which is similar to the finding by Call(2008).
10

banking business (Lin and McNichols, 1998), (2) to retain access to management (Lim, 2001),
and (3) to increase equity trading commissions (Irvine, 2004). Consistent with this, De Franco etal. (2009) find that bond analysts more frequently issue buy recommendations than sell
recommendations. Hence, I expect that the distribution of bond analysts’ forecasts will be
skewed to the right as well. However, since bond investors, compared to equity investors, aremore concerned about downside risk, such as negative surprise of actual cash flows and earnings,
which could be caused by preceding inflated forecasts, I argue that potential benefits of issuing
optimistic forecasts are smaller for bond analysts than for equity analysts. 12 Based on thesearguments, I propose that bond analysts provide more conservative (i.e., less optimistic) forecasts
than equity analysts:
H2: Ceteris Paribus, bond analysts’ forecasts on cash flows and earnings are less optimistic
than equity analysts’ forecasts on cash flows and earnings, respectively.

If bond analysts, compared to equity analysts, tend to provide less optimistic cash flow
and earnings forecasts to serve the demand of bondholders (H2), the smaller optimistic bias
implies a more leftward pattern in the distribution of signed forecast errors (i.e., forecasted minus
actual cash flows or earnings) for bond analyst forecasts than for equity analyst forecasts. I notethat such a pattern can be composed of both (i) a smaller positive forecast error toward zero error
for bond analyst forecasts than for equity analyst forecasts and (ii) a larger negative forecast
error from (near) zero error toward negative error for bond analyst forecasts than for equity
analyst forecasts. In the former case, it is obvious that bond analyst forecasts are more accurate
12This conjecture is consistent with the finding by Easton et al. (2009), who provide evidence that bond investors
react more strongly to bad news than to good news and thus bond analysts are more sensitive to bad news thanequity analysts. In addition, although De Franco et al. (2009) document that bond analysts’ view is moreconservative than that of equity analysts using recommendations, it is not clear, a priori, whether bond analysts’
forecasts are less optimistic than equity analysts’ forecasts, given the fact that the degree of individual analysts’
optimism in recommendations and forecasts could differ (Malmendier and Shanthikumar, 2007, 2014; Mikhail et al.,2007).
11than equity analyst forecasts but in the latter case it is possible that bond analyst forecasts areless accurate than equity analyst forecasts.
In H1a and H1b, I argue that bond analysts, relative to equity analysts, are more likely toprovide cash flow forecasts than earnings forecasts to meet the strong demand of bondholders for
future cash flow information. Since cash flows are the key focus of their forecast activities(McEnroe, 1996; Edmonds, Edmonds, and Maher, 2011; Mangen, 2013), bond analysts arelikely to put considerable efforts in their research to provide high quality cash flow forecasts.
Therefore, I posit that bond analysts’ cash flow forecasts are more accurate than those of equity
analysts. In contrast, I expect earnings forecasts are less of concerns for bond analysts than forequity analysts. Based on this conjecture, I offer the following hypothesis:
H3: Ceteris Paribus, bond analysts’ forecasts on cash flows (earnings) are more (less) accurate
than equity analysts’ forecasts on cash flows (earnings).

To better understand why bond analysts per se would forecast cash flows and earnings, it
is important to identify the economic determinants that are likely to drive bond investors’
demand on both forecast information. Bond investors’ demand for information on future cash
flows will be stronger when a firm’s ability to pay its debt obligation is more doubtful. Since thestability of future cash flow patterns is a critical concern for bondholders, bond investors are
more likely to demand for cash flow information for firms with higher volatility in cash flows(i.e. firms with higher uncertainty in future cash flows). I also conjecture that firms underliquidity constraint will generate more concerns regarding the viability of the firm than otherfirms (Sundram and Yermack, 2007; Cassell, Huang, Sanchez, and Stuart, 2012), causing
stronger demand for cash flow forecasts.

12However, since accruals, along with cash flows, play a positive role in explaining future
performance of the firm (Dechow, 1994; Dechow, Kothari, and Watts, 1998; Barth, Cram, andNelson, 2001), some bond analysts will not only provide cash flow forecasts but also earnings
forecasts. In fact, Easton et al. (2009) provide evidence on bond market reaction to earnings
surprises around earnings announcements. I therefore argue that although bond analysts are lessconcerned about earnings relative to cash flows, earnings still provide relevant information abouta firm’s ability to generate future cash flows particularly when the volatility or uncertainty of its
accruals is high and when the firm is subject to lower or negative earnings performance. 13 Basedon these arguments, I advance the following hypothesis:
H4a: Ceteris Paribus, bond analysts are more likely to provide cash flow forecasts for firms a)with greater volatility of cash flows and b) under more severe liquidity constraint.
H4b: Ceteris Paribus, bond analysts are more likely to provide earnings forecasts along withcash flow forecasts for firms a) with greater volatility of accruals, and b) with lower earnings.

3. Research Design
3.1. Propensity to issue cash flow and earnings forecasts for bond analysts versus equity
analysts (H1a and H1b)
H1a (H1b) predicts that the probability to issue cash flow (earnings) forecasts is greater(smaller) for bond analysts than for equity analysts. In order to test this prediction, I estimate thefollowing logistic regression model for firms covered by bond analysts and equity analysts,
where year fixed effects are included and the standard errors are clustered by firm (Petersen,2009):
Prob (CF1_F it =1) = G (α 0 + α 1 BondDummy it-1 + α 2 CFOVOL it-1 + α 3 CFO it-1 + α 4 ABSACC it-1
+ α 5 CAPINT it-1 + α 6 ALTMAN it-1 + α 7 SIZE it-1 + ε it-1 )
(1-1)
13

In contrast, prior studies find equity analysts are more likely to cover firms with higher earnings performance
than those with lower earnings performance (Yu, 2008).
13

Prob (EARN_F it =1) = G (α´ 0 + α´ 1 BondDummy it-1 + α´ 2 EARNVOL it-1 + α´ 3 EARN it-1
+ α´ 4 SIZE it-1 + ε it-1 )(1-2)

All observations are represented at firm-year level and must have at least one annual cash
flow or earnings forecast available. All independent variables are measured in the year prior to
the forecast year. The dependent variable, CF1_F, is an indicator variable that equals one ifeither bond analysts or equity analysts issue at least one forecast on operating cash flows (CFO)or free cash flows (FCF) during the fiscal year, and zero otherwise. EARN_F equals one if either
bond analysts or equity analysts issue at least one forecast on earnings (i.e., income before
extraordinary items) during the fiscal year, and zero otherwise. The main variable of interest,
BondDummy, is an indicator variable, which equals one if the forecast is issued by bond analysts,
and zero otherwise (i.e. if the forecast is issued by equity analyst). In the context of H1a andH1b, I expect α 1 to be positive and α´ 1 to be negative, which indicate that, the probability of
issuing cash flow (earnings) forecasts is greater for bond (equity) analysts than for equity (bond)analysts.
In regression model (1-1), I control for the factors that are expected to affect analysts’
incentive to issue cash flow forecasts. 14 I first include a proxy for future cash flow uncertainty(CFOVOL), which is measured as the firm-specific standard deviation of the operating cash
flows divided by average assets. Previous seven years are used to calculate the standard
deviation with the minimum requirement of three years. CFO, a proxy for degree of the liquidity
constraint, is also controlled, calculated as operating cash flows divided by average total assets. I
expect α 2 to be positive and α 3 to be negative, indicating that when the uncertainty about cash

14

Later, bond analysts’ incentive to issue cash flow forecasts is formally advanced as a hypothesis in H4a.
14

flows is high and when the liquidity constraint is severe, the probability of analysts issuing cash
flow forecasts is higher.
Next, I control for the determinants of analysts’ provision of a cash flow forecast
following the previous literature (e.g., DeFond and Hung, 2003), which shows that equity
analysts are more likely to issue cash flows forecasts for firms with (1) high earnings volatility15,(2) large absolute accruals, (3) high capital intensity, (4) poor financial health, and (5)heterogeneous accounting choices relative to their industry peers 16, consistent with the notionthat analysts are providing additional value-relevant information particularly when earnings areless useful in stock valuations. ABSACC is defined as the absolute value of earnings minus
operating cash flows divided by average total assets. Earnings are income before extraordinary
items. CAPINT is gross property, plant, and equipment (Gross PP&E) divided by total sales in
the previous year. Following Altman (1968), ALTMAN = 1.2 (Net working capital / Total Assets)+ 1.4 (Retained earnings / Total Assets) + 3.3 (Earnings before interest and taxes / Total Assets)+ 0.6 (Market value of equity / Book value of liabilities) + 1.0 (Sales / Total Assets). Consistent
with DeFond and Hung (2003), α 4 and α 5 , are expected to be positive, while α 6 is expected to be
negative. Prior literature finds that firm size (SIZE) proxies for firm risk and information
environment. SIZE is calculated as the natural logarithm of the equity market capitalization.
In regression model (1-2), I control for variables that are expected to affect analysts’
incentive to issue earnings forecasts. 17 EARNVOL, a proxy for uncertainty of earnings
performance, is the firm-specific standard deviation of the earnings before extraordinary items
I drop EARNVOL in model (1-1) since the correlation between EARNVOL and CFOVOL is high (ρ = 0.35).
Nevertheless, when I include EARNVOL, the coefficient on BondDummy is still positive (coeff. = 0.443, t-stat =2.65).
16
Accounting choice heterogeneity is the relative difference between the firm and industry group firms with respectto 1) inventory valuation method, 2) investment tax credit, 3) depreciation, 4) successful-efforts vs. full-cost for oil
and gas companies, and 5) purchase vs. pooling method for M&A firms. To maintain a reasonable sample size, I do
not control for the accounting choice heterogeneity in the main regression.
17
Again, bond analysts’ incentive to issue earnings forecasts is formally advanced as a hypothesis in H4b.
15

15

divided by average assets. Previous seven years are used to calculate the standard deviation withthe minimum requirement of three years. EARN, a proxy for earnings performance, is calculated
as earnings before extraordinary items divided by average total assets. I expect α´ 2 to be positiveand α´ 3 to be negative, indicating that when the uncertainty about earnings is high and whenearnings performance is low, the probability of bond analysts issuing earnings forecasts ishigher.
3.2. The bias in cash flow and earnings forecasts for bond analysts versus equity analysts(H2) 18
To compare the degree of forecast bias between bond and equity analysts’ forecasts, I
calculate the bias as the signed difference between per-share forecasted and actual values,
deflated by the actual value.
Bias it = (F it - A it ) / |A it |(2)

A it is the actual value per share for firm i and year t 19, and F it is the (consensus) forecasted value
per share for firm i and year t. The performance measure in this analysis is restricted to operating
cash flows (CFO), EBITDA, and earnings before extraordinary items (EARN). Although bondanalysts provide forecasts of free cash flows (FCF) and earnings before interest and tax (EBIT),
these forecast items are not available for equity analysts on I/B/E/S. I thus do not use these
forecasts in bias and accuracy (next section) analyses.
To calculate each of bond and equity analysts’ consensus forecasts, I use the most recent
forecast for each analyst for each firm-year. I then calculate the median consensus forecasts for18

