What makes a stock risky?
Evidence from sell-side analysts’ risk ratings
Daphne Lui
Lancaster University Management School
Lancaster LA1 4YX
44 (01524) 593 638
[email protected]
Stanimir Markov
Goizueta Business School
Emory University
Atlanta, GA 30322
(404) 727-5329
[email protected]
Ane Tamayo
London Business School
Regent’s Park
London NW1 4SA
44 (020) 7262 5050
[email protected]
April 26, 2006
Abstract
We examine the determinants and the informativeness of financial analysts’ risk ratings using a
large sample of research reports issued by Salomon Smith Barney, now Citigroup, over the
period 1997-2003. We find that the cross-sectional variation in risk ratings is largely explained
by variables commonly viewed as risk proxies such as idiosyncratic risk, size, leverage, and
accounting losses. We also find that the risk ratings can be used to predict future return volatility,
after controlling for other predictors of future volatility. Both findings establish the important
role of financial analysts as providers of information about investment risk.
We thank Teresa Dau, Arantza Urra and, especially, Inma Urra for excellent research assistance. We also
thank Sudipta Basu, Larry Brown, Marty Butler, Francesca Cornelli, Miles Gietzmann, Connie Kertz,
Michael Kimbrough, Grace Pownall, Henri Servaes, Greg Waymire, the seminar participants at Emory
University, The Hong Kong University of Science and Technology, London Business School, Tulane
University, Singapore Management University, the 16th Annual Conference on Financial Economics and
Accounting at the University of North Carolina, and the 29th Annual Congress of the European
Accounting Association in Dublin for their helpful comments.
1. Introduction
Information about investment risk is critical for making investment decisions. In a world
of uncertainty, the desirability of an investment depends not only on the expected payoff, but
also on the risk of the future payoffs. For that reason, in addition to forecasting the levels of
future cash flows, earnings, or stock prices, and providing stock recommendations, financial
analysts often provide information about investment risk.1 Despite Zmijewski’s (1993, p.337)
call for more research into how analysts make risk assessments, no prior research has
systematically studied the determinants of financial analysts’ risk assessments. This paper takes
the first step by providing evidence on the cross-sectional determinants and the usefulness of
analysts’ risk ratings.
Our main sample includes 6,098 research reports issued in the period 1997-2003 by
Salomon Smith Barney, now Citigroup, (or SSB henceforth), and available through Investext.
This brokerage house rates stocks as Low, Medium, High, and Speculative, based on price
volatility and predictability of financial results. To ensure that our results are not unique to SSB
and can be generalized to other information providers, we also analyze risk ratings issued by
Merrill Lynch and Value Line Investment Service.
We find that stock characteristics suggested in prior research as measures of risk are
important determinants of the risk ratings. Analysts rate stocks with high leverage and low
1
Currently, there are regulatory and litigation-related reasons for providing information about investment risks.
Following the bursting of the internet bubble in 2000, NYSE’s Rule 472 and NASD’s rule 2210 were amended to
require that research reports disclose “the valuation methods used, and any price objectives must have a reasonable
basis and include a discussion of risks” (Exchange Act Release # 48252 (July 29, 2003)). Since 2002, more than 60
class action suits alleging that analysts committed federal securities fraud in their research reports have been filed. In
dismissing the class action suit against Merrill Lynch and Henry Blodget, the court used the provided risk ratings
and qualitative discussions about specific sources of risk to conclude that the plaintiffs did not overcome “the
Bespeaks Caution Doctrine” (In Merrill Lynch and Co. Research Reports Securities Litigation, 272 F. Supp. 2d 351
(2003)). Under this doctrine, Merrill Lynch is protected from liability because it warned investors about the risks of
investing in the two concerned stocks (7/24 Real Media and Interliant), and there was no allegation of
misrepresentation that made Merrill Lynch’s cautionary statements fraudulent.
1
market capitalization as riskier, which supports the interpretation of these variables as measures
of risk rather than mispricing (e.g., Fama and French, 1992, 1993). Analysts also view firms
reporting losses as being riskier. Losses signal poor earnings prospects and could be capturing
distress risk as in Fama and French (1992). Finally, we find no evidence that either SSB or
Merrill Lynch analysts view high book-to-market stocks as being riskier but document that
Value Line analysts do.
Our evidence on beta, just like prior evidence on beta from the analysis of stock returns
(e.g., Kothari et al., 1995; Fama and French, 1992; Daniel and Titman, 1997; Easley et al., 2002),
is mixed. Univariate regressions and regressions that control for book-to-market, leverage and
size, suggest that high beta stocks are considered riskier by analysts. However, once we control
for idiosyncratic risk, defined as the standard deviation of stock returns unexplained by the
market, we find that only Value Line analysts view high beta stocks as being riskier. We
conclude that idiosyncratic risk plays a much bigger role as a determinant of the risk ratings than
beta.
We also examine whether analysts minimize the risks associated with purchasing stocks
of companies whose equities offerings their firm underwrote. In contrast with stock
recommendations (e.g., Dugar and Nathan, 1995; Lin and McNichols, 1998; Michaely and
Womack, 1999), risk ratings do not appear to be biased when underwriting relations exist.
Combined with the evidence that the same risk variables drive the ratings of sell-side analysts
and Value Line, this result suggests that even if sell-side analysts have incentives to minimize
investment risks, there are competitive forces holding these incentives in check.
Overall, we conclude that analysts’ risk ratings mainly incorporate information about
various stock characteristics commonly viewed as risk measures in the literature. The financial
2
analysts’ notion of risk is multidimensional, and very similar to the notion of risk used in the
academic literature.
In a world of costly information search and bounded rationality, gathering information
about risk and providing a single summary statistic (as financial analysts do) can have value.
The value of the risk ratings, however, would be even greater, if they provided information
incremental to the already-available information. Thus, we take the perspective of an investor
who is interested in predicting future volatility, and examine whether the risk ratings alone and in
the presence of other variables, predict the cross-sectional variation in future price volatility.
Our results suggest that the risk ratings alone explain almost 50% of the cross-sectional
variation in future return volatility. For the SSB sample, the spread in future volatility between
Low and Speculative risk stocks is 9.83% per month, which is large given a cross-sectional
standard deviation of future return volatility of 7.83% per month. The relation between future
return volatility and risk rating weakens when other predictors of future volatility are
incorporated, but the risk ratings remain incrementally informative about future volatility. For
example, controlling for past volatility, the difference in monthly volatility between Low and
Speculative risk stocks is 1.73%.
Controlling for other stock characteristics reduces the
difference to 1.42% per month.
The evidence on the incremental information content of risk ratings is not unique to SSB;
both Merrill Lynch’s and Value Line’s risk ratings help predict the cross-sectional variation of
future volatility. We conclude that our findings are consistent with the view that analysts play an
important role as providers of information about investment risks.
The evidence provided in this paper is useful for investors. It helps them understand
what information is included in (or excluded from) analysts’ risk assessments, so they can
3
optimally combine analysts’ information with their own information. In addition, our findings
on the predictive ability of analysts’ risk ratings suggest that investors can improve their
forecasts of future volatility by using analysts’ risk ratings in addition to other public
information.
Understanding how analysts determine their risk ratings is also of interest to researchers.
Since investors’ notions of risk are not observable, in developing and testing asset pricing
models researchers make various auxiliary assumptions whose validity may be hard to ascertain
(Brav and Heaton, 2002). In his presidential address to the American Finance Association, Elton
(1999) questions the tests’ continuous reliance on the assumption that realized returns are a good
proxy for expected returns, and calls for alternative ways of testing theories that do not use
realized returns. We believe that our analysis can potentially help us interpret existing evidence
about the relation between firm characteristics and average returns. For example, if analysts rate
small stocks as riskier than large stocks, then it is more likely that size is a risk proxy. This
assumes that SSB’s risk assessments influence, or are correlated with, marginal investor’s notion
of risk.2
Our analysis also complements the experimental literature on how individuals and
investors view risk (e.g., Alderfer and Bierman, 1970; Cooley, 1977; Mear and Firth, 1987;
Olsen, 1997; Bloomfield and Michaely, 2004). Unlike experimental and survey evidence, our
evidence comes from a market setting: research reports are supplied by analysts and demanded
by investors.3
2
This assumption is consistent with large body of evidence that analyst research reports contain information that
influences marginal investor’s earnings and cash flow expectations. For example, information provided by analysts
in the form of earnings forecasts and stock recommendations (Francis and Soffer 1997; Markov, 2001), cash flow
forecasts (Defond and Hung, 2003), price targets (Brav and Lehavy, 2003), and justifications of analyst opinion
(Asquith et al., 2005) has an effect on stock prices.
3
A general discussion of the advantages and disadvantages of experimental evidence vis-à-vis market evidence is
provided in Libby et al. (2002).
4
The rest of this paper is organized as follows. Section 2 describes the sample. The crosssectional determinants of analysts’ risk assessments are examined in section 3. Section 4
examines the informativeness of analysts’ risk ratings. Additional analyses are presented in
Section 5. Section 6 concludes the paper.
2. Sample and Variable Description
Our data on analysts’ risk assessments were hand-collected from analysts’ written reports
available on Investext. Well-known information providers such as IBES, First Call, and Zacks
gather and make available in electronic form various types of analysts’ provided information, but
not analysts’ risk assessments. Due to the large number of contributors to Investext we examined
research reports by seven major brokerages (Bear Stearns, Credit Suisse First Boston, Deutsche
Bank, Merrill Lynch, Morgan Stanley Dean Witter, Salomon Smith Barney, and Warburg Dillon
Read). Salomon Smith Barney (SSB) and Merrill Lynch have provided quantitative risk
assessments at least since 1997 and 1998. Credit Swiss First Boston and Morgan Stanley have
provided such risk ratings at least since 2004. The other three firms do not include risk ratings in
their reports, but do provide qualitative information about risk.
Our main sample includes reports issued by SSB over the period 1997-2003, which we
supplement with a sample of Merrill Lynch reports issued in 1998. We were not able to expand
the Merrill Lynch sample since Merrill Lynch had discontinued the practice of making its reports
available to Investext.
We acknowledge that evidence from the analysis of the reports of two investment firms
may not be generalizable to other investment research providers as the notion of risk may vary
across investment firms. Thus, we view the risk assessments of SSB and Merrill Lynch only as
5
useful proxies for the “consensus” risk assessment. The weight of these two firms in this
“consensus” assessment is likely to be significant for several reasons. First, these firms not only
employ a very large number of analysts, about 10% of all analysts on IBES in 2003, but are
viewed by institutional investors as the premier providers of investment research. In every year
in the period 1996-2005 SSB and Merrill Lynch, as well as Morgan Stanley and Credit Suisse
First Boston, were ranked by The Institutional Investor Magazine among the top 10 providers of
investment research. Second, SSB and Merrill Lynch have significant retail operations; together
they employ about 25,000 financial advisors (2003 SSB/Citigroup and Merrill Lynch Annual
Reports). Thus, their investment research reaches, and potentially influences, the opinions of
large numbers of individual investors.
The question why SSB and Merrill Lynch provide quantitative risk assessments while
other firms provide only qualitative information about risk is an important one. As a profit
maximizing entity, an investment firm would produce quantitative risk assessments as long as
the costs of producing them are lower than the revenues generated. What distinguishes these two
firms is that they both have very large private clients groups.4 We suggest that the benefits from
the provision of quantitative information about investment risks are perhaps greater for an
investment firm that derives a great portion of its revenues from serving individual investors.
Individual investors are more likely to find this information useful than large institutions with the
resources to generate it internally.5
4
In 1999 Merrill Lynch and SSB had client assets of $1,222 and $852 billion, followed by Charles Schwab and
Morgan Stanley Dean Witter with $595 and $529 billion, also providers of risk ratings. V. Kasturi Rangan, and
Marie Bell, “Merrill Lynch: Integrated Choice”, HBS # 500-090 (Boston: Harvard Business School Publishing,
2001), p. 25.
5
Explaining a firm’s choice to provide quantitative risk assessments would require that we sample the reports of all
firms, rather than the reports of only seven firms and gather data on firm characteristics potentially related to the
costs and benefits of providing such information such as size, the existence and importance of private clients group,
etc. This is an interesting venue for future research.
