Hype my Stock or Harm my Rivals? Another View on Analysts’ Conflicts of Interest

Michel Dubois, Andreea Moraru*

Abstract

We unravel a new form of conflicts of interest in the investment banking industry. We document a

significant gap between ratings for affiliated firms and their competitors (rivals) in the product market.

Specifically, brokers issue persistently higher ratings on firms with which they are affiliated compared to

their rivals. This behaviour is identified in both recommendations and price targets. Importantly, we

show that the Sarbanes-Oxley Act and the related financial regulations aiming at curbing the conflicts of

interests had no significant impact in reducing this gap. As such, affiliated brokers continue to indirectly

favour their clients. This form of conflict was devoid of adequate attention in prior research.

Furthermore, we find that investors are unaware of the existence of such conflict in the short-run.

Key words: Investment banks, Conflicts of interest, Financial regulation, Sarbanes-Oxley Act

JEL classification: G14, G24, G28, K22 ___________________________________________________________ *University of Neuchâtel, Pierre-à-Mazel 7, 2000-Neuchâtel, Switzerland. michel.dubois@unine.ch and

andreea.moraru@unine.ch.

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"Here we are, years later after the 'grand settlement'. Has anything changed? I would argue not

really. I'd say there's maybe change in form but not substance in terms of today's Wall Street research.

Back then if you were pitching for an IPO, the analysts and the bankers would come into the same

meeting. Guess what happens now? There are two meetings instead of one meeting. The analysts will

[now] come in separate from the bankers—so they're not in the room at the same time. [..] They are still

going to pitch for the IPO, just like what happened 15 years ago."

Jack Grubman (former Citigroup analyst), CNBC interview, May 31, 2013

1. Introduction

The underwriting relations with the affiliated firms were for a long period at the core of conflicts of

interest in the investment banking industry. By the time that most financial scandals broke out, there

was no surprise that the investment banks were rife with such conflicts of interest. Typically, brokers

respond to the conflicts they face by issuing optimistic stock recommendations on affiliated firms. The

positive coverage these firms enjoy subsequently allows them to pay substantial underwriting and

advising commissions to the broker.

In an attempt to whittle down these conflicts, a wave of regulatory initiatives emerged in recent years

and put investment banks to the fore. The Sarbanes-Oxley Act (SOX) was enacted in 2002 to restore

investors’ confidence in U.S. markets. More specifically, Section 501(a) of the bill relates to the codes of

conduct for securities analysts and requires the disclosure of conflicts of interest. NASD (now FINRA)

Rule 2711, NYSE Rule 427 and the Global Research Settlement Act (Global Settlement hereafter)

followed shortly to address analyst conflicts of interest related to the separation of the research and

investment banking departments. We refer hereafter generically to SOX for the more complex and

widespread regulatory initiatives that surged around its enactment.

The ongoing debate on the real effects of these regulations has triggered widespread attention of

both academics and professionals. However, mainstream research has been essentially inward looking

into the direct effect of the law on the over-optimism bias for affiliated firms. Moreover, the current

research has not incorporated yet a salient point: the alternative path that affiliated brokers might have

followed to continue favoring their clients. Specifically, affiliated brokers could alter their rating policy by

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issuing more unfavorable recommendations for their client firms’ rivals. As such, these clients could still

benefit from the discrimination of their rivals within a purely legal environment1.

This paper takes the affiliation relationship as the nexus of the analysis and treats the positive bias of

affiliated firms with respect to their rivals as the basic building block of the conflicts of interests. We ask

two questions. Do brokers provide significantly lower ratings for rival than for affiliated firms? Was SOX

effective in limiting this gap? These questions are important since they refer to a class of conflicts

unaddressed by the current regulation.

We investigate these issues by focusing on recommendations and price targets of affiliated brokers.

Consistent with our conjecture, we find that the proportion of favorable (unfavorable) recommendations

of affiliated firms is significantly higher (lower) that the one for rival firms both before and after SOX. In

line with previous studies, affiliated brokers became more pessimistic on average after the enactment of

SOX. Strikingly, after 2002, rival firms have the lowest (higher) proportion of favorable (unfavorable)

recommendations. Actually, the proportion of unfavorable recommendations before SOX is 2% for rival,

2% for affiliated and 3% for neutral firms while this proportion is 14%, 9% and 12% after SOX. A similar

pattern is revealed in price targets. This first set of results suggests that, while brokers comply with the

regulation and issue less optimistic ratings, they nevertheless continue to keep their clients on

significantly higher ratings than their clients’ rivals.