It is possible that bond analysts’ forecasts are simply mimicking equity analysts’ forecasts. However, this is not an
issue because based on randomly selected full-text bond and equity analysts’ reports I observe that earnings and/or
cash flow forecasts issued on the same date by the same investment bank differ between bond and equity analysts(See Appendix B).
19
I obtain qualitatively similar results when it is deflated by beginning-of-period or end-of-period price per share,
which is discussed in section 5.4.2.
16each firm-year, separately for bond analysts and equity analysts. 20 I first hand-collect the actual
value from individual bond reports. If the actual value is missing in bond reports, then the actual
value from I/B/E/S (for each CFO, EBITDA, or EARN) is used instead. 21 Next I compare the biasof forecasts between different groups (i.e., bond analysts versus equity analysts). This procedureensures that the forecast error difference between bond analysts and equity analysts are not due
to any alternative definition of actual or forecast values but to their different forecasting abilities(see Bradshaw and Sloan, 2002; Bhattacharya, Black, Christensen, and Larson,, 2003;
Abarbanell and Lehavy, 2007).
I test H2 with the following regression models, with year fixed effects included andstandard errors clustered by firm:
Bias_CFOF it = β 0 + β 1 (BondDummy) it + β 2 (CFOVOL) it + β 3 (CFO) it + β 4 (HORIZON) it+ β 5 (SIZE) it + β 6 (BM) it + β 7 (LEV) it + ε it
(3-1)
Bias_EBITDAF it = β´ 0 + β´ 1 (BondDummy) it + β´ 2 (EBITDAVOL) it + β´ 3 (EBITDA) it+ β´ 4 (HORIZON) it + β´ 5 (SIZE) it + β´ 6 (BM) it + β´ 7 (LEV) it + ε´ it(3-2)
Bias_EARNF it = β´´ 0 + β´´ 1 (BondDummy) it + β´´ 2 (EARNVOL) it + β´´ 3 (EARN) it+ β´´ 4 (HORIZON) it + β´´ 5 (SIZE) it + β´´ 6 (BM) it + β´´ 7 (LEV) it + ε´´ it(3-3)
In the above three equations, the dependent variables are forecast bias (Bias) for CFO(Bias_CFOF), EBITDA (Bias_EBITDAF), and EARN (Bias_EARNF), respectively. The
variable of interest in each of the three equations, BondDummy, is an indicator variable, which

20

I use the median consensus forecasts because Abarbanell and Lehavy (2003) find that using mean forecast errors
may lead to incorrect inference due to extreme forecast errors. Nonetheless, using either mean consensus forecasts
or the last forecasts does not alter my inferences, and the results are reported in Section 5.4.2.
21
The actual and forecast value collected from bond reports are in total amount (million). Since I/B/E/S data are allin per share value, I deflate the value in bond data by the number of primary or diluted shares in Compustat, whichfacilitates the comparison between bond and equity analysts’ values (Livnat and Mendenhall, 2006).
17equals one if the forecast is issued by the bond analyst, and zero otherwise (i.e. if the forecast isissued by equity analyst). Since Bias is the signed forecast error, a positive value of Bias meansthat the analyst forecast is optimistic, and a negative value of Bias represents that it ispessimistic. I conjecture that bond (equity) analyst forecasts are less (more) optimistic thanequity (bond) analyst forecasts for both cash flows and earnings. Accordingly, I expect β 1 , β´ 1 ,and β´´ 1 to be negative.
Note that I define cash flows in a broader definition which includes EBITDA as well as
CFO. EBITDA differs from earnings and operating cash flows because it eliminates the effectsof financing and accounting decisions by excluding payments for taxes or interest as well as
capital expenditures and depreciation. EBITDA also differs from free cash flows because it
excludes cash requirements for replacing capital assets. However, since 1) EBITDA was used toindicate the ability of a company to service debt, 2) the amount of depreciation and amortization
expenses comprises a substantial amount of total accruals (Dechow, 1994), and 3) the correlation(ρ = 0.757) between bond analysts’ EBITDA and Compustat CFO (“OANCF”) is significantly
greater than the correlation (ρ = 0.277) between bond analysts’ EBITDA and Compustat EARN(“IB”), EBITDA is considered cash flows rather than earnings, particularly for bondholders andbond analysts. 22,23
HORIZON is defined as the number of days between the earnings announcement date andthe forecast issuance date. Since analysts’ optimistic forecasts are walked down by the
management (Bartov, Givoly, and Hayn, 2002; Matsumoto, 2002; Cotter, Tuna, and Wysocki,
22

Historically, EBITDA first came into common use with leveraged buyouts (LBO) in the 1980s. In LBOs, the key
factor is cash generated by the firm prior to discretionary expenditures, as it is this cash that the buyer will use to pay
off the loans he or she used to purchase the firm, and EBITDA is the measure of cash flows from operations that canbe used to support debt payment at least in the short term.
23
In section 5.4.1., I use only the cases where EBITDA is not merely including taxes, interest, and depreciation andamortization but involving explicitly adjusting working capitals, and the results are qualitatively similar (See table11).
18

2006; Kross, Ro, and Suk, 2011; Kwak, Ro, and Suk, 2012), the coefficient on HORIZON is
expected to be positive. I also control for the volatility and the level of cash flows (or earnings).
CFOVOL (CFO), EBITDAVOL (EBITDA), and EARNVOL (EARN) are included in equations (31) – (3-3), respectively. Finally, BM and LEV are controlled for in the regression models. BM is
calculated as book value of equity divided by market value of equity. LEV equals the book valueof debt divided by the book value of equity. The rest of the variables are defined in the same way
as in regression models (1-1) and (1-2).
3.3. The accuracy of cash flow and earnings forecasts between bond analysts versus equity
analysts (H3)
In order to compare forecast accuracy between bond and equity analysts’ cash flow
forecasts and earnings forecasts, I calculate forecast accuracy by multiplying forecast error (i.e.,
the absolute value of forecast bias) by -1:
Accuracy it = (-1) × |F it - A it | / |A it |(4)

A it and F it are defined as in equation (2). In order to test H3, I estimate the following regressions
similar to those in Section 3.2:
Accu_CFOF it = γ 0 + γ 1 (BondDummy) it + γ 2 (CFOVOL) it + γ 3 (CFO) it + γ 4 (HORIZON) it+ γ 5 (SIZE) it + γ 6 (BM) it + γ 7 (LEV) it + ε it
(5-1)
Accu_EBITDAF it = γ´ 0 + γ´ 1 (BondDummy) it + γ´ 2 (EBITDAVOL) it + γ´ 3 (EBITDA) it+ γ´ 4 (HORIZON) it + γ´ 5 (SIZE) it + γ´ 6 (BM) it + γ´ 7 (LEV) it + ε´ it(5-2)
Accu_EARNF it = γ´´ 0 + γ´´ 1 (BondDummy) it + γ´´ 2 (EARNVOL) it + γ´´ 3 (EARN) it+ γ´´ 4 (HORIZON) it + γ´´ 5 (SIZE) it + γ´´ 6 (BM) it + γ´´ 7 (LEV) it + ε´´ it
(5-3)

19

The dependent variables are forecast accuracy (Accuracy) for CFO (Accu_CFOF),
EBITDA (Accu_EBITDAF), and EARN (Accu_EARNF), respectively. Because I multiply all theforecast accuracy measures by -1, a larger value indicates a more accurate forecast, and asmaller value indicates a less accurate forecast. Following H3, I expect both γ 1 and γ´ 1 to bepositive, and γ´´ 1 to be negative, indicating that bond (equity) analysts are more accurate
forecasting cash flows (earnings). 24
The rest of the variables are defined as in equations (3-1) – (3-3). I include CFOVOL,EBITDAVOL, and EARNVOL in equations (5-1) – (5-3), respectively, to control for analysts’
inherent difficulty in forecasting firm’s cash flows or earnings because the dependent variablesin the three models represent different performance measures. The coefficients on CFOVOL,EBITDAVOL, and EARNVOL are predicted to be negative, suggesting that, as the uncertainty of
future performance is greater for either cash flows or earnings, analysts’ forecasting abilities
deteriorate. I also include HORIZON, the number of days between analysts’ forecast date and theactual announcement date for the firm followed by the analyst, in order to control for the age ofeach forecast. Since analysts’ forecasting abilities improve as their forecast dates become closer
to actual earnings announcement dates (Brown, 2001), the coefficient on HORIZON is expected
to be negative. SIZE is expected to be positively associated with the accuracy because a greater
size indicates a better informational environment. Finally, BM and LEV are controlled for in the
regression models and expected to be negatively related to analysts’ accuracy because they may
represent for the inherent firm risk.
3.4. Determinants of bond analysts’ cash flow and earnings forecasts provision (H4a and
H4b)

24

Note, however, that in table 6 (univariate analysis) and figure 1 (intra-year change in forecasts), accuracy is not
multiplied by -1.
20In order to test H4a and H4b, I estimate the following logistic regression models only forfirms covered by bond analysts, including year fixed effects and cluster-adjusted standard errors:
Prob (CF_F it =1) = G (δ 0 + δ 1 CFOVOL it-1 + δ 2 CFO it-1 + δ 3 LEV it-1 + δ 4 EARNVOL it-1
+ δ 5 ABSACC it-1 + δ 6 CAPINT it-1 + δ 7 ALTMAN it-1 + δ 8 SIZE it-1
+ δ 9 MB it-1 + δ 10 DSIZE it-1 + δ 11 IMR it-1 + ε it-1 )(6-1)
Prob (EARN_F it =1) = G (δ´ 0 + δ´ 1 ACCVOL it-1 + δ´ 2 EARN it-1 + δ´ 3 ALTMAN it-1
+ δ´ 4 SIZE it-1 + δ´ 5 MB it-1 + δ´ 6 LEV it-1 + δ´ 7 DSIZE it-1
+ δ´ 8 IMR it-1 + ε it-1 )(6-2)

All observations are represented by firm-year level variables, and all independent
variables are measured in the year prior to the forecast year. I identify bond analysts’ issuance of
cash flow forecasts in two different ways. If bond analysts issue any forecast of operating cash
flows (CFO) or free cash flows (FCF) then CF1_F equals one, and zero otherwise.
Alternatively, CF2_F equals one if bond analysts provide CFO, FCF, or earnings before
interests, taxes, depreciation, and amortization (EBITDA) for firm i and year t, and zerootherwise, where cash flows are in a broader manner.
In regression model (6-1), the variables of my interest are CFOVOL and CFO. In thecontext of H4a, I expect δ 1 to be positive and δ 2 to be negative, which indicate that, as thevolatility of cash flows and the liquidity constraint are greater, the probability of bond analysts
issuing cash flow forecasts is higher.
I first control for the agency cost of debt because the presence of a conflict between
shareholders and bondholders (Jensen and Meckling, 1976) induces the agency cost of debt inwhich bondholders, who are typically interested in a less risky investment, may want to place
restrictions on the use of their money to reduce their risk. Bondholders with greater agency costsof debt are therefore more likely to demand information about future cash flows. Because firms
21with a higher leverage (LEV) place restrictions on the use of their money, these firms face greater
agency costs of debt and thus are more likely to demand information about future cash flows. I
expect δ 3 to be positive, indicating that as the agency cost of debt is the greater, the probability ofbond analysts issuing cash flow forecasts is higher. Similar to regression model (1-1), I control
for the determinants (i.e., EARNVOL, ABSACC, CAPINT, ALTMAN, and SIZE) of equity
analysts’ provision of a cash flow forecast following the previous literature (i.e., DeFond andHung, 2003). Next, following Johnston et al. (2009), I control for market-to-book ratio and debt
size. 25 MB equals equity market value divided by equity book value, which proxies for either 1)
growth opportunity or 2) financial distress as noted in Johnston et al. (2009). Therefore, the signof coefficient on MB (δ 9 ) is not unambiguous a priori. DSIZE is the natural logarithm of bookvalue of debt. Consistent with Johnston et al. (2009), δ 10 is expected to be positive. 26
Finally, since my sample on bond analysts’ debt reports collected from Investext may not
represent the (unobservable) population, relying solely on Investext as the source of debt
analysts’ reports is not completely free from the selection bias, although Investext is the only
source available for debt analysts’ information. This may be due to the possibility that debt
analysts have less incentive to provide their reports to Investext than equity analysts (Ronan,
2006; Gurun et al., 2011). To mitigate any potential sample selection bias, I estimate a probit
regression designed to capture the determinants of bond analysts’ reports. Specifically, I regressbond analysts’ probability to issue bond reports on determinants identified from Johnston et al.
(2009), such as SIZE, MB, LEV, INTCOV, and DSIZE. From this probit estimation, I calculate an
Inverse Mills Ratio (IMR) and then include this ratio in equation (6-1) and later in equation (6-2).
25

