CFA Certification Program and Sell-Side Analysts

Qiang Kang School of Business

University of Miami Phone: 305-284-8286 Fax: 305-284-4800 q.kang@miami.edu

Xi Li XL Partners, Inc Boston College

Phone: 617-467-4608 Fax: 617-467-4609 xli5@hotmail.com

Tie Su School of Business

University of Miami Phone: 305-284-1885 Fax: 305-284-4800

tie@miami.edu

JEL codes: J24, G24, G28 Keywords: Capital Markets, CFA, analyst

We are thankful for suggestions from Wayne Ferson, John Stowe, and seminar participants at the China International Conference in Finance. We gratefully acknowledge the data provided by I/B/E/S. Any errors are our own responsibility. Please address correspondence to Tie Su, Department of Finance, University of Miami, Coral Gables, FL 33124-6552. Phone: 305-284-1885. Fax: 305-284-4800. Email: tie@miami.edu.

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CFA Certification Program and Sell-Side Analysts

The learning from CFA program curriculum substantially improves analyst

recommendation performance. The positive impact is much larger for analysts covering smaller

companies or companies with thinner analyst coverage. Analysts improve their performance

when going through the CFA program as candidates but stop such improvements after

completing the program. The learning from the CFA program also significantly reduces risk-

taking and bias behavior in analyst recommendations. Moreover, CFA designations increase

analysts’ mobility to larger brokerage firms. The results survive various robustness checks,

including using different methods to deal with the selection bias.

JEL codes: J24, G24, G28 Keywords: Capital Markets, CFA, analyst

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“Lawyers have to pass the bar, doctors have medical school and even stockbrokers need a license before practicing their crafts. But stock analysts, who can make or break a company’s stock with their research, don’t need any credentials to hang their shingles on Wall Street.”

-- Kelleher (2001)

1. Introduction

Sell-side analysts are prominent in the investment process.1 In spite of their influence on

the market, however, neither brokerage firms nor the government have certification requirements

for analysts. This is surprising given such requirements exist in many other professions.

Following the sharp market correction since 2000 and the $1.4 billion Global Research Analyst

Settlement (Global Settlement thereafter) in 2003 between the largest ten brokerage firms and

regulators over the practice of exchanging biased research for investment banking business

[Smith, Craig, and Solomon (2003)], many people have argued for the certification of analysts

on investment knowledge and on ethics training. They argue that the lack of certification

requirements may result in inferior performance and excessively biased research.

The Chartered Financial Analyst (CFA) program is an existing voluntary certification

program offered by the CFA Institute, the trade association for buy-side and sell-side financial

analysts.2 Financial analysts receive the CFA designation after successfully completing the

program. By 2009, there are more than 85,000 CFA charterholders. In 2009, more than 200,000

candidates from over 150 countries registered to take the CFA examinations, with more than

60% of candidates from outside of North America. The CFA program and CFA charterholders

exert substantial influence on global financial markets, corporate decision-making, and the

1 Researchers make enormous efforts to identify analysts with more informative earnings forecasts and investment recommendations [e.g., Kothari (2001) and Lee (2001)], and investors pay millions of dollars to access analyst research. 2 Buy-side analysts working for money managers provide research for in-house use by money managers, and sell-side analysts working for brokerage firms provide research for the firms’ clients. Unless otherwise stated, we use the term “analysts” to refer to sell-side analysts in this paper. Also, we use “brokerage firm” or “firm” to refer to an analyst’s employer and we use “company” to refer to the entity that an analyst covers. Finally, we use “recommendation” and “forecasts” to refer to investment recommendations and earnings forecasts, respectively.

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specific training received by industry practitioners. Our paper investigates the degree to which

the CFA program affects the performance, behavior, and career outcomes of sell-side analysts.

Using a comprehensive sample of investment recommendations and earnings forecasts

over the 1994 to 2000 period,3 we find robust evidence that the CFA program has a significantly

positive impact on analyst recommendation performance. The positive impact is economically

meaningful: it is equivalent to an annualized excess return of 7.47 percentage points. The

positive impact is particularly significant for analysts covering smaller companies or companies

with thinner analyst coverage, consistent with the idea that the impact is greater for analysts that

face more opaque information environment. When we examine the subset of analysts who

complete the CFA program during our sample period, we find that they improve performance

significantly when going through the program, but they do not continue to improve performance

after finishing the program. Although on-the-job experience, as required by the CFA program,

and the learning through the CFA program curriculum could both improve analyst performance,

the positive impact that we observe is likely attributable to the latter because we control for

experience throughout our analysis. While the pass rate of the CFA exams has been declining,

we show that the effect of the CFA program has been consistent over time, which suggests that

the declining pass rate is not due to incumbent CFA charterholders trying to reduce the supply of

CFA charterholders in order to extract higher compensation. Taken together, the results suggest

that the improvement when going through the CFA program is likely due to the learning from the

preparation for the exams.

3 Given regulatory reforms on analyst research such as Regulation FD and Global Settlement in the early 2000s, we focus on the 1994-2000 sample period because it provides us a clean environment to identify the impact of the CFA program without the confounding influence of regulatory reforms. This is important for us to understand whether the CFA program can complement the regulatory reforms to improve analyst performance and behavior.

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We also examine the impact of CFA program on analyst behavior and career outcomes.

We find that the CFA program reduces risk-taking and bias behavior in recommendations. For

example, the CFA program increases the proportion of negative recommendations that analysts

make by about 15%. The CFA designation also significantly improves the probability that an

analyst will move up to a larger brokerage firm. Because larger firms are likely to offer higher

pay [Hong and Kubik (2003)], this evidence suggests potential benefits of the designation on

analyst compensation.

Our paper contributes to prior research on occupational regulation in two respects. First,

Kleiner (2000) notes the extant evidence on occupational certification and regulation is limited,

largely due to lack of data. Kleiner also writes, “Typically, direct estimates of the quality of a

service…are not available,” and further, sometimes, “it is not even altogether clear how one

would measure quality.” Our paper provides evidence on the securities industry. In comparison

to the difficulty of quality measurement in prior research, it is easy to produce objective

measures of performance and behavior for analysts. Second, the CFA program does not involve

formal specialty education as required by many certification programs widely examined so far,

such as those for lawyers, physicians, and public school teachers. Because formal education is

known to increase human capital, it is easier to separately examine the effects of certification

programs using the CFA program.

Our paper is also related to prior research on the determinants of analyst performance,

behavior, and career outcomes [e.g., Lys and Sohn (1990), Stickel (1995), Clement (1999),

Jacob, Lys, and Neale (1999), Hong and Kubik (2003), and De Franco and Zhou (2009)].

Compared to prior research, we examine the impact of the CFA program on analysts’ human

capital and career outcomes, measured respectively by the abnormal returns from investment

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recommendations and mobility among brokerage firms of different sizes. In particular, we focus

on investment recommendations instead of earnings forecasts because investment

recommendations provide an unequivocal assessment of companies by sell-side analysts and are

arguably more important to investors than earnings forecasts.

There is also significant practical importance to determining the effects of the CFA

program on analyst performance and behavior. First, investors have suggested that the CFA

program can be a mechanism to improve analyst performance and behavior. Some even argue for

the program to become a licensing requirement.4 The first step of any policy making is to

determine whether in fact the program benefits analyst performance and behavior. If it does, the

benefits could be substantial given that investors use analyst research extensively. Second,

completing the CFA program usually requires lengthy preparation and costs thousands of dollars.

Because in recent years more than 200,000 candidates from over 150 countries enroll for the

exams each year, it is important to know whether the substantial resources spent on preparing

and administrating the exams are justified. Third, as a result of the growing investors’ call for

ethics training following recent corporate scandals, business schools and certification programs

around the world have recently started to incorporate ethics training into their programs. Given

the short history of these training programs, it has been difficult to evaluate their effects on

behavior bias so far. In contrast, the CFA program has long included ethics training in its

curriculum, so this paper could shed light on the effectiveness of such training. Above all, our

study not only helps rationalize spending considerable resources on preparing and administrating

4 Many analysts became celebrities in the late 1990s for continuously recommending risky stocks that later reached the analysts’ hefty price targets before collapsing. The most well known among these analysts, Henry Blodget and Mary Meeker, were dubbed “King Henry” and “Queen of the Net,” respectively, for this reason [DeBaise and Wingfield (2001)]. Jack Grubman and Henry Blodget, star analysts in the nineties, were also later fined and barred from the securities industry for life for touting stocks with which their firms maintain investment banking relationships. Supporters of the CFA program point out that none of the three analysts have the CFA designation.

