Regulations and Brain Drain: Evidence from the Wall Street ... · brain drain in the sell-side...

Regulations and Brain Drain: Evidence from the Wall Street Star

Analysts’ Career Choices*

Yuyan Guan City University of Hong Kong

Faculty of Business Kowloon, Hong Kong SAR, China

[email protected]

Hai Lu University of Toronto

Rotman School of Management Toronto, Ontario, Canada

[email protected]

and

M.H. Franco Wong University of Toronto

Rotman School of Management Toronto, Ontario, Canada

[email protected]

January 2013

* We thank Sudipta Basu, Gus De Franco, Michael Halling, Karim Jamal, David Maber, Paul Healy,

Krishnagopal Menon, David Reeb, Gordon Richardson, Suraj Srinivasan, Kent Womack, and seminar participants at Boston University, City University of Hong Kong, Harvard Business School, Hong Kong University of Science and Technology, University of Alberta, University of Notre Dame, and University of Toronto for their helpful comments. We also thank Ying Cui and Youli Zou for their research assistance. Guan appreciates research start-up grant from the City University of Hong Kong. Lu and Wong acknowledge financial support from the Rotman School of Management at the University of Toronto and the Social Sciences and Humanities Research Council of Canada.


Analysts’ Career Choices

Abstract

Global Settlement along with related regulations in early 2000s prohibits the use

of investment banking revenue to fund equity research and compensate equity analysts.

We find that all-star analysts from investment banks are more likely to exit the profession

or move to the buy side after the regulations. The departed star analysts’ earnings

revisions and stock recommendations are more informative than the remaining analysts

who followed the same companies. To the extent that star analysts are superior to their

non-star counterparts in terms of research ability, the exit of star analysts represents a

brain drain in the sell-side equity research industry. These results are consistent with the

view that new regulations which were introduced to protect equity investors have an

unintended adverse effect on the investors due to a brain drain in investment banks.

JEL classification: G14; G24; G28; J24; J63; K22; K23 Keywords: Analysts; Turnover; Brain Drain; Global Settlement; Sarbanes-Oxley Act; Policy and regulation; Investment banks

1


Analysts’ Career Choices

1. Introduction

Sell-side analysts from investment banks have long been suspected of issuing

overly optimistic stock research in return for investment banking business, especially

during the internet bubble period in the late 1990s (e.g., Becker 2001; Morgenson 2001).

Regulators implemented a series of reforms in 2002-2003 to address the conflicts of

interest in equity research.1 One of the provisions from the Global Research Analyst

Settlement and Section 501 of the Sarbanes-Oxley Act requires investment banks to

physically separate their investment banking and research departments and restrict

interaction between them. In particular, banks’ senior management must set the budget of

the research department without input from investment bankers and without tying the

budget to investment banking revenues. Investment bankers cannot take part in

evaluating analysts’ job performance or determining their compensation. Research

analysts are prohibited from participating in investment banking activities or receiving

investment-banking-related compensation.

Since the reforms prohibit investment banks from using investment-banking

revenue to fund sell-side research and compensate sell-side analysts, it has led to a

reduction in the total compensation of sell-side analysts. Using compensation data from a

leading Wall Street investment bank, Groysberg et al. (2011) document that median

1 In May 2002, the U.S. Securities and Exchange Commission (SEC) approved the amendments to New York Stock Exchange Rule 351 (reporting requirements) and Rule 472 (communications with the public) and the National Association of Securities Dealers new Rule 2711 (Research Analysts and Research Reports). In July 2002, the U.S. Congress passed the Sarbanes-Oxley Act; section 501 of the Act addresses security analysts’ conflicts of interest. In December 2002, the SEC proposed enforcement actions against ten of the top U.S. investment banks and the so-called “Global Research Analyst Settlement” was finalized in April 2003.

2

analyst compensation (expressed in 2005 dollars) decreased from the peak of $1,148,835

in 2001 to $647,500 in 2005. More importantly, the drop mainly came from bonus

awards. In particular, median real bonuses declined from $940,007 in 2001 to $450,000

in 2005, while median real base salaries stayed roughly at $175,000 over the same time

period. The drop in compensation could cause a brain drain in investment banks and the

sell-side research profession, if high ability analysts exit sell-side research to pursue more

lucrative opportunities in other industries (Institutional Investor 2007; O’Leary 2007;

Pizzani 2009).

In this study, we examine whether star analysts depart from the sell-side

investment research industry as a consequence of the reforms. Star analysts have been

substantially affected by such regulations, since they are mostly involved with their

firms’ investment banking activities and their compensation, especially bonuses, was

largely tied to investment banking revenue (Krigman et al. 2001; Groysberg et al. 2011).

With a significant cutoff in their compensation, it is likely that star analysts would exit

sell-side equity research to pursue other lucrative opportunities. We treat star analysts as

a representative group of the best performers in the profession and, hence, we consider

their departure as evidence of a brain drain in the sell-side investment research industry. 2

Over the ten-year period from 1998 through 2007, we find that the percentage of

star analysts who left the sell side increases from 6.1% before the regulations to 12.4%

afterward. Using a logistic regression, we document that investment bank star analysts

are more likely than their non-star counterparts to leave sell-side research in the post-

reform period, holding constant the analysts’ forecast accuracy, optimism, experience,

2 Section 3 discusses existing empirical evidence consistent with the research of star analysts being better than those of their peers.

3

and affiliation. This result is robust to controlling for the confounding effects brought by

the drop in the overall revenue of investment banks and the change in investor’s

sentiment (Groysberg et al. 2011). In sum, these findings suggest that it has become more

difficult for investment banks and the sell-side research industry to retain star analysts

after the reforms.

To further investigate the loss of investment banking bonuses as an explanation for

the departure of star analysts, we test whether the reforms have a more pronounced effect

on star analysts specialized in industries with a high level of investment banking

activities. First, we document that the likelihood of star analysts exiting sell-side research

is positively associated with the level of investment banking activities in the core industry

of the star analysts. Second, we also find that the relative increase in the propensity of

star analysts leaving the sell side is increasing with investment banking activities in the

star analysts’ core industry.

The final test of our conjecture involves tracking the career choices of the departed

star analysts. We document that after the reforms, there is a significant jump in the

percentage of star analysts moving to or starting their own asset management, hedge

fund, private equity, or venture capital firms. In particular, 32.3% of the departed stars

moved to the buy side after the reforms, compared with 24.7% in the pre-reform period.

If investment banks still value their star analysts after the reforms, the banks could

pay them in some other ways, such as using firm-wide bonuses. In other words,

investment banks can try to retain their star analysts by promoting them to a managerial

position or transferring them to another department with a higher earnings potential. Our

results on the departure of star analysts are not supportive of this alternative. This could

4

be due to the fact that there are limited opportunities within the banks for the departed

stars.

We repeat the same set of empirical analyses on a sample of star analysts from non-

investment banks, including independent research firms, brokerage firms, and syndicate

banks.3 Since non-investment banks have no or very little investment banking business,

they are less affected by the reforms. We do not find an increase in the likelihood and

propensity of non-investment bank star analysts exiting the sell side or moving to the buy

side after the reforms. These findings provide triangulating evidence that the loss of

investment banking-related bonuses is a reason for the departure of investment bank star

analysts from the sell-side equity research industry.

The departure of star analysts is potentially harmful for the investors who use the

analysts’ research (Mattlin 2007; Pizzani 2009). Indeed, we show that the departed star

analysts provide more informative research. First, the market reacts more positively

(negatively) to the upward (downward) forecast revisions and recommendations changes

made by the departed stars than those issued by other analysts. Second, the market

response to the departed stars’ recommendation upgrades is complete within three trading

days, but a drift is detected for upgrades issued by other analysts who followed the same

companies. The latter is consistent with star analysts contributing to the informational

efficiency of the capital market, whereby new information generated by the departed star

analysts is impounded into stock prices more quickly. To the extent that buy-side firms

have limited capital to fully capitalize on the star analysts’ research findings, the reforms

3 We include syndicate bank analysts in this analysis, because syndicate banks involve only in distribution and, hence, they face little investment-banking incentives. The results are unchanged if syndicate bank analysts are excluded.

5

could prevent the information generated by the analysts from being fully impounded into

stock prices.4

This study adds to our understanding of the economic consequences of the new

regulations on the equity research industry. Prior research shows that these regulations

have made analysts’ stock recommendations less upwardly biased (e.g., Barber et al.

2006; Kadan et al. 2009; Guan et al. 2011) and more consistent with valuation based on

analysts’ earnings forecasts (Barniv et al. 2009; Bradshaw 2009; Chen and Chen 2009).

On the other hand, Boni and Womack (2002) and Boni (2006) suggest that the reforms

would cause research departments to reduce their coverage and quality of their research.

As Mehran and Stulz (2007) point out, it is important to identify and measure all the costs

and benefits to conduct a fair and comprehensive evaluation of the new regulations.5 In

this study, we identify an economic consequence of the reforms; namely, the new

regulations are associated with a brain drain in investment banks and the sell-side equity

research industry. We believe the brain drain effect should be taken into consideration in

an overall assessment of the efficacy of the reforms.