To shed more light on this issue, we run a difference-in differences model and estimate the bias in

affiliated and rival ratings, as compared to neutral ones, before and after the enactment of SOX. On a

five-notch scale, the relative recommendations for affiliated firms (rival) are 0.14 (0.7) higher than the

ones for neutral firms before SOX. The difference in the two biases is statistically. When assessing the

impact of SOX, we find that both the affiliated and rival bias drop significantly after 2002. However,

1 Evidence based on anecdotal accounts is pertaining to our investigation. In 2003, the French luxury group Louis

Vuitton Moet Hennessy (LVMH) sued Morgan Stanley for alleged unfair research from analyst Claire Kent that

favored direct competitor Gucci, with which the investment bank was involved at the time in an M&A deal. The star

luxury goods analyst downgraded LVMH in July 2002 from outperforming to neutral, while maintaining Gucci on

outperforming. In January 2004 the bank was ordered to pay $38 million, which it appealed. Finally, in a joint

declaration in 2007, LVMH and Morgan Stanley agreed to resume business relations and settled their five year

court battle without any paid compensation to either side. Moreover, the analyst’s reputation remained untarnished,

since two years after the suit, she ranked second in the Thomson Extel survey of luxury goods analysts. This case

illustrates the difficulty to prove financial analysts’ wrongdoing, if any, before the court. See, for instance, “Morgan

Stanley, LVMH tango in Paris”, by Ackman D., Forbes, May 27, 2003; “LVMH battles against Morgan Stanley in

court”, by Tagliabue, J., New York Times, November 18, 2003.

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brokers switched to a downward scale not only for affiliated firms, but also for rival firms, maintaining the

rating gap. Although of smaller magnitude prior 2002 SOX, affiliated brokers continue to issue

significantly higher relative recommendations on affiliated firms after SOX, while the bias in relative

recommendations for rival firms becomes significantly negative.

We go one step further and investigate whether investors recognize this new form of bias by

analyzing the short-term market reaction to recommendation changes before and after SOX. While

before SOX none of the two biases is recognized, after 2002 the short-term abnormal performance

associated with recommendations on affiliated firms is not significantly different from the one of neutral

firms. Moreover, after 2002, the abnormal return associated with upgraded recommendations for rival

firms is significantly lower than the one of affiliated and neutral firms. These findings suggest that, after

2002, investors most likely discount the positive bias on affiliated firms while they do not account for the

negative bias on rival firms.

This paper contributes to three streams of research. First, it uncovers a new form of conflicts in the

investment banking industry that was not addressed so far. Second, it sheds light on the unexpected

effects of the recent financial regulation. To the extent that conflicts of interest were mitigated, we find

evidence that brokers continue to favor their clients by following alternative strategies without breaching

the rules. Our analysis centers on the question raised by Mulherin (2007) of whether SOX had

unintended consequences. Finally, we built on the existing gap in the literature suggested by Ramnath,

Rock and Shane (2008) in better understanding the effects of the institutional and regulatory

environment on analysts’ output.

The reminder of the paper is organized as follows. Section 2 presents the hypotheses development

in the context of the existing literature. In Section 3, we provide details on the sample construction and

methodology. Section 4 presents and discusses the results. We extend the analysis to the short-term

market reaction in Section 5. Section 6 concludes.

2. Prior research and hypotheses development

Financial analysts play a key role in the dissemination of information to investors. Their production

(i.e., earning forecasts, price targets and stock recommendations) portends future stock performance

and reduces information asymmetry; see, e.g., Barber, Lehavy, McNichols, and Trueman (2001); Boni

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and Womack (2006); Brav and Lehavy (2003); Kadan, Madureira, Wang, and Zach (2013). Loh and

Mian (2006) emphasize the role of future earnings in predicting stock price movements and argue that

more accurate earnings forecasts are correlated with more profitable recommendations. More generally,

analysts’ reports receive the most attention from investors when they incorporate bad news; see, e.g.,

Asquith, Mikhail, and Au (2005).

From the perspective of accurately predicting market performance, analysts working for investment

banks are inherently subject to conflicts of interests. This bias was extensively investigated in prior

research. Central to this literature is the notion that the pernicious effects of conflicts of interest in

analysts’ output fall on investors and distort the markets2. Lin and McNichols (1998) identify the positive

bias in the long-term growth forecasts from affiliated brokers. They find that investors react more

negatively to neutral recommendations, consistent with the conjecture that markets interpret neutral

ratings from affiliated brokers simply as downgrades. Likewise, Dechow, Hutton, and Sloan (2000)

argue that long-term growth forecasts from affiliated brokers are systematically overoptimistic around

equity offerings, and the level of optimism is correlated with their fees. Not only equity offerings, but also

M&A deals, are prone to conflicts of interests. Kolasinski and Kothari (2008) note that affiliated brokers,

both with the acquirer and the target firm, upgrade their recommendations and publish optimistic reports

on the acquirer.

Stock recommendations, by their relative nature, are the most exposed to optimistic bias from

affiliated brokers. Michaely and Womack (1999) find that stocks recommended by affiliated brokers

perform more poorly that the ones from unaffiliated both before and after the recommendation

announcement. Moreover, the market does not recognize this bias. In the same vein, Barber, Lehavy,

and Trueman (2007) show that the average daily abnormal return from independent research firms with

favorable recommendations exceeds the one from investment banks. Additionally, during the bear

market of 2000s, these brokers were highly reluctant to downgrade stocks. Affiliated brokers are more

likely to distort upward recommendations to maximize commissions. In fact, Hong and Kubik (2003)

indicate that brokerage houses reward optimistic recommendations. Specifically, affiliated analysts that

try to peddle stocks are more likely to experience favorable career outcomes.

Besides underwriting relations, other factors such as trading fees, clients’ sophistication and brokers’

reputation are important determinants of the analysts’ bias. In particular, analysts working for retail 2 See Mehran and Stulz (2011) for a comprehensive survey on the literature of conflicts of interest in investment banking.