If interest coverage is included, instead of ALTMAN, to control for the financial distress, the coefficient on
INTCOV is negative and statistically significant at the 1% level (coeff.=-0.665, z-stat = -3.14).
26
Although individual bond analyst characteristics (e.g., experience, available resource, complexity, etc.) could
affect the decision to issue forecasts (Clement, 1999), the limitation on time-series and cross-sectional data does notallow me to calculate bond analysts’ characteristics in a meaningful way.
22

In regression model (6-2), the dependent variable, EARN_F, is an indicator variablewhich equals one if bond analysts forecast earnings (income before extraordinary items), andzero otherwise. All firm-year observations must have at least one cash flow forecast.
Corresponding to equation (6-1), I separately estimate equation (6-2) either when the sample isbased on CF1_F = 1 or when CF2_F = 1. The variables of interest are ACCVOL and EARN.27
ACCVOL is the firm-specific standard deviation of the total accruals divided by average assets.
Total accruals are calculated as the difference between income before extraordinary items andoperating cash flows. Previous seven historical years are used to calculate the standard deviation
with the minimum requirement of three years. Given H4b, I predict that the coefficient on
ACCVOL, δ´ 1 , to be positive and the coefficient on EARN, δ´ 2 , to be negative, which indicatesthat bond analysts are more likely to provide earnings forecasts when the accruals are highly
volatile and when earnings are low. The rest of the variables are the same as defined previously.
4. Sample and Descriptive Statistics
4.1. Sample
Due to the lack of sell-side corporate debt reports on an archived database, I manually
collect debt analysts’ reports from Investext, a provider of full-text bond analysts’ reports. 28 The
sample period for bond analysts’ reports spans from January 2001 to December 2010. The
beginning year, 2001, was chosen to mitigate the effect of structural changes in analysts’
forecasting behaviors driven by Reg FD (Kross and Suk, 2012), implemented in October, 2000.
Table 1 summarizes the sample selection process for bond reports. I search for debt
analysts’ reports using the following three criteria: 1) the asset class must be the fixed income, 2)Instead of EARN, I alternately use (i) ∆EARN, the change in income before extraordinary item (deflated byaverage total assets, or (ii) LOSS, a dummy variable which equals one if EARN is negative and zero otherwise. The(untabulated) results are qualitatively similar.
28
Investext is now available in Thomson ONE Banker. Investext also provides full-text equity analysts’ reports.
27

23

analysts’ reports must be issued within the “North America” region, and 3) industrial-,
geographic-, or macroeconomic-level research reports are excluded, thus only company-level
reports are included in my sample. I also eliminate debt reports 1) for non-US firms, 2) for closeend funds, convertible bonds, or derivatives, 3) covering multiple companies within two days
(see De Franco et al., 2009), 4) issued by equity analysts, and 5) issued by credit rating agencies,
either certified (e.g. Moody’s, Fitch) or non-certified (e.g. Morning Star, Rapid Ratings). After
this initial screening process, I obtain 10,660 corporate bond reports, from which I collect issue
dates of the reports, names of bond analysts, names of brokerage firms, names of firms followed,
and the actual and forecasted earnings and/or cash flows, if available. 29 Then I manually matchthe names of the firms in bond reports with those of COMPUSTAT files. The final sample fortesting H1a (H1b) includes 946 (997) firm-year observations for 392 (411) unique firms.
Equity analysts’ actual and forecast values are collected from I/B/E/S Unadjusted
Detail and Actuals Files, and company’s financial variables are obtained from Compustat North
America Fundamentals Annual File.
4.2. Descriptive statistics
Table 2 presents descriptive statistics on the characteristics of firms covered by bondanalysts in my initial sample. In panel A, the number of bond reports, the number of firms, thenumber of annual forecasts, and quarterly forecasts are tabulated by fiscal year. On average,bond analysts issue 1,066 reports and cover approximately 360 firms every year. The coverage ofbond analysts has slightly decreased after 2005, with an exception of 2008 (i.e., 1,234 bond29

The number of debt reports is 8,009 (between 1999 and 2004) in Johnston et al. (2009), and 28,378 (between 2002
and 2006) in De Franco et al. (2009). My conversations and correspondences with the authors of these papers andthe researchers of Thomson ONE Banker indicate that a significant number of banks removed their own reports fromthe database. However, I do not see any priori reason that this selection issue would systematically bias myempirical findings.
24

reports). 30 The sudden increase in the bond analyst coverage in 2008 may reflect the recent credit
crisis, which has triggered bond investors to demand more information on credit quality. Table 2,
panel A also shows that the frequency of bond analysts’ annual forecasts (3,863) is greater thanthat of their quarterly forecasts (2,378).
In panel B of table 2, I also compare the distribution of characteristics of firms coveredby bond and firms covered by equity analysts. I search for all firms between 2001 and 2010 andcalculate the firm characteristics at the firm-year level. Firms covered by bond analysts are larger
than those followed by equity analysts. The average market value for BA firms is $10.61 billion
whereas the average market value for EA firms is $5.50 billion. Consistent with Johnston et al.
(2009), BA firms have higher leverage, lower interest coverage, larger debt size, and lower
altman Z-score. In this full sample, BA firms are easier to forecast either cash flows or earnings
than EA firms, since (historical) volatility of earnings, accruals, and operating cash flows are
smaller for BA firms. The mean and median differences are all statistically significant at the 1%
level. Therefore, it is important to control for the difference in inherent operating uncertainty
between BA and EA firms when comparing the bias and accuracy between both analysts. Recall
that I identify BA and EA samples based on propensity score matching technique. Panel C
reports the firm characteristics difference based on the PS matched sample used for testing H1a.
The mean (median) difference in all variables between BA and EA firms are all statisticallyinsignificant, alleviating the concern that any difference in firm characteristics might drive mymain results. Although I do not report descriptive statistics for other PS matched samples, forbrevity, the results are similar to panel C.

30

This is consistent with the conjecture that Trade Reporting and Compliance Engine (TRACE), which was introduc
ed and expanded in 2005, acts as a substitute for debt research, thus possibly reduced the informational role of bondanalysts (Bessembinder and Maxwell, 2008).
25

Table 3 presents descriptive statistics on bond analysts and their forecasts in my initialsample. 31 Panel A indicates that the most active brokerage firms issuing corporate bond reports
are Deutsche Bank (38.81%), Bear Sterns and Co. Inc (11.78%), CIBC World Market Corp
(11.46%), Morgan Stanley (7.81%), and JP Morgan (5.58%), comprising about 75.44% of mysample. 559 unique bond analysts are employed by brokerage houses. However, (untabulated)
evidence reveals that more than half of the bond reports are issued by a team that comprises
multiple sell-side bond analysts. 32
Panel B reports the distribution of the number of analysts per firm and the number offirms covered per analyst. I compare the distribution between bond analysts and equity analysts.
The number of bond analysts per firm is measured in the individual basis while the number offirms covered by each bond analyst is measured in the team basis. The bond analyst coverage ismeasured based on the provision of bond analysts’ debt reports because many bond analysts do
not necessarily issue forecasts on cash flows or earnings, whereas the equity analyst coverage iscalculated based on the issuance of earnings forecasts since most equity analysts issue earnings
forecasts. Each firm appears to be followed by equity analysts approximately 8 (mediancomparison) to 10 (mean comparison) times more than by bond analysts. Also, the mean
(median) number of firms that each bond analyst covers in a fiscal year is 6 (10) while it is 11(32) for each equity analyst. This suggests that equity analysts are substantially more active
covering and analyzing a firm, which is not surprising given the prior finding that bond market isless liquid.

31

Since this study provides, for the first time, evidence on bond analysts’ forecasts, I provide detailed descriptive
information regarding the distribution of bond analysts and their forecasts in Table 3.
32
Later in this paper, I find that bond analysts are more accurate in forecasting cash flows than equity analysts. One
may argue that this superior forecasting ability is primarily driven by the fact that many bond analysts forecast cash
flows and earnings as a team. However, Brown and Hugon (2009) document evidence that team forecasters do notnecessarily outperform individual forecasters.
26

Panel C of Table 3 contains frequencies of bond analysts’ forecast items and thepercentage of each item out of total forecasts and total debt reports. Of total forecasts, bondanalysts are more likely to provide forecasts for EBITDA (98.45%), free cash flows (76.29%), or
operating cash flows (20.32%) than for earnings (15.92%), whereas equity analysts usuallyprovide forecasts for earnings than for EBITDA and free cash flows. Similarly, of total debt
reports bond analysts provide forecasts more frequently for EBITDA (35.68%) and/or free cash
flows (27.65%), or operating cash flows (7.36%) than for earnings (5.77%). This suggests thatbond analysts are more likely to provide the information on future cash flows from operations
that can be used to support debt payment than the information about future earnings. Although
not reported, unlike equity analysts bond analysts also issue forecasts on interest coverage andleverage. Finally, more than half of the forecasts (59.51%) have a horizon of shorter than a year
(Panel D), which indicates that bond investors have shorter-term perspectives than equity
investors. 33
5. Results
5.1. Results on the propensity to issue cash flow and earnings forecasts between bond analyst
and equity analysts.
Since the firms covered by bond and equity analysts in the sample could be inherently
different, it is possible that any (unobservable) firm characteristics drive the difference in theprobability to issue forecast. Considering this, I construct two different samples to test H1a andH1b. The first sample consists of firm-year observations where both bond analysts and equity
analysts issue at least one cash flow or earnings forecast for the same firm and same year: the“Exact Matching” sample. The second sample augments the first sample by adding matched-pair
33