This study also contributes to the strand of literature investigating the career

concerns of equity analysts. Prior studies have shown that analysts’ career outcomes are

affected by their forecast accuracy, optimism, and experience (Mikhail et al. 1999; Hong

et al. 2000; Hong and Kubik 2003; Wu and Zang 2009). We show that compensation is a

4 The brain drain might benefit large investors and their clients on the buy side (e.g., hedge funds). Moreover, certain investors (e.g., individuals) who rely on high-quality equity research from sell-side analysts could be harmed. This could potentially lead to wealth transfer from the small investors to their larger counterparts. Such a wealth transfer effect is implicit compared to the direct wealth transfer due to the misleading behaviour of some analysts documented in De Franco, Lu, and Vasvari (2007). This issue is beyond the scope of this study. 5 Recently, the SEC joined sanctioned investment banks in seeking the removal of the Chinese wall, suggesting that the SEC acknowledge the adverse effects imposed on investment banks. The motion was rejected in court (Craig and Scannell, 2010). Also, see Brakke (2010).

6

factor that leads to the voluntary departure of the best analysts from the sell-side

investment research profession. Annual pay for top analysts fell to about one-quarter by

2008 from its peak in 2000 (Robinson 2009). Our study provides systematic evidence

supporting the notion that the reforms affect the career choices of the star analysts when

the reforms reduce the earnings potential of sell-side analysts. More broadly, our study

adds to the economic literature by demonstrating how regulations and compensation

shocks affect human capital flows. Deuskar et al (2011) and Kostovetsky (2011) examine

whether good mutual fund managers would leave for hedge funds and the studies reach

different conclusions. Our study contributes to the debate by showing that a brain drain

indeed occurs following a compensation shock in investment research industry. In mutual

fund industry, good managers can be retained by allowing them to manage a hedge fund

side-by-side (Deuskar et al 2011). Such an alternative might be difficult to find within

investment research industry.

The next section describes our sample and data. Section 3 presents our analysis

modeling the propensity of the star analysts to depart the sell-side research industry after

the reforms. Section 4 investigates the career choices of star analysts after leaving sell-

side research. Section 5 examines the potential loss to investors who rely on the research

of the departed star analysts. Section 6 concludes.

2. Sample and Data

Our initial sample comes from I/B/E/S and covers the period from January 1998 to

December 2007. We divide the sample period into two subperiods: The pre-reform

period (January 1998 – December 2001) and the post-reform period (January 2002 –

7

December 2007). We examine the effect of reforms on analysts’ career choices between

the pre- and post-reform periods. As robustness checks, we also repeat our analyses with

2002 and 2003 excluded from the post-reform period. The reforms were proposed and

implemented during these two years and, hence, they might not have reached its full

effect by then. The results are similar with or without the inclusion of these two years in

the post-reform period.

We retrieve all analysts’ earnings forecast and stock recommendation data from

Thomson Financial’s I/B/E/S database. We use the 2006 I/B/E/S translation file to

identify the affiliation of each equity analyst and the name of each brokerage firm, which

allows us to have a sample spanning the period from January 1998 through December

2007.6

We define an analyst as a star if he/she is on Institutional Investor’s All-American

research team in a particular year or at least one of the last two years. Institutional

Investor publishes the list annually in its October issue and the ranking is based on votes

from buy-side portfolio managers.

Our final sample consists of the equity analysts from both investment banks and

non-investment banks. While the main focus of this study is on investment bank star

analysts, we use star analysts from non-investment banks to provide counterfactual

evidence to supplement our main tests. Investment banks are those firms classified as

investment banks by Nelson’s Directory of Investment Research and identified as lead or

co-lead underwriters by Thomson Financial’s SDC database. Non-investment banks

include independent research firms, brokerage firms, and syndicate banks. Independent

6 I/B/E/S stops providing the translation file for academic research after 2006. We use the 2006 translation file to identify analyst affiliation in 2007. Hence, we lose new investment banks (and their analysts) that are added to I/B/E/S in 2007.

8

research firms are those listed as such by Nelson and are not found in the SDC database

as lead/co-lead underwriter or manager/co-manager. Brokerage firms are those firms

classified by Nelson as a major institutional broker, major or small regional broker, or

investment bank/broker that is not identified as lead/co-lead underwriter or manager/co-

manager by SDC. Syndicate banks are those firms listed by Nelson as either investment

banks or brokers and identified by SDC as managers or co-managers, but not as lead or

co-lead underwriters. Syndicate banks involve only in distribution and, hence, they face

little investment-banking incentives. Our findings are not affected if we exclude

syndicate banks from the analysis.

Table 1 reports the number of analysts, number of star analysts, number of

investment banks, and number of companies covered each year in our sample period for

investment banks. Columns 1 to 4 show that the number of investment bank analysts does

not change much over time, with a high of 4,138 in year 2002 and a low of 3,320 in year

1998, and stays around 3,500 during the period 2004 – 2007. The number of investment

bank star analysts increases from 290 in year 1998 to 413 in year 2002, but it starts

decreasing since 2002 and reaches 305 in year 2007. The number of investment banks is

quite stable and ranges from a high of 132 in 1999 to a low of 107 in 2007. The number

of companies covered by investment banks decreases from 6,990 in year 1998 to 4,940 in

year 2003 and then increases gradually afterward. The pattern reflects the change of the

concentration of analysts following over time.

Columns 5 to 8 report the same information for non-investment banks. Even though

there are more non-investment banks than investment banks during the sample period

(1,430 versus 1,177), the number of analysts and star analysts from non-investment banks

9

are smaller comparing to those from investment banks. As a result, non-investment banks

cover fewer stocks than investment banks (36,017 versus 57,796).

3. Effect of the regulations on star analysts’ career concerns

Star analysts are voted by institutional investors based on a number of features,

such as industry knowledge, special services, earnings reports, stock recommendations,

etc. (Bagnoli et al. 2008). Prior research presents evidence that star analysts are superior

to their non-star peers. In particular, star analysts provide more accurate and timely

earnings forecasts, their status and coverage affect the market share of equity offerings of

investment banks, and their forecasts suffer less from conflict-of-interest due to their

concern for personal reputation (see, for example, Stickel 1992; Dunbar 2000; Krigman

et al. 2001; Gleason and Lee 2003; Bonner et al. 2007; Leone and Wu 2007; Clarke et al.

2007; Bagnoli et al. 2008; Fang and Yasuda 2009).

Consistent with the notion that star analysts possess higher ability (or are valued

more by their employers), Groysberg et al. (2011) find that star analysts receive 61%

higher total compensation and 100% higher bonus than their unranked counterparts in a

large, high-status investment bank. While the higher compensation might simply reflect

the fact that star analysts usually take part in their firms’ investment banking activities

(Krigman et al. 2001), their involvement in investment banking activities is an indication

that they have certain attributes that are considered desirable by both the investment

banks and their clients.

We examine the departure of star analysts from the sell-side investment research

industry as a consequence of the reforms. We treat star analysts as a representative group

10

of the best performers in the profession and, hence, their departure is considered a brain

drain in the sell-side investment research industry. An analyst is said to have left the sell-

side research profession in a particular year if he/she made earnings forecasts in that year

but stopped issuing forecasts the following year, according to data from I/B/E/S. We

further confirm his/her departure from sell-side research using Nelson’s Directory of

Investment Research.

Table 2, panel A presents summary statistics on the number of star analysts by year

and firm type. The panel also reports the number and percentage of star analysts who left

the sell-side investment research industry. Column (3) shows that the percent of

investment bank star analysts who left the industry is less than 8% in the first four years

of our sample period (i.e., the pre-reform period), peaks at 13.6% in years 2002 and 2003,

and stays relatively high since then.7 Panel B indicates that, on average, 6.1% and 12.4%

of star analysts left the industry during the pre-reform and post-reform periods,

respectively. On the contrary, we actually observe a drop in the percentage of star

analysts from non-investment banks leaving the sell-side research profession.

Specifically, column (6) in panel B reports that 19.6% of non-investment bank star

analysts left the sell side in the pre-reform period, compared with 12.4% in the post-

reform period.

Taken together, these statistics are consistent with the reforms having a

differential impact on the career choices of investment bank and non-investment bank

star analysts. We conjecture that the rise in the percent of star analysts who left

7 The slight increase in the percent of star analysts leaving the sell-side industry in 2001 could be due to Regulation Full-Disclosure (Reg FD), which became effective in late 2000. However, the increase in 2001 is small compared with those in the post reform period 2002–2007.

11

investment banks and sell-side research after the reforms could be attributed to the loss of

bonuses that are tied to investment-banking revenue.

We note that Wu and Zang (2009) investigate the effect of 76 mergers in the

financial industry during the period 1994 through 2004 on the career outcomes of sell-

side analysts. They find that accurate analysts are more likely to turn over and star

analysts are more likely to be promoted to an executive position. However, there are

only three, one, and two mergers completed in 2002, 2003, and 2004, respectively.