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the CFA exams, but also lends support to the increased practice of including ethics training in the

curriculum of education and certification programs.

The rest of the article is organized as follows. Section 2 provides a summary of the CFA

program and the related literature. Section 3 describes the data and the sample construction.

Section 4 discusses in detail the empirical strategy and presents the empirical results. Section 5

concludes.

2. The CFA Program and Related Literature

The CFA program has received heightened attention subsequent to a series of corporate

scandals around the turn of the century. Both the number of CFA charterholders and the number

of CFA candidates have grown substantially in recent years, regulators in countries such as the

United Kingdom, Singapore, Canada, and the U.S. have adopted the CFA designation as a

competency requirement, and the curriculum of the CFA program is being integrated into many

business school curricula. The CFA Institute advocates that all analysts be required to complete

its program both to improve research skills and to secure a commitment to abide by its ethics

code [Kelleher (2001)].

The CFA program seems to fit well with certification demand. Indeed, the CFA Institute

strives to set “a globally recognized standard for measuring the competence and integrity of

financial analysts.” According to the CFA Institute, “The CFA Program is comprised of three

levels, each culminating in an examination. You must pass each level sequentially, and fulfill

other requirements of the program…In general, each level of the program requires 250 hours of

preparation” through a “self-study curriculum.” The exams cover a substantial amount and

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diversity of material.5 The combined pass rate of the three levels of exams has been below 50%

in recent years. Other key requirements of the program include a bachelor degree and at least

three years of acceptable professional experience in the investment decision-making process

(four years for candidates that register for the 2005 program for the first time and all candidates

that remain in the program after 2007). CFA charterholders also need to pledge to the Code of

Ethics and are disciplined for violations with various measures, including loss of the designation.

While each of these requirements appears to be constructive, it is still unclear whether the

CFA program, or certification programs in general, can improve the performance and behavior

of those that are certified. On the one hand, the 750 hours of preparation that is generally

required to study the CFA program curriculum in order to pass the CFA exams may improve

analyst performance and behavior, given that formal schooling generally increases human capital

[see Card (1999) for a review]. Certification requirements may also establish a minimum

achievement standard, minimizing poor quality service and the associated risks of disruptions to

the financial markets. On the other hand, the self-studying from the CFA program curriculum

may have no effect on performance and behavior because self-studying is different from formal

schooling and because the curriculum may not be transformable to improved performance. A

code of ethics may not moderate analyst bias [Dobson (2003)]. Even worse, certification

requirements could create costly barriers to entry, which may deter qualified applicants from

entering, and help incumbents extract rents [e.g., Goldhaber and Brewer (2000) and Friedman

and Kuznets (1945)].

5 The four parts of its Candidate Body of Knowledge include ethical and professional standards, tools, asset valuation, and portfolio management. Each exam level covers all four parts, but with a different focus. Level I exams focus on tools, which include quantitative methods, economics, financial statement analysis, and corporate finance. Level II exams focus on asset valuation, which includes analyses of equity and debt investments, derivatives, and alternative investments. Level III exams focus on portfolio management. Each exam level also gives 10% to 15% weight to ethical and professional standards.

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Prior research on the impact of occupational regulation generally shows that while

licensing and certification requirements generate increased earnings in the affected industry, they

produce no improvements in the quality of the service provided or the quality of applicants [see

Rottenberg (1980) for a review of earlier literature and Angrist and Guryan (2004), Goldhaber

and Brewer (2000), Kleiner and Kudrle (2000), Kugler and Sauer (2005), Wilensky and Rossiter

(1983), and Wolverton and Epley (1999) for more recent examples].6 In particular, Young (1988)

shows that licensing rules are administered to advance the interest of licensed practitioners.

Similarly, Kandel and Lazear (1992) find that individuals react to the way that the rules are

administrated when choosing licensing locations.

The importance of the CFA program calls for a careful analysis of the program’s impact on

the human capital of (sell-side) analysts; it also stipulates distinguishing between alternative

explanations. The paper closest in spirit to ours is De Franco and Zhou (2009) 7. They report

somewhat mixed evidence when comparing the earnings forecasts of CFA charterholders and

non-charterholders. Specifically, the forecasts of CFA charterholders are 3-4% timelier and

bolder and generate greater short-term market reactions (about 0.66% more positive for positive

revisions and 3.30% more negative for the negative revisions) but are less accurate and similarly

optimistic. They also try to differentiate the signaling vs. human capital theories as the

explanation for the weakly better performance of CFA charterholders. They find that the

forecasts of CFA charterholders are timelier and bolder when compared to non-charterholders in

6 This literature differentiates between mandatory licensing and certification, such as mandatory licensing for lawyers and dentists, and voluntary certification for auto mechanics, physicians, and teachers. 7 Other prior studies usually use small samples and do not examine sell-side analysts. For 223 equity mutual funds in the 1988-1992 period, Shukla and Singh (1994) find that although funds managed by at least one CFA manager do not outperform the S&P 500 index, they are riskier yet better diversified and have higher risk-adjusted returns than funds without CFA managers. Brockman and Brooks (1998) find a positive correlation between the growth in the number of CFA charterholders and the growth in S&P 500 index in the 1963-1995 period. From a survey of 41 U.S. public pensions, Miller and Tobe (1999) find that pensions employing at least one CFA charterholder in investment teams have lower investment management costs than those without CFA charterholders.

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both the periods before and after the CFA designation. They conclude that the CFA

charterholders perform better in the pre-designation period and that the difference indicates

better innate ability of CFA candidates, providing supports for the signaling theory. They also

conclude that CFA charterholders perform better in the post-designation period and that the

difference is a reflection of the skills acquired through the CFA program, lending support to the

human capital theory.

Nevertheless, there could be other explanations for De Franco and Zhou (2009)’s evidence.

The performance difference before and after the CFA designation might be solely due to the

difference in innate ability, and the CFA analysts use the designation solely for the purpose of

signaling, instead of acquiring human capital. Alternatively, because the CFA program usually

takes years to complete, CFA analysts are likely to be participating in the program years before

the actual designations. Thus, the performance difference before the CFA designation might be

due to the human capital acquired in the process of completing the CFA program during the pre-

designation period and the acquired human capital enables the CFA analysts to continue to

outperform after the designations, without any signaling effect.

Different from De Franco and Zhou (2009), we examine the question whether the CFA

program increases the human capital of sell-side analysts, and we focus on investment

recommendations instead of earnings forecasts. To carefully differentiate the signaling vs.

human capital theories, we draw from prior research and explicitly control for innate ability with

analyst fixed effects, in addition to some common control variables, in cross-sectional

regressions [e.g., Hausman and Taylor (1981), and Jacob, Lys, and Neale (1999)]. Analyst fixed

effects are likely to adjust for the analyst-specific characteristics, particularly those related to

innate ability, that we are unable to include in regressions due to data constraints. Our results

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thus control for analyst fixed effects and suggest that the CFA program significantly improves

analyst human capital in terms of their making more profitable recommendations.

We also examine the changes in performance within both the pre- and post-designation

periods for the subset of analysts who complete the CFA program during our sample period.

These time series results are unaffected by innate ability because we examine the same analysts

in both periods. We find that the subset of CFA analysts improve performance significantly

when going through the program but do not continue to improve performance after finishing the

program, which provides further support for the positive effect of the CFA program on the

human capital of sell-side analysts.

Complementing De Franco and Zhou (2009), we focus on examining investment

recommendations instead of earnings forecasts. Investment recommendations and earnings

forecasts contain independent information, and many people consider recommendations to be

more important to investors than forecasts [e.g., Francis and Soffer (1997), and Womack (1996)].