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brokerage houses are more optimistic than analysts serving institutional investors only, while the

optimism is offset by the reputation of the bank and the presence of institutional investors; see, e.g.,

Cowen, Groysberg, and Healy (2006); Ljungqvist, Marston, Starks, Wei, and Yan, (2007). Additionally,

legal enforcement and sanctions play a key role in mitigating the magnitude of conflicts of interest; see,

e.g., Dubois, Frésard, and Dumontier (2013).

The evidence so far suggests that brokers’ optimism bias towards their clients is not innocuous, and

can hurt issuers and investors alike by limiting the objectivity and independence of securities research.

This salient feature was integrated into an extensive series of regulations meant to restore the public

confidence in the U.S. markets. Adopted by the SEC in July 2002, SOX requires public disclosure of

conflicts of interest together with the securities analysts’ protection from retaliation of their employers in

the event of unfavorable research reports. Based on the notion that investors were the purported victims

of analysts’ bias, NASD and NYSE established additional rules to separate research analysts from the

pressure of investment banking personnel. More specifically, NASD Rule 2711 and NYSE Rule 472,

generally referred to as the Self-Regulatory Organizations Rules, were adopted to improve the

objectivity and transparency in research. Importantly, the first requires to disclose the ratings’

percentage distribution scale for the entire universe of securities covered, and particularly for the

affiliated firms to which the brokers provide investment banking services. Finally, on April 28, 2003 the

Global Settlement was enforced as an agreement among the SEC, NASD, the NYSE, the New York

State Attorney General, and ten of the largest Wall Street investment banks to settle charges alleging

misleading or fraudulent research. As part of the Global Settlement, these firms are required to

physically separate their investment banking from research departments and to fund research

independently from the investment banking services. Additionally, analysts are not allowed anymore to

attend pitches and road shows with investment bankers in the promotion of the IPOs and are required to

disclose all the ratings on the issuers3.

3 The ten firms were: Bear, Stearns & Co., Inc.; Citigroup Global Markets Inc. (f/k/a Salomon Smith Barney, Inc.);

Credit Suisse First Boston LLC; Goldman, Sachs & Co.; J.P. Morgan Securities Inc.; Lehman Brothers Inc.; Merrill

Lynch, Inc; UBS Warburg LLC; U.S. Bancorp Piper Jaffray, Inc. Source:

http://www.sec.gov/news/speech/factsheet.htm. Another two firms – Deutsche Bank Securities and Thomas Weisel

Partners - joined the settlement on August 26, 2004. The settling firms were also required to pay disgorgement and

civil penalties of $1.4 billion. Source: http://www.sec.gov/news/press/2004-120.htm.

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A rich literature has emerged around the overarching question of the efficacy of these regulations4.

Ertimur, Sunder, and Sunder (2007) argue, among others, that the profitability of “buy” and “hold”

recommendations of affiliated analysts increased following these regulatory initiatives. Barber, Lehavy,

McNichols, and Trueman (2006) show an increase in the proportion of unfavorable stock

recommendations in the aftermath of the NASD Rule 2711, which requires brokers to release their

ratings distribution. For example, the ratio of “buy” to “sell” recommendations decreased from 35 to 1 in

the 1996-2000 period to less than 3 to 1 in the 2000-2003 period. Likewise, after the passage of U.S.

regulations, affiliated analysts are less likely to issue optimistic recommendations compared to

independent analysts. As such, optimistic (neutral and pessimistic) recommendations are more (less)

informative; see Kadan, Madureira, Wang, and Zach (2009). Buslepp, Casey, and Huston (2012)

indicate an improvement in the relation between recommendations and the residual income model

valuation estimates after the passage of the Global Settlement. On the flip side, the independent

research funded by the agreement seems to be of lower quality than the one of investment banks.

From another perspective, little is known about the unintended consequences of the regulations on

brokers’ behavior. One fact that gains increased public attention is the orphaned stock impact,

especially for small firms. To the extent that brokers have to allocate high costs to comply with legal

requirements, and most importantly, since the funding of research is limited due to its separation from

investment banking, many stocks have lost immediate analysts coverage. To illustrate, Morgan Stanley

and Merrill Lynch cut the number of North American covered stocks by 26% and 30% respectively by

April 20045. According to Mehran and Stulz (2007), the increased costs of producing analyst services

result ultimately in a decrease in the efficiency of financial markets, since they can adversely affect

valuations and make it difficult for companies to finance their growth. However, since there is no legal

obligation to cover stocks, there is no wrongdoing in adopting this strategy.

At the first glance, the aforementioned literature is consistent with a reduction of the optimism bias

for affiliated brokers. It is important to stress that conflicts of interests were inadvertently understood as

optimistic recommendations on affiliated firms. However, inflating affiliated stock recommendation is just

one outcome of a bundle of possibilities relating to any other type of broker’s misbehavior in trying to

peddle their clients’ stocks and generate commissions. Consistent with this conjecture, Carapeto and

4 See Mulherin (2007) for an extensive analysis of the costs and benefits of regulating financial markets. 5 See, for example, “Change comes slowly to Wall Street research”, by Lee, M. and Metaxas, J., CNBC, April 26,

2004.