Hence, our post-reform period results are not likely driven by the phenomenon

documented in Wu and Zang (2009).

3.1. Logistic regression analysis of star analysts’ propensity to exit

To formally examine the impact of the reforms on the loss of star analysts from

the sell-side research industry, we use logistic regression analysis to estimate the

propensity of analysts to exit sell-side research firms. The regression model is specified

as follows:

(1)

where D is an indicator variable that equals one in the post-reform period, zero otherwise.

STAR is an indicator variable that equals one for analysts who are on the Institutional

Investor All-American research team in the current year or at least one of the last two

years, zero otherwise.

Equation (1) is a difference-in-differences regression model, which is used to

address potential confounding time effects that affect all analysts. In particular, it allows

us to compare the change in the propensity of star analysts to leave sell-side research in

12

the post-reform period with that of their non-star counterparts. Hence, this research

design removes the confounding effects that are common across star and non-star

analysts. A positive coefficient on D × STAR, the difference-in-differences estimate, is

consistent with star analysts exhibiting a larger increase, relative to their non-star peers,

in their propensity to leave the sell side in the post-reform period.

We control for other factors that might affect the propensity of analysts to leave

the sell-side research industry. Mikhail et al. (1999) and Hong et al. (2000) find that

forecast accuracy and experience are associated with job separation. Hong and Kubik

(2003) also show that forecast optimism affects job separation, especially for analysts

who cover stocks underwritten by their firms. Hence, we control for annual earnings

forecast accuracy, forecast optimism, and forecast experience in the logistic regression.

In particular, accurate forecasters might leave the industry for other lucrative

opportunities and poorly performing analysts might be fired. Following Hong and Kubik

(2003) and Wu and Zang (2009), we include TOPACCU and BOTACCU into the logistic

regression. TOPACCU (BOTACCU) is an indicator variable that equals one if the

analyst’s relative forecast accuracy score is in the top 10% (bottom 10%) of all analysts,

zero otherwise. Same as these studies, we measure relative forecast accuracy for each

analyst i as the analyst’s average forecast accuracy in year t,itAccuracy .

itAccuracy is

computed by averagingijtAccuracy across all companies followed by analyst i in period t.

In particular,

1100 100 ,

1

ijt

ijt

jt

RankAccuracy

NumberFollowing

− = − ×

−

13

whereijtRank is analyst i’s forecast accuracy rank for company j in period t, and

jtNumberFollowing is the number of analysts following company j in period t. We use

the last forecast made by each analyst for the same company and forecast period. By

construction, this measure controls for differences in the composition of companies

followed by the analysts and for differences in forecasting period.

We calculate relative annual forecast optimism, RFOPT, as in Cowen et al.(2006),

using the following equation:

,( )

t k t k

ijt jtt k

ijt t k

jt

FORECAST FORECASTRFOPT

STDDEV FORECAST

− −

−

−

−

=

where t k

ijtFORECAST− is analysts i’s forecast of company j’s performance for period t, as

of t-k. t k

jtFORECAST

− and ( )t k

jtSTDDEV FORECAST− are, respectively, the average and

standard deviation of all forecasts for company j and period t, as of t-k.8 We compute

t k

ijtRFOPT− only for companies that are followed by at least three analysts and we use only

the first forecast made by each analyst for the same company and forecast period.

t k

ijtRFOPT− is then averaged across all companies followed by analyst i in period t to

compute analyst i’s average relative forecast optimism at period t, t k

itRFOPT− . By

construction, this measure controls for company- and time-specific factors that affect

forecast optimism across analysts.

Analyst experience, EXPERIENCE, is the average number of years the analyst

has issued earnings forecasts or recommendations for the companies they follow.

SANCTIONED is an indicator variable that equals one for analysts affiliated with a

8 We winsorize forecast optimism at the 1st and 99th percentiles, because some standard deviations (i.e., the deflator) are extremely small. Results are qualitatively similar if we scale this measure by stock price.

14

sanctioned investment bank and zero otherwise.9 The reforms target these sanctioned

banks and, hence, analysts from these banks could react to the reforms differently as a

result of their visibility.

There are two confounding factors that might also explain the brain drain

phenomenon. First, investment banks are facing decreasing performance in the post-

reform period because the profitability of their trading division is adversely affected by a

drop in soft dollar arrangements, trading volume, and commission rate (Goldstein et al.

2009). As a result, bonuses tied to firm-wide profit are adversely affected, making it

difficult to use firm-wide bonuses to retain star analysts. Second, investor sentiment

varies substantially in our sample period (Cen et al. 2012). The sentiment affects trading

commission (Groysberg et al. 2011).

We control for these two factors by including TOTREV and SENTIMENT in the

logistic regressions. TOTREV is the revenue of the firms in the industries with 4-digit SIC

codes of 6200 (security and commodity brokers, dealers, exchange and services), 6211

(security brokers, dealers and flotation companies), and 6282 (investment advice).

SENTIMENT is the lagged value of the annual Baker and Wurgler (2006) index.

Table 3, columns 1 to 4 present summary statistics on the explanatory variables of

the logistic regression for the sample of all investment bank analysts and the subsample

of star analysts by subperiod. In both subperiods, about 12% of all the analysts are star

analysts. Average TOPACCU is about 10% in both subperiods for all analysts in the

9 The sanctioned investment banks are Bear Stearns, Credit Suisse First Boston, Goldman Sachs, Lehman Brothers, J.P. Morgan Securities, Merrill Lynch, Morgan Stanley, Citigroup Global Markets (formerly known as Salomon Smith Barney), UBS Warburg, and U.S. Bancorp Piper Jaffray. Deutsche Bank Securities Inc. and Thomas Weisel Partners LLC later joined the Global Settlement in 2004 (SEC 2004). We classify them as non-sanctioned investment banks in our main analysis. However, the results are qualitatively the same if they were included in the sanctioned bank category.

15

sample, but less than 10% for the star analyst subsample. This result is consistent with

forecast accuracy being not a main factor in the ranking criteria of star analysts (Bagnoli

et al. 2008). The mean and median of relative forecast optimism, RFOPT, of all star

analysts is 0.02, greater than that of all the investment bank analysts in the pre-reform

period. In the post period, the relative forecast optimism of star analysts is reduced and

becomes the same as that of all the investment bank analysts. In terms of experience, the

mean experience of star analysts is about 10 years in both pre- and post- period, much

greater than that of all the investment bank analysts, which is about 7 years. Both the

number of funds and total revenue increase in the post-reform period. In addition, the

majority of the star analysts are from the ten sanctioned investment banks in both pre-

and post- reform periods, making up 75% and 81%, respectively.

Columns 5 to 8 in table 3 show the same information for non-investment banks.

Most of variables for non-investment bank analysts are similar to those for investment

bank analysts with several exceptions. Average BOTACCU is higher for non-investment

banks than investment banks in both sub-periods, indicating that non-investment bank

analysts are less accurate than their investment bank counterparts. Regarding forecast

optimism, RFOPT, non-investment bank analysts are relatively more optimistic than

other analysts who followed the same companies, consistent with prior results in Cowen

et al. (2006).

3.2. Results of logistic regression

Table 4 summarizes the results of the logistic regression (1), which are based on a

sample of 24,991 and 6,025 analyst-year observations from investment banks and non-

investment banks, respectively. As shown in columns (1) to (4) of table 4, the estimated

16

coefficient on D is positive, suggesting that investment bank analysts on average are

more likely to leave the profession in the post-reform period. The estimated coefficients

on the STAR variable are statistically negative, suggesting that star analysts are less likely

than their non-star colleagues to leave the profession during the pre-reform period.

However, the estimated coefficients on the interactive term D×STAR, our main variable

of interest, are positive and statistically significant, suggesting that the increase in the

propensity of star analysts to leave the sell-side research profession after the reform is

more than the corresponding increase for non-star analysts. The latter supports our

conjecture that more star analysts left the sell side in the post-reform period, after taken

into consideration of the confounding events that affect both star and non-star analysts.

We obtain the aforementioned results after controlling for other determinants of

job separation, promotion, or demotion of sell-side analysts (Mikhail et al. 1999; Hong et

al. 2000; Hong and Kubik 2003; Wu and Zang 2009). Adding to these studies, we

document statistically positive coefficients on BOTACCU, indicating that inaccurate

(bottom 10%) analysts are more likely to exit the profession. Relative forecast optimism,

RFOPT, has little effect on the propensity of analysts to leave the profession. On the

other hand, analyst experience, EXPERIENCE, significantly reduces the propensity to

exit sell-side research.