Also, investment recommendations provide an unequivocal assessment of companies by sell-side

analysts. In comparison, because there are several dimension of earnings forecast performance

such as accuracy, timeliness, and boldness, it is difficult to draw an unambiguous conclusion in

case of divergent results among the various dimensions of performance. Moreover, our approach

tracks the performance of investment recommendations from the recommendation dates to

revision dates and thus simulates an investor’s hypothetical investment experience of following

analysts’ investment recommendations. In contrast, it is difficult to assess the impact of greater

accuracy by a few cents and of greater timeliness by a fraction of days. Further, bias is more

severe in recommendations than in forecasts [e.g., Lin and McNichols (1998)] and is the focus of

the $1.4 billion regulatory settlement noted earlier. In untabulated results, we find evidence that

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CFA charterholders outperform non-charterholders in terms of forecast accuracy, but this result

disappears after controlling for analyst fixed effects, indicating that this difference is likely due

to other differences between CFA charterholders and non-charterholders. Finally, we examine

the impact of the CFA designation on career outcomes.

3. Data and Analyst Characteristics

3.1. Data

We obtain our primary data from the Institutional Brokers Estimate System (I/B/E/S).

The I/B/E/S database provides individual name, brokerage affiliation, earnings forecasts, and

stock recommendations of each analyst, as well as a unique code for each analyst that allows us

to track analysts should they change affiliations. Its earnings forecast database starts in 1983, and

its stock recommendation database starts in October 1993. I/B/E/S provides standardized

recommendations, with integer ratings from 1 through 5 corresponding to “strong buy,” “buy,”

“hold,” “underperform,” and “sell,” respectively.

Following Clement (1999) and Jacob, Lys, and Neale (1999), we exclude analysts with

forecasts in the I/B/E/S database prior to 1984 to avoid a left-censored bias in the experience

measure. We also exclude analysts with only “hold” recommendations. We restrict our sample to

the period from 1994 through 2000 for two reasons. First, the recommendations data start in late

1993. Second, given regulatory reforms on analyst research such as Regulation FD and Global

Settlement in the early 2000s, our sample can provide the evidence on the impact of the CFA

program without the confounding influence of regulatory reforms. This is important, for

example from the viewpoint of regulatory reforms, because we want to understand whether it is

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possible for the CFA program to complement the above regulatory reforms to improve analyst

performance and behavior.

Our sample selection procedure yields a sample of 4,051 analysts and 15,178 analyst-year

observations. Because the I/B/E/S database sometimes assigns multiple codes to a single analyst,

merging the data for these analysts reduces the sample to 4,019 analysts. For estimation purposes

we require CRSP stock returns and create a three-month recommendation portfolio within each

year (details to be discussed in Section 3.2), which further reduces the sample to 3,510 analysts

and 12,398 analyst-year observations.

The I/B/E/S database only provides the last name and first initial of analysts. We search

news articles in databases such as Lexis-Nexis and ProQuest to find the first name of each

analyst. If our search results in multiple analysts with the same first and last names, we match

information on brokerage firm affiliation with the I/B/E/S database to identify the analyst. We

then hand-collect information about whether and when analysts receive the CFA designation

from the annual Membership Directory of Association for Investment Management and

Research.

Panel A of Table 2 reports the proportion of analysts by CFA designation. Of the 3,474

sample analysts, 1,259 analysts, or about 36%, are CFA charterholders. Given there are presently

no certification requirements for analysts, the high proportion of analysts having completed the

CFA program is an anecdotal evidence that CFA designation is beneficial to analysts.

3.2. Measures of Performance, Risk-Taking, and Bias

We first create an analyst’s recommendation portfolio following prior research [e.g., Li

(2005), Emery and Li (2009)]. An analyst’s recommendation portfolio is made up of long (short)

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positions in stocks the analyst rated 1 or 2 (4 or 5). Stocks are added to the portfolio on the

recommendation date, and removed from the portfolio on the date of any revision to the rating of

3. A stock’s classification changes when a superseding recommendation alters the stock’s

classification. For example, a revision from 1 or 2 to 4 or 5 is a revision, whereas an upgrade

from 2 to 1 is not a revision because the stock would already be classified as a long position.

Reiteration of a previous recommendation does not change a stock’s classification. Returns

within each year accumulate from the recommendation date until either (1) the date of revision,

or (2) the end of the year, if there is no revision during the remainder of the year. CRSP daily

returns for each recommendation are equally weighted to calculate the portfolio’s return. We

require a minimum time period of three months for the overall recommendation portfolio within

each year for estimation purposes.8 We estimate the Carhart (1997) model

Rit = αi + ∑4j=1 jRjt + εit , (1)

where Rit is the return on the recommendation portfolio of analyst i in excess of the three-month

T-bill return on day t, αi is the multifactor model Jensen’s alpha which measures the average

daily abnormal return on the portfolio of analyst i given the daily frequency of our data, j is the

regression coefficient for factor j, Rjt is the return of factor j on day t, and εit is an error term for

the portfolio of analyst i on day t. The factors are the return on the CRSP value-weighted

NYSE/AMEX/Nasdaq market index in excess of the three-month T-bill return, the size and

book-to-market factors of Fama and French (1993), and the return momentum factor of Carhart

(1997). Prior research identifies these factors as related to systematic risk or investment styles

that have nothing to do with the contribution of skill. We include them in our analysis to avoid

rewarding analysts for simply exploiting these factors.

8 Our approach is similar to that of WSJ rankings which gives a weight of 2, 1, 0, -1, and -2 to stocks that the analysts rated 1 through 5. Alternative weighting schemes of recommended stocks do not affect our results.

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We use ALPHA, the intercept of the Carhart model regression, and INFORATIO, the t-

statistic of the intercept, to measure analyst recommendation performance. INFORATIO, which

stands for “information ratio,” is essentially the Sharpe ratio in a multifactor model setting. This

metric is used extensively as a performance measure, because it controls for both systematic and

idiosyncratic risks of an investment. Following Chevalier and Ellison’s (1999) examination of the

risk-taking behavior of fund managers, we measure analyst risk-taking in recommendations with

RESIRISK, the residual return standard deviation in the Carhart model regression.

Because one objective of the recent efforts to reduce analyst bias following the $1.4

billion settlement mentioned above is to increase the proportion of negative recommendations

[Opdyke (2002)], we use the percentage of negative recommendations among an analyst’s

recommendations (PCTSELL), including both underperforms and sells, to measure the bias in

investment recommendations.

For part of our analysis, we also control for analyst performance and behavior reflected in

earnings forecasts. We use Hong, Kubik, and Solomon’s (2000) relative forecast accuracy

(ACCURACY) and relative forecast boldness (BOLDNESS) to measure performance and risk-

taking behavior in earnings forecasts. We use Hong and Kubik’s (2003) relative forecast

optimism (OPTIMISM) on a one-year basis to measure analyst bias. These relative measures

account for both the forecasts of the other analysts covering the same stock and the number of

other analysts covering a particular stock. See the Appendix for details about the construction of

these variables.

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3.3. Control Variables

We use several control variables. Following Stickel (1992), among others, we measure

analyst reputation using IISTAR and WSJSTAR, dummy variables that equal one if the analyst is

an Institutional Investor (I.I.) All-American and Wall Street Journal (WSJ) All-Star analyst,

respectively, and zero otherwise.9 Jacob, Lys, and Neale (1999) use the number of research

reports issued by an analyst (NREPORT) to measure the timeliness of reports, which should

proxy for the willingness of analysts to exert effort. Clement (1999) and Jacob, Lys, and Neale

(1999) argue that an increase in the number of companies covered by one analyst

(NCOMPANY), that is, broader coverage, increases task complexity. Jacob, Lys, and Neale

(1999) also argue that broader coverage broadens industry knowledge. Stickel (1995) and Hong

and Kubik (2003) use brokerage firm size (BROKERSIZE) as a proxy for marketing ability and

the reputation of analysts’ firms, respectively. We use COMPANYSIZE as a proxy for the

information environment of the companies under coverage, because prior research argues that

smaller companies have a more opaque information environment due to less information

disclosure, and less news and research coverage [Stickel (1995)]. We also use COVERAGE, the

average number of analysts that cover the same firm as a particular analyst does, as another

measure of the information environment [e.g., Piotroski and Roulstone (2004)]. In addition, we

include EXPERIENCE, the number of years that an analyst has been submitting reports to

I/B/E/S, to measure the impact of learning-by-doing [e.g., Clement (1999) and Jacob et al.

(1999)].