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Gietzmann (2011) bring into light another type of affiliation relationship in the U.K. practice of the

“corporate broker”. According to the U.K. Listing Authority, every listed company on the London Stock

Exchange must have an appointed broker to advice communication with the stock market on an

ongoing basis. The corporate broker has unique access to private information and participates to board

meetings. The authors note that, although the Chinese walls were created between the corporate

brokers and the investment bank activities, these brokers may easily know whether the management is

considering a SEO and thus provide the information to their affiliated underwriters. More recently, Lee

(2013) identifies a new source of conflicts in the parent-subsidiary relationship. Not only analysts at the

parent investment bank provide positively biased recommendations for the client firms, but also those

employed in the broker’s subsidiaries. Similar with extant literature, their recommendations have worse

or no investment value. As such, since conflicts may arise in different forms, limiting brokers’ incentives

to gain underwriting commissions by any means does not imply to mitigate exclusively the

overoptimistic bias towards the affiliated firms.

We start from the premise that favoring the affiliated firms by boosting their ratings is only one of the

many forms of biased research to gain underwriting commissions. Specifically, we investigate whether

brokers provide positive ratings for affiliated firms as compared to their rivals. Recent findings suggest

that peer firms play a central role in a firm’s financing decisions through their characteristics and their

own financing policies; see Frésard (2011), Leary and Roberts (2013). To the extent that affiliated

brokers provide less-than-stellar ratings on their clients’ rivals, they continue to curry favor with their

clients by means of this strategy. Our objective is thus to explore the existence and to commensurate

the magnitude of such conflicts of interests that materialize through the brokers’ hostile attitude towards

their client firms’ rivals. We therefore state the first null hypothesis as follows.

Hypothesis 1. Before SOX, affiliated brokers provide similar ratings for their clients and their clients’

rivals.

To go further, we investigate whether SOX and the related regulation had an effect in mitigating the

potential rating gap between affiliated and rival firms. The objective of these laws was pinned down to

the limitation of the positive bias towards client firms and no other form of conflicts was addressed.

While these regulations ostensibly reduced the optimistic bias for affiliated firms, we expect that they

inadvertently created a de facto situation in which other forms of the same conflict continue to exist. The

second null hypothesis is the following.

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Hypothesis 2. The adoption of SOX had no significant impact on the rating gap between affiliated

and rival firms.

Different attempts were made in detecting and then whittling down the overoptimistic bias towards

affiliated firms in the investment banking industry. However, as noted previously, the decrease in the

optimistic bias does not preclude the decrease of conflicts. To the best of our knowledge, no study to

date addresses the question of whether affiliated brokers use different strategies to favor their clients,

nor whether these innovations involve sidestepping the current regulatory framework.

3. Data and methodology

3.1. Sample construction

We focus on stock recommendations and price targets for several reasons. First, recommendations

were at the core of the scandals and the regulations put in place to curb these conflicts are mainly

centered on recommendations. The Institutional Brokers’ Estimate System (I/B/E/S) translates on a

single five-point scale a multitude of specific rating scales, which opens the possibility that the relative

nature of one recommendation differs from one broker to another; see Kadan et al. (2009); Kadan et al.

(2013). We integrate price targets to our analysis since recommendations have no mutually-

agreed-upon interpretation. As such, price targets allow us to identify the bias of affiliated brokers

without a concern for misinterpretation. Moreover, there is not much known about conflicts of interest

related to price targets at present. Indeed, while some studies focus on the performance of price

targets, they open the field for the question of whether price targets are subject to conflicts of interest as

well; see, e.g., Brav and Lehavy (2003); Gleason, Johnson and Li (2013).

The initial sample consists of all outstanding recommendations and price targets from the I/B/E/S

U.S. Historical Detail files for the period January 1996 to December 20086. I/B/E/S started collecting

recommendations in October 1993 and the data is scant for the first three years. Price targets enter the

database as of January 1999. Since our dataset is left censored, it is not possible to know how many

brokers followed the same firm during the last twelve months. As such, we conduct our analysis for

6 Following Ljungqvist, Malloy and Marston (2009a), a recommendation (price target) is outstanding if it has been

confirmed by the broker in the I/B/E/S review date field in the last twelve months and has not been stopped by the

broker in the I/B/E/S Stopped file.

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recommendations (price targets) on the period January 1997 (2000) to December 2008. The data

collection stops in 2008 since this is the last year for which we can obtain information on competitor

firms from the Hoberg and Phillips data library7,8.