We allow for sanctioned bank fixed effects to control for unobservable

heterogeneity across the 10 sanctioned investment banks. As shown in column (1), the

estimated coefficient on the sanctioned bank dummy, SANCTIONED, is positive and

statistically significant, implying that analysts from sanctioned banks are more likely to

leave. However, the ten sanctioned banks might be affected by the Global Settlement

17

differently, depending on their business mixes. For example, a sanctioned bank might be

affected more by the drop in investment banking business and, hence, exhibits a higher

departure rate. We replace the sanctioned bank dummy with individual sanctioned bank

dummies to allow for heterogeneity among the different sanctioned investment banks.

Notwithstanding these additional control variables, the results on our main variable of

interest, D×STAR, remain unchanged. We also re-estimate the regression model after

controlling for the total revenue of the investment banking industry and investor

sentiment. The results are reported in column (2) of table 4. Neither TOTREV nor

SENTIMENT exhibits a significant effect on the likelihood of an analyst leaving the sell-

side industry. Nonetheless, the estimated coefficients on our variable of interest,

D×STAR, remain statistically positive, suggesting that the key results are not attributed to

these potentially confounding factors.

Since our main variable of interest is an interaction term in a nonlinear (logistic)

model, we follow the method of Ali and Norton (2003) and Norton et al. (2004) to

estimate the observation-specific interaction effects of D and STAR. Untabulated results

support the hypothesis that star analysts have a significantly greater propensity to leave

the sell-side profession in the post-reform period. The mean estimated marginal effects

are between two and five percents across the four model specifications, while the

maximum marginal effects are between eight and 11 percents. In other words, the

average star analyst is around 4% more likely to leave the sell side after the reforms,

which is economically large relative to the attrition rate of around 6.1% in the pre-reform

period 1998-2001.10

10 We report this analysis for information purpose only. We do not tabulate these results because there are debates on whether this adjustment is necessary (Greene 2010; Kolasinski and Siegel 2010).

18

If the aforementioned results for investment bank star analysts are driven by the

loss of investment banking-related compensation, we should expect to see the reforms

have less effect on the exit of non-investment bank star analysts. We present the

corresponding regression results in columns (3) and (4) of table 4. As reported, non-

investment bank analysts in general are not more likely to leave the sell-side research

industry in the post-reform period. Furthermore, in contrast to their investment bank

counterparts, star analysts at non-investment banks appear to be less likely than their non-

star peers to leave the sell-side research industry in the post-reform periods. The

estimated coefficient on D×STAR is statistically negative at the 5% significance level.

This finding lends further support to the conjecture that the reforms inducing investment

bank star analysts to leave sell-side research, although the results should be interpreted

cautiously due to the small number of star analysts in non-investment banks.

3.3. Effect of investment banking business in an analyst’s core industry on her exit

In our above regression analysis, we control for the investment banking revenue.

To strengthen our argument that the departure of star analysts is indeed due to the

investment banking incentives, we conduct two additional analyses. We consider that the

level of investment banking activities in an analysts’ core industry have two effects on

the analysts’ career choices.

First, the post-reform period (2002-2007) coincides with a downturn in

investment-banking market activities. Hence, our findings that the likelihood and

propensity of star analysts leaving the investment research industry rise after the reform

could be attributed to the decreasing demand for their services after investment-banking

deals dried up shortly after the reforms. To investigate how the initial public offering

19

(IPO) and seasoned equity offering (SEO) markets have changed in the post reforms

period, we extract the numbers of IPOs and SEOs and the corresponding proceeds from

Thomson Financial’s SDC database for our sample period 1998 – 2007. Figure 1 shows

that both the numbers and proceeds of IPO and SEO deals are low in years 2002 and

2003. However, both the numbers and proceeds of investment banking deals started

recovering in 2004 and reached or surpassed the 1999 levels in 2007. The latter indicates

that the increase in the percent of investment bank star analysts who left the sell-side

research industry in the post-reform period cannot be attributed to investment banking

market conditions (even though this might be the case in 2002 and 2003).11

Second, we argue that star analysts are more likely to leave sell-side research after

the opportunity to earn investment banking-related compensation is cutoff by the reform.

If our conjecture is correct, we expect the reforms to have a more pronounced effect on

analysts who are specialized in industries with a high level of investment banking

activities.12 We test this implication by examining whether the level of investment

banking activities in an analyst’s core industry is associated with the likelihood and

propensity of analysts leaving the sell side after the reforms.13 We define an analyst’s

core industry (based on 2-digit SIC) as the one in which the majority of the companies

covered by the analyst come from. We retrieve from the SDC database the IPO and SEO

proceeds raised by companies in each 2-digit SIC industry over our sample period and

use it to capture the level of investment banking activities for each analyst’s core industry.

11 We note that this explanation would require that analysts and their banks could work around the rule prohibiting research analysts from participating in investment banking businesses after the reforms (O’Leary 2007). Otherwise, investment banking market condition should have no effect on the demand for sell-side research analysts after the reforms. 12 Industries with a high level of investment banking activities are also likely to exhibit high stock market activities, which attracts buy-side firms. 13 We thank Paul Healy for suggesting this analysis.

20

We first report simple statistics on the percentage of star analysts who left by

industry quartiles formed by ranking industry-level IPO and SEO proceeds in both the

pre- and post-reform periods. Panel A of table 5 shows that the mean proceeds for

industries in the lowest and highest quartiles are $83 and$12,472 million, respectively, in

the pre-reform periods. The corresponding figures are $141 and $7,378 million in the

post-reform period. In the pre-reform period, there is no clear pattern that the percentage

of star analysts who departed is greater for those specialized in industries with heavy

investment banking activities. In contrast, during the post-reform period, there is a

monotonic relation between the percentage of star analysts who left and the investment

banking activities of the analysts’ core industry. Specifically, the percentage of star

analysts who left increases from 6.08% for star analysts with core industry in the lowest

quartile of investment banking activities to 13.14% for those in the highest quartile.

In panel B, we use an alternative classification scheme to get a more balanced

number of star analysts in each industry quartile. Specifically, we classify the star

analysts into quartiles based on the total IPO and SEO proceeds in the core industry of

the analyst. Similar to panel A, we find that in the post-reform period, the percentage of

star analysts who departed increases monotonically with the level of investment banking

activities in the analysts’ core industry. It ranges from 9.84% for star analysts in the

lowest industry quartile to 13.73% for those in the highest quartile. However, there is no

such a pattern in the pre-reform period. Overall, the descriptive statistics support our

conjecture that the increased likelihood of star analysts leaving the profession is due to

the prohibition of compensating analysts based on a bank’s investment banking revenue.

21

To formally test the effect of investment banking activities in an analyst’s core

industry on star analysts’ career choice, we include LOGIB and D×STAR×LOGIB into the

logistic equation (1).14 The variable LOGIB is logarithm of one plus the revenue of

investment banking business (IPOs and SEOs) for the core industry that each individual

analyst provides research service. Table 6 presents the regression results. We find a

statistically positive coefficient (p=0.08) on D×STAR×LOGIB in the regressions for

investment banks, suggesting that the relative increase in the propensity of star analysts

exiting sell-side research is increasing with the level of investment banking activities in

the core industry of the star analysts. The results are reported in columns (1) and (2).

However, the estimated coefficients on D×STAR×LOGIB as reported in columns (3) and

(4) are insignificant in the regressions for non-investment banks. Overall, these results

provide further support for our conjecture that the brain drain effect we documented in

the post-reform period is associated with the loss of funding support from investment

banking business.

4. Career choices of star analysts who exited the sell-side research industry

While the results presented in Tables 2, 4, and 6 strongly suggest a rise in the

percentage and propensity of star analysts departing sell-side research after the reforms,

they do not address whether they are being forced out because of funding cuts or are

leaving voluntarily to pursue other opportunities. Only the latter is consistent with our

conjecture that the reforms lead to a brain drain in investment banks, because star

14 We also include DxSTAR and STARxLOGIB into the regression as control variables to avoid the correlated omitted variables issue.

22

analysts lose investment-banking-related bonuses after the reforms (e.g., see O’Leary

2007 and Robinson 2009).

To further investigate the reason behind the exit of star analysts, we keep track of

all 348 investment bank star analysts and 25 non-investment bank stars analysts who left

the sell-side investment research industry during our sample period from 1998 through

2007. We first use the I/B/E/S translation file to extract the initial and last name of these

star analysts. We then look up the full name of these analysts from Nelson’s Directory of

Investment Research and the corresponding issues of the Institutional Investor. Given

the full names, we check the Nelson’s Directory to determine whether these star analysts

indeed left the sell-side research industry or are promoted to a managerial position in

their firms or other sell-side research firms. Finally, we search Factiva and Google to

identify the first job the departed star analysts took after they left the sell-side equity

research industry.

Based on the information collected, we classified these departed star analysts into

one of the following eight categories:

(A) Promoted to a managerial position within the firm or to another firm. This category

includes analysts that are listed as key executives in Nelson’s Directory of Investment

Research. For example, Benjamin Bowler of Merrill Lynch was promoted to Co-

Head of Global Equity Research.