9 Formerly, WSJ published two sets of rankings. One was based on investment recommendations and the other was based on earnings forecasts. WSJ stopped providing the ranking based on earnings forecasts in 2002. For brevity, we only present the results based on the WSJ’s investment recommendation-based rankings. The results for the WSJ’s rankings based on earning forecasts are similar and are available upon request.

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3.4. Summary Statistics

Panel B of Table 2 reports summary statistics on the measures of analyst performance,

behavior, and control variables. Compared to non-charterholders, CFA charterholders are

significantly different in the following aspects. CFA charterholders have smaller ALPHA and

RESIRISK and issue a greater proportion of negative recommendations. The proportion of I.I.

stars is lower among CFA charterholders, whereas that of WSJ stars is higher among CFA

charterholders. On average, CFA charterholders have about 1.8 years more experience, issue

more research reports, and cover more companies.

4. Testing Methodologies and Empirical Results

4.1. Empirical Strategy

To assess the impact of the CFA program on the performance and behavior of analysts,

we focus mainly on estimating the following model:

0 1 2 3 4

5 6 7 8

9 .

t t t t t

t t t t

t t

Dependent Variable a a CFA a IISTAR a WSJSTAR a EXPERIENCE

a COVERAGE a NREPORT a NCOMPANY a BROKERSIZE

a COMPANYSIZE Year Effects Analyst Effects Brokerage Firm Effects

(2)

Here, the dependent variables include the measures of analyst performance, ALPHA and

INFORATIO, and the measures of analyst risk-taking and bias behavior, RESIRISK and

PCTSELL. As Table 2 shows that CFA charterholders and non-charterholders are different

across several characteristics and to the extent that these characteristics affect analyst

performance and behavior, we include these characteristics to control for performance/behavior

differences that are unrelated to the CFA program. We also include year dummies to control for

time variations in performance/behavior that are related to changes in macroeconomic

conditions.

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The specification of Equation (2) deserves a further discussion. First, the key parameter

of interest in this model is the coefficient on CFA, a binary variable that equals one for the years

that an analyst is a CFA charterholder and zero otherwise. Clearly, the choice of becoming a

CFA charterholder is not random. Heckman (1979) explains that using non-randomly selected

samples when estimating behavioral relations results in a unique type of “omitted variables” bias,

namely, selection bias. For example, CFA analysts may have different innate abilities and

motivations from non-charterholders, differences that may affect the initial decision of whether

to go through the CFA program, which therefore makes it difficult to know whether the potential

performance differences between CFA and non-CFA analysts would be due to the CFA program.

Prior research uses econometric methods such as instrumental variables, the fixed effects model,

and Heckman’s (1979) two-step procedure to control for selection bias [Heckman, Ichimura,

Smith, and Todd (1998)]. Here, appropriate instrumental variables such as measures of innate

ability are not available.10 In addition, unlike events such as mergers and acquisitions, the choice

to finish the CFA program again is not available to CFA analysts who have already finished the

program, and this non-reversibility is likely to render Heckman’s (1979) procedure inaccurate.

Moreover, the timing of an analyst’s decision to go through the CFA program is unknown,

further weakening the efficacy of Heckman’s procedure.

Given the limitations of the alternative methods to deal with the selection bias in this

particular scope of study, we therefore adopt the fixed effects model to control for the potential

selection bias. Hausman and Taylor (1981) argue that the fixed effects model offers a common,

unbiased technique for controlling for omitted variables in panel data sets. Jacob, Lys, and Neale

10 Matching methodology would face essentially the same issue as the instrumental variable approach. Matching based on aspects other than proxies for innate ability is unlikely to be useful here. Further, because CFA charterholders are about 36% of our sample analysts, the choices in the non-charterholder sample are too limited to provide decent matches.

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(1999) use analyst fixed effects to adjust analyst aptitude and innate ability. Accordingly, we

include analyst fixed effects in Equation (2). We also add brokerage firm fixed effects to address

omitted brokerage characteristics that may affect analyst performance and behavior. In Section

4.6, we also conduct several tests à la Heckman’s (1979) two-step procedure, combined with the

fixed-effects model; the results are qualitatively similar.

Second, the inclusion of analyst experience is particularly interesting. As we discuss

above, CFA candidates may improve performance due to the learning from the CFA program

curriculum, or due to work experience as required by the CFA program. Prior literature finds

mixed evidence with respect to the extent of learning-by-doing as measured by experience. For

example, Clement (1999) find that forecast accuracy increases with experience, whereas Jacob et

al. (1999) find that the experience effect disappears after adjusting for analyst aptitude. By

controlling for analyst experience, we effectively focus our study on the impact that corresponds

to the learning from the CFA program curriculum.

Third, because each level of the CFA exams is offered once a year and analysts need to

have at least three years of relevant work experience,11 it takes at least three years to finish the

CFA program. Because there is no time limit within which a candidate has to complete the

program, and thus analysts may take a few years off between the preparations for the exams, the

actual time for an analyst to finish the program may be well more than three years. Further, some

analysts may finish the exams before gaining the required experience, while other analysts may

meet the minimum experience requirement before finishing all three exams. Likely due to these

reasons, 30% of the analysts who entered the CFA program in 1990 obtained their designation

within five years according to the CFA Institute, whereas 19% of the analysts who entered the

CFA program in 1996 obtained their designation within five years. The lengthy period of time to 11 Since 2003, Level I exams have been offered twice a year in some sites.

19

complete the program creates a non-charterholder sample that is tainted with CFA candidates.

For example, if a particular analyst receives the CFA designation in year t, this analyst may be a

CFA charterholder, a CFA candidate, or a non-candidate, during the few years before the

designation. While we cannot clearly measure the impact of this bias on our estimates, this bias

will only hinder our ability to detect the performance difference between CFA charterholders and

non-charterholders in our paper. Thus, any beneficial effects that we may find for the CFA

program are likely to provide only an estimate of the lower bound of these benefits.12

Someone may concern that our model specification still suffers from the omitted-variable

bias. Ideally, Equation (2) should include analyst characteristics such as measures of

education/innate ability (e.g., MBA degree and SAT score), other types of experience, or

indicators for other important certifications (e.g., Certified Public Accountant designation).

However, these characteristics, interesting by themselves, are extremely difficult, if not

impossible, to obtain for the universe of analysts. To the extent that the analyst status does not

change on these aspects (e.g., an analyst has an MBA degree or a CPA designation when

entering our sample, or the analyst never obtains those characteristics), our use of fixed-effects

models helps lessen and address such concern.

4.2. Does the CFA Program Improve Analyst Performance?

We first look at the effects of the CFA program on analyst performance measured by

ALPHA and INFORATIO, respectively. We report the results of estimating the fixed effects

model in the first two columns of Table 3. The coefficient estimate of CFA is positive and

12 When we exclude from the non-charterholder sample all the analyst-year observations for which CFA analysts are likely to be still CFA candidates (e.g., four years before they obtain their designations), we find slightly stronger benefits of the CFA program. We also find similar results when we combine the CFA candidate sample with the CFA sample.

20

significant at the 1% level for ALPHA and INFORATIO. Thus, the CFA program significantly

improves analyst recommendation performance.

The impact of the CFA program is also economically significant. Note the coefficient

estimate of ALPHA is essentially a measure of the daily return performance in basis points. Thus,

the increase in the annualized excess return by completing the CFA program is 7.47 percentage

points (= (1 + 0.0286%) 252 - 1). In Table 3, the magnitude of the coefficient estimates of CFA is

2.86 and 0.15 for ALPHA and INFORATIO, respectively. In comparison, the average ALPHA

and INFORATIO for CFA charterholders are 1.93 and 0.16, respectively, in Panel B of Table 2.

If the CFA program helps analyst performance through improved analytic skills, the

benefits should be greater for CFA charterholders who face a more opaque information

environment. Prior research argues that analysts that cover smaller companies and companies

with less coverage from other analysts should face a more opaque information environment. We

therefore expect these analysts to benefit more from the CFA program. We test this conjecture by

adding the interaction of CFA with the two information environment measures, COMPANYSIZE

and COVERAGE, in Equation (2). In columns (3)-(4) of Table 3, we report the results of

estimating Equation (2) when we add the interaction of CFA and COMPANYSIZE. In columns

(5)-(6) of Table 3, we report the results when we add the interaction of CFA and COVERAGE.