Unlike equity analysts who emphasize the ‘long-term’ growth as well as short-term earnings, many bond analysts
do not discuss ‘long-term’ growth in their reports.
27

observations which are obtained using a propensity-score matching method: the “PS Matching”
sample. To the additional matched-pair observations, firm-year observations where bond analysts
issued at least one cash flow or earnings forecast but any equity analyst do not cover the firm
year are matched with the matched counterparts, which are selected among firm-year
observations where equity analysts issued at least one cash flow or earnings forecast but any
bond analyst do not cover the firm year, by ensuring that the propensity of bond analysts andequity analysts to issue the forecast is closest. Specifically, I estimate the following probit model:
Prob (BondDummy=1) it = G (θ 0 + θ 1 SIZE it-1 + θ 2 MB it-1 + θ 3 LEV it-1 + θ 4 INTCOV it-1
+ θ 5 DSIZE it-1 + ε it )(7)
A binary dependent variable, BondDummy, equals one if a cash flow (earnings) forecast
is issued by bond analysts, and zero if not (i.e., issued by equity analysts). The explanatory
variables, SIZE, MB, LEV, INTCOV, and DSIZE are included following the determinant model inJohnston et al. (2009). All coefficients are statistically significant and of the hypothesized sign as
in Johnston et al. (2009). Based on these estimates, I employ a “nearest neighbor” matchingprocedure without replacement such that each of bond observation is matched with an equity
observation having the closest propensity score for BondDummy. Since I match on the propensity
score only, the “matched” pairs can be from different years and industries.
Table 4 reports the univariate analysis on the difference between bond and equity
analysts in the probability of issuing cash flow and earnings forecasts. Panels A and B show thefrequency difference in forecasting cash flows for the exact matching sample and for the PS
matching sample, respectively, whereas Panel C and D separately show the frequency differencein forecasting earnings for the two samples. There are 946 (997) firm-year observations where28both bond and equity analysts provided at least one annual cash flow (earnings) forecast. 34 The
propensity score matching procedure generated additional 179 (192) matched-pair firm-year
observations.
The results in panels A and B reveal that bond analysts are more likely to provide cash
flow forecasts than equity analysts. Panel A indicates that 80.4% (= 761/946) of bond analysts
provide cash flow forecasts whereas only 73.8% (= 699/946) of equity analysts provide earnings
forecasts for the exact sample. Panel B also shows that 80.5% (= 906/1,125) of bond analysts
provide cash flow forecasts while 32.0% (= 791/1,125) of equity analysts do so for the PS
matched sample. Further, panel A shows that the frequency (n = 185) where bond analysts issue
cash flow forecasts and equity analysts do not is higher than the frequency (n = 123) where
equity analysts issue cash flow forecasts and bond analysts do not (McNemar test statistics =12.48, p-value < 0.001). Similarly, panel B also indicates bond analysts issue cash flow forecasts
absent equity analysts’ cash flow forecasts (n = 251) more often than equity analysts issue cash
flow forecasts absent bond analysts’ cash flow forecasts (n = 136). However, for earnings
forecasts the results are quite the opposite. Almost all equity analysts provide earnings forecasts
whereas only 32.5% (= 324/997) of bond analysts provide earnings forecasts for the exact
sample as shown in panel C and 32.0% (= 381/1,189) for the PS matched sample as seen in panel
D. The off-diagonal frequency differences are statistically significant in both panels C and D.
The logistic regression results are reported in Table 5. The variable of my interest is
BondDummy. A positive coefficient on BondDummy where the dependent variable is CF1_F
indicates that bond analysts, relative to equity analysts, are more likely to provide cash flow
forecasts. On the other hand, a negative coefficient on BondDummy where the dependent

34

The slight difference in sample size is due to the restriction that the cash flow (earnings) forecast analysis requires
no missing values of control variables such as CFOVOL and CFO (EARNVOL and EARN).
29

variable is EARN_F suggests that equity analysts, relative to bond analysts, are more likely toprovide earnings forecasts. For both cases, I estimate the models alternately with and without
control variables and present the results separately in order to provide some assurance thatmulticollinearity is not driving the overall results. In column (1) – (4), BondDummy is all
positive and statistically significant at the 1% level. For example, column (2) shows that thecoefficient on BondDummy is 0.443 (t-statistic = 2.65), which is supportive of H1a. In contrast,
BondDummy is negatively associated with EARN_F (e.g., coefficient = -8,073, t-stat = -7.83 incolumn (6)), consistent with H1b. In sum, both table 4 and 5 provide strong evidence that therelative importance of cash flow (earnings) information is greater (smaller) for bond analysts
than for equity analysts.
5.2. Comparison results for forecast bias and accuracy between bond and equity analysts
5.2.1. Univairate Analysis
Similar to the analysis in section 5.1., I first identify observations where both bond andequity analysts issue at least one forecast for the same firm and same year (“Exact Matching”).
Then I augment the sample by including additional matched-pair observations using propensity
scores (“PS Matching”) for firm-year observations where bond analysts issued annual cash flowor earnings forecasts but are not exactly matched with equity analyst sample. 35 Using a binary
dependent variable that equals one if a cash flow (or earnings) forecast is issued, and zero if not,I estimate the following probit models:
Prob (CFO_F=1) it = G (λ 0 + λ 1 SIZE it-1 + λ 2 MB it-1 + λ 3 LEV it-1 + λ 4 INTCOV it-1
+ λ 5 DSIZE it-1 + λ 6 HORIZON it-1 + λ 7 CFOVOL it-1 + ε it )
(8-1)

35

The number of exactly matched firm-year observations for CFO, EBITDA, and EARN is 146, 163, and 248,
respectively. The number of firm-year observations included by propensity score matching procedure for CFO,
EBITDA, and EARN is 65, 590, and 13, respectively.
30

Prob (EBITDA_F=1) it = G (λ´ 0 + λ´ 1 SIZE it-1 + λ´ 2 MB it-1 + λ´ 3 LEV it-1 + λ´ 4 INTCOV it-1
+ λ´ 5 DSIZE it-1 + λ´ 6 HORIZON it-1 + λ´ 7 EBITDAVOL it-1 + ε´ it )(8-2)
Prob (EARN_F=1) it = G (λ´´ 0 + λ´´ 1 SIZE it-1 + λ´´ 2 MB it-1 + λ´´ 3 LEV it-1 + λ´´ 4 INTCOV it-1
+ λ´´ 5 DSIZE it-1 + λ´´ 6 HORIZON it-1 + λ´´ 7 EARNVOL it-1 + ε´´ it )(8-3)
SIZE, MB, LEV, INTCOV, and DSIZE are included and defined as the same way in model(7). I augment the model by including HORIZON and CFOVOL (EBITDAVOL or EARNVOL)
because my focus is on the comparison of the forecast quality between bond and equity analysts.
Table 6 reports the univariate analysis on the difference in forecast bias and error
between bond and equity analysts. 36 Panels A, B, and C show the mean difference in forecastingbias and accuracy for CFO, EBITDA, and EARN, respectively. 37 In each panel, I compare themean difference between bond and equity analysts, separately, for the full and the matchedsamples. The results in panels A and B reveal that bond analysts are providing cash flow
forecasts in a more conservative manner than equity analysts. Specifically, panel A shows thatthe mean difference in CFO bias is 0.298 (t-statistic = 4.14) for the full sample and 0.588 (tstatistic = 2.71) for the matched sample. Similarly, panel B indicates that the mean difference in
EBITDA bias is 0.187 (t-statistic = 9.75) for the full sample and 0.225 (t-statistic = 10.58) for thematched sample. Panel C presents comparison results of forecast bias in earnings between bondand equity analysts. The results in both full and matched samples show that bond analysts’
earnings forecasts are also less optimistic than equity analysts’ forecasts (mean difference =36

Note that in Table 6, accuracy is not multiplied by -1, thus a greater value of error implies lower quality offorecast accuracy.
37
Using Wilcoxon rank sum test produces qualitatively similar (untabulated) results. In addition, χ2 test also
confirms that the frequency where bond analysts are 1) more conservative or 2) more (less) accurate forecasting cash
flows (earnings) than equity analysts is greater than the frequency for the opposite case.
31

0.225 or 0.337). Overall, CFO, EBITDA, and EARN forecasts are less optimistic for bondanalysts than for equity analysts, which supports H2.
Next, consistent with H3, panels A and B of Table 6 indicate that bond analysts are moreaccurate forecasting CFO and EBITDA than equity analysts. For example, panel A show that themean difference in CFO forecast error between bond and equity analysts is 0.191 (t-statistic =2.87) in the full sample and 0.753 (t-statistic = 3.12) in the matched sample. As a supplementary
test, I compare the CFO forecast error between analyst forecasts and forecast based on a timeseries model, which was proposed by Barth, Cram, and Nelson (2001) and used by Givoly et al.
(2009) and Call et al. (2013). 38 (Untabulated) results document that both bond and equity analyst
forecasts are more accurate than forecast based on a time-series CFO model. In the matchedsample, CFO forecast error of the time-series model is 1.866 (1.629) larger than bond (equity)
analysts’ CFO forecast accuracy and the difference is statistically significant at 1% level. More
importantly, the difference-in-difference average error between bond and equity analysts, is0.237 (=1.866 – 1.629), implying that bond analysts are more accurate than equity analysts withrespect to forecasting CFO. Finally, panel C presents comparison results of forecast error inearnings between bond and equity analysts. The results in both full and matched samples showthat bond analysts’ earnings forecasts are less accurate than equity analysts’ forecasts (meanerror difference = -1.279 or -0.883), which is supportive of H3.
I plot the intra-year change in both forecast bias and error for bond and equity analysts in
Figure 1. For each firm-year, I keep the earliest forecast issued after the previous year’s earnings
announcement and the last forecast issued before the current year’s earnings announcement. All
The predicted operating cash flows are estimated from the following time-series model: CFOit = λ1 + λ2CFOit-1 +λ3∆ARit-1 + λ4∆INVit-1 + λ5∆APit-1 + λ6DEPit-1 + λ7Otherit-1 + εit. All variables are scaled by average assets. I estimate
the model for each industry and year with a requirement of at least 20 observations available. Then I calculate thetime-series CFO forecast accuracy with the absolute difference between the predicted operating cash flows and theactual operating cash flows, deflated by the absolute value of actual operating cash flows.

38

32

firm-year observations of forecast bias and error are used in this analysis and all firms must haveat least two forecasts by both bond and equity analysts within the fiscal period. Panels A, B, andC graph forecast bias and errors of CFO, EBITDA, and EARN, respectively. There are several
observations to note. First, forecast optimism for both bond and equity analysts is lessened
throughout the year (Bartov et al., 2002; Cotter et al., 2006; Kross and Suk, 2012), and forecast
accuracy improves toward the actual earnings announcement day (Brown, 2001; Kwak et al.,2012). Second, except for the first forecast in earnings, for all three performance measures (i.e.,
CFO, EBITDA, and EARN) bond analysts exhibit less forecast optimism compared to equity
analysts, which is additional evidence supportive of H2. Third, equity analysts are better (worse)
earnings (cash flow) forecasters than bond analysts (H3), as shown in panel A, B., and C. In sum,
both Table 6 and Figure 1 are generally consistent with H2 and H3.
5.2.2. Multivariate Analysis
I first conduct the regression analysis to examine whether bond analysts are less
optimistic (i.e., more conservative) than equity analysts in cash flow and earnings forecasts.
Results are reported in Table 7. Note that the sample is based on matched sample as previously
described and a negative coefficient on BondDummy indicates that bond analyst forecasts are
more conservative than equity analyst forecasts. Results in columns (1), (2), and (3) show thatbond analysts are more pessimistic than equity analysts in forecasting CFO (coefficient = -0.602,
t-statistic = -2.36), EBITDA (coefficient = -0.149, t-statistic = -4.53), and EARN (coefficient = 0.481, t-statistic = -3.20), which supports H2. Turning to the control variables, HORIZON is
positively associated with forecast optimism, possibly because both analysts’ optimism is walked
down by the management. As expected, CFO, EBITDA, and EARN are all negatively associated33