(B) Transfer laterally to a different department within the firm. This category includes

analysts who stayed with the same firm after leaving sell-side research (i.e., no longer

found in I/B/E/S), but they are not listed as analyst or key executives in Nelson’s

23

Directory. For example, Anatol Feygin of Bank of America Securities became the

firm’s Head of Global Commodity Strategy.

(C) Moved to a buy side, hedge fund, private equity, or venture capital firm. This

category includes analysts who either moved to or started their own asset

management, hedge fund, private equity, or venture capital firms. For example,

William Julian left Smith Barney Citigroup in 2003 to become the founding partner

of Calimar Capital Management, a hedge fund focused on small cap stocks.

(D) Started own investment research firm or moved to another research firm that is not

covered by I/B/E/S. For example, John Tumazos left Prudential Equity Group in

2008 to found Very Independent Opinions, LLC.

(E) Moved to a non-financial industry. This category includes the analysts who left the

financial industry. For example, Mark Rowen left Prudential Equity Group to join

eBay as its vice president of investor relations.

(F) Retired or fired for causes. Besides those who announced retirement, this category

also includes a few analysts who were banned from the financial services industry

(e.g, Henry Blodget of Merrill Lynch) or fired for causes (e.g., Peter Caruso of

Merrill Lynch for tipping off clients).

(G) Still a sell side analyst but not found in I/B/E/S. Some analysts are no longer covered

by I/B/E/S, but we found their names on the Nelson directory. Not all investment

research firms are covered by I/B/E/S.

(H) Not enough information to classify. We are not able to find enough information on

56 analysts to classify them into one of the above categories. This category includes

these “unclassified” analysts.

24

Table 7 presents the number and percentage of the departed star analysts in each of

the above categories by subperiod. In particular, we are interested in investigating

whether the percent of star analysts moving to these categories changes after the reforms

and whether the change is consistent with star analysts moving to more lucrative careers.

Elite analysts are known to leave the sell-side industry to manage money as portfolio

managers at the buy side (see, e.g., Groysberg et al. 2008). It is generally believed that

money managers at the buy side can earn more than sell-side star analysts. This is

especially true after the reforms, which prohibit tying analyst compensation to

investment-banking revenues. Hence, we consider an increase in the percent of departed

stars who moved to the buy side as evidence that star analysts exit the sell-side industry

to pursue more lucrative opportunities. We define buy side to include asset management,

hedge fund, private equity, or venture capital firms.

Panel A in table 7 reports the findings for departed star analysts from investment

banks. Columns 1 and 2 show that there are a total of 85 star analysts who left the sell-

side profession in the pre-reform period (1998 – 2001): 17.6% are promoted to a

managerial position, 5.9% transferred to a different department in the same firm, 24.7%

moved to the buy side, and 18.8% moved to a non-financial industry. Columns (3) and

(4) present the corresponding statistics for the post-reform period (2002 – 2007) and

column (5) shows the change relative to the pre-reform period. Specifically, columns (3)

and (4) show that of the 263 star analysts who exit the sell-side profession in the post-

reform period, 32.3% moved to the buy side, which is a significant 7.6% point increase

relative to that in the pre-reform period.15 This is also the largest percentage point

15 Under the null hypothesis that the reforms have no effect on the career choices of star analysts, the percent changes are zero. We assume that the percent changes from these eight categories are drawn from

25

increase among the eight categories. If potential compensation is higher on the buy-side,

this finding is consistent with star analysts leaving sell-side research to pursue more

lucrative opportunities on the buy side after the reforms.

If star analysts are valuable for the investment banks in other capacities, the

investment banks will make an effort to keep their stars by promoting them to a

managerial position in the research department or transferring them to a different

department with a higher earnings potential (e.g., investment banking or asset

management). Panel A provides little support that investment banks are able to retain the

star analysts in other capacities. Specifically, the percent of star analysts who are

promoted (category A) decreases by 0.9% points and those who moved to another

department (category B) increases by 1.7% points; both percentage changes are not

distinguishable from zero.

To further support our conjecture that the reforms cause the brain drain in

investment banks, we perform the same analysis on non-investment banks. Under our

conjecture, non-investment banks should be least affected. Panel B, column (5) shows

that there is an insignificant 3.3% point reduction in the percent of non-investment bank

star analysts who moved to the buy side during the post-reform period. We also find a

significant increase (decrease) in the percent of non-investment bank star analysts who

moved to the non-financial industries (were promoted to a managerial position).

However, the results presented in panel B should be interpreted with caution, given the

small sample of star analysts who departed non-investment banks.

a student’s t-distribution, with zero mean. Hence, we estimate the standard error for statistical inference using the standard deviation of the percent changes across these eight categories. We also try more finely defined categories and the findings (not tabulated) are qualitatively similar to those reported here.

26

Taken together, the evidence documented in this section suggests that the sell-side

equity research profession loses more of their star analysts to the buy side after the

reforms. We attribute this “brain drain” phenomenon to the series of regulations that

ban analysts from participating investment-banking related activities.

5. Potential effect of the brain drain on investors

Star analysts are voted by institutional investors each year and are considered to be

the most valuable analysts in the profession. Their departure suggests arguably a loss of

human capital in the industry. In this section, we examine one aspect of the value of star

analysts which can be measured directly with public data. Specifically, we investigate the

adverse effect of the brain drain in the investment research industry on the investors who

relied on the research of the departed star analysts. We compare the informativeness of

research conducted by the departed star analysts with that of other analysts who cover the

same companies and remain in the sell-side research industry (i.e., non-departed

analysts). We measure informativeness by the market responses to analysts’ forecast

revisions and stock recommendations. We analyze the data from the year before the star

analysts depart and compute the mean of the differences across year. t-statistics are

computed by dividing the mean of the differences by the standard error of the annual

differences.

5.1. Informativeness of forecast revisions

Table 8, Panel A summarizes our findings on the informativeness of forecast

revisions for both departed and other analysts. Columns 1 indicates that, on average, the

departed star analysts revise upward their earnings forecasts 714 times and downward

27

794 times in a year. Columns 3 to 5 report the 3-day market-adjusted abnormal returns

from day –1 to day +1, where day 0 is the day when the analysts issue their revisions.

For upward revisions, the market reacts more to the revisions issued by the departed stars

than to those issued by other analysts. The associated numbers are 1.76% and 1.45%,

respectively, with the difference being statistically positive. Moreover, the upgrade

revisions of the departed stars are more informative that those of other analysts in all ten

years. For downgrade revisions, the market again reacts stronger to the revisions made

by the departed star analysts. The 3-day market-adjusted abnormal return is –2.24%

versus –2.04%, with the difference being statistically significant. These results are

consistent with Gleason and Lee (2003) that forecasts made by star analysts elicit a

stronger immediate price response, suggesting that star analysts help impound earnings

news into price faster.

Columns 6 to 8 present the market reactions over the post-revision window from

day +2 to day +21. There is no distinguishable difference between the stars and their

peers, as their upward revisions lead to significantly positive drifts of about 1% each.

Indeed, the differences are positive in five of the ten years and negative for the other five

years. As for downward revisions, we find no significant drift or reversal and the

difference between the departed stars and staying analysts is statistically zero. These

results are inconsistent with those in Gleason and Lee (2003), who find a less pronounced

post-revision price drift for forecasts made by star analysts.

In sum, Table 8, Panel A shows that the stock market responds to both upward and

downward forecast revisions made by the departed stars more strongly in the 3-day

window around their forecast revisions. However, post-revision price drifts are not

28

statistically different between the departed stars and staying analysts. Taken together,

these results are consistent with earnings revisions made by star analysts being more

informative than those made by other analysts following the same set of companies.

5.2. Informativeness of stock recommendation changes

Table 8, Panel B shows the evidence that both the recommendation upgrades and

downgrades issued by the departed stars are more informative than other analysts

following the same companies. Recommendation upgrades are those revised upward to

buy/strong buy or initiated at buy/strong buy. Recommendation downgrades are those

revised downward to buy/hold/sell/strong sell or initiated at hold/sell/strong sell. Column

(1) indicates that, on average, the departed star analysts revise upward their

recommendations 110 times and downgrade their recommendations 139 times in a year.

Columns (3) to (5) report the 3-day market-adjusted abnormal returns, CAR(–1,+1), from

day –1 to day +1, where day 0 is the day when the analysts issue their recommendations.

The results show that the market reacts more positively to departed stars’ upgrades than

those of their peers in the three days around the upgrades, with the difference being

significantly positive, and positively so in eight of the ten years. Similarly, the

downgrades of the departed stars also lead to a more negative reaction from the stock

market. Although the difference is statistically insignificant, it is more negative in eight

of the ten years, which cannot be due to chance alone based on a binomial test.

Columns (6) to (8) of panel B, table 8 present the market reactions over the post-

revision window from day +2 to day +21, CAR(+2,+21). There is a significantly positive

drift for recommendation upgrades made by other analysts but not for those issued by the

departed stars. While the difference is indistinguishable from zero, there are significantly

29

more years with a lower market reaction. In other words, the market’s responses to the

departed analysts’ upgrades are faster and more complete than those of other analysts.