The coefficient estimates of both types of interaction terms are negative for both performance

measures. The coefficient estimate of the interaction between CFA and COMPANYSIZE is

significant at the 5% level for ALPHA, whereas that of the interaction between CFA and

COVERAGE is significant at the 1% level for ALPHA and at the 5% level for INFORATIO.

These results are consistent with the hypothesis that analysts that face a more opaque information

environment benefit more from the CFA program.

21

In summary, the CFA program significantly improves recommendation performance, and

its economic significance is substantial. The average impact of the CFA program on performance

is equivalent to an increase of 7.47 percentage points in the abnormal returns generated by

analyst recommendations. The positive impact is particularly strong for analysts that face an

opaque information environment. Among the variables that we examine, the magnitude of the

coefficient estimates of CFA suggests that the CFA program has the largest positive effect on

analysts’ recommendation performance.

4.3. The Improvement Rate in Performance before and after Finishing the CFA Program

We further examine the changes in performance of analysts before and after their

finishing the CFA program and receiving the designation. For this purpose, we focus on the

analysts who earned the CFA designation during the 1995 to 2000 period. Specifically, we define

a new variable YTOCFA, the number of years away from the completion of the CFA program.

For example, YTOCFA is equal to -3 for the third year before the designation and to +3 for the

third year after the designation. We then use the following model to examine the relation

between annual changes in the three performance measures and YTOCFA either for the three

years before analysts complete the CFA program (i.e., the pre-designation period) or for the three

years after the completion of the CFA program (i.e., the post-designation period):

0 1 2 3

4 5 6 .t t t t

t t t t

DependentVariable a a YTOCFA a EXPERIENCE a NREPORT

a NCOMPANY a BROKERSIZE a COMPANYSIZE

(3)

In this analysis, we include analysts who are in our sample for at least two consecutive years.

This requirement allows us to calculate the change in performance measures.

Note in Equation (3), we do not include change in experience because it is always equal

to one year. Thus, our approach here naturally eliminates any effects on analyst performance

22

changes that are due to the accumulation of experience. We do include EXPERIENCE to control

for an analyst’s experience. One concern is that if learning-by-doing effects do exist, they may

weaken as analysts gain more experience. Thus, because analysts have less experience before

they complete the CFA program, we need to ensure that our results do not reflect a natural

plateau in the learning curve of analysts. By including EXPERIENCE, any effects of the CFA

program that we observe should be attributable to the learning from the CFA program

curriculum, as opposed to experience.

The above approach has several advantages. First, it eliminates concerns about selection

bias by focusing on those analysts who earned CFA designations. Second, a gradual

improvement in analyst performance in the years leading up to the designation would lend

additional support to the positive effect of the CFA program on analyst performance. Further, if

CFA charterholders do not continue to improve performance in the post-designation period, i.e.,

after they finish the CFA program, the performance improvement in the pre-designation period is

likely to be due to the learning from the CFA program curriculum while one is going through the

CFA program. Finally, the coefficient estimates of YTOCFA provide estimates for the

performance improvement rate of CFA candidates. The disadvantage of this approach is that the

resulting sample size is small.

Over the 1995 to 2000 period, 563 analysts received the CFA designations. Of these

analysts, we obtain a sample of 232 analysts (415 analyst-year observations) in the pre-

designation period and a sample of 474 analysts (878 analyst-year observations) in the post-

designation period, respectively.

We report the results in Table 4. The coefficient estimates of YTOCFA are positive and

significant at the 5% level for both ALPHA and INFORATIO for the pre-designation period.

23

Further, the estimates of YTOCFA are insignificant for both performance measures in the post-

designation period. Thus, CFA candidates tend to improve their recommendation performance

significantly while going through the CFA program but they do not continue to improve after

finishing the CFA program. Taken together, the performance improvement in the pre-designation

period is likely to be attributable to the CFA program. These results are consistent with our

findings in Section 4.2.

To understand the economic significance of these results, the increase in the annualized

excess return of going through the CFA program is 5.62 percentage points (= (1 + 0.0217%) 252 -

1) per year. Thus, the total improvement over the three-year period would be a substantial 16.86

percentage points. Given the small sample in this analysis, we have to be cautious when

interpreting coefficient estimates. Nonetheless, the results provide strong evidence that the CFA

program significantly improves analyst recommendation performance.

4.4. Risk-Taking and Bias

We then examine separately the effects of the CFA program on risk-taking and bias

behavior. Column (1) of Table 5 reports the results of estimating Equation (2) with RESIRISK as

the dependent variable. The coefficient estimate of CFA for RESIRISK is -0.49 basis points and

is significant at the 1% level. In comparison, RESIRISK is 10.70 basis points for CFA

charterholders in Panel B of Table 2. Because the CFA program reduces RESIRISK of CFA

charterholders by 4.38% (= 0.49 / (10.70 + 0.49)), the economic significance of the CFA

program on analyst risk-taking behavior seems to be modest.

Column (2) of Table 5 presents the results of estimating Equation (2) with PCTSELL as

the dependent variable. The coefficient estimate of CFA is positive and significant at the 10%

24

level for PCTSELL. Again, for comparison, Panel B of Table 2 reports that the average

proportion of negative recommendations is 4.75% for CFA charterholders. Thus, completing the

CFA program significantly increases the proportion of negative recommendations made by CFA

analysts by about 15% (= 0.63 / (4.75 – 0.63)).

To summarize, the CFA program significantly reduces analyst risk-taking and bias in

recommendations, suggesting additional benefits of completing the CFA program. The

program’s effect on analyst bias is particularly interesting. Research demonstrates strong analyst

bias in the presence of investment banking relationships [e.g. Lin and McNichols (1998) and

Michaely and Womack (1999)]. As a consequence, regulators and investors have exerted

considerable effort to reduce analyst bias, particularly after the $1.4 billion settlement we discuss

above. The CFA program can therefore be a valuable tool to reduce analyst bias. The negative

effect of the CFA program on bias behavior also provides support for the increased interest in

incorporating ethics training in the curriculum of education and certification programs.

4.5. Career Outcomes

The popularity of the CFA program indicates its potential benefit to enhancing analysts’

career outcomes. According to the annual surveys of the CFA Institute, CFA charterholders with

10 or more years of experience earn about 21% more than their non-CFA contemporaries. About

90% of CFA charterholders say the designation broadens their career opportunities or chances

for promotion. In this section, we examine the impact of completing the CFA program on analyst

career outcomes. Prior research examines job separations such as termination from the analyst

profession [Hong et al. (2000)] and job mobility among brokerage firms [Hong and Kubik

(2003)]. We examine the determinants of job mobility using the following ordered probit model:

25

0 1 1 2 1 3 1

4 1 5 1 6 1

7 1 8 1 9 1

Pr

t t t

t t tt

t t t

a a CFA a IISTAR a WSJSTAR

a ACCURACY a INFORATIO a EXPERIENCEJob Mobility

a NREPORT a NCOMPANY a COMPANYSIZE

Year Effects

(4)

where Job Mobility is equal to 0 if an analyst was working for a brokerage firm that was above

the 95th percentile in terms of the number of analysts employed in year t-1 and moves in year t to

a firm below the 95th percentile; 1 if an analyst switches between firms below the 95th percentile

or switches between firms above the 95th percentile from year t-1 to year t; 2 if an analyst does

not change brokerage firms; and 3 if an analyst was working for a firm below the 95th percentile

in year t-1 and moves to a firm above the 95th percentile in year t. We define job mobility in this

way because larger brokerage firms are likely to offer higher pay [Hong and Kubik (2003)]. We

include other analyst characteristics to isolate the effects of the CFA designation.

Table 6 reports the results of estimating Equation (4). The coefficient estimate of CFA is

positive and significant at the 1% level, which suggests that the CFA designation enhances

analyst job mobility. Because the coefficient estimates of the ordered probit model only provide

limited information about the marginal effects of particular variables and are therefore hard to

interpret, we follow Greene (1997) and calculate the marginal effects to understand the economic

significance of the CFA designation. The marginal effect of the CFA designation is -0.38%,

-1.18%, 1.07%, and 0.49%, when Job Mobility is equal to 0 through 3, respectively. In

comparison, the corresponding expected probability for the four outcomes of Job Mobility is

2.20%, 11.09%, 83.77%, and 2.94%, respectively.