6
2.1 Definition of risk rating
The notion of risk as expected price volatility is common to all four brokerages. Since our
main sample consists of SSB’s research reports, the rest of our discussion discusses SSB’s risk
ratings policy.
From 1997 to September 2002 SSB rated stocks using 5 categories:
“L (Low risk): predictable earnings and dividends, suitable for conservative investor.
M (Medium risk): moderately predictable earnings and dividends, suitable for average equity
investor.
H (High risk): earnings and dividends are less predictable, suitable for aggressive investor.
S (Speculative): very low predictability of fundamentals and a high degree of volatility, suitable
for sophisticated investors with diversified portfolios that can withstand material losses.
V (Venture): indicates a stock with venture capital characteristics that is suitable for sophisticated
investors with high tolerance for risk and broadly diversified investment portfolios.” 6
From September 2002 onwards, the firm no longer assigned stocks to the Venture
category. After 2002 all stocks are rated as Low [L], Medium [M], High [H], or Speculative [S].7
The risk ratings and the forecasts of total return (price appreciation plus dividends) are the basis
for the stock recommendations. Stocks with risk ratings Low, Medium, High, and Speculative are
rated Buy when the analyst forecasts total return of at least 10% or more, 15% or more, 20% or
more, and 35% or more respectively; Hold when the analyst forecasts total return of 0%-10%,
0%-15%, 0%-20%, and 0%-35% respectively; Sell when the analyst forecasts negative return.
6
Spencer Grimes, Liberty Media Group, Salomon Smith Barney, December 29, 1998, via Thomson
Research/Investext, accessed January 30, 2006.
7
Lanny Baker and William Morrison, Amazon.com, Citigroup Smith Barney, July 22 2004, via Thomson
Research/Investext, accessed January 30, 2006.
7
The premise of the recommendation is the fundamental trade-off between risk and return: the
higher the risk, the higher the expected return.
2.2 Descriptive Statistics
SSB reports are available on Investext from 1997. We downloaded all company and
industry reports issued by SSB in the months of January and February for every year in the
period 1997-2003. The total number of reports is 10,722 reports. We downloaded only January
and February reports because of cost considerations and because most of the reports in
subsequent months cover the same firms and provide similar risk ratings. In other words, there is
not enough month-to-month variation in the risk ratings to make it worthwhile to add additional
months.
Since SSB usually provides risk ratings for several companies in its industry reports, our
initial sample consists of 25,778 firm-year observations. The number of firm-year observations
with non-missing exchange tickers and risk ratings is 24,423. Because the risk ratings do not
change much over a short time horizon, and many of our variables are available on an annual or
quarterly basis, our sample includes only unique firm-year observations. After deleting duplicate
firm-years and companies with missing COMPUSTAT or CRSP data, we are left with 6,098
unique firm-year observations. For each report, we coded the report date, the analyst’s name, the
quantitative risk assessment, the company’s exchange ticker, and the company’s name. These
research reports were authored by 235 analysts8, covering 2,279 unique firms (see Panel A of
Table 1 for details of sample construction). As mentioned before, SSB analysts use the following
categories in rating the stocks: Low risk, Medium risk, High risk, Speculative, and Venture.
8
Some industry reports contain risk ratings of multiple firms, but do not state the names of the analysts who issued
them.
8
Given the very low frequency of Venture ratings (28 firm-year observations over the sample
period), we combine Speculative and Venture ratings into a single category, Speculative. We
assign 1 to the lowest risk rating and 4 to the highest.
To construct firm characteristic variables, we obtain stock returns from CRSP and
accounting data from COMPUSTAT.
We construct three measures of risk, stock beta,
idiosyncratic risk, and total volatility, using pre-report data. To minimize the loss of
observations, we estimate these variables using one year of daily stock returns (with a restriction
of a minimum of 60 daily observations).9 We calculate debt-to-equity ratios, book-to-market
ratios, and market capitalization using data from the latest fiscal year prior to the report dates.
We construct an indicator for negative earnings by summing earnings before extraordinary items
for the rolling four quarters prior to the report dates. Since recently listed firms are likely to be
riskier, we also construct an indicator variable to identify firms that went public in the two years
before the report date. The IPO dates are obtained from SDC Platinum. Finally, prior research
shows that affiliated analysts’ stock recommendations are biased. To test whether their risk
ratings are also biased, we include an indicator variable for SSB affiliation. The definitions of all
variables are outlined in Appendix 1. To mitigate the effect of outliers, we winsorize beta, bookto-market ratio, debt-to-equity ratio, idiosyncratic risk, and total volatility variables at the +/- 1%
level.
Panel B of Table 1 reports risk ratings by industries. We classify our sample firms into
five industries (manufacturing; utilities; wholesale, retail and some services; finance; other)
based on the definition employed by Fama and French10. We restrict the classification to five
industries to ensure that we have a reasonable number of firms in each industry and some
9
The average number of daily observations used to estimate beta and volatility is 247.
See http://mba.tuck.dartmouth.edu/pages/faculty/ken.french/Data_Library/det_5_ind_port.html
10
9
variation in risk ratings within each industry. As shown in Panel A of Table 1, we have only 281
Low risk observations with only 27 in industry 2, utilities (Panel B, Table 1). Using a more
refined industry classification, such as the Fama-French ten industry classification, would result
in some industries with virtually no low-risk firms.
Panel C of Table 1 describes the sample distribution of risk ratings. Overall, the
distribution of the risk ratings is skewed toward riskier categories, with 47% of the stocks rated
as High risk and 12% of the stocks rated as Speculative or Venture. Less than 5% of the stocks
are rated as Low risk. We observe a change toward rating more stocks as risky, with the change
taking place in 2001, the same year the bull market ended. For example, in the first half of the
sample time period (1997-2000), Low risk ratings represent more than 5% of all the ratings,
while in the second half (2001-2003) they range between 4.1% and 1.5%. The frequency of
Medium risk ratings varies between 35% and 44% in the first half and 31% and 33% in the
second half of the sample time period. The frequency of Speculative or Venture ratings increased
from about 10% to about 17%. Exploring the reasons for this change in the risk rating policy is
left for future research.
Panel A of Table 2 shows that the sample firms are typically large, with an average
market capitalization of $11 billion. The mean stock beta is 0.90, which indicates that our firms
have lower systematic risk than the market. The mean pre-report daily volatility of the stocks is
3.12% (14.40% per month), and the mean post-report volatility (postvol12) is about the same at
3.07% (14.92% per month). The sample firms have a mean debt-to-equity ratio of about 1.4 and
a mean book-to-market ratio of 0.5. The mean risk rating is 2.7, while the median is 3.
Panel B of Table 2 shows the firm characteristics associated with each risk rating. As
expected, the total stock volatility increases with analysts’ perception of the riskiness of a stock.
10
Idiosyncratic risk and beta also increase with the risk ratings. Finally, stocks that are rated as
riskier tend to have high book-to-market ratios and higher leverage.
The correlations between the variables are shown in Table 3. Risk ratings are positively
and significantly associated with beta and total risk. Consistent with the notion that size captures
risk, size is strongly negatively correlated with risk ratings, indicating that analysts perceive
smaller firms to be much riskier. The correlations between risk ratings and both book-to-market
and debt-to-equity ratios are weaker, but they increase once negative book-to-market and debtto-equity ratios are excluded from the sample.
3. Cross-Sectional Determinants of Risk Ratings
In this section, we examine the cross-sectional determinants of analysts’ risk ratings. In
particular, we regress financial analysts’ risk ratings on variables commonly viewed as measures
of risk. The dependent variable, rrating, is coded as 1, 2, 3, and 4, where high values represent
higher risk.
3.1. Motivation
Next, we introduce and briefly motivate the set of variables used in our empirical
analysis. Details on how the variables are defined and measured are provided in Appendix 1.
Market beta. According to the Capital Asset Pricing Model (Sharpe, 1964; Lintner, 1965;
Black, 1972), the expected return on an asset is a positive linear function of its market beta, and
beta alone suffices to explain the cross-section of expected returns. In other words, beta is the
only source of priced risk.
11
Empirical evidence on the validity of the CAPM is mixed. In tests that include market
beta, stock characteristics such as size (e.g., Banz, 1981; Fama and French, 1992), leverage (e.g.,
Bhandari, 1988; Fama and French, 1992), and book-to-market (e.g., Stattman, 1980; Fama and
French, 1992) help explain the cross-section of average stock returns. Furthermore, the evidence
is mixed even when beta is the only variable used to explain average returns. For example, while
Kothari, et al. (1995) find a positive relation between beta and average stock returns, Fama and
French (1992) find no relation between beta and average returns and Easley et al. (2002) find a
negative relation.
One problem with tests of asset pricing models is their reliance on ex-post (realized)
returns as a proxy for expected returns (Elton, 1999). Brav et al. (2003) address this problem by
focusing on analysts’ target prices as an alternative proxy for expected stock returns. They find a
positive relation between their expected return proxy and beta, which validates beta as a priced
source of risk. Bloomfield and Michaely (2004) conduct experiments to elicit the views of
investment professionals on beta, and find that high beta stocks are indeed perceived as being
riskier.
Size.
Since Banz (1981), there has been a lot of evidence that small (low market
capitalization) firms outperform large firms after controlling for differences in market beta (e.g.,
Chan and Chen, 1991; Fama and French, 1992; Daniel and Titman, 1997).
There is no
consensus in the literature on whether size captures a risk factor. For example, Fama and French
(1992) argue that the relation between size and average returns arises because size is a proxy for
undiversifiable risk. Size may capture a relative-prospects effect, as in Chan et al. (1985) and
Chan and Chen (1991).
The earnings prospects of distressed firms are more sensitive to
12
economic conditions, which results in a distress factor that is priced in expected returns.
Conversely, Daniel and Titman (1997) maintain that the relation between size and average
returns does not arise from the co-movement of small stocks with pervasive risk factors.
According to them, it is the characteristic (rather than the covariance structure) that appears to
explain the cross-sectional variation in the returns.
Empirical evidence relying on alternative proxies for ex-ante returns supports the
interpretation of size as a risk factor (e.g., Brav et al., 2003).
This interpretation is also
supported by the experimental evidence in Bloomfield and Michaely (2004), which shows that
Wall Street professionals perceive small stocks as being riskier.
Book-to-market. Fama and French (1992), among others, provide evidence that high
book-to-market (value) stocks earn higher returns. While the empirical phenomenon is fairly
robust, its interpretation has been controversial. One interpretation is that the higher returns are a
compensation for financial distress risk. Firms that are distressed tend to do especially poorly
when the economy is in a recession and the marginal utility of consumption is low. Investors
would hold these stocks only if they are compensated for this additional risk, which results in
greater expected returns (Fama and French, 1992, 1995).
The behavioral interpretation is that book-to-market “captures the unraveling (regression
toward the mean) of irrational market whims about the prospect of firms” (Fama and French,
1992, p. 429). According to this interpretation, investors overestimate the growth prospects of
growth (low book-to-market) stocks relative to value (high book-to-market) stocks. As these
expectations are corrected, returns on value stocks turn out to be higher. Value strategies yield
13
higher returns because they exploit the suboptimal behavior of the typical investor, and not
because they are fundamentally riskier.
The behavioral interpretation is supported by empirical evidence provided in Lakonishok
et al. (1994), Daniel and Titman (1997), and La Porta et al. (1997), among others. The
experimental evidence in Bloomfield and Michaely (2004) also points towards the behavioral
explanation. Wall Street professionals appear to view book-to-market as a mispricing indicator
rather than a risk factor. The evidence from an analysis of analyst price target provided in Brav
et al. (2003) also supports the behavioral interpretation. Brav et al. (2003) find that analysts
expect high book-to-market stocks to earn lower returns, which contradicts the rational
interpretation of book-to-market as a risk proxy.
Leverage.
The leverage effect (high debt-to-equity stocks earn higher returns than
predicted by the CAPM) was first documented by Bhandari (1988). He shows that leverage
captures a source of risk different from market risk. Fama and French (1992) argue that leverage
is also related to financial distress risk and that, as a measure of risk, it overlaps substantially
with the book-to-market factor. Campbell et al. (2005) provide evidence that leverage is an
important predictor of firms’ sensitivities to market cash flows, and advocate a greater role of
leverage in determining cost of capital in models where investors are more averse to cash flow
risk than to discount risk (Campbell and Vuoltenaho, 2005).