Although total market responses to recommendation upgrades made by departed stars and

other analysts from day −1 to day +21 are almost identical (2.96% vs. 3.09%), 92% of the

market response is completed within the three-day window for departed stars while only

68% is completed in the same window for other analysts. In addition, recommendation

downgrades do not cause a significant drift or reversal for either the departed stars or

their remaining peers.

6. Concluding remarks

This paper examines the impact of a series of regulations on the career choices of

star analysts. In the post-reform period, we find a significant increase in the percentage

and likelihood of investment bank star analysts leaving the sell-side investment research

profession for the buy-side. We suggest that such a movement is due to the drop in

earnings potential at investment banks as a result of the reforms and that the regulations

unintentionally cause a brain drain at investment banks. We also show that the forecast

revisions and recommendation changes issued by the departed star analysts are more

informative than the staying analysts who covered the same company. The loss of star

analysts could thus adversely affect the consumers of these analysts’ research and the

companies covered by these analysts. In summary, this study provides the first evidence

supporting the concern that the regulations have led to a brain drain in investment banks

and the corresponding sell-side equity research industry.

30

References

Ai, C. and E. C. Norton, 2003. Interaction terms in logit and probit models. Economics

Letters 80: 123-129.

Bagnoli, M., S.G. Watts, and Y. Zhang, 2008. Reg-FD and the competitiveness of all-star

analysts. Journal of Accounting and Public Policy 27: 295-316. Baker M., and J. Wurgler, 2006. Investor sentiment and the cross-section of stock returns.

Journal of Finance 61: 1645-80. Barber, B.M., R. Lehavy, M. McNichols, and B. Trueman, 2006. Buys, holds, and sells:

The distribution of investment banks’ stock ratings and the implications for the profitability of analysts’ recommendations. Journal of Accounting and Economics 41: 87-117.

Barniv, R., O-K. Hope, M.J. Myring, and W. B. Thomas, 2009. Do analysts practice what

they preach and should investors listen? Effects of recent regulations. Accounting

Review 84: 1015-1039. Becker, D., 2001. Speech by SEC Staff: Analyzing Analysts. Remarks before the

Committee on Federal Regulation of Securities of the American Bar Association, August 7, 2001 (Available from http://www.sec.gov/news/speech/spch510.htm).

Boni, L., 2006. Analyzing the analysts after the global settlement. In Fuchita, Y., Litan,

R. E. (Eds.), Financial Gatekeepers: Can They Protect Investors? Brookings Institution Press and the Nomura Institute of Capital Markets Research.

Boni, L. and K. Womack, 2002. Wall Street’s credibility problems: Misaligned incentives

and dubious fixes? Brookings-Wharton Papers on Financial Services, 2002, 93–130.

Bonner, S., A. Hugon, and B. Walther, 2007. Investor reaction to celebrity analysts: The

case of earnings forecast revisions. Journal of Accounting Research 45: 481-513. Bradshaw, M.T., 2009. Analyst information processing, financial regulation, and

academic research. Accounting Review 84: 1073-1083. Brakke, T., 2010. Lessons from the Global Settlement. CFA Magazine 21:12-13. Cen, L, H. Lu, and L., Yang, 2012. Investor sentiment, disagreement, and breadth-return

relationship. Management Science, forthcoming. Chen, C-Y. and P.F. Chen, 2009. NASD Rule 2711 and changes in analysts’

independence in making stock recommendations. Accounting Review 84: 1041-1072.

31

Clarke, J., A. Khorana, A. Patel, and P. Rau, 2007. The impact of all-star analyst job

changes on their coverage choices and investment banking deal flow. Journal of

Financial Economics 84: 713-737. Cowen, A., B. Groysberg, and P. Healy, 2006. Which types of analyst firms are more

optimistic? Journal of Accounting and Economics 41: 119-146. Craig, S. and K. Scannell, 2010. SEC tried to ease curbs: Judge rejects bid by agency,

Wall Street to soften landmark 2003 analyst deal. The Wall Street Journal, March 17.

De Franco, G., H. Lu, and F. Vasvari, 2007. Wealth transfer effects of analysts’

misleading behavior. Journal of Accounting Research 45: 71-110. Deuskar, P., J. Pollet, Z. Wang, and L. Zheng, 2011. The good or the bad? Which mutual

fund managers join hedge funds? Review of Financial Studies 24: 3008-3024. Dunbar, C., 2000. Factors affecting investment bank initial public offering market share,

Journal of Financial Economics 55: 3-41. Fang, L., and A. Yasuda, 2009. The effectiveness of reputation as a disciplinary

mechanism in sell-side research. Review of Financial Studies 22: 3735-3777. Gleason, C.A. and C.M.C. Lee, 2003. Analyst forecast revisions and market price

discovery. The Accounting Review 78: 193-225. Goldstein, M.A., P. Irvine, E. Kandel, and Z. Wiener, 2009. Brokerage commissions and

institutional trading patterns. Review of Financial Studies 22(12):5175-5212. Greene, W., 2010. Testing hypotheses about interaction terms in nonlinear models.

Economics Letters 107: 291-296. Groysberg, B., P. Healy, and C. Chapman, 2008. Buy-side vs. Sell-side analysts’ earnings

forecasts. Financial Analysts Journal 64: 25-39. Groysberg, B., P. Healy, and D. Maber, 2011. What drives sell-side analyst compensation

at high-status investment banks? Forthcoming, Journal of Accounting Research. Guan, Y., H. Lu, and M.H.F, Wong, 2011. Conflict-of-Interest Reforms and Investment

Bank Analysts’ Research Biases. Forthcoming in Journal of Accounting, Auditing,

and Finance. Hong, H., J. Kubik, and A. Solomon. 2000. Security Analysts’ Career Concerns and

Herding of Earnings Forecasts. Rand Journal of Economics 31: 121–44.

32

Hong, H., and J. Kubik, 2003. Analyzing the Analysts: Career Concerns and Biased

Earnings Forecasts. The Journal of Finance 58: 313–52. Institutional Investor, 2007. 2007 All America Research Team. October, Page 1. Kadan, O., L. Madureira, R. Wang, and T. Zach, 2009. Conflicts of Interest and Stock

Recommendations: The Effects of the Global Settlement and Related Regulations. Review of Financial Studies 22: 4189-4271.

Kolasinski, A.C. and A.F. Siegel, 2010. On the economic meaning of interaction term

coefficients in non-linear binary response regression models. Working paper, University of Washington.

Kostovetsky, L. 2011. Brain drain: are mutual funds losing their best minds? Working

paper, University of Rochester. Krigman, L., W. Shaw, and K. Womack, 2001. Why do firms switch underwriters?

Journal of Financial Economics 60: 245-284. Leone, A. and J. Wu, 2007. What does it take to become a superstar? Evidence from

institutional investor rankings of Financial Analysts. Working paper, University of Miami and University of Rochester.

Mattlin, B., 2007. The 2007 All America Research Team. Institutional Investor 41, 10,

49-52. Mehran, H. and R.M. Stulz, 2007. The economics of conflicts of interest in financial

institutions. Journal of Financial Economics 85: 267-296. Mikhail, M., B. Walter, and R. Willis, 1999. Does Forecast Accuracy Matter to Security

Analysts? The Accounting Review 74: 185–91. Morgenson, G., 2001. SEC leader cites conflicts of analysts at large firms. The New York

Times, August 1. C1 Norton, E. C., H. Wang, and C. Ai, 2004. Computing interaction effects and standard

errors in logit and probit models. The Stat Journal 4(2): 154-167. O’Leary, C. 2007. The cracks in the Chinese wall: Four years after SEC settlement, is

Street research withering in the shadows? Investment Dealer Digest, 73, 9:18-22. Pizzani, L., 2009. Settling down. CFA Magazine, March-April, 41-44. Robinson, E., 2009. Research Renegades, Bloomberg Markets, November, 33-44.

33

Securities and Exchange Commission, 2004. Deutsche Securities Inc. and Thomas Weisel Partners LLC settle enforcement actions involving conflicts of interest between research and investment banking, press release, August 26, 2004.

Stickel, S., 1992. Reputation and performance among security analysts, Journal of

Finance 47: 1811-1938. Wu, J., and A. Zang. 2009. What determine financial analysts’ career outcomes during

mergers? Journal of Accounting and Economics 47: 59-86.

34

Table 1

Statistics on the number of equity analysts, securities firms, and companies being covered

The sample consists of the analysts from investment and non-investment banks in the period from January 1998 to December 2007. Sell-side analyst data are from Thomson Financial’s I/B/E/S database. An analyst is considered a star in a particular year if he/she is on the Institutional Investor All-American research team list in that year or at least one of the last two years. Investment banks are those listed as investment banks by Nelson’s Directory of Investment Research and identified as lead or co-lead underwriters by Thomson Financial’s SDC database. Non-investment banks include independent research firms, brokerage firms, and syndicate banks.