In May 2002, NASD 2711 Rule imposed the disclosure of the percentage of all securities rated by

each broker to whom they would assign a “buy”, “hold” or “sell” rating. Consequently, during this year

many brokers changed their rating scale and issued recommendations in bulk for the firms they were

covering at the moment9. We count for this structural break for the top 100 largest brokers (in terms of

total number of recommendations issued during the entire period). In total, these brokers count for 80%

of the observations. We check for cases when the broker stopped the totality or the majority of her

coverage and resumed the coverage in the subsequent days on a different rating scale. We check one

year post-resumption to see if the broker continues to use the new rating scale. If this is the case, we

assume that this broker uses the new rating scale starting from the resumption day. The

recommendations issued in bulk on the new rating scale are removed from the dataset since they do

not bring additional information; see Loh and Stulz (2011). We identify 21 brokers that resume coverage

in 2002, with a change from four- or five-point scale to three- or four-point scale. For eight brokers these

days are identical with the ones in Kadan et al. (2009)10. One of these brokers changes again the rating

scale in 2008. We check whether the structural break of 2002 is the only event with rating scale

changes. As such, we find additional changes in rating scales in 2000 (one broker), 2001 (one broker),

and between 2003 and 2008 (24 brokers). Finally, to keep the interpretation of recommendations more

straightforward, we reverse the I/B/E/S scale and code recommendations from one (strong sell) to five

(strong buy), so that higher values correspond to more aggressive recommendations.

We perform several adjustments on the price targets sample. First, to preserve a common

interpretation of the variable across brokers, we fix the forecast horizon and keep only the one-year-

ahead price targets. These observations count for 98% of the initial sample. We next extract the current

7 Available at http://alex2.umd.edu/industrydata/industryclass.htm. 8 Ljungqvist et al. (2009a) identify changes in recommendation archives from one I/B/E/S download to another

during the 2000-2007 period; subsequently Thomson Reuters had announced that it have solved the problem. We

collect the data for both recommendations and price targets as of December 2011 snapshot so that they are not

subject to this issue. 9 We find no evidence of this structural break in price targets. 10 Unlike Kadan et al. (2009), for one of the eight brokers we find no subsequent change in her five-point rating

scale.

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prices from CRSP and transform price targets, as well as all stock prices to USD11. Since we work with

unadjusted data in both I/B/E/S and CRSP, we also account for splits and adjust the price targets and

current prices accordingly. Finally, we compute for each observation the price target to current price

ratio and winsorized at 1% on both tails.

3.1.1. Identification of affiliated recommendations (price targets)

Brokers’ conflicts of interest are determined with data on IPO, SEO, public debt issues and M&A

deals obtained from the Security Data Company Platinum (SDC) database and syndicated loans from

LPC’s DealScan (LPC). We follow Ljungqvist, Marston and Wilhelm (2006) and form corporate families

so that we give credit to a parent firm for any form of investment banking relationship of its subsidiaries

with a given bank. In addition, since many banks were the product of mergers and acquisitions during

the sample period, we account for all the bank’s predecessors when identifying an investment banking

relation.

To identify an affiliated recommendation (price target), we proceed as follows. First, we match the

firms in I/B/E/S with those from SDC (LPC) by the firm’s CUSIP parent (official ticker) at a given date.

Second, since each database provides a different form of the brokers’ name, we match by hand the

brokers’ names in SDC and LPC with those from the I/B/E/S Broker’s Translation file. When the

matching between two names is not unique, we search for additional information on the identity and the

type of the broker in the Internet and on Nelson’s Directory of Investment Research12. In April 2008,

I/B/E/S stopped providing the names of the brokers in the database. Therefore, we identify 99 out of 930

distinct brokers for which the names are not available. Though the majority of these I/B/E/S codes could

not be matched in SDC or LPC via the Nelson’s Directory of Investment Research, these brokers count

for less than 2% of the total observations. To assure a maximum consistency in the names matching,

11 We check previously for any inconsistencies between the firm’s currencies reported in I/B/E/S and CRSP in the

same day and remove around 0.1% of the observations. We apply this filter since, irrespective of the currency in

which the broker issues the price target, it remains unclear what the firm’s underlying currency is in the respective

day. 12 For example, Cantor Fritzgerald is identified as “Cantor Fritgera” in I/B/E/S, “Cantor Fritzgerald Inc” in SDC and

“Cantor Fritzgerald Securities” in LPC. Additionally, we identify cases in which, for the same name in I/B/E/S, there

is more than one entry in the Nelson’s Directory. For instance, Renaissance Capital in I/B/E/S can be linked to

either Renaissance Capital LLC (independent research firm) or Renaissance Capital Ltd (investment bank). In such

case, we go one step further and check the analysts’ name employed at each house and link them to the analyst’s

name in I/B/E/S to choose the correct matching firm.

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we use a pessimistic matching procedure, so that we form a link between two databases only when the

two names clearly depict the same broker.

Finally, a broker is identified as conflicted at the time of the recommendation (price target) issue if

she is a lead or co-manager for at least one IPO, SEO, public debt issue, financial advisor for an M&A

deal, lender or co-lender in a syndicated loan with the given firm from one year prior to one year after

the issue date. We impose this interval of time in line with the restrictions of SOX (sect. 501-b3), which

requires the disclosure of any existing conflicts between the issuer and the broker during the one year

period preceding the issue date. Ljungqvist, Marston and Wilhelm (2009b) show that appointments for

co-management allow banks to establish relationships with the issuers and gain more mandates in the

near future. For this reason, we do not restrict the definition of conflicts solely to lead managers and

extend the period of potential conflicts to one year after the issue date.