with the forecast bias (i.e, Bias_CFO, Bias_EBITDA, Bias_EARN), respectively, implying thatanalysts’ forecasts becomes less optimistic for lower performance in CFO, EBITDA, and EARN.
Next, the regression results from testing H3 are reported in Table 8. According to H3,bond analysts are predicted to be more (less) accurate in forecasting cash flows (earnings) thanequity analysts. Since -1 is multiplied for the forecast accuracy measure, a larger (smaller) valueof the measure indicates forecasts are more (less) accurate. Therefore, BondDummy is expected
to be positive (negative) when the dependent variable is cash flow (earnings) accuracy. The
coefficient on BondDummy is positive and significant in the first model (coefficient = 0.844, tstatistic = 3.33) and in the second model (coefficient = 0.152, t-statistic = 4.66), consistent with
H3 that bond analysts are better CFO and EBITDA forecasters than equity analysts. Lastly, thethird model shows that bond analysts are less accurate in forecasting earnings than equity
analysts, given the coefficient on BondDummy is negative and significant (coefficient = -0.925, tstatistic = -5.33), which is consistent with H3. HORIZON is also negatively related to accuracy,i.e., as the distance between the forecast date and the actual earnings announcement date islonger, the accuracy of the forecast is lower. SIZE is positively associated with forecast accuracy,
indicating that larger firms have better informational environments, thus enabling analysts topredict future performance more accurately. In sum, the results in Table 7 and 8 are consistent
with H2 and H3, respectively.
5.3. Results on the determinants of bond analysts’ cash flow and earnings forecasts provision
The results from estimating the logistic regression (equation (6-1)) is reported in Table 9.
I implement two different models: In the first model, CF1_F is the dependent variable and
CF2_F is the dependent variable in the second model. Within each model, I estimate the models
alternately with and without control variables (and Inverse Mills Ratio). Consistent with H4a,34

bond analysts issue cash flow forecasts if there is greater uncertainty in cash flows. The
coefficient on CFOVOL is positive and statistically significant in all eight specifications, whichindicates that the uncertainty in cash flows is an important factor that affects bond analysts’
decision on issuing cash flows forecasts. The loading on CFO is negative and significant in all
specifications, suggesting that bond analysts are more likely to issue cash flows forecasts if thefirm is under liquidity constraint. As regards to the control variables, the positive effects of LEV
on CF1_F and CF2_F are observed only in columns (1), (3), (5) and (7), where other control
variables are omitted. Thus, LEV might be statistically insignificant due to multicollinearity inother specifications. 39 ABSACC (ALTMAN) are positively (negatively) related to CF1_F and
CF2_F. In sum, the results are supportive of my hypothesis that bond analysts tend to issue cash
flow forecasts to meet bond investors’ demand.
Table 10 presents the results from testing the determinants of bond analysts’ earnings
forecast provision. All observations must have at least one cash flow forecast, since my interest
is to examine why bond analysts issue an earnings forecast conditional on the issuance of a cash
flow forecast. As discussed in section 3.4., I use two samples: one when CF1_F=1 and the otherwhen CF2_F=1. As H4b predicts that bond analysts are more likely to provide earnings
forecasts when the volatility of accruals is high, the results indicate that the coefficient on
ACCVOL is positive and statistically significant in all eight columns. Specifically, the coefficienton ACCVOL ranges from 5.14 to 5.95 with Z-statistic = 2.25 to 2.50 when the sample is based on
CF1_F (i.e., when CF1_F = 1). The level of earnings (EARN) is negatively associated with theprobability of issuing earnings forecast (EARN_F) in all eight models, suggesting thatconditional on cash flow forecast provision, bond analysts are more likely to provide an earnings

39

The Pearson correlation between LEV and ALTMAN (SIZE) is -0.55 (-0.42).
35

forecast when the earnings are lower, which supports H4b. Overall, the results in Table 9 and 10
support both H4a and H4b.
5.4. Sensitivity Analysis
5.4.1. Alternative definition of Earnings before Interest, Tax, and Depreciation and Amortization(EBITDA)In this paper, I define cash flows in a broader way which includes EBITDA as well as
CFO and FCF for reasons explained in section 3.2. However, one may argue that EBITDA might
be conceptually closer to earnings rather than cash flows. In order to lessen any potential concernthat EBITDA does not proxy for cash flows, I identify cases where bond analysts’ definition of
EBITDA involves explicit adjustment of working capitals to earnings based on full-text bondreports. More specifically, if EBITDA adds not only depreciation and amortization but also
adjusts ‘working capital’, ‘deferred items’, ‘non-cash items’, ‘non-recurring items’ or ‘otheritems’ to operating income (or EBIT), then I consider it closer to the definition of cash flows
rather than earnings. Out of 3,803 bond reports which include bond analysts’ forecasts of
EBITDA, 1,902 (50.0%) of EBITDA forecasts by bond analysts are considered cash flows basedon this alternative definition EBITDA. I leave only these observations and repeat the analyses intable 6 – 8 for EBITDA.
Table 11 reports the univariate and regression results for both forecast bias and accuracy
tests using the alternative definition of EBITDA. Panel A shows that the mean difference in
EBITDA forecast bias between bond and equity analysts is 0.200 (t-statistic = 10.43) in the fullsample and 0.363 (t-statistic = 3.94) in the matched sample. Similarly, the mean difference inforecast accuracy between bond and equity analysts is 0.260 (t-statistic = 12.28) in the fullsample and 0.461 (t-statistic = 4.33) in the matched sample. More importantly, BondDummy isstill negatively associated with Bias_EBITDA (t-statistic = -3.61) and positively associated with36

Accu_EBITDA (t-statistic = 3.79) in panel B. Therefore, my main empirical findings that bondanalysts are less optimistic in forecasting cash flows than equity analysts and that bond analysts
are more accurate forecasting cash flows than equity analysts are insensitive to more rigorous
definition of EBITDA.
5.4.2. Influential outliers, alternative forecast consensus, alternative deflator of forecast
variables, and alternative values of cash flows and earnings
In this section, I conduct various robustness checks and their results are reported in Table
12. For brevity, I only focus on the robustness on the main variable, BondDummy, althoughcontrol variables are included (but not reported) in the regression model. First, recall that I
winsorized all continuous variables at the top and bottom 1% level in my main tests. To mitigate
the concern that my results are driven by influential outliers, I truncate all continuous variables at
the top and bottom 1% level and repeat the tests in Table 7 and 8. The coefficients on
BondDummy are still consistent with my predictions all at the 5% significance level. Untabulated
results show that dropping observations 1) where the absolute studentized residual is larger than
2 and 2) where the Cook’s D is greater than (4/N) do not alter my main inferences.
Second, instead of using the median forecast as the consensus, I use the last forecast
issued by each bond or equity analyst as the forecast consensus for each firm-year (O’Brien,1990). The coefficients on BondDummy are still consistent with my predictions all at the 5%
significance level. Additionally, when I use the mean forecast value as the consensus, the results
are qualitatively similar. For example, BondDummy is negatively related to Bias_CFO(coefficient = -0.675, t-statistic = -2.41), Bias_EBITDA (coefficient = -0.178, t-statistic = -4.81),
and Bias_EARN (coefficient = -0.550, t-statistic = -3.53). The associations between BondDummyand each of Accu_CFO (t-statistic = 3.31), Accu_EBITDA (t-statistic = 4.99), and Accu_EARN (tstatistic = -4,88) are also robust to using mean consensus forecasts.
37

Third, following prior literature (Givoly et al., 2009), I also deflate forecast bias andaccuracy by previous year’s stock price rather than actual value as a sensitivity check. The
results are generally consistent with my predictions except for the cases where the dependent
variable is Bias_EARN or Accu_CFO, although the sign is as expected. I also deflated by current
year’s stock price and get similar results.
Finally, many actual values in bond reports are missing. To obtain a larger sample size at
the cost of increasing measurement error, I repeat the analyses by retrieving the actual values
from Compustat if CFO, EBITDA, or EARN is missing in both bond reports and I/B/E/S. Morespecifically, net cash flows from operating activities (OANCF), operating income before
depreciation (OIBDP), or income before extraordinary items (IB) in Compustat-Annual File areused, for CFO, EBITDA, or EARN, respectively. Panel A and B in table 12 indicates that theresults are robust to alternative definition of actual values for bond analysts, expect for the firstcolumn in panel A. When I use only the actual values from bond reports, as expected, the sample
size decreases from 422 to 212 (CFO), from 1,506 to 1,108 (EBITDA), and from 522 to 214(EARN). In this case, except for the second first and third columns in Panel A, all thecoefficients on BondDummy are significant at the 5% level.
6. Concluding Remarks
Using bond analysts’ forecast data, this study examines the determinants and propertiesof their forecasts, in comparison with equity analysts. I first document that the probability toissue cash flow (earnings) forecasts is greater (smaller) for bond analysts than for equity
analysts. I also find that bond analysts have conservative view on future firm performance inorder to meet bondholders’ asymmetric demand on good news and bad news. In addition,although bond analysts’ earnings forecast accuracy is lower than that of equity analysts, bond38

analysts’ cash flow forecasts are more accurate than equity analysts. Finally, bond analysts are
more likely to provide a forecast on future cash flows when the uncertainty and the constraint ofcash flows are greater, and bond analysts’ decision to provide earnings forecasts, along with cash
flow forecasts, is associated with firm’s uncertainty of accruals and earnings performance.
This study contributes to the recent growing literature on the informational role of bondanalysts. To the best of my knowledge, this study is the first to document debt analysts’ efforts
on forecasting firms’ future earnings and cash flows. This paper also adds to the literature onother information intermediaries, such as stock analysts. In particular, considering the debate on
the quality of analysts’ cash flow forecasts, this study shows that equity analysts are inferior tobond analysts with respect to cash flow forecast accuracy. In conclusion, this study elevates ourunderstanding of the informational role bond analysts play in the bond market through theirforecasting activities, which are different in various aspects from the role of equity analysts.
Future research is encouraged to examine the interaction between equity and bond analysts inforecasting earnings and/or cash flows.

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43

Appendix A: Variables Description
Variable

Definition

ABSACCThe absolute value of earnings minus operating cash flows divided by average
total assets. Earnings are Income before extraordinary items.

Accuracy(-1) × |F it - A it | / |A it |, where A it = actual value per share for firm i and year t,
and F it = (consensus) forecasted values per share for firm i and year t.

Accu_CFOF

Accuracy where the actual and forecasted values are for operating cash flows.

Accu_EBITDAF

Accuracy where the actual and forecasted values are for earnings before
interest, tax, and depreciation and amortization.

Accu_EARNF

Accuracy where the actual and forecasted values are for earnings before
extraordinary items.

ACCVOLThe firm-specific standard deviation of the total accruals divided by averageassets. Total accruals are calculated as the difference between net income before
extraordinary items and operating cash flows. Previous seven historical years
are used to calculate the standard deviation with the minimum requirement of
three years.

ALTMAN

1.2 (Net working capital / Total Assets) + 1.4 (Retained earnings / Total Assets)+ 3.3 (Earnings before interest and taxes / Total Assets) + 0.6 (Market value ofequity / Book value of liabilities) + 1.0 (Sales / Total Assets).

Bias(F it - A it ) / |A it |, where A it = actual value per share for firm i and year t, and F it
= (consensus) forecasted values per share for firm i and year t.

Bias_CFOF

Bias where the actual and forecasted values are for operating cash flows.