Investment Banks Non-investment Banks

Number of Analysts

(1)

Number of star analysts

(2)

Number of securities

firms (3)

Number of companies

covered (4)

Number of Analysts

(5)


(6)

Number of securities

firms (7)

Number of companies

covered (8)

1998 3,320 290 124 6,990 753 15 109 3,892

1999 3,663 336 132 6,799 797 13 131 3,845

2000 3,793 366 125 6,262 805 11 102 3,492

2001 3,962 395 114 5,328 775 12 123 2,975

2002 4,138 413 109 5,027 736 13 109 2,783

2003 3,831 412 113 4,940 869 17 138 3,093

2004 3,449 361 118 5,266 960 19 183 3,562

2005 3,504 324 120 5,564 1,071 25 197 3,976

2006 3,553 313 115 5,744 1,021 26 178 4,146

2007 3,590 305 107 5,876 1,022 21 160 4,253

Total 36,803 3,515 1,177 57,796 8,809 172 1,430 36,017

35

Table 2

Statistics on star analysts who left the sell-side equity research industry by year and subperiod

The table presents the number and percentage of star analysts who left the sell-side equity research industry. An analyst is considered a star analyst in a particular year if he/she is on the Institutional Investor All-American research team list in that year or at least one of the last two years. An analyst is said to have left the sell-side research industry in a particular year if he/she made earnings forecasts in the current year but stopped issuing forecasts the following year and his/her departure is confirmed based on information from the Nelson’s Directory of Investment Research.

Investment banks Non-investment banks


(1)


who left (2)

Percent of star analysts

who left (3)


(4)


who left (5)

Percent of star analysts

who left (6)

Panel A: Statistics by year

1998 290 10 3.4% 15 1 6.7%

1999 336 22 6.5% 13 2 15.4%

2000 366 24 6.6% 11 4 36.4%

2001 395 29 7.3% 12 3 25.0%

2002 413 56 13.6% 13 2 15.4%

2003 412 56 13.6% 17 2 11.8%

2004 361 44 12.2% 19 2 10.5%

2005 324 28 8.6% 25 4 16.0%

2006 313 39 12.5% 26 4 15.4%

2007 305 40 13.1% 21 1 4.8%

Panel B: Statistics by period

Pre-reform (1998-2001) 1,387 85 6.1% 51 10 19.6%

Post-reform (2002-2007) 2,128 263 12.4% 121 15 12.4%

36

Table 3

Descriptive statistics of regression variables

STAR is an indicator variable that equals to one for analysts on the Institutional Investor All-American research team list in a particular year or at least one of the last two years. TOPACCU (BOTACCU) is an indicator variable that equals one for analysts whose relative forecast accuracy scores are in the top 10% (bottom 10%), and zero otherwise. Relative annual earnings forecast accuracy and relative forecast optimism, RFOPT, are calculated as described in the text. EXPERIENCE is the forecasting experience of an analyst and is defined as the number of years that analyst appears in the I/B/E/S database. SANCTIONED is an indicator variable that equals one if the analyst is affiliated with a sanctioned investment banks; zero otherwise. The sanctioned investment banks are Bear Stearns, Credit Suisse First Boston, Goldman Sachs, Lehman Brothers, J.P. Morgan Securities, Merrill Lynch, Morgan Stanley, Citigroup Global Markets (formerly known as Salomon Smith Barney), UBS Warburg, and U.S. Bancorp Piper Jaffray. TOTREV is the revenue (in $ millions) of the firms in the industries with SIC codes 6200, 6211, and 6282. LOGIB is logarithm of one plus the revenue of investment banking business (IPOs and SEOs) for the core industry that each individual analyst provides research service.

Investment banks Non-investment banks

Star analysts All analysts Star analyst All analysts

Mean

(1) Std. Dev.

(2) Mean

(3) Std. Dev.

(4) Mean

(5) Std. Dev.

(6) Mean

(7) Std. Dev.

(8)

Panel A: Pre-reform period

(1998 – 2001)

STAR 1.00 0.00 0.12 0.33 1.00 0.00 0.02 0.14

TOPACCU 0.07 0.25 0.10 0.30 0.04 0.20 0.09 0.29

BOTACCU 0.03 0.17 0.10 0.30 0.11 0.31 0.14 0.34

RFOPT 0.02 0.36 -0.01 0.47 0.02 0.45 0.02 0.49

EXPERIENCE 10.35 5.30 7.11 5.25 9.32 5.68 6.64 5.11

SANCTIONED 0.75 0.43 0.35 0.48 0.00 0.00 0.00 0.00

TOTREV 390,667 90,325 387,495 90,768 373,137 91,107 385,474 92,058

LOGIB 7.09 3.12 7.43 2.55 7.16 2.50 6.74 3.48

Panel B: Post-reform period

(2002 – 2007)

STAR 1.00 0.00 0.12 0.33 1.00 0.00 0.03 0.17

TOPACCU 0.05 0.22 0.10 0.30 0.07 0.26 0.10 0.29

BOTACCU 0.03 0.07 0.10 0.30 0.06 0.24 0.15 0.36

RFOPT -0.02 0.35 -0.02 0.50 0.06 0.45 0.02 0.55

EXPERIENCE 10.69 5.72 7.00 5.23 6.25 3.93 6.44 5.41

SANCTIONED 0.81 0.39 0.37 0.48 0.00 0.00 0.00 0.00

TOTREV 482,464 139,058 499,863 142,268 520,958 139,750 517,921 139,154

LOGIB 7.18 2.56 7.40 1.96 7.58 2.14 6.63 3.20

37

Table 4

Logistic regressions of analysts who left the sell-side investment research industry

D is an indicator variable that equals one in the post-reform period (January 2002 – December 2007) and zero in the pre-reform period (January 1998 – December 2001). STAR is an indicator variable that equals one for analysts on the Institutional Investor All-American research team list in a particular year or at least one of the last two years. TOPACCU (BOTACCU) is an indicator variable that equals one for analysts whose relative forecast accuracy scores are in the top 10% (bottom 10%), and zero otherwise. Relative forecast accuracy and relative forecast optimism, RFOPT, are calculated as described in the text. EXPERIENCE is the forecasting experience of an analyst and is defined as the number of years that analyst appears in the I/B/E/S database. SANCTIONED is an indicator variable that equals to one if the analyst is affiliated with a sanctioned investment banks; zero otherwise. The sanctioned investment banks are Bear Stearns, Credit Suisse First Boston, Goldman Sachs, Lehman Brothers, J.P. Morgan Securities, Merrill Lynch, Morgan Stanley, Citigroup Global Markets (formerly known as Salomon Smith Barney), UBS Warburg, and U.S. Bancorp Piper Jaffray. Ten sanctioned bank dummies are included in columns (2) and (4). TOTREV is the revenue of the firms in the industries with SIC codes 6200, 6211, and 6282. SENTIMENT is the investor sentiment index of Baker and Wurgler (2006). Standard errors are clustered by securities firm. p-values are reported in square brackets. *, **, and *** denote statistically different from zero, respectively, at the 10%, 5%, and 1% level, using a two-sided test.


(1) (2) (3) (4)

Intercept -1.62 *** -1.56 *** -1.03 *** -1.21 ***

[0.00] [0.00] [0.00] [0.00]

D 0.14 ** 0.22 -0.19 * 0.08

[0.02] [0.12] [0.06] [0.76]

STAR -1.27 *** -1.27 *** -0.16 -0.13

[0.00] [0.00] [0.24] [0.32]

D×STAR 0.72 *** 0.72 *** -0.42 ** -0.45 **

[0.00] [0.00] [0.05] [0.04]

TOPACCU 0.06 0.06 0.13 0.14

[0.27] [0.27] [0.19] [0.18]

BOTACCU 0.58 *** 0.58 *** 0.45 *** 0.45 ***

[0.00] [0.00] [0.00] [0.00]

RFOPT -0.03 -0.03 -0.06 -0.06

[0.39] [0.41] [0.31] [0.32]

EXPERIENCE -0.01 *** -0.01 *** -0.03 *** -0.04 ***

[0.00] [0.00] [0.00] [0.00]

SANCTIONED 0.23 *** 0.24 ***

[0.00] [0.00]

TOTREV (×100,000) 0.02 -0.01

[0.40] [0.90]

SENTIMENT 0.05 0.23 *

[0.59] [0.10]

Pseudo-R2(%) 1.91 1.92 1.27 1.42

38

Table 5

Descriptive statistics on the relation between star analysts’ career choice and investment banking business of their

core industry

This table presents the number and percentage of star analysts who left the sell-side equity research industry for the quartiles classified based upon the total proceeds from IPOs and SEOs. In Panel A, we classify the core-industry into quartiles based upon total proceeds from IPOs and SEOs and assign the star analysts to each quartile accordingly. Panel B presents statistics when we classify star analysts into quartiles based upon total proceeds from IPOs and SEOs of their core-industry.