Turning to the performance measures, ACCURACY and INFORATIO both increase the

chance of favorable career outcomes when switching among brokerage firms, with significance

levels of 1% and 10%, respectively. Our result for ACCURACY is consistent with Hong and

26

Kubik (2003). The marginal effect of forecast accuracy is -0.01%, -0.04%, 0.04%, and 0.02%

when Job Mobility is equal to 0 through 3, respectively, whereas the same marginal effect of

recommendation performance is -0.01%, -0.03%, 0.29%, and 0.13%, respectively. Thus, the

economic significance of recommendation performance is as important as that of forecast

accuracy.

With respect to other significant independent variables, the chances of a favorable career

outcome declines with experience and the number of research reports, and increases with the

number of companies covered. Although we do not tabulate here the marginal effects of these

variables for brevity, CFA has the largest marginal effect among all the variables. In column (2)

of Table 6, we present the results when we include the measures of analyst behavior in Equation

(4). None of the four measures of analyst behavior is significant at conventional levels.

To summarize, the CFA designation significantly increases the probability of favorable

career outcomes as measured by mobility to larger brokerage firms. Because larger firms are

likely to offer higher pay [Hong and Kubik (2003)], our results suggest that completing the CFA

program benefits analyst compensation.

4.6. Robustness Analysis

To determine whether our results are robust, we perform a battery of sensitivity tests. For

brevity the results are not reported in the text and are available upon request.

We use different methods to address the selection bias embedded in obtaining the CFA

designation. As someone may concern that our above approach of using fixed effects is too

conservative to deal with the selection bias, we combine Heckman’s (1979) two-step procedure

with the fixed-effects model as in Fich and Shivdasani (2005). Specifically, in the first step, we

27

estimate the following probit model, where Selection equals one for all the sample years during

which an analyst is a CFA charterholder by the end of our sample period, and zero otherwise:

0 1 2 1 3 1 4 1

5 6 1 7 1 8 1

9 1 10 1 11 1 .

t t t t

t t t

t t t t

Selection a a FEMALE a IISTAR a WSJSTAR a EXPERIENCE

a COVERAGE a NREPORT a NCOMPANY a BROKERSIZE

a COMPANYSIZE a INFORATIO a ACCURACY

(5)

In the second step, we include the resulting inverse Mill’s ratio in Equation (2). We use Selection

instead of CFA as the dependent variable because becoming a CFA charterholder is a non-

reversible event. In addition to this sensitivity test, we also use two alternate tests to control for

the potential selection bias. First, we use CFA as the dependent variable in the first-step probit

model, i.e., Equation (5), and repeat the second step. Second, instead of using Heckman’s (1979)

two-step procedure, we directly include Selection as one additional explanatory variable in

Equation (2). If CFA analysts perform better only because they are different from non-

charterholders, the significance of CFA should disappear with the inclusion of Selection. The

results from all the three tests are similar to the results reported above for the fixed-effects

model.

We conduct tests with a variety of other performance measures. We use alternative

methodologies to measure recommendation performance and risk-taking behavior, such as the

value-weighted analyst portfolio, the market model, and the Fama-French (1993) three-factor

model. Because analysts cover related industries, we create an analyst-specific index by

matching the stocks in individual analyst portfolios with industry indexes based on two-digit SIC

codes. We replace the market index with the analyst-specific industry indexes in the factor

models to control for industry momentum. To address the potential effect of bias on analyst

performance, we exclude IPO research coverage in cases in which the brokerage house of an

28

analyst is the lead underwriter. None of the performance measurements qualitatively affect our

results.

We also test different model specifications. Because some of the dependent variables are

fractions, e.g., PCTSELL, we also perform logistic regressions for these variables. Moreover, in

addition to panel regressions, we also use Fama-MacBeth (1973) regressions to examine

performance and behavior. These different specifications do not modify our conclusions.

Chevalier and Ellison (1999) use several measures to examine the risk-taking behavior of

mutual fund managers, namely, residual return standard deviations from the market model

regression in which the mutual fund returns are the dependent variables, and absolute deviations

of market betas and residual return standard deviations of individual managers from the means of

these two variables across all managers within a year. Similarly, we examine the same measures

for analysts. The untabulated results are similar to those for the residual return standard

deviation.

We also apply different methodologies to investigate the improvement rate in

performance. First, for each analyst who obtains the CFA designation during our sample period,

we find a matched analyst based on performance in the third year before the CFA analysts obtain

their designation. We include only the matched analysts who do not become CFA charterholders

by the end of 2000. We then compare the performance improvement rate of the matched analysts

in the three years around the year that the corresponding CFA analysts finish the CFA program.

This method is conservative because the matched sample may include analysts who are CFA

candidates and who attain their designation after 2000. In contrast to our findings for CFA

analysts in Section 4.3, the improvement rate of matched analysts is not significantly different

between the first and last three years of the performance measurement period.

29

Instead of using regressions, another way to examine the performance improvement rate

around the CFA designation is to compare the performance of the same group of analysts before

and after the designation. Because this approach requires that analysts survive a minimum

number of years, it results in a very small sample. For example, the sample for the three years

around the designation would require inclusion in our sample for seven consecutive years and

would leave us with only seven analysts. Further, this approach cannot address the potential

influence of other factors such as experience. Nonetheless, we use this approach to examine

whether there is any consistent performance improvement in the years around the designation.

We find performance improvement in the pre-designation period but little performance

improvement in the post-designation period. The results are statistically insignificant, which is

not surprising given the small sample.

We examine the change in performance for the period from three years before to three

years after the designation. The choice of three-year lag can be arbitrary. Alternative choices of

lags yield similar results.

It is possible that individual forecasts and recommendations do not occur randomly

across firms or over time. For example, Stickel (1990) and Welch (2000) find evidence of

herding in forecasts and recommendations, respectively. Interdependence across forecasts or

recommendations is likely to introduce cross-sectional correlation in performance measures and,

in turn, inflate test statistics. To address this potential problem, we exclude the forecasts within

three days of any prior forecast issued for the same stock. We also exclude recommendations

with overlapping time spans between the recommendation and revision dates with any prior

recommendation for the same stock. While the resulting sample is a much smaller subset of the

original sample, we obtain similar results from this smaller sample.

30

For job mobility, we define top brokerage firms differently using other percentiles or the

annual IPO deal volume. We find similar results. The I.I. magazine argues that “because of

ratings by this magazine and others, research is the most closely monitored Wall Street field of

all, making it eminently clear who the outstanding analysts are, male or female” [Galant (1996)].

We thus modify Equation (3) by replacing the dependent variable with an indicator variable that

equals one if analysts are I.I. stars in the subsequent year and zero otherwise. We find that the

CFA designation has no impact on increasing the probability of becoming an I.I. star.

We also estimate Equation (4) by adding analyst and brokerage firm fixed effects and

find similar results. Because the coefficient estimates of fixed effect probit models are

inconsistent [e.g., Greene (1997) and Greene (2004)], the results are not presented in the paper

but are available upon request.

Given the significant benefit of the CFA designation on investment recommendations, a

natural question is whether the designation also has similar effects on earnings forecasts. In

untabulated results, we do not find any effect of the CFA program on analyst performance and

behavior as reflected in earnings forecasts: ACCURACY, BOLDNESS, and OPTIMISM. Our

results are somewhat different from De Franco and Zhou (2009), probably due to sampling

differences.

5. Conclusion

Sell-side analysts are prominent in the investment process. With the onset of market

malaise in 2000 and following the $1.4 billion settlement between regulators and the largest ten

investment banks over the exchange of biased analyst research for investment banking business,

investors have called for certification of analysts on investment knowledge and ethics training.

31

Using a comprehensive sample of investment recommendations and earnings forecasts over the

period from 1994 to 2000, we investigate whether the Chartered Financial Analyst (CFA)

program affects the performance, behavior, and career outcomes of analysts.

We find that the CFA program benefits analyst recommendation performance

significantly. Specifically, completing the CFA program improves the annualized risk-adjusted

returns on the recommendations of CFA analysts by 7.47 percentage points. CFA charterholders

that cover smaller companies and companies with less coverage from other analysts experience

even greater performance improvements, consistent with the idea that the benefits of the CFA

program are greater for analysts that face a more opaque information environment. Focusing on

those analysts that complete the CFA program during our sample period, we find that their

performances substantially improve while they are going through the program as candidates, but

such improvements appear to stop upon their completing the program. Because we control for

experience throughout our paper, these effects are likely attributable to the learning from the

CFA program curriculum rather than to the accumulated experience as required by the CFA

program.