3.1.2. Identification of rival recommendations (price targets)

Our objective is to identify recommendations (price targets) issued on direct competitors of the

affiliated firms. To do so, we use the text-based network industry classification (TNIC) from Hoberg and

Phillips data library; see Hoberg and Phillips (2010a); Hoberg and Phillips (2010b). We proceed in two

steps. First, for each recommendation (price target), we check whether the firm represents a TNIC

competitor for at least one affiliated firm of the broker at the time of the recommendation (price target) is

issued. This set constitutes the list of potential rivals.

Second, given the limited resources of even the largest investment banks, it is highly improbable that

an affiliated broker targets this entire set of her client’s peers. Indeed, the TNIC score has an average

(median) of 68 (22) competitors per each firm-year. Moreover, and most importantly, the playing field of

competition does not imply the product market exclusively. From an investor’ perspective, two firms

compete in the stock market if they are close substitutes in her portfolio. Consistent with this

perspective, affiliated brokers rely on a set of highly substitutable firms for their customers and only

follow a subset of the entire panel of the client’s product market competitors. As such, for each affiliated

firm-year, we retain as rivals the first five TNIC competitors with which the affiliated firm has the highest

return correlation.

This definition allows us to identify both the direct competitors in the product market and the closest

substitutes in terms of portfolio management. The choice for the firms with the highest correlation in

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returns is also based on the affiliated brokers’ objective. Specifically, we conjecture that brokers

indirectly favor affiliated firms by issuing negative ratings on their rivals that an investor could easily

replace in her portfolio with the client firm. Following this definition, for each affiliated firm, we use a

dynamic set of rivals that changes every year; see Table A1 for an illustration of the top five rivals for

Pfizer.

3.1.3. Final sample

While the universe of the brokers issuing recommendations and price targets in I/B/E/S is largely

formed of investment banks, these brokers can also be independent brokerage and/or independent

research houses. Our interest is to explore the existence of the bias in affiliated and rival

recommendations and price targets within affiliated brokers, and for this reason we limit our analysis on

investment banks. As such, all brokers covering an affiliated or rival firm at least once during the period

are classified as affiliated brokers. Otherwise they are dropped from our sample. All in all, we identify

246 (196) investment banks providing recommendations (price targets). Not surprisingly, these count for

more than 97% of the initial observations. Barber et al. (2007) find similar results with 241 investment

banks for their recommendations sample.

Finally, the sample on investment banks’ recommendations and price targets consist of affiliated,

rival, neutral (i.e., neither affiliated nor rival) and both affiliated and rival firm-broker observations. While

a firm can be categorized as both affiliated and rival for a broker, it is not clear whether, and to what

extent, the corresponding recommendations are biased. Therefore, they are excluded from the sample.

We lose roughly 3% (5%) of the observations in recommendations (price targets) by imposing this last

filter.

3.2. Empirical methodology

To test our hypotheses, we examine the brokers’ bias on their relative ratings and estimate the

following difference-in-differences model.

, , 1 , , 2 , , 3 4 , ,

5 , , , , , ,δ'Xb i t i t b i t b i t t b i t t

b i t t b i t b i t

Y Rival Affiliated SOX Rival SOXAffiliated SOX

α η β β β β

β ε

= + + + + + ×

+ × + + (1)

where subscripts b, i, t stand for broker, firm, and time respectively.

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The dependent variable, , ,b i tY , is the recommendation (price target to current price ratio) issued by

broker b on firm i in quarter t minus the mean outstanding recommendation (price target to current price

ratio) from the rest of the brokers covering firm i in the prior twelve months. For each firm-broker

observation, we retain the most recent outstanding recommendation (price target) during the quarter.

Moreover, to ensure the time matching of recommendations (price target) among brokers, we follow

Ljungqvist et al. (2007) and keep the most recent recommendation over the last twelve months when

computing the consensus.

We focus our analysis at the broker level for two reasons. First, the issuer-bank relationship is the

first stage through which conflicts of interests materialize. Second, and most importantly, the financial

regulations target primarily the research at the investment banking level. Since analysts working for

affiliated brokers implicitly comply with these changes, we do not focus on their individual

characteristics.

The magnitude of the conflicts of interest at the firm-broker level is estimated by the Affiliated

dummy. This variable equals one if the broker is a lead manager, co-manager, financial advisor, lender

or co-lender for the firm in a least one IPO, SEO, public debt offering, M&A deal or syndicated loan one

year prior or one year post the recommendation (price target) issue date. The dummy variable Rival

counts for the bias in rival recommendations (price targets). It takes the value of one if two conditions

are fulfilled: a) the firm is a potential rival of an affiliated firm, and b) its one-year daily past returns are

among the five highest correlated with the affiliated firm’s daily returns. Since our sample consists of

three groups, the Rival (Affiliated) dummy captures the difference in the relative rating between rival

(affiliated) and neutral firms. To account for the influence of the U.S. regulations on brokers’ bias, we

introduce SOX, a dummy variable that equals one if the recommendation (price target) is issued after

the adoption of SOX (i.e., July 31st, 2002).

In line with prior studies, the additional variables (X) control for other sources of bias in brokers’

recommendations and price targets. First, since large institutions have more resources to support

research and, supposedly, more access to private information, we follow Stickel (1995) and control for

the size of the broker based on the number of analysts employed over the past quarter (BrokerSize).