Industry

Quartile

Mean Total

Proceeds

(million dollars)

Number of Star

Analysts

Number of Star

Analysts who

left

Percentage of

Star Analysts

who left

Panel A: Classify the star analysts into quartiles based upon total proceeds from IPOs and SEOs of their core-industry

Pre-reform Period 4 12,472 800 52 6.50%

3 1,495 291 14 4.81%

2 307 113 8 7.08%

1 83 94 3 3.19%

Post-reform Period 4 7,378 1309 172 13.14%

3 1,132 333 40 12.01%

2 422 256 30 11.72%

1 141 148 9 6.08%

Panel B: Classify the core-industry into quartiles based upon total proceeds from IPOs and SEOs

Pre-reform Period 4 18,836 342 17 4.97%

3 7,191 286 23 8.04%

2 2,609 356 20 5.62%

1 415 313 15 4.79%

Post-reform Period 4 10,177 488 67 13.73%

3 4,628 555 80 14.41%

2 1,892 494 53 10.73%

1 422 508 50 9.84%

39

Table 6

Effect of investment banking business of analyst’s core industry

LOGIB is logarithm of one plus the revenue of investment banking business (IPOs and SEOs) for the core industry that each individual analyst provides research service. All the other variables are defined in the same way as described in table 4. Standard errors are clustered by securities firm. p-values are reported in square brackets. *, **, and *** denote statistically different from zero, respectively, at the 10%, 5%, and 1% level, using a two-sided test.


(1) (2) (3) (4)

Intercept -1.62 *** -1.57 *** -1.03 *** -1.14 ***

[0.00] [0.00] [0.00] [0.00]

LOGIB -0.01 -0.01 -0.01 -0.01

[0.54] [0.53] [0.53] [0.51]

DxSTARxLOGIB 0.08 * 0.08 * 0.01 0.02

[0.08] [0.08] [0.86] [0.84]

D 0.14 ** 0.22 -0.20 ** 0.17

[0.02] [0.11] [0.05] [0.56]

STAR -1.26 *** -1.26 *** -0.13 0.57

[0.00] [0.00] [0.28] [0.13]

D×STAR 0.72 *** 0.71 *** -0.38 -0.52

[0.00] [0.00] [0.11] [0.46]

DxLOGIB -0.00 -0.00 -0.01 -0.01

[0.71] [0.73] [0.59] [0.58]

STARxLOGIB -0.06 -0.07 -0.09 -0.10 *

[0.11] [0.11] [0.15] [0.09]

TOPACCU 0.06 0.06 0.14 0.14

[0.28] [0.28] [0.17] [0.17]

BOTACCU 0.58 *** 0.58 *** 0.44 *** 0.44 ***

[0.00] [0.00] [0.00] [0.00]

RFOPT -0.03 -0.03 -0.06 -0.06

[0.38] [0.40] [0.29] [0.30]

EXPERIENCE -0.01 *** -0.01 *** -0.03 *** -0.04 ***

[0.00] [0.00] [0.00] [0.00]

SANCTIONED 0.24 *** 0.24 ***

[0.00] [0.00]

TOTREV (×100,000) 0.02 -0.004

[0.40] [0.94]

SENTIMENT -0.01 0.23 *

[0.93] [0.09]

Pseudo-R2(%) 1.93 1.94 1.37 1.53

40

Table 7

Descriptive statistics on the career choice of star analysts who left the sell-side research industry

We keep track of star analysts who left the sell-side research profession in the period 1998 through 2007 and classify their career choice into one of eight categories: (A) Promoted to a managerial position within the firm or to another firm; (B) Transferred laterally to a different department within the firm; (C) Moved to a buy-side, hedge fund, private equity, or venture capital firm; (D) Started own investment research firm or moved to another research firm not covered by I/B/E/S; (E) Moved to a non-financial industry; (F) Retired or fired for cause; (G) Still a sell-side analyst but not found in I/B/E/S; and (H) Not enough information to classify. We first use the Nelson’s Directory of Investment Research to verify that the analysts have left sell-side research and check whether they are promoted to a managerial position in investment research. Next, we search Factiva and Google to identify the first job these analysts took after they left the sell-side research industry. To compute the t-statistics, we estimate the standard error using the standard deviation of the percent changes across these eight categories, assuming that the percent changes from these eight categories are drawn from a normal distribution with zero mean.

Category Pre-reform period

1998 – 2001 Post-reform period

2002 – 2007

# who left

(1) % who left

(2) # who left

(3) % who left

(4) (4) – (2)

(5) t-stat (6)

Panel A. Investment banks

A. Promoted 15 17.6 44 16.7 -0.9 -0.7

B. To another department 5 5.9 20 7.6 1.7 1.3

C. To buy side 21 24.7 85 32.3 7.6 5.6

D. To another research 3 3.5 8 3.0 -0.5 -0.4

E. To non-financial 16 18.8 40 15.2 -3.6 -2.7

F. Retired or fired 7 8.2 23 8.7 0.5 0.4

G. Not on I/B/E/S 3 3.5 6 2.3 -1.2 -0.9

H. Lack information 15 17.6 37 14.1 -3.6 -2.7

Total 85 100.0 263 100.0

Panel B. Non-investment banks

A. Promoted 3 30.0 2 13.3 -16.7 -4.2

B. To another department 0 0.0 1 6.7 6.7 1.7

C. To buy side 5 50.0 7 46.7 -3.3 -0.8

D. To another research 0 0.0 1 6.7 6.7 1.7

E. To non-financial 0 0.0 2 13.3 13.3 3.4

F. Retired or fired 0 0.0 1 6.7 6.7 1.7

G. Not on I/B/E/S 0 0.0 0 0.0 0.0 0.0

H. Lack information 2 20.0 1 6.7 -13.3 -3.4

Total 10 100.0 15 100.0

41

Table 8 Short- and long-window market responses to earnings revisions and stock recommendations

Panel A (B) presents the market responses to revisions of annual earnings forecasts (change in stock recommendations) of 373 departed star analysts and other analysts who followed the same companies in the year prior to the star analysts leaving sell-side equity research. Recommendation upgrades are those revised upward to buy/strong buy or initiated at buy/strong buy. Recommendation downgrades are those revised downward to buy/hold/sell/strong sell or initiated at hold/sell/strong sell. CAR(–1,+1) is the market-adjusted abnormal returns from day –1 to day +1 and CAR(+2,+21) is the market-adjusted returns from day +2 to day +21, where day 0 is the date when the analysts revise their earnings forecasts (recommendations). The t-statistic is calculated using the Fama-MacBeth approach. *, **, and *** denote statistically different from zero at the 10%, 5%, and 1% level, respectively, using a two-sided t-test. #Positive is the number of years with positive difference in the 10-year period from 1998 through 2007. p-value is the probability of having #Positive smaller than the observed number, using a binomial test.

Number CAR(–1,+1) CAR(+2,+21)

Departed stars (1)

Other analysts

(2)

Departed Stars (3)

Other analysts

(4)

Difference (3) – (4)

(5)

Departed Stars (6)

Other analysts

(7)

Difference (6) – (7)

(8)

Panel A: Earning Revisions

Upward revisions

Mean 714 7,671 1.76% 1.45% 0.31% 1.06% 0.99% 0.07%

t-statistic 10.41*** 11.33*** 4.08*** 3.99*** 3.34*** 0.39

#Positive 10 5

p-value 0.00*** 0.38

Downward revisions

Mean 794 8,394 -2.24% -2.04% -0.20% 0.44% 0.31% 0.13%

t-statistic -7.61*** -8.99*** -1.74* 1.51 1.15 0.79

#Positive 4 6

p-value 0.17 0.17

Panel B: Stock recommendations

Upgrades

Mean 110 11,975 2.73% 2.11% 0.62% 0.23% 0.98% -0.75%

t-statistic 8.42*** 10.24*** 2.31** 0.49 3.50*** -1.38

# Positive 8 3

p-value 0.01*** 0.05*

Downgrades

Mean 139 11,289 -4.37% -3.99% -0.38% 0.11% -0.14% 0.25%

t-statistic -6.68*** -8.08*** -0.85 0.17 -0.60 0.34

# Positive 2 7

p-value 0.01*** 0.05*

42

Figure 1

The number and proceeds of investment banking deals, 1998 – 2007

The figure shows the number and proceeds of IPOs and SEOs for the period from 1998 through 2007. Data are extracted from Thomson Financial’s SDC database. Panel A: Number of IPOs and SEOs

0

200

400

600

800

1000

1200

1400

1600

1800

2000

1998 1999 2000 2001 2002 2003 2004 2005 2006 2007

Year

IPO#

SEO#

Total#

Panel B: Proceeds from IPOS and SEOs

0

50000

100000

150000

200000

250000

300000

350000

1998 1999 2000 2001 2002 2003 2004 2005 2006 2007

Year

IPO$

SEO$

Total$

Regulations and Brain Drain: Evidence from the Wall Street ... · brain drain in the sell-side...

Documents

Transcript of Regulations and Brain Drain: Evidence from the Wall Street ... · brain drain in the sell-side...