Idiosyncratic risk. Idiosyncratic risk emerges as a theoretical measure of risk in Levy
(1978), Merton (1987), and Malkiel and Xu (2004). The basic idea is that in the absence of full
diversification, idiosyncratic risk will result in variability of investor wealth, or consumption,
14
and thus be priced in equilibrium.11 The numerous tests of whether idiosyncratic risk is priced
have yielded mixed evidence. For example, Lehman (1990), Goyal and Santa-Clara (2003),
Malkiel and Xu (2004), and Spiegel and Wang (2005) find evidence consistent with idiosyncratic
risk being priced while Ang et al. (2005) and Bali et al. (2005) find the opposite.
Even if idiosyncratic risk is not priced in equilibrium, however, it could still matter to
investors pursuing active investment strategies to profit from mispricing in individual stocks,
investors in the options markets, and especially to individual investors who tend to hold only
handful of stocks in brokerage accounts. Curcuru et al. (2004) report that in 2001 13.7% of all
households held undiversified portfolios. They use data from the Survey of Consumer Finances
and define undiversified households as having more than 50% of their equity holdings in
brokerage accounts with fewer than 10 stocks. If undiversified individual investors are an
important consumer of analyst research, which seems to be the case for Merrill Lynch and SSB,
then it would make sense to provide risk assessments that depend on idiosyncratic risk.
Recent IPO. Lewellen and Shanken (2002) show that estimation risk (or uncertainty
about the parameters underlying a model) is priced in equilibrium. Since information about
future cash flows for a recent IPO firm is scarce, holding the stock of an IPO would subject an
investor to more estimation risk. Other theoretical papers, however, argue that estimation risk
can be diversified away (e.g., Bawa et al., 1979; Coles and Lowenstein, 1988).
Negative earnings.
Negative earnings and accounting information in general, are
important predictors of bankruptcy (Beaver, 1966; Zmijewski, 1984), and may thus be
11
A positive relation between idiosyncratic risk and expected returns is also predicted by Barberis and Huang (2001)
and Ou-Yang (2004).
15
informative about financial distress risk. Negative earnings, in particular, may signal poor
earnings prospects and could capture distress risk as in Fama and French (1992).
Investment banking relationship. Prior research shows that analysts whose employers
have underwriting relationships with the companies that they cover issue more optimistic stock
recommendations than unaffiliated analysts (e.g., Dugar and Nathan, 1995; Lin and McNichols,
1998; Michaely and Womack, 1999). This raises the question of whether analysts bias their risk
assessments by understating the investment risks associated with buying these stocks. We
construct an affiliation indicator variable, which is equal to one, when SSB underwrote a firm’s
equity offering one year before or after the report date, and zero otherwise. We interpret a
negative coefficient on this variable as evidence of biased risk assessments.
Industry membership. In general, firms in the same industry have similar characteristics.
If analysts assign the same risk ratings to firms in the same industry, then it is possible that the
risk measures “explain” the risk ratings only due to their systematic variation across industries.
Including industry dummies effectively industry-adjusts the risk measures, and increases the
hurdle for documenting a relation between the various risk measures and the risk ratings.
3.2. Empirical Analysis
Panel A of Table 4 presents the ordered logit estimates of our regressions. In all six
models we control for industry and time effects by including industry and year dummies. The
reported standard errors (in parenthesis) are White (1980) heteroskedasticity-adjusted and robust
to within-analyst correlation (Rogers (1993) /clustered standard errors). To help interpret the
16
logit coefficients, Panel B of Table 4 contrasts the probabilities that an analyst will issue Low,
Medium, High, and Speculative ratings when all continuous independent variables are at their
sample means, and the dummy independent variables are set to zero with the revised
probabilities when an independent variable is increased by its standard deviation or set to 1.
The first model examines whether high beta stocks are viewed as riskier by financial
analysts. The coefficient on beta, β1, is positive and statistically significant at 1% level. The
pseudo R-squared of this model is 6.8%.12 Next, we add the Fama-French risk proxies: size and
book-to-market in model (ii), and size and leverage in model (iii). Negative equity makes the
interpretation of book-to-market and leverage difficult. Rather than dropping the observations,
we include negative book-to-market and leverage observations, but estimate separate intercepts
and slope coefficients for positive and negative observations. We have 169 observations of
stocks with negative book values of equity.
The addition of size and book-to-market in model (ii), and size and leverage in model (iii)
improves the model’s fit; it increases the pseudo R-squared of the model to 23%. Small firms
and high leverage firms are viewed as riskier by SSB analysts, which supports the Fama-French
interpretation of size and leverage as risk measures. We find no evidence that high book-tomarket stocks are viewed as riskier, which seems to contradict the risk interpretation of the bookto-market effect. This finding, however, could be due to biased coverage by analysts. In
particular, high book-to-market firms could include firms that are financially distressed and firms
that have high ratio of assets-in-place to growth options in their investment opportunity sets and
are not necessarily distressed (Smith and Watts, 1992). If analysts discontinue coverage of the
former, but continue coverage of the latter, then we may fail to document the hypothesized
12
Without the industry dummies, the pseudo R-squared is 4.78% (4.05% without year and industry dummies). These
results are not tabulated for brevity.
17
relation between book-to-market stocks and risk ratings. The results do not change when we
include both book-to-market and leverage in the same model (model (iv)), except for the
negative book-value-of-equity dummy which becomes statistically insignificant.
The coefficients on beta, size, and leverage in model (4) appear economically significant
while the coefficient on book-to-market does not (Panel B of Table 4). For example, one
standard deviation change in beta reduces probabilities of Low and Medium risk from 3.81% and
50.60% to 1.57 % and 30.83%, and increases the probabilities of High and Speculative risk from
43.05% and 2.54% to 61.52% and 6.09%. In contrast, the effect of a one standard deviation
change in book-to-market on the probability of any risk assessment is within 3% of the base case
probability of that risk assessment.
In models (ii), (iii), and (iv) we document a positive and statistically significant
coefficient on beta, which suggests that analysts indeed view high beta stocks as being riskier. It
is possible, however, that this effect is due to beta proxying for idiosyncratic risk (the variance
component of returns unexplained by the market) as the correlation between beta and
idiosyncratic risk, reported in Table 3, is about 61%. We find that idiosyncratic risk is a more
significant determinant of the risk ratings than beta. The pseudo R-squared of a model that
includes only idiosyncratic risk, model (v), is 21% while that of model (i), which includes only
beta, is only 7%.13
Model (vi) includes all variables from models (iv) and (v) plus three additional variables:
dummy variables for a recent initial public offering (IPO), negative accounting earnings, and
SSB being an affiliated broker to the firm-year observation. The only finding that is not robust
to including additional variables is the consistently positive coefficient on beta in models (i)
13
Excluding the industry dummies exacerbates the difference; the pseudo R-squared of model (v) drops to 20.33%
(18.50% without year and industry dummies), while the pseudo R-squared of model (i) drops to 4.78% (4.05%
without year and industry dummies).
18
through (iv). This coefficient becomes statistically insignificant, which suggests that controlling
for other risk measures, and idiosyncratic risk in particular, high beta stocks are not viewed as
riskier.14 The analysis of marginal effects in Panel B of Table 4 further supports the view that
idiosyncratic risk plays a more important role as a determinant of the risk ratings than beta. For
example, one standard deviation change in beta (idiosyncratic risk) changes the probabilities of
High and Speculative risk from 60.13% and 3.06% to 61.79% (75.99%) and 3.31% (10.64%)
respectively. As expected, we find that firms with losses are viewed as riskier by financial
analysts. Interestingly, the role of negative earnings is not subsumed by size, which contrasts
with Fama and French’s (1992) finding for stock returns. Finally, we find no evidence that recent
IPOs are viewed as riskier.
We find no evidence that SSB analysts rate the stocks they are affiliated with differently
(model (vi)). We find that when affiliate (together with industry and year dummies) is the only
explanatory variable in the regression, there is a weak positive relation between risk ratings and
affiliation, but this effect disappears once we control for other variables.15 We do not believe that
this result is due to low power. In our sample, SSB acted as underwriter in 306 cases (around 5%
of the total sample), and we are able to document that when an underwriting relation exists stock
recommendations are more favorable. Results are not tabulated for brevity.
A distinctive feature of the ordered logit model is that the risk ratings are viewed as
naturally ordered, and assumes that the relationship between the outcomes and the independent
variables is the same for different outcomes The multinomial logit model does not presume the
existence of a natural order in the risk ratings, and allows different relationships between
different outcomes and the independent variables.
14
This result is robust to estimating beta using weekly returns.
The slope coefficient is 0.2582 and the standard error 0.1588. The pseudo R-square is 3.78%. These results are not
tabulated for brevity.
15
19
We report the results of multinomial logit regressions in Panel C of Table 4. The
multinomial logit estimates can be viewed as the logit estimates from the simultaneous
estimations of three logit models: the first model compares Low risk and Medium risk stocks; the
second model compares Medium risk and High risk stocks; the third model contrasts High risk
vs. Speculative or Venture stocks. A positive coefficient means that the riskier outcome is more
likely to be observed with an increase in the corresponding independent variable.
The most interesting result in the multinomial logit regressions concerns beta. Beta is
statistically significant in all three logit regressions. When beta is high, stocks are more likely to
be rated Medium risk than Low risk, and High Risk rather than Medium risk. Surprisingly, high
beta stocks are less likely to be rated Speculative or Venture than High risk. These results suggest
that the effect of beta depends on the risk rating outcomes, which explains why we find no
relation between the latent risk variable and beta in the ordered logit regression (model (vi)). The
relation between risk and other firm characteristics such as size and leverage is monotonic as
expected. In the case of leverage and accounting losses, however, not all coefficients are
statistically significant. This could be due to the loss of efficiency in the multinomial logit
estimations as the number of parameters is three times higher than the number of parameters in
the ordered logit estimations.
In sum, analysts’ risk ratings are highly multi-dimensional, with idiosyncratic risk, size,
leverage, and negative earnings capturing distinct dimensions of risk. We find no evidence that
the existence of an underwriting relation results in biased risk ratings. We conclude that risk
ratings incorporate mainly information about firm characteristics commonly viewed as risk
proxies.
20
4. Informativeness of Risk Ratings
4.1. Motivation
In general, we think of financial analysts as both aggregating already-available
information, and generating new information. The empirical analysis in this section examines
the extent to which risk ratings predict the cross-sectional variation in future return volatility. If
risk ratings help predict the cross-sectional variation in volatility in the presence of other
predictors of volatility, then we can say that risk ratings are incrementally informative. In other
words, financial analysts provide information about future volatility that is incremental to the
information provided by other variables.
Our focus on the predictions of total return volatility is motivated by SSB’s definition of
risk ratings as being based on price volatility and predictability in fundamentals, and our finding
that past volatility is an important determinant of their risk ratings. We do not claim that
everyone ought to treat the risk ratings as predictors of future volatility, but only that such
predictions may be of interest to investors involved in active investment strategies, investors who
are not fully diversified, and investors in the options markets, among others.
4.2. Empirical Analysis
In our analysis, we regress post-report volatility on risk ratings and other predictors of
volatility, and interpret the coefficient on risk ratings as a measure of its incremental information
content. Post-report volatility is defined as the natural logarithm of the standard deviation of
daily returns after the date of an analyst report (see appendix for details).
21
We use the natural logarithm of the standard deviation, logpostvol, instead of the standard
deviation, postvol, for two reasons.16 First, the distribution of postvol is truncated at zero and
highly skewed. As a result, the residuals from the regression of postvol on the predictive
variables violate the OLS regression assumptions. In contrast, the distribution of logpostvol is
more symmetric and the residuals from the regression are well-behaved.
Second, using
logpostvol ensures that the predicted volatility is always positive, a condition that is not always
met when postvol is used as the dependent variable.17
4.1.1. Horizon effects
The first question we address is whether analysts’ risk ratings are informative about
future volatility at different return horizons. It is well known that return volatility varies over
time and has temporary and persistent components. Thus, we first examine whether the risk
ratings alone can predict post-report volatility at time horizons of 3, 6, 9, and 12 months,
logpostvol3 to logpostvol12. The risk rating is a discrete variable taking the values 1, 2, 3, and 4
(Low risk, Medium risk, High risk, and Speculative or Venture). Hence, the parameter estimates
from our regressions represent the difference in post-report volatility for stocks whose risk
ratings differ by one.