Further, the CFA program significantly improves analyst behavior by reducing risk-

taking and bias in recommendations. For example, going through the CFA program increases the

proportion of negative recommendations made by analysts by about 15%. The CFA designation

also significantly increases the probability of favorable career outcomes as measured by mobility

to larger brokerage firms. Because larger firms are likely to offer higher compensation, mobility

to larger firms should increase analyst compensation. Thus, the designation is likely to benefit

analyst compensation.

32

In conclusion, the CFA program substantially benefits the performance, behavior, and

compensation of a large population of analysts. Given the importance of analyst research, the

documented impact of the CFA program cannot be underestimated and can rationalize spending

considerable resources in preparing and administrating the CFA exams. The fact that the CFA

program significantly reduces analyst bias also lends support to the increased practice of

including ethics training in the curriculum of education and certification programs, and suggests

that the CFA program can be a valuable tool to reduce analyst bias.

33

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37

Appendix. The Construction of ACCURACY, BOLDNESS, and OPTIMISM,

Let Fi,j,t be the last earnings per share (EPS) forecast of year-end earnings issued by

analyst i on company j between January 1st and July 1st of year t. Let the actual year-t EPS of

company j be Aj,t, let the company’s stock price be Pj, and let F-i,j,t be the average Fi,j,t made by all

other analysts except analyst i. Thus, F-i,,j,t is a measure of the consensus forecast.

We calculate analyst forecast error as | Fi,j,t - Aj,t | / Pj and an analyst’s deviation from

consensus as | Fi,j,t - F-i,j,t |. We can then construct ACCURACY and BOLDNESS according to the

following procedure, which we illustrate using the case of ACCURACY. First, all analysts that

cover company j in year t are assigned a ranking based on their forecast errors for that company.

For example, the best analyst (the one with the smallest forecast error) receives a rank of 1, the

second-best receives a rank of 2, and so on. If more than one analyst has the same forecast error,

each analyst in the tie receives the midpoint value of the ranks they take up. Second, we calculate

the score measure

, ,, ,

,

1100 [ ] 100

1i j t

i j tj t

RankScore

N

,

where Nj,t is the number of analysts who cover company j in year t. The relative accuracy

measure, ACCURACY, is the average accuracy scores over all the companies covered by analyst i

in year t.

We also create an indicator variable Ii,j,t that equals one if Fi,j,t - F-i,j,t > 0. The relative

optimism measure, OPTIMISM, is the average of these indicator variables over all the companies

covered by analyst i.

38

Table 1. Variable Definitions This table defines the variables in our paper. All variables are calculated within a calendar year. See the Appendix for details on the construction of ACCURACY, BOLDNESS, OPTIMISM, and an analyst’s recommendation portfolio. We measure performance and risk-taking behavior of individual analysts using the Carhart (1997) four-factor model regression in which the daily returns on the recommendation portfolios of individual analysts are regressed on the CRSP value-weighted NYSE/AMEX/NASDAQ market index returns in excess of the three-month T-bill returns and the size, book-to-market, and momentum factors.

CFA Dummy variable that equals one for the years that an analyst is a CFA charterholder, and zero otherwise.

ALPHA The intercept of the Carhart (1997) model regression in basis points.

INFORATIO t-statistic for the intercept of the Carhart (1997) model regression.

RESIRISK Residual return standard deviation of the Carhart (1997) model regression in basis points.

PCTSELL Percentage of “sells” and “underperforms” among the analyst’s recommendations.

ACCURACY Hong et al.’s (2000) measure of relative earnings forecast accuracy.

BOLDNESS Hong et al.’s (2000) measure of boldness in earnings forecasts.

OPTIMISM Hong and Kubik’s (2003) measure of relative earnings forecast optimism on a one-year basis.

IISTAR and WSJSTAR

Dummy variable that equals 1 if the analyst is an Institutional Investor All-American or Wall Street Journal All-Star analyst, respectively, and 0 otherwise.

NREPORT Logarithm of the number of research reports that an analyst issues.

NCOMPANY Logarithm of the number of companies that an analyst covers.

BROKERSIZE Logarithm of the number of analysts employed by the analyst’s house. For analysts who switch houses within a given year, we use the time-weighted average of the two houses.

COMPANYSIZE Logarithm of the mean market capitalization of the companies that an analyst covers at the end of the prior calendar year.

COVERAGE Logarithm of the average number of analysts that cover the same companies that an analyst covers at the end of the prior calendar year.

EXPERIENCE Number of years that an analyst has been submitting reports to I/B/E/S.

39

Table 2: Summary Statistics of the Variables

This table reports the characteristics of sample analysts. See Table 1 for variable definitions. The sample consists of 12,270 analyst-year observations for 3,474 unique analysts. In Panel A, we report the number of analysts in each category and the number as a percentage of the overall sample of 3,474 analysts. In Panel B, we conduct a t-test for the difference in the means of variables between CFA charterholders and non-charterholders. We report the significance level of the t-tests under the column for CFA charterholders. ***, **, and * indicate that the t-statistics are significant at the 1%, 5%, and 10% levels, respectively. The data are from January 1994 through December 2000. Panel A. CFA Composition

CFA Non-CFA Sum

Number of analysts 1,259 (36.24%) 2,215 (63.76%) 3,474 (100.00%) Panel B. Analyst Characteristics

Variable Full Sample Non-CFA CFA

ALPHA (basis points) 2.20 2.33 1.93 ***

INFORATIO 0.17 0.17 0.16

ACCURACY 50.13 50.18 50.01

RESIRISK (basis points) 12.26 13.00 10.70 ***

BOLDNESS 50.19 50.20 50.19

PCTSELL (%) 4.18 3.91 4.75 ***

OPTIMISM 48.01 47.95 48.16

IISTAR (%) 12.77 12.83 12.64 **

WSJSTAR (%) 8.28 7.34 10.27 ***

EXPERIENCE 6.29 5.70 7.52 ***

COVERAGE 8.76 8.87 8.54 ***

NREPORT 12.96 12.63 13.67 ***

NCOMPANY 14.56 13.87 15.99 ***

BROKERSIZE 40.74 42.25 37.55 ***

COMPANYSIZE ($Billion) 6.31 6.34 6.26 N 12,270 8,315 3,955

40

Table 3. Analyst Performance and the CFA Program This table reports the results of estimating the following fixed effects model in which the dependent variables are measures of analyst performance: ALPHA and INFORATIO.

0 1 2 3 4

5 6 7 8 9

10

*t t t t t

t t t t t

t

Dependent Variable a a CFA a CFA InformationUncertainty a IISTAR a WSJSTAR

a EXPERIENCE a COVERAGE a NREPORT a NCOMPANY a BROKERSIZE

a COMPANYSIZE Year Effects Analyst Effects Brokerage Firm E

.tffects

The measure of information uncertainty is the average size of companies that an analyst covers and the average number of analysts covering the companies that an analyst covers. The coefficient estimates are in basis points when ALPHA is the dependent variable. See Table 1 for variable definitions. Heteroscedasticity-consistent t-statistics are reported in parentheses. ***, **, and * indicate that t-statistics are significant at the 1%, 5%, and 10% levels, respectively. The data are from January 1994 through December 2000.