We include the number of brokers who issued at least one recommendation (price target) on the stock

over the past quarter to capture the stock’s information environment (Ln(#brk)). To account for the fact

that brokers may become optimistic about a stock because it is performing well or because of market-

15

wide optimism, we include the stock’s market adjusted return (PastFirmPerf) and market return

(PastMktPerf) for the preceding six months; see Jegadeesh, Kim, Krische and Lee (2004); Kadan et al.

(2009). We also include a dummy variable that equals one if the broker initiates the coverage for the

firm (Initiation). Table A2 details the construction of all the variables.

In line with our first hypothesis, we should observe no significant difference between the affiliated

and rival groups before SOX ( 2 1β β= ). By fixing both the group and the time dimensions, we test

whether there are significant differences across groups and across periods. This effect is captured by

5 4β β− . According to our second hypothesis, the difference between the affiliated and rival bias (as

compared to neutral firms) before and after SOX should not be significantly different from zero (i.e,

5 4 0β β− = ). In other words, affiliated brokers continue to provide higher ratings for their affiliated as

compared to their rivals.

To account for the time trend and time-invariant firm heterogeneity we include a vector of firm fixed

effects ( iα ) and quarter dummies ( tη ). We further adjust the standard errors for heteroskedasticity and

within-firm dependence; see Petersen (2009). We use the above specification for two reasons. First, to

identify the presence of a firm and/or broker effect, we compute the standard errors of the model

including time dummies by clustering at each of these second dimensions. Additionally, we assess the

magnitude of the correlation between each group with the observed covariates. While we cannot reject

the hypothesis of the presence of each effect, the correlation between the broker specific effect and the

observables is close to zero, whereas the correlation between the firm specific effects and the

observables is not. This suggests that the estimates from fitting a firm fixed effect model (i.e., a model

which assumes that the broker-error component and the variables of interest are uncorrelated) should

be preferred to a broker fixed effects model13.

13 While no standard methodology has been so far developed for estimating three-way error-component models,

this topic has started to gain some attention in recent years; see Andrews, Schank and Upward (2006); Gormley

and Matsa (2013). We confirm further the implication of using firm and time fixed effects by fitting a three-way fixed

effects model which groups all unique firm-broker combinations (i.e., by defining a “spell”; see Andrews et al.,

2006). This model eliminates both the unobserved firm- and broker-error components, while using time dummies.

The estimates from this model are generally close to those of the firm fixed effects model with time dummies,

implying that the broker specific component and the covariates are uncorrelated. The results are available upon

request.

16

4. Results

4.1. Univariate results

Table 1 presents the descriptive statistics on the recommendations and price targets. Panel A

reports the distribution of recommendations by year and by rating. Two notable features emerge from

the data. Firstly, the proportion of both strong sell and sell recommendations is around 3% before 2002.

Consistent with previous findings, this remarkably low number corresponds to brokers’ general

reluctance to issue unfavorable ratings on the covered firms during that period; see, e.g., Barber,

Lehavy, McNichols and Trueman (2003); Jegadeesh and Kim (2006). Secondly, the distribution

becomes more balanced after 2002 with a decrease (increase) in the proportion of strong buy and buy

(strong sell and sell) recommendations. To illustrate, the percentage of buy (sell) recommendations

went from 37% (2%) in 2000 to 23% (9%) in 2008. Simultaneously, the percentage of hold

recommendations increased substantially (e.g., from 28% in 2000 to 47% in 2008), suggesting that

brokers became generally more pessimistic in the aftermath of the regulations enacted around SOX.

The distribution by year is quite smooth, with a peak in the number of recommendations in 2002. The

number of covered firms increases from 2,712 in the previous year to 2,900, implying a higher coverage

intensity for this year. The fact that we still depict a higher number of recommendations after controlling

for resumption in light of the change in rating scales is also directly related to the almost 25% of the top

100 brokers (in terms of the number of recommendations issued) which do not resume their coverage

(in the I/B/E/S Stopped file) when issuing recommendations on different scales. Barber et al. (2006) use

data from FirstCall and identify a similar peak in the number of recommendations from an average of

57,000 between 1997-2001 to more than 84,000 in 2002.

[Insert Table 1 about here]

The distribution by years of the price target to current price ratio is depicted in Panel B. The majority

of the observations lie between a targeted increase of 7% and 30% in the one-year-ahead price,

whereas the mean (median) of the ratio is 1.24 (1.17) for the entire period. We identify a drop in the

median ratio subsequent to 2002 from 1.43 in 2002 to a steady ratio of around 1.16 between 2003 and

2007. This decrease in price targets confirms the idea that affiliated brokers became more pessimistic in

the post regulation era. Compared to recommendations, price targets are issued twice more frequently.

Similar to Brav and Lehavy (2003), price targets contain fewer covered firms (5,335 compared to 7,378)

and fewer brokers (196 compared to 246) compared to recommendations.