Our results are presented in Panel A, Table 5. The risk ratings help predict the crosssectional variation in post-report volatility at different time horizons, with the R-squared
increasing with the horizon (from 46% for the three-month volatility to 48% for the twelve-
16
A similar model for volatility is explored in Andersen et al. (2001), and Shanken and Tamayo (2005).
In our model, logpostvol is a linear function of the predictive variables, Logpostvol = f(predictive variables).
Taking the exponential of logpostvol, we obtain postvol, Postvol = exp[logpostvol] = exp[f(predictive variables)],
which is positive. For the distributional properties of logpostvol vs. postvol, see French et al. (1987) and, more
recently, Andersen et al. (2001).
17
22
month volatility).18 The economic significance of the ratings as a predictor of volatility is
substantial, and also increases with the horizon. For example, the difference in three-month
post-report volatility between Low risk and Speculative stocks is 1.96% per day (9.20% per
month, assuming 22 trading days per month), an amount we view as economically significant
since it exceeds the cross-sectional standard deviation in the post report volatility of 1.76% per
day (Panel A of Table 2), or 8.26% per month.19 The economic significance of the risk ratings is
larger for the twelve-month horizon. The difference in twelve-month volatility between Low risk
and Speculative stocks is 2.10% per day (9.83% per month), which is 1.3 times larger than the
cross-sectional standard deviation in post-report volatility of 1.67% per day (Panel A of Table 2),
or 7.83% per month.20 These results suggest that analysts are better at predicting long-term
components of volatility. Hence, in the remaining tables we focus on the twelve-month volatility
horizon.
4.1.2. Incremental Informativeness
Our evidence so far suggests that risk ratings (together with year and industry dummies)
explain close to 50% of the cross-sectional variation in twelve-month post-report volatility
(Panel A, Table 5). We have also shown that the risk ratings can be partly explained by publicly
available information (Table 4). In this section, we examine whether the predictive ability of the
risk ratings comes solely from their correlation with publicly available information, or whether
18
Excluding the industry and year dummies, the R-squared ranges from 22% (3-month horizon) to 24% (12-month
horizon).
19
1.96% per day is the difference in volatility for stocks with rrating=4 and rrating=1. It is estimated as follows.
First, we compute the expected log-volatility when rrating=1. By taking the exponential function of the result, we
obtain the expected volatility when rrating=1. We do the same for rrating=4, and then obtain the difference in the
expected volatilities. Finally, the monthly volatility is obtained by multiplying the daily volatility by sqrt(22),
assuming 22 trading days per month.
20
The difference in volatility between Low and High risk stocks is 2.06 per day (9.64% per month) at the six-month
horizon, and 1.99% per day (9.35% per month) at the nine-month horizon.
23
the risk ratings are incrementally informative.
The results are presented in Panels B and C of
Table 5.
The first model in Panel B examines the source of the predictive ability of risk ratings by
decomposing risk ratings into expected and unexpected components based on model (vi) from
Table 4. The expected risk rating (errating) is the part correlated with the information variables
included in the model, while the unexpected risk rating (uerrating) is the part orthogonal to these
information variables. We examine whether the unexpected and expected components of risk
ratings are equally informative about future return volatility. This simple decomposition of risk
ratings increases the R-squared from 48% (model (iv) in Panel A) to 65% (model (i) in Panel B).
We find that errating and uerrating are both statistically significant, which suggests that the
predictive ability of the risk ratings is a result of analysts aggregating already-available
information as well as providing new information. The economic significance of errating is
substantial. For example, a one-standard deviation increase in errating increases the predicted
volatility by 51.6% (assuming the other variables are at their means). The economic significance
of the unexpected component of risk is also substantial. A one-standard deviation increase in
uerrating increases the predicted volatility by 10.2% (assuming the other variables are at their
means)
It is well known that volatility is autoregressive; that is, past volatility helps predict future
volatility (e.g., French et al., 1987). For our sample, we find that pre-report volatility alone
explains 79% of the cross-sectional variation in post-report volatility (model (ii)). Hence, in
models (iii) and (iv), we re-examine our previous results on the predictive ability of risk ratings
after controlling for past volatility. Although the explanatory power does not increase much
relative to model (ii), we find that the coefficient of risk rating is both statistically and
24
economically significant. In particular, a coefficient of 0.0453 (model (iii)) yields a difference in
monthly volatility between Low risk and Speculative stocks of 1.73% per month (assuming that
the other variables remain at their means), which is fairly large given a cross-sectional standard
deviation of future monthly volatility of 7.83% (from Table 2, panel A). We also find that, even
after controlling for past volatility, both the expected and unexpected components of risk ratings
remain informative, suggesting that analysts provide information incremental to what is already
available.
Finally, in model (v) we examine whether the risk rating adds information about postreport volatility after controlling for past volatility and other potential predictors of future
volatility. We find that although the risk rating coefficient is smaller, it remains statistically and
economically significant.
For example, in the presence of pre-report volatility and other
predictors of volatility, the difference in post-report volatility between Low risk and Speculative
stocks is 1.42% per month.
Up to this point, our analysis of the information content of risk ratings has assumed that
post-report volatility increases linearly with risk ratings. In other words, the difference in postreport volatility between Low risk and Medium risk stocks (ratings 1 and 2) is the same as the
difference in post-report volatility between High risk and Speculative stocks (ratings 3 and 4).
Since this restriction may be empirically invalid, we define a set of indicator variables, rrating2,
rrating3, and rrating4, and estimate post-report volatility for each risk rating.21 The results are
presented in Panel C of Table 5. The intercept of these regressions is an estimate of the postreport volatility for a stock rated as Low risk (rrating=1), and the parameter estimates on
rrating2, rrating3, and rrating4 represent the difference between post-report volatility between a
21
The indicator variable rrating1 is equal to 1 when the risk rating is 1 (low risk), and 0 otherwise. The other
indicator variables are defined similarly.
25
stock rated as Low risk, and a stock with a risk rating of Medium risk, High risk, and Speculative
or Venture.
In all the specifications (Models (i), (ii), and (iii) of Panel C, Table 5), we find that the
relation between risk ratings and post-report volatility is monotonic: stocks viewed as riskier
have higher post-report volatility. The relation appears non-linear, however. For example,
before controlling for other predictive variables (model (i)), the difference in the coefficients of
Speculative or Venture and High risk (rrating4-rrating3) is 0.4199, which translates into a
difference in future monthly volatility of 7.10%. The difference in future monthly volatility
between High and Medium risk stocks (rrating3-rrating2) is 3.12%. Finally, the difference in
future monthly volatility between Medium and Low risk stocks (rrating2-rrating1) is 1.48% per
month.
Models (ii) and (iii) of Panel C, Table 5, correspond to Models (iii) and (v) from Panel B.
The inclusion of the predictors of post-report volatility diminishes the informational content of
the risk ratings, but the difference in post-report volatility between Speculative and Low risk
stocks remains statistically and economically significant in both models.
For example,
controlling for pre-report volatility (model (ii)), the difference in post-report volatility between
Speculative and Low risk stocks is 1.65% per month. Controlling also for other predictive
variables reduces the difference to 1.29% per month.
Overall, we find evidence that financial analysts provide information about future
volatility that is incremental to publicly available information. This evidence further supports
the view of financial analysts as important information intermediaries in capital markets.
26
5. Additional Analyses
5.1.1. Robustness Checks
We conduct several robustness tests. First, we examine the sensitivity of our results to the
exclusion of financial firms. Since the book-to-market and the leverage ratios of financials are
not comparable to those of the other industries, we re-run our analyses excluding financials. We
obtain similar results, although book-to-market is marginally significant (at 7% level) as a
determinant of the risk ratings. The volatility results remain unchanged. Finally, we analyze the
incremental informativeness of the risk ratings for 3-month, 6-month, and 9-month volatility
horizons. The pattern we observe is similar to the pattern reported in Panel A of Table 5. The
incremental information content of risk ratings decreases as we shorten the forecast horizon, but
even in the case of 3-month volatility it is statistically significant.
In order to examine whether our findings are time period specific, we also conduct our
analysis every year. We find that risk ratings are informative in every year in the period 19982003. Fama and MacBeth analysis (with Newey-West adjustments) yields a coefficient on the
risk ratings of 0.0402, with a standard error of 0.0063, which is comparable to our pooled
regression result.22 The only difference is that, in addition to risk ratings, only past volatility and
negative earnings can predict the cross-section of future volatility.
5.2. Merrill Lynch and Value Line
In this sub-section we examine whether our findings on SSB extend to other information
providers and to other time periods. In particular, we examine the determinants and the
informativeness of risk ratings provided by Merrill Lynch in 1998 and Value Line Investment
22
See Petersen (2005) for a comparative analysis of Fama-Macbeth and clustered errors approaches.
27
Survey over the period 1991-2003. An important distinction between sell-side analysts,
employed at SSB and Merrill Lynch, and Value Line analysts is that Value Line analysts are not
prone to the sell-side’s conflicts of interest. This makes the Value Line ratings a valuable
benchmark against which to examine the usefulness of sell-side analysts’ risk ratings.
Both Merrill Lynch and Value Line base their risk assessments on a stock’s volatility and
financial strength. For example, Merrill Lynch rates a stock as Average Risk if “the stock is
expected to entail price risk similar to the market as a whole. Issues of such companies are
characterized by relatively good balance sheet and capital structures that are appropriate for the
company’s particular industry or industries. The company has demonstrated the ability to
produce above average sales, profits, and other measures of leadership within its industry.”23
Merrill Lynch uses four categories: Low, Average, Above Average, or High Risk. Value Line’s
Safety Rank, from 1 (Low Risk) to 5 (High Risk), combines a ranking of past volatility and a
measure of the company’s financial strength.
Panel A of Table 6 describes the frequency distribution of Merrill Lynch’s and Value
Line’s risk ratings. Similar to SSB, the risk ratings are skewed toward high risk. Merrill rates
59% of all stocks as Above Average or High risk; 35% are rated Average and only 5.6% are rated
Low risk. Value Line risk ratings are similarly asymmetric: 48% of the stocks have a ranking of
3, 40% have rankings of 4 and 5 (High risk), and the remaining 12% have rankings of 1 and 2
(Low risk). Similar to SSB, Value Line assigns fewer stocks to lower risk rankings (1, 2, and 3)
and more stocks to higher risk rankings (4 and 5) over time. For example, in every year from
1991 through 1995 at least 15% of the stocks were assigned a ranking of 2, while in the period
1996-2003 the percentage of stocks assigned a ranking of 2 ranges between 10.7% and 7.8%. In
23
In recent years Merrill Lynch has switched to a policy of assessing risk based on a quantitative volatilityforecasting model. An analyst, however, can deviate from the prediction of the model, especially if he or she thinks
that the stock warrants a riskier rating.
28
the period 1991-1995 the percentage of stocks assigned a ranking of 4 varies between 14.1% and
11.9% while in the period 1996-2003 the percentage of stocks assigned a ranking of 4 ranges
between 28.9% and 36%.
Panel B of Table 6 reports our findings on the determinants of Merrill Lynch’s and Value
Line’s risk ratings. All firm characteristics influencing SSB’s risk ratings (idiosyncratic risk,
leverage, size, and accounting losses) similarly influence Merrill Lynch’s ratings. An additional
determinant of Merrill’s risk ratings is the IPO variable. Merrill views recent IPOs as inherently
riskier. Value Line’s risk ratings incorporate the same information that SSB’s ratings incorporate
plus information about book-to-market and beta. Value Line rates low book-to-market stocks and
high beta stocks as riskier, which is supportive of the hypothesis that beta and book-to-market
are measures of risk.
Both Merrill’s and Value Line’s risk ratings have incremental information content (Panel
C of Table 6). Based on this evidence we conclude that our evidence is not unique to SSB and
that it can be extended to other information providers such as Merrill Lynch and Value Line. The
fact that risk ratings issued by sell-side analysts appear to be similar to the ones issued by Value
Line suggests that even if sell-side analysts have incentives to minimize investment risks and
issue boilerplate disclosures, there are competitive forces holding them in check.