Bias_EBITDAF

Bias where the actual and forecasted values are for earnings before interest, tax,
and depreciation and amortization (EBITDA).

Bias_EARNF

Bias where the actual and forecasted values are for earnings before
extraordinary items.

BondDummy

An indicator variable which equals to one if the forecast is issued by bondanalyst, otherwise (i.e. if the forecast is issued by equity analyst).

BM

Equity book value divided by equity market value.

CAPINT

Gross property, plant, and equipment (Gross PP&E) divided by total sales in theprevious year.

CFO

Operating cash flows divided by average total assets.

44

Appendix A (cont’d)
CF1_F

An indicator variable that equals one if (bond) analysts provide a forecast either
on operating cash flows or on free cash flows, and zero otherwise.

CF2_F

An indicator variable that equals one if bond analysts provide any forecast on
operating cash flows, free cash flows, or earnings before interest, tax, anddepreciation and amortization, and zero otherwise.

CFO_F

An indicator variable that equals one if analysts provide a forecast on operating
cash flows, zero otherwise.

CFOVOLThe firm-specific standard deviation of the operating cash flows divided byaverage assets. Previous seven years are used to calculate the standard deviation
with the minimum requirement of three years.

DSIZE

The natural logarithm of book value of debt.

EARN

Income before extraordinary items divided by average total assets.

EARN_FAn indicator variable which equals one if (bond) analysts forecast earnings,
zero otherwise.

EARNVOL

The firm-specific standard deviation of earnings divided by average assets.
Previous seven years are used to calculate the standard deviation with theminimum requirement of three years.

EBITDA

Earnings before depreciation and amortization divided by average assets.

EBITDA_F

An indicator variable which equals one if analyst forecast EBITDA, zerootherwise.

EBITDAVOL

The firm-specific standard deviation of earnings before depreciation andamortization divided by average assets. Previous seven years are used to
calculate the standard deviation with the minimum requirement of three years.

HORIZON

The difference (in days) between the earnings announcement date and theforecast issuance date.

INTCOV

Operating income divided by interest payments.

LEV

The book value of debt divided by book value of equity.

LOSS

Dummy variable which equals one if EARN is negative, zero otherwise.

MB

Equity market value divided by equity book value.

SIZE

The natural logarithm of the equity market capitalization, which equals thenumber of shares outstanding times the end of year price.

45

Appendix B. Sample reports of debt and equity analysts’ cash flow and earnings forecasts
1. Bond analyst report [excerpted from Deutsche Bank (2008) covering RSC Holdings Inc.]

2. Equity analyst report [excerpted from Deutsche Bank (2008) covering RSC Holdings
Inc.]

46

Figure 1. Intra-year Change in Forecast Bias and Forecast Accuracy between Bond
Analysts and Equity Analysts
Panel A. Forecast Item – Operating Cash Flows (CFO)

Panel B. Forecast Item – Earnings before Interest, Tax, and Depreciation andAmortization (EBITDA)47

Panel C. Forecast Item – Earnings (EARN)

Figure 1 plots the intra-year improvement of forecast bias and accuracy for both bond and equity analysts. Panel A,
B, and C graphs the forecast bias and accuracy of CFO, EBITDA, and EARN, respectively. All firms must have atleast two forecasts by both bond and equity analysts within the fiscal period. For each firm and year, I keep theearliest forecast issued after previous year’s earnings announcement and the last forecast issued before the current
earnings announcement. All observations that are able to calculate the bias and accuracy are used in this analysis.
Bias it = (F it – A it ) / |A it |, where A it = actual value per share for firm i and year t, and F it = forecasted value per sharefor firm i and year t. Accuracy it = |F it - A it | / |A it |. All variables are winsorized at the top and bottom 1%.

48

Table 1. Sample Construction
Number ofbond analyst
reports

Number of
firm-year
observations

Number of
unique firms

12,478

4,396

1,847

(1,818)

(808)

(623)

10,660

3,588

1,224

2-1. Less: Missing Compustat data

(2,642)(832)

Cash Flow Forecast Determinant Test (H1a)

946

392

Initial Reports from Investext: 2001 - 2010
1. Less: Reports issued
1) for non-US firms2) for close-end fund, convertible bond,or derivative3) within two days by the same analyst(i.e. duplicate report)
4) for multiple firms within a single report5) at macro or industry level6) by equity analysts7) simultaneously by equity analysts8) by credit rating agencies (e.g., Fitch, Rapid)
Bond Reports before Merging with Compustat

This table presents the sample selection process. I obtain all bond reports from Investext, a provider of full-text bondanalysts’ reports between 2001 and 2010. The initial bond reports were downloaded using the following three search
criteria: 1) asset class must be fixed income, 2) reports must be issued within the “North America” region, and 3)
industrial-, geographic-, or macroeconomic-level research reports are excluded.

49

Table 2. Descriptive Statistics on Firm Characteristics
Panel A: Distribution by Year

Fiscalyear

Number ofbond reports

Number offirms with bond reports

Number ofannual forecasts

Number ofquarterly forecasts

Frequency

Percent

Frequency

Percent

Frequency

Percent

Frequency

Percent

2001
2002
2003
2004
2005
2006
2007
2008
2009
2010

415
1,157
1,831
2,327
1,222
540
755
1,234
614
565

3.89%
10.85%
17.18%
21.83%
11.46%
5.07%
7.08%
11.58%
5.76%
5.30%

227
409
543
621
471
252
279
382
240
171

6.31%
11.38%
15.10%
17.27%
13.10%
7.01%
7.76%
10.63%
6.68%
4.76%

93
405
662
875
423
199
297
457
244
208

2.41%
10.48%
17.14%
22.65%
10.95%
5.15%
7.69%
11.83%
6.32%
5.38%

60
179
358
639
256
144
171
308
133
130

2.52%
7.53%
15.05%
26.87%
10.77%
6.06%
7.19%
12.95%
5.59%
5.47%

Total

10,660100.00%3,595

100.00%3,863

100.00%2,378

100.00%50

Table 2. Descriptive Statistics on Firm Characteristics (Continued)
Panel B: Firm Characteristics (Full Sample)

Variables# of Obs.
(BA)# of Obs.
(EA)

Mean
(BA)

Mean
(EA)

Diff.
(BA - EA)

P-value1

Median(BA)

Median(EA)

Diff.
(BA - EA)

P-value2

MV ($ billion)
ROA
LOSS
MB
LEV
INTCOVDSIZE
ALTMANVOLEARN
VOLACCVOLCFO

2,180
2,180
2,180
2,180
2,180
2,180
2,180
2,180
2,180
2,180
2,180

28,14828,14828,14828,14828,14828,14828,14828,14828,14828,14828,148

10.61
0.01
0.31
3.87
0.65
3.99
8.21
1.96
0.05
0.05
0.04

5.50
-0.02
0.30
4.20
0.35
64.98
5.94
3.64
0.12
0.10
0.09

5.11
0.03
0.00
-0.32
0.30
-60.98
2.27
-1.68
-0.07
-0.05
-0.05

<.0001<.00010.68940.6890<.0001<.0001<.0001<.0001<.0001<.0001<.0001

2.72
0.02
0.00
1.71
0.57
2.37
8.21
1.68
0.03
0.03
0.03

0.72
0.04
0.00
2.03
0.31
4.38
5.93
2.89
0.05
0.05
0.05

2.00
-0.01
0.00
-0.32
0.26
-2.01
2.28
-1.21
-0.02
-0.02
-0.02

<.0001<.00010.6894<.0001<.0001<.0001<.0001<.0001<.0001<.0001<.0001Panel C: Firm Characteristics (PS Matched Sample)

Variables# of Obs.
(BA)# of Obs.
(EA)

Mean
(BA)

Mean
(EA)

Diff.
(BA - EA)

P-value1

Median(BA)

Median(EA)

Diff.
(BA - EA)

P-value2

MV ($ billion)
ROA
LOSS
MB
LEV
INTCOVDSIZE
ALTMANVOLEARN
VOLACCVOLCFO

1,125
1,125
1,125
1,125
1,125
1,125
1,125
1,125
1,125
1,125
1,125

1,125
1,125
1,125
1,125
1,125
1,125
1,125
1,125
1,125
1,125
1,125

6.87
0.00
0.40
2.38
0.66
2.34
7.81
1.63
0.06
0.06
0.04

5.74
0.00
0.38
2.33
0.64
2.50
7.83
1.64
0.06
0.06
0.05

1.13
0.00
0.02
0.04
0.02
-0.16
-0.02
-0.01
-0.01
-0.01
0.00

0.5136
0.67340.28080.7666
0.11350.22240.76980.83290.35250.34270.4430

1.41
0.01
0.00
1.57
0.62
1.82
7.78
1.54
0.04
0.04
0.04

1.41
0.01
0.00
1.57
0.61
1.94
7.77
1.56
0.04
0.04
0.04

0.00
0.00
0.00
0.01
0.01
-0.13
0.01
-0.02
0.00
0.00
0.00

0.88010.26750.28070.92170.15070.13280.84180.75470.73550.9987
0.992651

This table reports descriptive statistics on the firms covered by bond analysts and equity analysts. Panel A tabulates the annual distribution of the number of bondreports, the number of firms with bond reports, the number of annual bond analysts’ forecasts, and the number of quarterly bond analysts’ forecasts. The sample
is based on 10,660 bond reports before matching with Compustat file. Panel B compares the industrial distribution between firms covered by bond analysts andequity analysts. The industry definition is based on the Fama and French (1997) 48-industry classification. Industries with less than 2% of the overall sample are
combined and reported as “others.” Both samples are constructed from the intersection of Investext, I/B/E/S, and Compustat. The number of observation is at the(unique) firm-level. Panel C and D compare the difference in characteristics between firms covered by bond analysts and firms covered by equity analysts. In
panel C, all firm-year observations available between 2001 and 2010 are used. In panel D, firm-year observations are based on the sample used for testing H1a.
First, firm-year observations where both bond and equity analysts issue at least one forecast for the same firm and same year are identified and used. Second,firms with bond analysts’ forecasts are matched with firms covered by equity analysts using the propensity matching technique. SIZE, MB, LEV, INTCOV, and
DSIZE are used to identify the closest match. If the mean (median) difference is statistically significant at the 10% level, then the mean (median) difference andthe p-value are in bold. The first p-value is based on t-statistics from two-tailed tests, and the second is based on Wilcoxon-Rank Sum tests. Refer to Appendix A
for the definitions of variables.