ALPHA INFORATIO ALPHA INFORATIO ALPHA INFORATIO (1) (2) (3) (4) (5) (6)

CFA t 2.86 *** 0.15 *** 3.55 *** 0.17 *** 8.50 *** 0.41 *** (3.70 ) (3.23 ) (3.96 ) (3.45 ) (3.15 ) (3.09 ) CFA t*COMPANYSIZE t -0.95 *** -0.02 (-2.68 ) (-1.20 ) CFA t* COVERAGE t -2.75 *** -0.12 ** (-2.40 ) (-2.04 ) IISTAR t 0.13 -0.01 0.10 -0.01 0.10 -0.01 (0.23 ) (-0.08 ) (0.17 ) (-0.10 ) (0.17 ) (-0.11 ) WSJSTAR t -1.10 *** -0.16 *** -1.10 *** -0.16 *** -1.09 *** -0.16 *** (-2.48 ) (-3.84 ) (-2.49 ) (-3.84 ) (-2.47 ) (-3.83 ) EXPERIENCE t -0.37 0.02 -0.45 0.02 -0.44 0.02 (-0.38 ) (0.28 ) (-0.47 ) (0.25 ) (-0.45 ) (0.23 ) COVERAGE t -0.85 0.03 -0.80 0.03 0.05 0.07 (-1.22 ) (0.66 ) (-1.16 ) (0.69 ) (0.06 ) (1.46 ) NREPORT t -0.12 0.01 -0.11 0.01 -0.13 0.01 (-0.46 ) (0.48 ) (-0.41 ) (0.50 ) (-0.48 ) (0.47 ) NCOMPANY t -0.90 0.01 -0.97 0.01 -0.91 0.01 (-1.32 ) (0.13 ) (-1.42 ) (0.08 ) (-1.33 ) (0.12 ) BROKERSIZE t -0.95 -0.11 ** -0.92 -0.11 ** -0.92 -0.11 ** (-1.41 ) (-2.35 ) (-1.37 ) (-2.34 ) (-1.36 ) (-2.32 ) COMPANYSIZE t 0.17 -0.02 0.46 * -0.01 0.16 -0.02 (0.75 ) (-1.06 ) (1.72 ) (-0.52 ) (0.72 ) (-1.08 ) Intercept 3.83 0.04 5.54 0.09 2.73 -0.01 (0.52 ) (0.09 ) (0.76 ) (0.19 ) (0.37 ) (-0.02 ) R-square 0.40 0.32 0.40 0.32 0.40 0.32N 12,270 12,270 12,270 12,270 12,27 12,270

41

Table 4. The CFA Program and Improvement Rate in Analyst Performance

This table reports on the relation between the changes in analyst performance and the changes in analyst characteristics for the three years before and after they obtain the CFA designation, respectively, for CFA analysts who obtain their designation over the 1995 to 2000 period. We report the results of estimating the following model

0 1 2 3

4 5 6 .t t t t

t t t t

Dependent Variable a a YTOCFA a EXPERIENCE a NREPORT

a NCOMPANY a BROKERSIZE a COMPANYSIZE

See Table 1 for variable definitions. YTOCFA measures the number of years from the designation. For example, for the third year before (after) the designation, it equals -3 (+3), respectively. Columns (1)-(2) report the results for the three years before the designation. Columns (3)-(4) report the results for the three years after the designation. The coefficient estimates are in basis points when ALPHA is the dependent variable. The models are estimated with ordinary least squares. Heteroscedasticity-consistent t-statistics are reported in parentheses. ***, **, and * indicate that t-statistics are significant at the 1%, 5%, and 10% levels, respectively. The data are from January 1994 through December 2000.

Before the Designation After the Designation ΔALPHA t ΔINFORATIO t ΔALPHA t ΔINFORATIO (1) (2) (3) (4)

YTOCFA t 2.17 ** 0.23 ** 1.11 0.07 (2.03 ) (2.12 ) (1.06 ) (0.88 ) Experience t 1.27 0.04 0.40 0.06 (0.69 ) (0.25 ) (0.28 ) (0.50 ) ΔCOVERAGE t 2.15 0.11 4.52 * 0.09 (0.62 ) (0.32 ) (1.85 ) (0.56 ) ΔNREPORT t 1.71 0.16 0.15 0.03 (1.23 ) (1.19 ) (0.13 ) (0.30 ) ΔNCOMPANY t -2.62 -0.31 0.39 0.16 (-0.68 ) (-0.81 ) (0.14 ) (0.98 ) ΔBROKERSIZE t -2.36 -0.20 -1.92 -0.13 (-0.57 ) (-0.49 ) (-0.91 ) (-1.00 ) ΔCOMPANYSIZE t 1.99 0.22 -1.19 -0.05 (1.35 ) (1.51 ) (-1.43 ) (-0.86 ) Intercept 0.58 0.23 -0.79 -0.04 (0.15 ) (0.59 ) (-0.25 ) (-0.19 ) R-square 0.01 0.01 0.01 0.01 N 415 415 878 878

42

Table 5. Analyst Behavior and the CFA Program This table reports the results of estimating the following fixed effects model in which the dependent variables are measures of analyst risk-taking and bias behavior.

0 1 2 3 4

5 6 7 8

9 .

t t t t t

t t t t

t t

Dependent Variable a a CFA a IISTAR a WSJSTAR a EXPERIENCE

a COVERAGE a NREPORT a NCOMPANY a BROKERSIZE

a COMPANYSIZE Year Effects Analyst Effects Brokerage Firm Effects

See Table 1 for variable definitions. The coefficient estimates are in basis points when RESIRISK is the dependent variable. The coefficient estimates are in percentages when PCTSELL is the dependent variable. Heteroscedasticity-consistent t-statistics are reported in parentheses. ***, **, and * indicate that t-statistics are significant at the 1%, 5%, and 10% levels, respectively. The data are from January 1994 through December 2000.

Risk-Taking Behavior Bias Behavior RESIRISKt PCTSELLt (1) (2)

CFA t -0.49 *** 0.64 * (-2.38 ) (1.93 ) IISTAR t 0.01 0.55 ** (-0.02 ) (2.15 ) WSJSTAR t 0.23 ** -0.16 (2.17 ) (-0.97 ) EXPERIENCE t -2.73 *** 0.60 (-8.19 ) (1.31 ) COVERAGE t 0.12 0.89 *** (0.54 ) (3.08 ) NREPORT t 0.33 *** 1.47 *** (3.62 ) (9.31 ) NCOMPANY t -5.96 *** -0.68 ** (-24.21 ) (-2.21 ) BROKERSIZE t 0.11 -0.26 (0.55 ) (-0.66 ) COMPANYSIZE t -0.35 *** -0.14 (-4.77 ) (-1.41 ) Intercept 27.87 *** -0.64 (11.07 ) (-0.17 ) R-square 0.76 0.72 N 12,270 12,270

43

Table 6. Mobility among Brokerage Firms This table reports the results of estimating the following ordered probit model about the mobility of analysts among brokerage firms:

0 1 1 2 1 3 1

4 1 5 1 6 1

7 1 8 1

9 1

Pr

t t t

t t tt

t t

t

a a CFA a IISTAR a WSJSTAR

a ACCURACY a INFORATIO a EXPERIENCEJob Mobility

a NREPORT a NCOMPANY

a COMPANYSIZE Year Effects

.

The dependent variable, Job Mobility, is 0 if an analyst was working for a brokerage firm that was above the 95th percentile in terms of the number of analysts employed in year t-1 and moves in year t to a firm below the 95th percentile; 1 if an analyst switches between firms below the 95th percentile or switches between firms above the 95th percentile from year t-1 to year t; 2 if an analyst does not change brokerage firms; and 3 if an analyst was working for a firm below the 95th percentile in year t-1 and moves to a firm above the 95th percentile in year t. See Table 1 for variable definitions. ***, **, and * indicate that t-statistics are significant at the 1%, 5%, and 10% levels, respectively. The data are from January 1994 through December 2000.

Ordered Probit Model

Coefficientt-statistic Coefficientt-statistic

CFA t-1 0.07*** (2.43) 0.07*** (2.43)

IISTAR t-1 -0.06 (-1.38) -0.06 (-1.38)

WSJSTAR t-1 -0.01 (-0.13) -0.01 (-0.12)

ALPHA t-1 0.02* (1.69) 0.02* (1.70)

ACCURACY t-1 0.01*** (2.62) 0.01*** (2.43)

EXPERIENCE t-1 -0.07*** (-2.81) -0.07*** (-2.84)

COVERAGE t -0.01 (-0.11) -0.01 (-0.13)

NREPORT t-1 -0.15*** (-5.77) -0.15*** (-5.79)

NCOMPANY t-1 0.12*** (4.11) 0.12*** (4.13)

COMPANYSIZE t-1 0.01 (0.49) 0.01 (0.55)

RESIRISK -0.01 (-1.09)

BOLDNESS -0.01 (-0.17)

PCTSELL 0.02 (0.42)

OPTIMISM -0.04 (-0.60)

INTERCEPT 1.89*** (15.31) 1.90*** (14.32)

Pseudo-RSQ 0.13 0.15

N 10,129 10,129