6. Concluding remarks
This study takes the first step in understanding the role of financial analysts as providers
of information about investment risks in capital markets. Using a sample of 6,098 risk ratings
issued by Salomon Smith Barney, now Citigroup, during the period 1997-2003, we examine the
degree to which analysts’ quantitative risk assessments are determined by firm characteristics
29
often used by researchers as measures of risk. We find that idiosyncratic risk, size, leverage, and
accounting losses are important determinants of analysts’ risk ratings. To the extent that analysts’
notion of risk is correlated with marginal investor’s notion of risk, the evidence suggests that
idiosyncratic risk, size, leverage and losses are indeed measures of risk. Overall, the firm
characteristics we examine explain more than 28% of the variation in risk ratings, which
suggests that financial analysts gather and process information about investment risk in
determining the risk ratings.
Viewing the risk ratings as a predictor of future return volatility, we examine the extent to
which the ratings provide information incremental to publicly available information about future
volatility. We find that risk ratings have incremental information content about future volatility,
even after controlling for various predictors of future volatility. An investor thus can use risk
ratings to improve her forecast of cross-sectional differences in future volatility.
We find similar evidence about the determinants and informativeness of risk ratings when
we analyze Merrill Lynch reports issued in 1998 and Value Line Safety Rankings issued in the
period 1991-2003. This further supports the view that analysts indeed play an important role as
providers of information about investment risks.
Our findings are especially interesting given the widely held belief that sell-side analysts
played an important role in creating the stock market bubble in the late 90s. In particular, due to
their undisclosed conflict of interests, sell-side analysts are alleged to have contributed to the
bubble by hyping stocks, and minimizing, or failing to discuss the risks involved in purchasing
securities. This belief led to important changes in the regulatory environment in which financial
30
analyst activities take place.24 The evidence from the analysis of SSB’s and Merrill Lynch’s risk
ratings, however, contradicts this commonly held belief as the risk assessments provide useful
information about investment risks and are driven by the same factors driving Value Line’s risk
ratings. We caution our readers against viewing this evidence as conclusive as we have no formal
test of whether risk assessments in the bubble period differ systematically from the post-bubble
period due to compromised independence in the pre-bubble period, nor do we show that analysts
provided “sufficient” amount of information about investment risks. We leave such analyses for
future research.
Finally, our investigation of the role of financial analysts as providers of information
about risk complements prior investigations of financial analysts as providers of information
about expected cash flows and expected returns. Merging these two strands of research to
examine how analysts actually make the risk-return trade-off is a promising area for future work.
24
In his Testimony on Global Research Analyst Settlement Before the Senate Committee on Banking, Housing and
Urban Affairs, William H. Donaldson, Chairman of the U.S. Securities & Exchange Commission discusses various
regulatory actions whose purpose is to ensure that analysts’ provide objective research to investors. According to
him, “Although the monetary relief secured in the settlements is substantial, unfortunately the losses that investors
suffered in the aftermath of the market bubble that burst far exceed the ability to compensate them fully.”
31
References
Alderfer, C., and H. Bierman, 1970, Choices with risk: Beyond the mean and variance, The
Journal of Business 43 (3), 341-353.
Andersen, T., T. Bollerslev, F.X. Diebold, and H. Ebens, 2001, The distribution of realized stock
return volatility, Journal of Financial Economics 61, 43-76.
Ang, A., R. Hodrick, Y. Xing, and X. Zhang, 2005, The cross-section of volatility and expected
returns, Journal of Finance 61, 259-299.
Asquith, P., M. Mikhail, and A. Au, 2005, Information content of equity analyst reports, Journal
of Financial Economics 75 (2), 245-490.
Bali, T., N. Cakici, X. Yan, and Z. Zhang, 2004, Does idiosyncratic risk really matter? Journal of
Finance 60, 905-929.
Banz, R., 1981, The relationship between return and market value of common stocks, Journal of
Financial Economics 9 (1), 3-18.
Barberis, N., and M. Huang, 2001, Mental accounting, loss aversion, and individual stock
returns, Journal of Finance 56, 1247-1292.
Bawa, V., S. Brown, and R. Klein, 1979, Estimation risk and optimal portfolio choice, NorthHolland, Amsterdam.
Beaver, W., 1966. Financial ratios as predictors of failure, Journal of Accounting Research 4, 71111.
Bhandari, L., 1988, Debt/Equity ratio and expected common stock returns: Empirical evidence,
Journal of Finance 43 (2), 507-528.
Black, F., 1972, Capital market equilibrium with restricted borrowing, Journal of Business 45
(3), 444-455.
Bloomfield, R., and R. Michaely, 2004, Risk or mispricing? From the mouths of professionals,
Financial Management 33 (3): 61-81.
Brav, A., and J.B. Heaton, 2002, Competing theories of financial anomalies, Review of Financial
Studies 15 (2), 575-606.
Brav, A., and R. Lehavy, 2003, An empirical analysis of analysts’ target prices: Short-term
informativeness and long-term dynamics, Journal of Finance 58 (5), 1933-1967.
Brav, A, R. Lehavy, and R. Michaely, 2003, Using expectations to test asset-pricing models,
Working Paper, Duke University, Durham, NC.
32
Campbell, J., C. Polk, and T. Vuolteenaho, 2005, Growth or glamour? Fundamentals and
systematic risk in stock returns, Working Paper, Working Paper, Nortwestern University.
Campbell, J., and T. Vuolteenaho, 2004, Good beta, bad beta, American Economic Review 94,
1249-1275.
Chan, K.C. and N. Chen, 1991, Structural and return characteristics of small and large firms,
Journal of Finance 46 (4), 1467-1484.
Chan, K.C., N. Chen, and D. Hsieh, 1985, An exploratory investigation of the firm size effect,
Journal of Financial Economics 14 (3), 451-471.
Coles, J., and U. Loewenstein, 1988, Equilibrium pricing and portfolio composition in the
presence of uncertain parameters, Journal of Financial Economics 22 (2), 279-303.
Cooley, P., 1977, A Multidimensional analysis of institutional investor perception of risk,
Journal of Finance 32 (1), 67-78.
Curcuru, S., J. Heaton, D. Lukas, and D. Moore, 2004, Heterogeneity and portfolio choice,
Forthcoming in the Handbook of Financial Econometrics.
Daniel, K. and S. Titman, 1997, Evidence on the characteristics of cross sectional variation of
stock returns, Journal of Finance 52 (1), 1-33.
Defond, M., and M. Hung, 2003, An empirical analysis of analysts’ cash flow forecasts, Journal
of Accounting and Economics 35 (1), 73-100.
Dugar, A., and S. Nathan, 1995, The effects of investment banking relationships on financial
analysts’ earnings forecasts and investment recommendations, Contemporary Accounting
Research (Fall) 131-160.
Easley, D., S. Hvidkjaer, and M. O’Hara, 2002, Is information risk a determinant of asset
returns?, Journal of Finance 57 (5), 2185-2221.
Elton, E, 1999, Expected return, realized return and asset pricing tests, Journal of Finance 54 (4),
1199-1220.
Fama, E., and K. French, 1992, The cross-section of expected stock returns, Journal of Finance
47 (2), 427-465.
Fama, E., and K. French, 1993, Common risk factors in the returns on stocks and bonds, Journal
of Financial Economics 33 (1), 3-56.
Fama, E., and K. French, 1995, Size and book-to-market factors in earnings and returns, Journal
of Finance 50 (1), 131-155.
33
Francis, J., and L. Soffer, 1997, The relative informativeness of analysts’ stock recommendations
and earnings forecasts revisions, Journal of Accounting Research 35 (2), 193-211.
French, K., G. Schwert, and R. Stambaugh, 1987, Expected stock returns and volatility, Journal
of Financial Economics 19 (1), 3-29.
Goyal, A., and P. Santa-Clara, 2003, Idiosyncratic risk matters!, Journal of Finance, 58 (3), 9751007.
Kothari, S.P., J, Shanken, and R. Sloan, 1995, Another look at the cross-section of expected
stock returns, Journal of Finance 50 (1), 185-224.
Lakonishok, J., A. Shleifer, and R. Vishny, 1994, Contrarian investment, extrapolation, and risk,
Journal of Finance 49 (5), 1541-1578.
La Porta, R., J. Lakonishok, A. Shleifer, and R. Vishny, 1997, Good news for value stocks:
Further evidence on market efficiency, Journal of Finance 52 (2), 859-874.
Lehman, B., 1990, Residual risk revisited, Journal of Econometrics 45, 71-97.
Levy, H., 1978, Equilibrium in an imperfect market: A constraint on the number of securities in a
portfolio, American Economic Review 68 (4), 643-658.
Lewellen, J., and J. Shanken, 2002, Learning, asset-pricing tests, and market efficiency, Journal
of Finance 57 (3), 1113 – 1145.
Libby, R., R. Bloomfield, and M. Nelson, 2002, Experimental research in financial accounting,
Accounting, Organizations, and Society 27 (8), 775-810.
Lin, H., and M. McNichols, 1998, Underwriting relationships, analysts’ earnings forecasts and
investment recommendations, Journal of Accounting and Economics 25, 101-128.
Lintner, J., 1965, The valuation of risk assets and the selection of risky investments in stock
portfolios and capital budgets, Review of Economics and Statistics 47 (1), 13-37.
Malkiel, B., and Y. Xu, 2004, Idiosyncratic risk and securities returns, Working Paper,
University of Texas at Dallas.
Markov, S., 2001, Financial analyst stock recommendations and earnings forecast revisions,
Doctoral Dissertation, University of Rochester.
Mear, R., and M. Firth, 1987, Cue usage and self-insight of financial analysts, The Accounting
review 62 (1), 176-182.
34
Merton, R., 1987, A simple model of capital market equilibrium with incomplete information,
Journal of Finance 42 (3), 483-510.
Merrill Lynch & Co. Research Reports Securities Litigation, 272 F. Supp. 2d 351 (2003 U.S.
Dist)
Merrill Lynch & Co., 1997, Policy and procedures manual,
http://www.oag.state.ny.us/investors/merrill/merrill_exhibits_part1.pdf, accessed February 11,
2006
Michaely, R., and K.L. Womack, 1999, Conflicts of Interest and the Credibility of Underwriter
Analyst Recommendations, Review of Financial Studies 12 (4), 653-686.
Olsen, R., 1997, Investment risk: The experts’ perspective, Financial Analysts Journal 53 (2), 6266.
Ou-Yang, H., 2005, An equilibrium model of asset pricing and moral hazard, Forthcoming in
Review of Financial Studies.
Petersen, M., 2005, Estimating standard errors in finance panel data sets: Comparing approaches,
Working Paper, Northwestern University.
Rogers, W., 1993, Regression standard errors in clustered samples, Stata Technical Bulletin 13,
19-23
Shanken, J., and A. Tamayo, 2005, Dividend yield, risk and mispricing: a Bayesian analysis,
Working paper, London Business School.
Sharpe, W., 1964, Capital Asset Prices; A theory of market equilibrium under conditions of risk,
Journal of Finance 19 (3), 425-442.
Smith, C., and R. Watts, 1992, The investment opportunity set and corporate financing, dividend,
and compensation policies, Journal of Financial Economics 32 (3), 263-292.
Spiegel, M., and X. Wang, 2005, Cross sectional variation in stock returns: liquidity and
idiosyncratic risk, Working Paper, Yale University.
Stattman, D., 1980, Book values and stock returns, The Chicago MBA: A Journal of Selected
Papers 4, 25-45.
Value Line Investment Survey, 2005, A short guide toward using The Value Line Investment
Survey, http://www.valueline.com/pdf/quickgd.pdf, accessed on February 11, 2006.
White, H., 1980, A heteroskedasticity-consistent covariance matrix estimator and a direct test for
heteroskedasticity, Econometrica 48 (4), 817-830.
35
Zmijewski, M., 1984, Methodological issues related to the estimation of finanical distress
prediction models, Journal of Accounting research 22, 59-82.
Zmijewski, M., 1993, Comments on ‘Earnings forecasting research: its implications for capital
markets research’ by L. Brown, International Journal of Forecasting 9, 337-342.