52

Table 3. Descriptive Statistics on Bond Analysts and Their Forecasts
Panel A: Distribution of Brokerage Firms
Number of
Bond Reports

Percentage

Number of
Bond Analysts

Percentage

Bear Sterns and Co. Inc
CIBC World Markets Corp
CREDIT SUISSE
Deutsche BankHSBC
JP Morgan
Keybanc Capital Markets
Morgan Stanley
Morgan Keegan & Co
RBC Capital Markets
UBS
Others*

1,256
1,222
150
4,137
240
595
486
833
308
483
815
135

11.78%
11.46%
1.41%
38.81%
2.25%
5.58%
4.56%
7.81%
2.89%
4.53%
7.65%
1.27%

59
30
31
85
22
59
7
104
14
29
58
61

10.55%
5.37%
5.55%
15.21%
3.94%
10.55%
1.25%
18.60%
2.50%
5.19%
10.38%
10.91%

Total

10,660100.00%559

100.00%Investment Banking

Panel B: Number of Bond (or Equity) Analysts per Firm and Number of Firms per Bond(or Equity) Analysts.
Number of analysts per firm

N

Mean

25%

Median75%

Bond analyst (as an individual)

1,224

4.42

2

3

6

Equity analyst

7,342

47.57

7

24

62

Number of firms per analyst

N

Mean

25%

Median75%

Bond analyst (as a team)

517

10.46

1

6

16

Equity analyst

10,793

32.36

3

11

41

53

Table 3. Descriptive Statistics on Bond Analysts and Their Forecasts (Continued)
Panel C: Distribution of Forecast Items
Number of forecasts% of forecasts
out of total forecasts% of forecasts
out of total reports

EBITDA

3,803

98.45%

35.68%

Free Cash Flows

2,947

76.29%

27.65%

Operating Cash Flows

785

20.32%

7.36%

EBIT

738

19.10%

6.92%

Earnings

615

15.92%

5.77%

Total Forecasts

3,863

100.00%36.24%

Total Reports

10,660-100.00%

Forecast item

Panel D: Forecast Horizon (between the forecast release date and the target fiscal year end
date)

Time
(in years)

Number of
Forecasts

Percent

Year < 1

2,299

59.51%

1 ≤ Year < 2

1,310

33.91%

2 ≤ Year < 3

195

5.05%

Year ≥ 3

59

1.53%

Total

3,863

100.00%Panel A tabulates the distributions of brokerage firms employing bond analysts. The sample is based on 10,660 bondreports before matched with Compustat file between 2001 and 2010. *Others include brokerage firms with less than
1% of the sample, such as CITI, ING bank, Societe Generale, or Unicredit Research etc. Panel B presents the
distribution of analysts for each firm and the distribution of firms for each analyst. I compare the distribution
between bond analysts and equity analysts. The number of bond analysts per firm is measured in the individual basis
while the number of firms covered by each bond analyst is measured in the team basis. The definition of bondanalyst coverage is based on the debt report provision whereas the definition of equity analyst coverage is based on
the earnings forecast provision. Panel C contains frequencies of bond analysts’ forecast items and the percentage ofeach item out of total forecasts and total debt reports. Panel D reports the forecast horizon, the forecast release daterelative to the end of targeted fiscal year measured in years. For example, if the target fiscal year end is December
31, 2013 and the forecast release date is September 21, 2012, then the horizon is calculated as 1.25 (year).

54

Table 4. The Propensity to Issue Cash Flow (Earnings) Forecast between Bond Analyst andEquity Analyst: Univariate Analysis
Panel A. Cash Flow Forecast: Exact Matching

EquityAnalyst1
0
Total

Bond Analyst
0
123
62
185

1
576
185
761

Total
699
247
946

McNemar Test Statistic (S) = 12.48*** [p-value = <.0001]
Panel B. Cash Flow Forecast: PS Matching

EquityAnalyst1
0
Total

Bond Analyst
0
136
83
219

1
655
251
906

Total
791
334
1,125

McNemar Test Statistic (S) = 34.17*** [p-value = <.0001]
Panel C. Earnings Forecast: Exact Matching

EquityAnalyst1
0
Total

Bond Analyst
0
672
1
673

1
324
0
324

Total
996
1
997

McNemar Test Statistic (S) = 672.00*** [p-value = <.0001]
Panel D. Earnings Forecast: PS Matching

EquityAnalyst1
0
Total

Bond Analyst
0
805
3
808

1
381
0
381

Total
1,186
3
1,189

McNemar Test Statistic (S) = 805.00*** [p-value = <.0001]
This table presents the 2 × 2 contingency tables tabulating the frequency of issuing cash flow or earnings forecasts
by bond analysts and equity analysts. All observations must have at least one annual forecasts by both bond andequity analysts. Panel A and B report the number of cash flow forecast provisions where exact matching procedure
is used in panel A and propensity matching procedure is implemented in panel B. Panel C and D report the numberof earnings forecast provisions where exact matching procedure is used in panel C and propensity matchingprocedure is used in panel D. McNemar test statistic is used to determine whether the difference in marginal
probabilities is statistically significant.

55

Table 5. The Propensity to Issue Cash Flow (Earnings) Forecast between Bond Analyst and Equity Analyst: Multivariate Test
Independent
Variables

Predicted
Sign

Intercept
BondDummy(+)

Dependent Variable: CF1_F
Exact Matching
PS Matching
(1)
(2)
(3)
(4)
2.295***(6.18)
0.417***(2.68)

CFOVOLCFO
ABSACCCAPINT
ALTMAN-0.963*(-1.70)0.443***(2.65)
0.002(0.28)
0.723(0.61)-0.948(-1.14)0.690***(3.33)-0.125*(-1.84)0.479(0.91)
0.559***(4.09)-2.421***(-3.79)0.597***(4.03)
0.006(0.69)
1.924*(1.77)-0.896(-1.25)0.480***(2.68)-0.111*(-1.91)

Predicted
Sign

(–)

Dependent Variable: EARN_F
Exact Matching
PS Matching
(5)
(6)
(7)
(8)
4.025***(7.82)-8.050***(-7.82)

3.291***(4.65)-8.073***(-7.83)2.631***(2.58)-7.485***(-10.27)

1.824*(1.67)-7.503***(-10.28)

Yes

0.007
(0.31)-1.759*(-1.83)0.102(1.51)
Yes

Yes

0.007
(0.35)-1.332(-1.53)0.107*(1.88)
Yes

1,994
54.37%

1,994
54.65%

2378
54.32%

2378
54.57%

ACCVOL
EARN

Year Fixed EffectYes

0.398***(7.61)YesN
Pseudo R2

1,890
9.00%

1,890
14.20%

SIZE

Yes

0.367***(8.56)
Yes

2,250
10.28%

2,250
15.43%

Table 5 reports the logistic regression results of H1a and H1b. The regression is implemented for firm-year observations between 2001 and 2010 where allobservations must have at least one annual forecast available. All independent variables are winsorized at the top and bottom 1%. Refer to Appendix A for thedefinitions of variables. Year dummies are included and standard errors are clustered by firm. Z-statistics are reported in the parenthesis below the coefficients.
***, **, and * denote significance at the 1%, 5%, and 10% levels, respectively, from two-tailed tests.

56

Table 6. Univariate Analysis of Forecast Bias and Error Comparison between Bond Analysts and Equity Analysts
Panel A: Operating Cash Flows (CFO)
Full Sample

Matched Sample

Bias(1) Equity Analyst(2) Bond Analyst

N

Mean

N

Mean

N

Mean

Error
N
Mean

15,027215

0.283-0.01515,027
215

0.701
0.510

211
211

0.593
0.004

211
211

diff.(1) - (2)t-statistic

Error

0.298
4.14***

Bias

0.191
2.87***

0.588
2.71***

1.268
0.515
0.753
3.12***

Panel B: Earnings Before Interest, Taxes, and Depreciation and Amortization (EBITDA)
Full Sample

Matched Sample

Bias(1) Equity Analyst(2) Bond Analyst

Error

Bias

Error

N

Mean

N

Mean

N

Mean

N

Mean

6,507
796

0.216
0.029

6,507
796

0.362
0.137

753
753

0.143
0.030

753
753

0.272
0.140

diff.(1) - (2)t-statistic

0.187
9.75***

0.225
10.58***

0.112
4.52***

0.132
5.41***

Panel C: Earnings (EARN)
Full Sample

Matched Sample

Bias(1) Equity Analyst(2) Bond Analyst
diff.(1) - (2)t-statistic

Error

Bias

Error

N

Mean

N

Mean

N

Mean

N

Mean

49,334267

0.096-0.12949,334
267

0.252
1.531

261
261

0.084
-0.254

261
261

0.284
1.166

0.225
1.27

-1.279-4.91***

57

0.337
2.79***-0.883-6.60***

This table presents univariate test results of comparing the average bias and error between bond and equity analysts. All tests are conducted for firm-year
observations between 2001 and 2010. Both Bias and Error are winsorized at the top and bottom 1%. Bias it = (F it – A it ) / |A it |, where A it = actual value per sharefor firm i and year t, and F it = median consensus forecasted value per share for firm i and year t. Error it = |F it - A it | / |A it |. Panels A, B, and C report the forecast
bias and error of CFO, EBITDA, and EARN, respectively, for each of panel full and matched samples. I define the matched sample from the following two steps:
First, firm-year observations where both bond and equity analysts issue at least one forecast for the same firm and same year are identified and used. Second,firms with bond analysts’ forecasts are matched with firms covered by equity analysts using the propensity matching technique. SIZE, MB, LEV, INTCOV,DSIZE, HORIZON, and CFOVOL (EBITDAVOL or EARNVOL) are used to identify the closest match. If the mean difference is statistically significant at the 10%
level from two-tailed tests, then the mean difference and the t-statistics are in bold. ***, **, and * denote significance at the 1%, 5%, and 10% levels,
respectively, from two-tailed tests.

58

Table 7. Regression Results of Forecast Bias Comparison between Bond and Equity Analysts
Dependent Variable:
Bias_CFO

Dependent Variable:
Bias_EBITDA

Dependent Variable:
Bias_EARN

Coeff.

t-statCoeff.

t-statCoeff.

t-stat-0.142-0.11

0.458***

4.24-0.298-0.54-0.602**-2.36-0.149***-4.53-0.481***-3.20

CFOVOL

-3.212-0.81

CFO-4.237*** -2.73

EBITDAVOL

0.094

0.37

EBITDA-1.180***-5.94

EARNVOL-1.322

-0.94

EARN

-0.431-0.89

Independent
Variables

Predicted
Sign

Intercept
BondDummy(–)

HORIZON

0.003**

2.19

0.0003*1.71

0.002**

2.00

SIZE

0.021

0.19

-0.036***-4.24-0.041-0.82

BM0.819*

1.82

0.010

0.32

0.107

0.98

LEV

0.219

0.65

-0.018-0.36-0.150-0.49

Year Fixed Effect

Yes

Yes

Yes

N

422
6.31%

1,506
7.54%

522
6.20%

Adj. R2

This table presents the regression results for forecast bias comparison between bond and equity analysts. The
regression is implemented for firm-year observations where bond analyst sample is matched with equity analyst
sample. Refer to table 6 for more detail in the matching procedure. All variables, except for BondDummy, arewinsorized at the top and bottom 1%. Refer to Appendix A for the definitions of variables. Column (1) contains
comparison test results between bond and equity analysts for CFO. Column (2) tabulates comparison test results
between bond and equity analysts for EBITDA. Column (3) reports comparison test results between bond and equity
analysts for EARN. Year dummies are included, and standard errors are clustered by firm. ***, **, and * denotesignificance at the 1%, 5%, and 10% levels, respectively, from two-tailed tests.