36
Appendix 1
Variables definition
rrating
rrating1
rrating2
rrating3
rrating4
errating
uerrating
prevol
postvol3
postvol6
postvol9
postvol12
beta
idiorisk
BM
If an analyst rates a stock low risk, then rrating=1
If an analyst rates a stock medium risk, then rrating=2
If an analyst rates a stock high risk, then rrating=3
If an analyst rates a stock speculative or venture, then rrating=4
If an analyst rates a stock low risk, then rrating1=1, else rrating1=0
If an analyst rates a stock medium risk, then rrating2=1, else rrating2=0
If an analyst rates a stock high risk, then rrating3=1, else rrating3=0
If an analyst rates a stock speculative or venture, then rrating4=1, else rrating4=0
The expected risk rating of a stock is estimated using the model of the determinants of
risk ratings in model (vi) in Table 4
The unexpected risk rating of a firm is taken as the observed risk rating less the expected
risk rating estimated using the model of the determinants of risk ratings in model (vi) in
Table 4
The pre-report total volatility of a stock is the standard deviation of a stock's daily return
for a period of twelve months prior to the date of the analyst's report. A minimum of 60
daily returns observations are required to estimate total volatility
The three-month post-report total volatility of a stock is the standard deviation of a
stock's daily return for a period of three months after the date of the analyst's report. A
minimum of two calendar months of daily returns observations are required to estimate
total volatility
The six-month post-report total volatility of a stock is the standard deviation of a stock's
daily return for a period of six months after the date of the analyst's report. A minimum
of five calendar months of daily returns observations are required to estimate total
volatility
The nine-month post-report total volatility of a stock is the standard deviation of a stock's
daily return for a period of nine months after the date of the analyst's report. A minimum
of eight calendar months of daily returns observations are required to estimate total
volatility
The twelve-month post-report total volatility of a stock is the standard deviation of a
stock's daily return for a period of twelve months after the date of the analyst's report. A
minimum of eleven calendar months of daily returns observations are required to estimate
total volatility
Stock beta is estimated using daily returns observations prior to the date of the analysts'
reports from the regression Rit = α + betai * Rmt + ε. A minimum of 60 daily returns are
required
The idiosyncratic risk of a stock is the portion of the volatility of the stock's returns
unexplained by the market
The book-to-market ratio of a firm is calculated using data in the most recent fiscal year
prior to the date of the analyst's report. Book value of equity = Compustat #60,
market value of equity = Compustat #24 * Compustat #25
37
Appendix 1 (continued)
DE
Dneg
MV
IPO
negINC
affiliate
Ind1
Ind2
Ind3
Ind4
Ind5
The debt-to-equity ratio of a firm is calculated using data in the most recent fiscal year
prior to the date of the analyst's report. Book value of equity = Compustat #60,
Total debt = Compustat #9 + Compustat #34
Dneg is a dummy variable which takes the value of one if BM and/or DE are negative
(both due to negative book values of equity) and zero otherwise
The market value of a stock is calculated using data in the most recent fiscal year prior to
the date of the analyst's report. Market value of equity = Compustat #24 * Compustat #25
IPO is a dummy variable which takes the value of one if a firm had its initial public
offering within two years prior to the date of the analyst's report. It takes the value of
zero otherwise
negINC is a dummy variable which takes the value of one if the sum of a firm's previous
four quarters' earnings before extraordinary items is negative, and zero otherwise
affiliate is a dummy variable which takes the value of one if Salomon Smith Barney
(including Salomon Brothers, Smith Barney, Salomon Smith Barney, and Citigroup)
underwrote a firm’s equity offering one year before or after the report date, and zero
otherwise
Manufacturing firms in Fama-French 5 Industry Portfolios – SIC codes from 2000-3999
Utilities in Fama-French 5 Industry Portfolios – SIC codes from 4900-4999
Wholesale, retail, and some services firms in Fama-French 5 Industry Portfolios – SIC
codes from 5000-5999 and 7000-7999
Finance firms in Fama-French 5 Industry Portfolios – SIC codes from 6000-6999
Other firms in Fama-French 5 Industry Portfolios – firms not classified as in Ind1, Ind2,
Ind3, and Ind4
38
Table 1
Sample composition
Panel A: Sample Construction
The data on analysts’ risk assessments comes from analysts’ research reports available on
Investext. The initial sample consists of 25,778 firm-year observations obtained from 10,722
company or industry research reports issued by Salomon Smith Barney analysts on US firms in
the period of January and February, 1997 to 2003. After deleting duplicate firm-year
observations and those with missing data, the final sample consists of 6,098 observations. This
table reconciles the initial and the final samples.
Less: duplicate firm-years
Less: missing COMPUSTAT or CRSP data
Total number of observations in the sample
Number of unique firms
Number of unique analysts
Number of
observations
25,778
755
480
120
24,423
17,593
732
6,098
2,279
235
Low risk
Medium risk
High risk
Speculative or Venture
Total number of observations in the sample
281
2,204
2,856
757
6,098
Total number of firm-years
Less: listed on foreign exchanges (from industry reports)
Less: missing exchange tickers
Less: missing risk ratings
39
Table 1 (continued)
Panel B: Risk ratings by industry for the final sample (6,098 observations)
The sample consists of 6,098 firm-year observations over the period of 1997-2003 followed by
Salomon Smith Barney analysts. The firms are grouped into five industries based on the
definition of Fama-French 5 portfolios. See Appendix 1 for the definition of the variables.
Ind1
Ind2
Ind3
Ind4
Ind5
Low risk
Medium risk
High risk
139 (6.2%)
27 (9.3%)
31 (3.0%)
61 (4.5%)
23 (2.0%)
792 (35.2%)
139 (47.8%)
221 (21.3%)
743 (55.1%)
309 (26.4%)
1,005 (44.7%)
101 (34.7%)
642 (61.8%)
474 (35.1%)
634 (54.1%)
Speculative or
Venture
313 (13.9%)
24 (8.2%)
144 (13.9%)
71 (5.3%)
205 (17.5%)
Total
2,249
291
1,038
1,349
1,171
Panel C: Risk ratings by year for the final sample (6,098 observations)
1997
1998
1999
2000
2001
2002
2003
19972003
Low risk
Medium risk
High risk
54 (8.3%)
45 (5.7%)
42 (5.4%)
46 (5.5%)
43 (4.1%)
36 (3.5%)
15 (1.5%)
288 (44.1%)
318 (40.0%)
318 (41.3%)
300 (35.7%)
346 (33.1%)
325 (32.0%)
309 (31.5%)
281 (4.6%)
2,204 (36.2%)
40
Total
258 (39.5%)
353 (44.5%)
330 (42.9%)
414 (49.3%)
518 (49.5%)
492 (48.5%)
491 (50.1%)
Speculative or
Venture
53 (8.1%)
78 (9.8%)
80 (10.4%)
80 (9.5%)
139 (13.3%)
162 (16.0%)
165 (16.9%)
2,856 (46.8%)
757 (12.4%)
6,098
653
794
770
840
1,046
1,015
980
Table 2
Descriptive Statistics
Panel A: Descriptive statistics for the final sample
The sample consists of 6,098 firm-year observations over the period of 1997-2003 followed by
Salomon Smith Barney analysts. The variables beta, prevol, postvol3, postvol6, postvol9,
postvol12, idiorisk, BM, and DE are winsorized at +/- 1% level. See Appendix 1 for the
definition of the variables. N is the number of observations.
rrating
beta
prevol
postvol3
postvol6
postvol9
postvol12
idiorisk
BM
DE
MV ($mil)
Mean
2.6705
0.9001
0.0312
0.0299
0.0293
0.0306
0.0307
0.0287
0.5000
1.3974
10,571
Standard
Deviation
0.7493
0.6251
0.0168
0.0176
0.0165
0.0166
0.0167
0.0167
0.4486
3.6810
28,659
Q1
2
0.4487
0.0195
0.0177
0.0178
0.0191
0.0190
0.0168
0.2306
0.2503
753
41
Median
3
0.7756
0.0268
0.0253
0.0248
0.0263
0.0264
0.0243
0.4159
0.7243
2,275
Q3
3
1.1735
0.0382
0.0369
0.0360
0.0376
0.0375
0.0360
0.6464
1.4970
7,469
N
6,098
6,098
6,098
6,045
5,933
5,803
5,699
6,098
6,098
6,098
6,098
Table 2 (continued)
Panel B: Descriptive statistics by risk ratings
The sample consists of 6,098 firm-year observations over the period of 1997 to 2003 followed
by Salomon Smith Barney analysts. The number of observations is reduced to 6,045, 5,933,
5,803, and 5,699 respectively for variables postvol3, postvol6, postvol6, and postvol9. The
variables beta, prevol, postvol3, postvol6, postvol9, postvol12, idiorisk, BM, and DE are
winsorized at +/- 1% level. See Appendix 1 for the definition of the variables.
Risk rating
Low
Medium
High
Speculative / Venture
Mean
0.6580
0.7225
0.9525
1.3093
Standard
deviation
0.3761
0.4192
0.6473
0.8444
Q1
0.3527
0.4015
0.4739
0.6819
Median
0.6782
0.6837
0.8044
1.1582
Q3
0.9348
0.9711
1.2635
1.8834
prevol
Low
Medium
High
Speculative / Venture
0.0187
0.0224
0.0333
0.0537
0.0054
0.0084
0.0149
0.0201
0.0149
0.0163
0.0230
0.0385
0.0178
0.0212
0.0303
0.0516
0.0215
0.0270
0.0406
0.0690
postvol3
Low
Medium
High
Speculative / Venture
0.0193
0.0222
0.0320
0.0485
0.0080
0.0106
0.0171
0.0218
0.0142
0.0148
0.0203
0.0328
0.0173
0.0197
0.0284
0.0447
0.0216
0.0271
0.0391
0.0615
postvol6
Low
Medium
High
Speculative / Venture
0.0189
0.0217
0.0314
0.0483
0.0065
0.0099
0.0158
0.0196
0.0147
0.0150
0.0204
0.0341
0.0175
0.0195
0.0278
0.0450
0.0208
0.0261
0.0382
0.0600
postvol9
Low
Medium
High
Speculative / Venture
0.0198
0.0229
0.0329
0.0495
0.0056
0.0098
0.0160
0.0199
0.0162
0.0162
0.0217
0.0346
0.0186
0.0212
0.0294
0.0461
0.0225
0.0271
0.0403
0.0611
postvol12
Low
Medium
High
Speculative / Venture
0.0199
0.0231
0.0331
0.0492
0.0055
0.0099
0.0163
0.0200
0.0165
0.0163
0.0216
0.0345
0.0187
0.0213
0.0295
0.0453
0.0226
0.0275
0.0405
0.0606
beta
42
Table 2 (continued)
Panel B: Descriptive statistics by risk ratings (continued)
Risk rating
Low
Medium
High
Speculative / Venture
Mean
0.0163
0.0198
0.0308
0.0515
Standard
deviation
0.0055
0.0084
0.0147
0.0197
Q1
0.0123
0.0135
0.0205
0.0367
Median
0.0149
0.0183
0.0279
0.0495
Q3
0.0191
0.0243
0.0383
0.0660
BM
Low
Medium
High
Speculative / Venture
0.3126
0.4492
0.5218
0.6355
0.2091
0.2999
0.4495
0.7357
0.1624
0.2543
0.2336
0.1903
0.2701
0.4099
0.4391
0.4433
0.4196
0.5867
0.6962
0.8765
DE
Low
Medium
High
Speculative / Venture
0.8922
1.3442
1.5012
1.3483
2.1588
2.5563
4.1718
4.7579
0.3293
0.4182
0.1661
0.0032
0.5888
0.8470
0.6861
0.3954
1.2407
1.5156
1.4834
1.6722
MV
Low
Medium
High
Speculative / Venture
44,581
14,264
6,548
2,371
66,369
31,455
19,691
6,850
7,376
1,809
616
216
17,791
4,565
1,568
540
50,048
11,852
4,356
1,672
idiorisk
43
Table 3
Correlation matrix of key variables
The sample consists of 6,098 firm-year observations over the period of 1997 to 2003 followed by Salomon Smith Barney analysts. The number of
observations is reduced to 6,045, 5,933, 5,803, and 5,699 respectively for variables postvol3, postvol6, postvol6, and postvol9. The variables beta, prevol,
postvol3, postvol6, postvol9, postvol12, idiorisk, BM, and DE are winsorized at +/- 1% level. See Appendix 1 for the definition of the variables.
Upper triangle: pearson correlation, lower triangle: spearman correlation
rrating
Beta
prevol
postvol3
postvol6
0.2932
0.5678
0.4595
0.4899
rrating
<0.0001
<0.0001
<0.0001
<0.0001
0.2389
0.6610
0.5270
0.5593
beta
<0.0001
<0.0001
<0.0001
<0.0001
0.5669
0.5335
0.8041
0.8287
prevol
<0.0001
<0.0001
<0.0001
<0.0001
0.4753
0.4491
0.8256
0.9503
postvol3
<0.0001
<0.0001
<0.0001
<0.0001
0.5043
0.4665
0.8420
0.9574
postvol6
<0.0001
<0.0001
<0.0001
<0.0001
0.5039
0.4531
0.8156
0.8811
0.9419
postvol9
<0.0001
<0.0001
<0.0001
<0.0001
<0.0001
0.4947
0.4438
0.7954
0.8606
0.9238
postvol12
<0.0001
<0.0001
<0.0001
<0.0001
<0.0001
0.5767
0.4665
0.9902
0.8328
0.8537
idiorisk
<0.0001
<0.0001
<0.0001
<0.0001
<0.0001
0.0891
-0.1853
-0.0661
-0.0755
-0.0872
BM
<0.0001
<0.0001
<0.0001
<0.0001
<0.0001
-0.1186
-0.2382
-0.2924
-0.2461
-0.2641
DE
<0.0001
<0.0001
<0.0001
<0.0001
<0.0001
-0.4590
0.1682
-0.2350
-0.2045
-0.2261
logMV
<0.0001
<0.0001
<0.0001
<0.0001
<0.0001
Postvol9
0.4921
<0.0001
0.5394
<0.0001
0.8005
<0.0001
0.8767
<0.0001
0.9436
<0.0001
44
0.9861
<0.0001
0.8327
<0.0001
-0.0975
<0.0001
-0.2561
<0.0001
-0.2360
<0.0001
postvol12
0.4834
<0.0001
0.5200
<0.0001
0.7727
<0.0001
0.8417
<0.0001
0.9142
<0.0001
0.9778
<0.0001
0.8170
<0.0001
-0.1048
<0.0001
-0.2602
<0.0001
-0.2418
<0.0001
idiorisk
0.5758
<0.0001
0.6146
<0.0001
0.9957
<0.0001
0.8097
<0.0001
0.8344
<0.0001
0.8088
<0.0001
0.7841
<0.0001
-0.0608
<0.0001
-0.2955
<0.0001
-0.2774
<0.0001
BM
0.1561
<0.0001
-0.0946
<0.0001
0.0986
<0.0001
0.0988
<0.0001
0.0817
<0.0001
0.0570
<0.0001
0.0506
<0.0001
0.1067
<0.0001
0.1873
<0.0001
-0.3639
<0.0001
DE
0.0217
0.0910
-0.0470
0.0002
-0.0796
<0.0001
-0.0650
<0.0001
-0.0725
<0.0001
-0.0683
<0.0001
-0.0694
<0.0001
-0.0813
<0.0001
0.0341
0.0077
0.0200
0.1178
logMV
-0.4673
<0.0001
0.1274
<0.0001
-0.2785
<0.0001
-0.2342
<0.0001
-0.2428
<0.0001
-0.2538
<0.0001
-0.2535
<0.0001
-0.3146
<0.0001
-0.3683
<0.0001
0.0074
0.5631
Table 4
Determinants of risk ratings
Panel A: Ordered logit regressions
This panel presents the ordered logit regression estimates from regressing Salomon Smith
Barney analysts’ risk ratings (rrating) on a set of variables (see Appendix I for the definition of
the variables). The sample consists of 6,098 firm-year observations over the period of 19972003. The variables beta, idiorisk, BM, and DE are winsorized at +/- 1% level. The intercepts
for different outcomes are not reported for brevity. Year and industry dummies are included in
the regressions but also not reported. Standard errors (in parentheses) are White (1980)
heteroskedasticity-adjusted and robust to within analyst correlation (Rogers (1993) /clustered
standard errors). *, ** denote p-value <= 5%, and 1% respectively for two-sided tests.
Beta
β1
logMV
β2
Dneg
β3
BM
β4
Dneg * BM
β5
DE
β6
Dneg * DE
β7
idiorisk
β8
IPO
β9
negINC
β10
affiliate
β11
Pseudo R squared
negBM
Prob > χ2
negDE
Prob > χ2
(i)
0.8677**
(0.1151)
0.0677
β4+β5=0
(ii)
1.4423**
(0.1332)
-0.7761**
(0.0470)
0.4621
(0.3844)
0.1384
(0.1426)
-3.1058
(1.6033)
0.2230
(iii)
1.4568**
(0.1327)
-0.7986**
(0.0443)
1.3520**
(0.3305)
0.0720**
(0.0119)
-0.0229
(0.0372)
(iv)
1.4602**
(0.1329)
-0.7740**
(0.0471)
0.6363
(0.5911)
0.2140
(0.1478)
-3.1994
(1.7950)
0.0746**
(0.0120)
-0.0721
(0.0418)
0.2282
0.2293
0.0491
0.1619
-2.9854
0.0944
0.0025
0.9520
-2.9674
0.0620
β6+β7=0
45
(v)
(vi)
0.1332
(0.1720)
-0.5378**
(0.0551)
0.5416
(0.7064)
0.0006
(0.1463)
-0.2385
(2.1771)
0.0750**
(0.0124)
-0.0832
(0.0519)
100.7601** 79.5225**
(6.7620)
(8.5132)
0.0551
(0.1610)
0.6199**
(0.1324)
0.0732
(0.1380)
0.2063
0.2801
-0.2379
0.9123
-0.0082
0.8702
Table 4 (continued)
Panel B: Marginal effects for selected variables in ordered logit models (iv) and (vi) in Panel A
This table presents the marginal effects of changing the independent variables (see Appendix I for the definition of the variables). The base case probabilities
are calculated using Panel A’s coefficients after setting all continuous independent variables to their mean values and all dummy independent variables to 0,
using manufacturing (Ind1) firm observations in 1997. We re-calculate these probabilities for a one standard deviation change (continuous independent
variable) or 0 to 1 change (dummy independent variable) in an independent variable, with the rest of the variables held at their sample means. The variables
beta, idiorisk, BM, and DE are winsorized at +/- 1% level.
Model (iv)
Risk Rating
1
2
3
4
Base case probabilities
3.81%
50.60%
43.05%
2.54%
beta
1.57%
30.83%
61.52%
6.09%
logMV
12.91%
68.79%
17.61%
0.69%
BM
3.47%
48.55%
45.19%
2.78%
DE
2.92%
44.64%
49.13%
3.31%
Model (vi)
Risk Rating
1
2
3
4
Base case probabilities
1.91%
34.90%
60.13%
3.06%
beta
1.76%
33.14%
61.79%
3.31%
logMV
4.64%
54.66%
39.46%
1.25%
BM
1.91%
34.89%
60.14%
3.06%
DE
1.46%
29.20%
65.36%
3.99%
46
idiorisk
0.51%
12.85%
75.99%
10.64%
IPO
1.81%
33.73%
61.24%
3.23%
negINC
1.04%
22.82%
70.60%
5.54%
Affiliate
1.78%
33.35%
61.59%
3.28%
Panel C: Multinomial logit regressions
This panel presents the multinomial logit regression estimates from regressing Salomon Smith
Barney analysts’ risk ratings (rrating) on a set of variables (see Appendix I for the definition of
the variables). The sample consists of 6,098 firm-year observations over the period of 19972003. The variables beta, idiorisk, BM, and DE are winsorized at +/- 1% level. Year and
industry dummies are included in the regressions but not reported. The coefficients represent the
incremental effect from the base outcome to the alternative outcome. Standard errors (in
parentheses) are White (1980) heteroskedasticity-adjusted and robust to within analyst
correlation (Rogers (1993) /clustered standard errors). *, ** denote p-value <= 5%, and 1%
respectively for two-sided tests.
Intercept 1
β0
Beta
β1
logMV
β2
Dneg
β3
BM
β4
Dneg * BM
β5
DE
β6
Dneg * DE
β7
idiorisk
β8
IPO
β9
negINC
β10
affiliate
β11
Pseudo R squared
‘Medium risk’ vs. ‘Low ‘High risk’ vs. ‘Medium ‘Speculative/Venture’ vs.
risk’ (base outcome)
risk’ (base outcome) ‘High risk’ (base outcome)
3.7212**
1.9253**
-2.2852**
(1.2316)
(0.5847)
(0.5855)
1.1203*
0.4223*
-0.4630**
(0.5150)
(0.2038)
(0.1699)
-0.6076**
-0.5373**
-0.1742**
(0.1298)
(0.0681)
(0.0677)
-3.0654
-0.0231
0.3880
(2.1172)
(1.0668)
(0.5851)
1.7192
0.1760
-0.1568
(0.9038)
(0.2429)
(0.1466)
-35.8350**
-1.6347
-0.0451
(10.5670)
(3.6802)
(1.6319)
0.1585
0.0962**
0.0269
(0.1832)
(0.0181)
(0.0141)
-0.2990
-0.1771
-0.0274
(0.2688)
(0.1004)
(0.0513)
112.4434**
79.8536**
76.0308**
(26.4776)
(12.7259)
(7.3084)
0.3404
0.3540
-0.0250
(0.8185)
(0.2101)
(0.1954)
-0.1843
0.3569*
0.9019**
(0.3846)
(0.1590)
(0.1435)
0.5820
0.2112
-0.4288
(0.6087)
(0.1903)
(0.3126)
0.3083
47
Table 5
Informativeness of risk ratings
Panel A: OLS analysis of the informativeness of risk ratings at different post-report
volatility intervals
This table presents the OLS estimates from regressing the logarithm of post-report total
volatility at different intervals (logpostvol3 to logpostvol12) on Salomon Smith Barney’s risk
ratings (see Appendix I for the definition of the variables). The sample consists of firm-year
observations over the period of 1997-2003, and its size ranges from 5,699 to 6,045 observations.
Year and industry dummies are included in the regressions but not reported. Standard errors (in
parentheses) are White (1980) heteroskedasticity-adjusted and robust to within firm correlation
(Rogers/clustered standard errors). *, ** denote p-value <= 5%, and 1% respectively for twosided tests.
Dependent variable
Intercept
δ0
rrating
δ1
R squared
N. observations
logpostvol3
-4.5728**
(0.0349)
0.2940**
(0.0093)
0.4623
6045
48
logpostvol6
-4.5630**
(0.0344)
0.3000**
(0.0092)
0.4667
5933
logpostvol9
-4.5418**
(0.0332)
0.2916**
(0.0090)
0.4783
5803
logpostvol12
-4.4926**
(0.0336)
0.2917**
(0.0092)
0.4751
5,699
Table 5 (continued)
Panel B: OLS analysis of the informativeness of risk ratings
This table presents the OLS estimates from regressing the logarithm of twelve-month postreport total volatility (logpostvol12) on a set of predictive variables (see Appendix I for the
definition of the variables). Models (i) and (iv) include two predictive variables that decompose
risk rating (rrating) into an expected component (errating) and an unexpected component
(uerrating). The sample consists of 5,699 firm-year observations over the period of 1997-2003.
Year and industry dummies are included in the regressions but not reported. Standard errors (in
parentheses) are White (1980) heteroskedasticity-adjusted and robust to within firm correlation
(Rogers/clustered standard errors). *, ** denote p-value <= 5%, and 1% respectively for twosided tests.