59

Table 8. Regression Results of Forecast Accuracy Comparison between Bond and Equity Analysts
Dependent Variable:
Accu_CFO
Coeff.
t-statDependent Variable:
Accu_EBITDA
Coeff.
t-stat-1.041-0.80-0.351***-3.42

0.844***

3.33

0.152***

4.66

CFOVOL-1.698-0.33

CFO

8.290***

4.81

EBITDAVOL-0.562**-2.23

EBITDA

1.383***

6.57

Independent
Variables

Predicted
Sign

Intercept

Predicted
Sign

Dependent Variable:
Accu_EARN
Coeff.
t-stat-1.475**-2.55-0.925***-5.33

EARNVOL-1.733

-1.13

EARN

-0.145-0.28

BondDummy

(+)

(–)

HORIZON-0.003**-2.33-0.001***

-4.14

0.001

0.64

SIZE

0.109

0.96

0.027***

3.10

0.120**

2.28

BM-0.848*

-1.76

0.009

0.26

0.053

0.67

LEV

-0.554-1.37-0.020-0.42-0.027-0.08

Year Fixed EffectN
2

Adj. R

Yes

Yes

Yes

422
12.70%

1,506
10.20%

522
11.18%

This table presents the results of forecast accuracy comparison between bond and equity analysts. The regression is
implemented for firm-year observations where bond analyst sample is matched with equity analyst sample. Refer totable 6 for more detail in the matching procedure. All variables, except for BondDummy, are winsorized at the top
and bottom 1%. Refer to Appendix A for the definitions of variables. Column (1) contains comparison test results
between bond and equity analysts for CFO. Column (2) tabulates comparison test results between bond and equity
analysts for EBITDA. Column (3) reports comparison test results between bond and equity analysts for EARN. Yeardummies are included, and standard errors are clustered by firm. ***, **, and * denote significance at the 1%, 5%,
and 10% levels, respectively, from two-tailed tests.

60

Table 9. Determinants of Bond Analysts’ Issuance of Cash Flow Forecasts
Independent
Variables

Predicted
Sign

Intercept
CFOVOL

(+)

CFO

(–)

LEV

(1)
-1.205(-0.95)10.200***(4.04)-2.622***(-5.41)0.725***(2.79)EARNVOL
ABSACCCAPINT
ALTMAN
SIZE
MB
DSIZE

Dependent Variable: CF1_F(2)(3)
3.231**(2.45)
5.479**(2.14)-1.478***(-2.76)-0.409(-1.25)0.005(0.52)
2.200**(1.96)-0.019(-0.37)-0.217***(-2.85)-0.212**(-2.00)-0.001(-0.07)-0.204*(-1.65)

IMR
Year Fixed Effect
N with (without) cash flow forecasts
Total N
Pseudo R2

Yes
755 (1,055)
1,810
6.23%

Yes
755 (1,055)
1,810
11.97%-4.366***(-3.55)6.848***(2.64)-2.586***(-5.45)
1.693***(5.54)

1.453***(6.33)
Yes
755 (1,055)
1,810
9.13%

(4)

(5)

14.450***(2.74)
5.931**(2.31)-1.379**(-2.54)-2.365**(-2.40)0.005(0.51)
2.182*(1.92)-0.027(-0.51)-0.220***(-2.82)-0.105(-0.95)0.024(1.10)-0.964***(-2.77)-2.860**(-2.23)Yes
755 (1,055)
1,810
12.25%-1.485(-1.13)13.940***(4.99)-2.486***(-5.05)0.904***(3.15)

Yes
937 (873)
1,810
5.86%

Dependent Variable: CF2_F(6)(7)
3.926***(2.94)
8.318***(2.97)-1.140**(-2.06)-0.501(-1.39)0.001(0.11)
2.838**(2.38)-0.038(-0.69)-0.240***(-2.96)-0.297***(-2.65)0.007(0.42)-0.212(-1.63)Yes
937 (873)
1,810
14.08%-5.356***(-4.15)9.820***(3.50)-2.493***(-5.28)2.111***(5.87)

1.774***(7.07)
Yes
937 (873)
1,810
10.19%(8)
17.480***(3.20)
8.988***(3.17)-1.024*(-1.84)-2.871***(-2.84)0.001(0.12)
2.822**(2.34)-0.047(-0.85)-0.237***(-2.89)-0.175(-1.50)0.038*(1.69)-1.126***(-3.12)-3.454***(-2.59)Yes
937 (873)
1,810
14.47%

This table reports the logistic regression results of why bond analysts issue cash flow forecasts. The regression is implemented for firm-year observations
between 2001 and 2010. All independent variables are winsorized at the top and bottom 1%. Refer to Appendix A for the definitions of variables. Year dummies
are included and standard errors are clustered by firm. Z-statistics are reported in the parenthesis below the coefficients. ***, **, and * denote significance at the
1%, 5%, and 10% levels, respectively, from two-tailed tests.
61

Table 10. Determinants of Bond Analysts’ Issuance of Earnings Forecasts together with Cash flow Forecasts

Independent
Variables

Predicted
Sign

Intercept
ACCVOL

(+)

EARN

(–)

(1)
-1.412***(-3.63)5.259**(2.27)-4.795***(-3.75)

ALTMAN
SIZE
MB
LEV
DSIZE
IMR
Year Fixed Effect
N with (without) earnings forecasts
Total N
Pseudo R2

Yes
114 (641)
755
19.10%

Dependent Variable: EARN_F
Sample Based on CF1_F
(2)
(3)
(4)
-0.883-1.764** 26.930*(-0.68)(-2.16)(1.85)
5.316**
5.143**
5.952**(2.30)(2.25)(2.50)-5.370*** -5.018*** -5.231***(-3.33)(-3.77)(-3.18)0.013
0.014(0.10)(0.11)-0.628***-0.538***(-3.22)(-2.62)0.064*
0.154***(1.71)(2.79)-2.732***-8.467***(-4.24)(-2.78)0.711***-0.985(2.83)(-1.06)0.221-6.963*(0.50)(-1.92)Yes
Yes
Yes
114 (641)
114 (641)
114 (641)
755
755
755
22.87%
19.17%
23.44%(5)-1.339***(-3.65)4.380**(2.30)-4.291***(-3.81)Yes
132 (805)
937
15.41%

Sample Based on CF2_F(6)(7)-0.321-1.769**(-0.28)(-2.47)4.874***
4.146**(2.62)(2.18)-4.343*** -4.584***(-2.90)(-3.85)0.0003(0.00)-0.697***(-3.78)0.042(1.20)-2.784***(-4.87)0.725***(3.17)
0.272(0.71)Yes
Yes
132 (805)
132 (805)
937
937
19.52%
15.52%(8)
34.290**(2.37)
5.823***(3.06)-4.100***(-2.69)0.010(0.09)-0.578***(-2.95)0.156***(2.93)-9.997***(-3.28)-1.391(-1.50)-8.644**(-2.42)Yes
132 (805)
937
20.26%

This table contains the logistic regression results of why bond analysts issue earnings forecasts. The regression is implemented for firm-year observations
between 2001 and 2010, and must have at least one cash flow forecast. All independent variables are winsorized at the top and bottom 1%. Refer to Appendix A
for the definitions of variables. Year dummies are included and standard errors are clustered by firm. Z-statistics are reported in the parenthesis below thecoefficients. ***, **, and * denote significance at the 1%, 5%, and 10% levels, respectively, from two-tailed tests.

62

Table 11. Alternative Definition of Earnings before Interest, Tax, and Depreciation and Amortization (EBITDA)
Panel A: Univariate Analysis
Full Sample(1) Equity Analyst(2) Bond Analyst

Matched Sample

Bias
N
Mean
6,507
0.216
413
0.016

Error
N
Mean
6,507
0.362
413
0.102

Bias
N
Mean
384
0.381
384
0.018

Error
N
Mean
384
0.566
384
0.105

0.200
10.43***

0.260
12.28***

0.363
3.94***

0.461
4.33***

diff.(1) - (2)t-statPanel B: Regression Analysis

Independent Variables

Predicted
Sign

Intercept
BondDummy

Dependent Variable:
Bias_EBITDA
Coeff.
t-stat0.891***(–)

Predicted
Sign

2.77
(+)

Dependent Variable:
Accu_EBITDA
Coeff.
t-stat-0.824**-2.50

0.408***

3.79

-3.61

EBITDAVOL-0.339***
0.094

0.88

-1.788-1.26

EBITDA-1.428**-2.00

1.766**

2.17

HORIZON

0.001**

2.17-0.002**

-2.56

SIZE

-0.086***-2.93

0.074**

2.27

BM-0.117

-1.19

0.157

1.45

LEV

-0.089

-0.58

0.066

0.39

Year Fixed Effect

Yes

Yes

N

768
5.79%

768
6.62%

Adj. R263

In table 11, the empirical tests on H2 and H3 using EBITDA is repeated based on alternative definition of EBITDA. More specifically, observations are used
only if EBITDA adds not only depreciation and amortization but also adjusts ‘working capital’, ‘deferred items’, ‘non-cash items’, ‘non-recurring items’, or‘other items’. Panel A presents the univariate test results of comparing the average bias and accuracy between bond and equity analysts. Panel B tabulates theresults of forecast bias and accuracy comparison between bond and equity analysts. The regression is implemented for firm-year observations where bondanalyst sample is matched with equity analyst sample. Refer to table 6 for more detail in the matching procedure. All variables, except for BondDummy, arewinsorized at the top and bottom 1%. Refer to Appendix A for the definitions of variables. Column (1) contains bias comparison test result between bond andequity analysts. Column (2) tabulates accuracy comparison test results between bond and equity analysts. Year dummies are included, and standard errors are
clustered by firm. ***, **, and * denote significance at the 1%, 5%, and 10% levels, respectively, from two-tailed tests.

64

Table 12. Influential Outliers, Alternative Forecast Consensus, Alternative Deflator of Forecast Variables, and Alternative
Values of Cash Flows and Earnings
Panel A: Forecast Bias
Dependent Variable:
Bias_CFO

Independent Variable: BondDummy(1) Truncation at the top and bottom 1%(2) Last forecast as consensus(3) Prior year price as deflator(4) Compustat values in BA actual value

Dependent Variable:
Bias_EBITDA

Dependent Variable:
Bias_EARN

N

Coeff.

t-statN

Coeff.

t-statN

Coeff.

t-stat

342
422
420
524

-0.734**-0.555**-0.128**-0.144

-2.42
-2.25
-2.31
-1.25

1,190
1,506
1,502
2,174

-0.101***-0.109***-0.028***-0.144***

-4.04
-3.98
-3.75
-3.84

434
522
520
542

-0.342***-0.460***-0.022-0.457***

-2.74
-3.11
-1.39
-3.08

Panel B: Forecast Accuracy
Dependent Variable:
Accu_CFO

Independent Variable: BondDummy(1) Truncation at the top and bottom 1%(2) Last forecast as consensus(3) Prior year price as deflator(4) Compustat values in BA actual value

Dependent Variable:
Accu_EBITDA

Dependent Variable:
Accu_EARN

N

Coeff.

t-statN

Coeff.

t-statN

Coeff.

t-stat

342
422
420
524

0.807***
0.898***-0.0470.406***

2.74
3.44
-0.96
2.66

1,190
1,506
1,502
2,174

0.063***
0.116***
0.024***
0.158***

2.85
4.38
2.62
4.43

434
522
520
542

-0.520***-0.899***-0.109***-0.942***

-4.85
-5.30
-6.42
-5.63

This table contains the results on various robustness tests for H2 and H3. The regression is implemented for firm-year observations where bond analyst sample ismatched with equity analyst sample. Refer to table 6 for more detail in the matching procedure. For brevity, only the coefficients on BondDummy are reported,
although control variables are included in the regression models. Panel A presents the regression results for forecast bias comparison between bond and equity
analysts and panel B presents the regression results for forecast accuracy comparison between bond and equity analysts. Year dummies are included, andstandard errors are clustered by firm. ***, **, and * denote significance at the 1%, 5%, and 10% levels, respectively, from two-tailed tests.

65