Intercept
δ0
rrating
δ1
errating
δ2
uerrating
δ3
logprevol
δ4
logMV
δ5
Dneg
δ6
BM
δ7
Dneg * BM
δ8
DE
δ9
Dneg * DE
δ10
IPO
δ11
negINC
δ12
R squared
negBM
Prob > F
negDE
Prob > F
(i)
-3.6480**
(0.0341)
(ii)
-0.4578**
(0.0306)
(iii)
-0.7471**
(0.0440)
0.0453**
(0 .0049)
0.1992**
(0.0075)
0.0578**
(0.0092)
0.6516
(iv)
-0.8442**
(0.0506)
0.8506**
(0.0075)
0.8069**
(0.0090)
0.0484**
(0.0050)
0.0360**
(0.0055)
0. .7700**
(0.0132)
0.7858
0.7888
0.7895
δ7+δ8=0
(v)
-0.8823**
(0.0516)
0.0370**
(0.0054)
0.7780**
(0.0099)
0.0021
(0.0021)
-0.0602
(0.0628)
0.0227*
(0.0102)
-0.1743
(0.1559)
0.0009
(0.0008)
-0.0142**
(0.0058)
0.0285**
(0.0136)
0.0713**
(0.0105)
0.7923
-0.1516
0.3285
-0.0133**
0.0238
δ9+δ10=0
49
Table 5 (continued)
Panel C: OLS analysis of the informativeness of risk ratings dummy variables
This table presents the OLS estimates from regressing the logarithm of twelve-month postreport total volatility (logpostvol12) on a set of predictive variables (see Appendix I for the
definition of the variables) and a set of dummy variables (rrating1, rrating 2, rrating3,
rrating4). The dummy variable rrating1 is equal to 1 when the risk rating is 1 (low risk), and 0
otherwise. The other dummy variables are defined analogously. The sample consists of 5,699
firm-observations over the period of 1997-2003. Year and industry dummies are included in the
regressions but not reported. Standard errors (in parentheses) are White (1980)
heteroskedasticity-adjusted and robust to within firm correlation (Rogers/clustered standard
errors). *, ** denote p-value <= 5%, and 1% respectively for two-sided tests.
Intercept
rrating2
γ1
rrating3
γ2
rrating4
γ3
Logprevol
γ4
logMV
γ5
Dneg
γ6
BM
γ7
Dneg * BM
γ8
DE
γ9
Dneg * DE
γ10
IPO
γ11
negINC
γ12
R squared
(i)
-4.0663**
(0.0326)
0.1526**
(0.0272)
0.4132**
(0.0282)
0.8331**
(0.0318)
(ii)
-0.6937**
(0.0411)
0.0255**
(0.0113)
0.0714**
(0.0120)
0.1275**
(0.0167)
0.8046**
(0.0093)
0.4969
0.7889
rrating2=rrating3=rrating4=0 γ1=γ2=γ3=0 F= 378.83**
Prob > F
0.0000
NegBM
γ7+γ8=0
Prob > F
NegDE
γ9+γ10=0
Prob > F
50
F= 28.16**
0.0000
(iii)
-0.8389**
(0.0486)
0.0276**
(0.0118)
0.0696**
(0.0131)
0.1010**
(0.0181)
0.7780**
(0.0100)
0.0021
(0.0021)
-0.0610
(0.0628)
0.0228**
(0.0102)
-0.1788
(0.1560)
0.0009
(0.0008)
-0.0141**
(0.0058)
0.0288
(0.0136)
0.0720**
(0.0107)
0.7923
F= 16.25**
0.0000
-0.1560
0.3147
-0.0132*
0.0239
Table 6
Merrill Lynch and Value Line Analyses
Panel A: Frequencies of risk ratings
Merrill Lynch
The sample consists of 1,036 firms over the period of January-June 1998 followed by Merrill
Lynch analysts. Merrill Lynch rates stocks in four risk categories: Low, Average, Above
average, or High risk.
Low
Average
58 (5.6%)
367 (35.4%)
Above
average
432 (41.7%)
High risk
Total
179 (17.3%)
1,036
Value Line
The sample consists of 37,136 firm-year observations over the period of 1991-2003 followed by
Value Line analysts. Value Line divides stocks into five ranks, 1 being the stocks with the
lowest risks and 5 the stocks with the highest risks.
Year
1991
1992
1993
1994
1995
1996
1997
1998
1999
2000
2001
2002
2003
19912003
1 (Lowest)
91 (8.0%)
89 (7.6%)
93 (7.7%)
93 (7.6%)
93 (7.4%)
103 (3.9%)
113 (3.1%)
116 (2.9%)
100 (2.4%)
96 (2.2%)
81 (1.9%)
95 (2.3%)
115 (3.0%)
2
183 (16.0%)
183 (15.6%)
187 (15.6%)
186 (15.2%)
192 (15.3%)
282 (10.7%)
297 (8.3%)
317 (7.8%)
332 (7.9%)
345 (7.8%)
360 (8.5%)
365 (8.9%)
369 (9.5%)
3
672 (58.7%)
700 (59.8%)
720 (60.0%)
747 (61.1%)
779 (62.1%)
1,361 (51.6%)
1,615 (45.0%)
1,798 (44.2%)
1,831 (43.6%)
1,856 (41.9%)
1,892 (44.7%)
1,970 (48.3%)
1,884 (48.4%)
4
161 (14.1%)
148 (12.6%)
153 (12.7%)
158 (12.9%)
150 (11.9%)
775 (29.4%)
1,225 (34.1%)
1,467 (36.0%)
1,504 (35.8%)
1,581 (35.7%)
1,489 (35.2%)
1,272 (31.2%)
1,127 (28.9%)
5 (Highest)
37 (3.2%)
51 (4.4%)
48 (4.0%)
39 (3.2%)
41 (3.3%)
118 (4.4%)
340 (9.5%)
371 (9.1%)
435 (10.3%)
555 (12.4%)
413 (9.7%)
378 (9.3%)
399 (10.2%)
Total
1,144
1,171
1,201
1,223
1,255
2,639
3,590
4,069
4,202
4,433
4,235
4,080
3,894
1,278 (3.4%)
3,598 (9.7%)
17,825 (48%)
11,210 (30.2%)
3,225 (8.7%)
37,136
51
Panel B: Determinants of risk ratings – Ordered logit regressions
This table presents the ordered logit estimates from regressing analysts’ risk ratings (rrating) on
a set of variables (see Appendix I for the definition of the variables). For Merrill Lynch, the
sample consists of 1,036 firms over the period of January-June 1998. Merrill Lynch rates stocks
in four risk categories. For Value Line, the sample consists of 37,136 firm-year observations
over the period of 1991-2003. Value Line divides stocks into five ranks. The variables beta,
idiorisk, BM, and DE are winsorized at +/- 1% level. Industry dummies are included in all the
regressions but not reported. Year dummies are included in the Value Line regressions but not
reported. The intercepts for different risk rating outcomes are not reported for brevity. Standard
errors (in parentheses) are White (1980) heteroskedasticity-adjusted and robust to within-analyst
(firm) correlation for Merrill Lynch (Value Line) regressions. *, ** denote p-value <= 5%, and
1% respectively for two-sided tests.
Beta
β1
logMV
β2
Dneg
β3
BM
β4
Dneg * BM
β5
DE
β6
Dneg * DE
β7
idiorisk
β8
IPO
β9
negINC
β10
affiliate
β11
Pseudo R squared
negBM
Prob > F
negDE
Prob > F
MERRILL LYNCH
(i)
(ii)
1.4271**
-0.3519
(0.2320)
(0.3065)
-1.1382**
-0.6958**
(0.0829)
(0.0937)
-1.9208
-1.4578
(1.0722)
(1.3398)
0.2276
0.9609
(0.3858)
(0.4295)
-16.2526** -17.2828**
(5.0824)
(5.5766)
0.2129**
0.2287**
(0.0531)
(0.0640)
-0.7817**
-0.6849**
(0.1530)
(0.1981)
120.7454**
(14.6334)
0.7113*
(0.2884)
1.0072**
(0.3361)
0.1936
(0.3917)
0.2712
0.3329
β4+β5=0 -16.0250**
0.0014
β6+β7=0 -0.5688**
0.0001
52
-16.3219**
0.0032
-0.4562*
0.0121
VALUE LINE
(i)
(ii)
1.8009** 0.9496**
(0.0327)
(0.0371)
-0.8542** -0.5171**
(0.0158)
(0.0186)
1.3518** 1.0180**
(0.2092)
(0.2982)
0.2714** 0.3477**
(0.0339)
(0.0381)
-1.2552** -1.0861**
(0.1742)
(0.2243)
0.2394** 0.2632**
(0.0132)
(0.0153)
-0.4256** -0.5181**
(0.0470)
(0.0613)
69.2810**
(1.9024)
0.3292
(0.2400)
0.3564**
(0.0383)
0.2947
0.3619
-0.9838**
0.0000
-0.1862**
0.0000
-0.7384**
0.0008
-0.2549**
0.0000
Panel C: OLS analysis of the informativeness of risk ratings
This table presents the OLS estimates from regressing the logarithm of twelve-month postreport total volatility (logpostvol12) on a set of predictive variables (see Appendix I for the
definition of the variables). For Merrill Lynch, the sample consists of 977 firms over the period
of January-June 1998 and the risk ratings range from 1 to 4. For Value Line, the sample consists
of 34,704 firm-year observations over the period of 1991-2003 and the risk ratings range from 1
to 5. The regressions in columns (ii) include risk ratings dummy variables (rrating1- rrating 5).
The dummy variable rrating1 is equal to 1 when the risk rating is 1 (low risk) and 0 otherwise.
The variables beta, idiorisk, BM, and DE are winsorized at +/- 1% level. Industry dummies are
included in all the regressions but not reported. Year dummies are included in the Value Line
regressions but not reported. Standard errors (in parentheses) are White (1980)
heteroskedasticity-adjusted and robust to within-firm correlation for Value Line regressions. *,
** denote p-value <= 5%, and 1% respectively for two-sided tests.
Intercept
δ0
rrating
δ1
rrating2
δ2
rrating3
δ3
rrating4
δ4
rrating5
δ5
logprevol
δ6
logMV
δ7
Dneg
δ8
BM
δ9
Dneg * BM
δ10
DE
δ11
Dneg * DE
δ12
IPO
δ13
negINC
δ14
R squared
MERRILL LYNCH
(i)
(ii)
-0.5256**
-0.5144**
(0.1185)
(0.1092)
0.0333**
(0.0121)
0.0630*
(0.0258)
0.0903**
(0.0295)
0.1185**
(0.0396)
0.7636**
(0.0247)
-0.0226**
(0.0061)
-0.4183**
(0.1206)
0.0020
(0.0376)
-0.9633*
(0.3890)
0.0123**
(0.0037)
-0.0586**
(0.0158)
0.0680*
(0.0289)
-0.0055
(0.0230)
0.7480
53
0.7657**
(0.0251)
-0.0219**
(0.0061)
-0.4221**
(0.1213)
0.0008
(0.0377)
-0.9805*
(0.3902)
0.0123**
(0.0037)
-0.0590**
(0.0158)
0.0697*
(0.0287)
-0.0029
(0.0235)
0.7483
VALUE LINE
(i)
(ii)
-1.2800** -1.2650**
(0.0222)
(0.0209)
0.0397**
(0.0024)
0.0086
(0.0065)
0.0541**
(0.0061)
0.1305**
(0.0078)
0.1119**
(0.0102)
0.7205**
0.7095**
(0.0049)
(0.0052)
-0.0128** -0.0125**
(0.0009)
(0.0009)
0.0098
0.0138
(0.0189)
(0.0190)
0.0076**
0.0116**
(0.0029)
(0.0029)
-0.0484* -0.0680**
(0.0196)
(0.0198)
-0.0007
0.0003
(0.0008)
(0.0008)
0.0022
-0.0002
(0.0047)
(0.0046)
0.0285
0.0325
(0.0191)
(0.0193)
0.1127**
0.1082**
(0.0039)
(0.0039)
0.8218
0.8227
Panel C (continued)
rrating2=rrating3=
rrating4(=rrating5) δ2=δ3=δ4(=δ5)
Prob > F
=0
negBM
δ9+δ10=0
-0.9613*
Prob > F
0.0131
negDE
δ11+δ12=0
-0.0463**
Prob > F
0.0023
54
F=3.44*
0.0164
-0.9797*
0.0117
-0.0467**
0.0023
-0.0408*
0.0344
0.0015
0.7570
F=109.17**
0.0000
-0.0564**
0.0037
0.0001
0.9823