17

Brokers map the recommendation ratings on their assessment of the stock’s expected return within

twelve months relative to a benchmark (i.e., the market index, industry index or the own stock’s current

performance). In other words, favorable (i.e., strong buy and buy) and unfavorable (i.e., strong sell and

sell) recommendations derive from favorable and unfavorable views on the stock’s future relative

performance. As such, to classify a price target as favorable (unfavorable), we proceed backwards and

map the cut-off points in price targets to each recommendation category by the corresponding

percentile. We conduct this analysis on the set of brokers providing both price targets and

recommendations in I/B/E/S. Table 2 details the translation for each category. All price target to current

price ratios above (below) 1.22 (0.95) are classified as favorable (unfavorable); see Brav and Lehavy

(2003) for a similar approach.

[Insert Table 2 about here]

We note that the affiliated ratings count for 6% (8%) for recommendations (price targets) issues. This

number corresponds to Malmendier and Shanthikumar (2007) who find around 7% of affiliated

recommendations in U.S14. Rival recommendations (price targets) count almost double, with 16% (17%)

of the total sample. Specifically, for each recommendation (price target) issued on an affiliated firm, 2.6

ratings are issued on a rival firm. The rest of the observations are neutral (77% and 76% for the

recommendations and price target samples respectively).

Having identified the corresponding levels for favorable (unfavorable) recommendations and price

targets, we detail the distribution of each category by affiliation in Table 3. Panel A breaks down the

distribution of recommendations for each type of rating. In line with prior studies (see, e.g., Kadan et al,

2009; Ljungqvist et al., 2007), we find that affiliated recommendations have the highest percentage

favorable views in all years before 2002. They account for 73% in the pre-SOX period, whilst rival and

neutral firms count for 67% and 64% of favorable recommendations over the same period. The highest

proportion of favorable recommendations is on affiliated firms while the percentage of favorable

recommendations is lower for rival than for affiliated firms.

We observe a dramatic change after 2002. While the proportion of favorable recommendations drops

sharply for all three types of firms, brokers continue to have the most favorable views on affiliated firms.

14 Malmendier and Shantikumar (2007) define a recommendation as affiliated if the broker was an IPO (SEO) lead

or co-underwriter in the past five (two) years, a co-underwriter of equity, a bond lead underwriter in the past year, or

was involved in a SEO in the next two years with the covered firm.

18

Rival firms now have the lowest proportion of buy and strong buy recommendations among all three

types of firms. To illustrate, the proportion changes to 38%, 44% and 42% for rival, affiliated and neutral

firms respectively after SOX. This pattern is complemented by the evolution of unfavorable (i.e., sell and

strong sell) recommendations. As expected, affiliated brokers are the most reluctant to issue negative

views on their clients during the entire period. Nevertheless, after 2002, the proportion of unfavorable

ratings increases for all types of firms and brokers issue the highest proportion of sell and strong sell on

rival firms. Actually, the proportions of unfavorable recommendations went from 2% to 14%, from 2% to

9% and from 3% to 12% for rival, affiliated and neutral firms respectively in the periods before and after

SOX.

Panel B depicts a similar trend in price targets. We remark a significant decrease (increase) in

favorable (unfavorable) percentages after 2002 for all types of firms. Here, furthermore, the proportion

of favorable price targets for rival firms is the lowest over the entire period. As with recommendations,

the proportion of unfavorable price targets spikes from 3% to 10%, from 2% to 6% and from 4% to 9%

for rival, affiliated and neutral firms respectively after the enactment of SOX.

At the first glance, affiliated brokers seem to have complied with the regulations to come forth with

less (more) optimistic (pessimistic) research on their clients. Looking more closely, however, the

downward switch in ratings for clients went simultaneously with a greater downward switch for rivals.

Thus the fact that affiliated brokers became generally more pessimistic appears to have had no effect

on the existing rating gap between client and rival firms.

[Insert Table 3 about here]

4.2. Multivariate results

We start by ascertaining the affiliated and rival bias from the baseline specification in Table 4. Panel

A reports the results for the recommendations in column (1). The coefficient on affiliated brokers

(Affiliated) is positive and significant. On a five-notch scale, the relative recommendations for client firms

are 0.14 higher than the ones for neutral firms before SOX. This result is similar to what has been

documented in the literature; see, e.g., Ljungqvist et al. (2007); Kadan et al. (2009). The average effect

of rival firms (Rival) is positive and significant, suggesting that prior to the enactment of SOX affiliated

brokers issued higher relative recommendations also for their clients’ rivals compared to neutral firms.

The positive bias in relative recommendations for rival firms can be driven from the proximity and the

19

common business characteristics with the affiliated firms. However, the magnitude of the bias (0.07) is

half of the one on affiliated firms. The difference of the bias between affiliated and rival firms before

SOX is statistically and economically significant (F-test 15.02, p-value 0.00). Therefore, we reject our

first hypothesis.

When assessing the impact of SOX, we notice that the regulation significantly decreases the bias in

the relative recommendations for affiliated and rival firms as compared to neutral firms. The coefficients

on Affiliated x SOX and Rival x SOX are both negative and significant. Interestingly, the two coefficients

are not significantly different (F-test 0.50, p-value 0.52), suggesting that the magnitude of the pessimism

induced by the regulations was similar for both types of firms. Strikingly, the F-test on the overall effect

of each bias after SOX reveals that brokers continue to issue higher relative recommendations for

affiliated firms as compared to neutral firms (0.14 - 0.10 = 0.04 statistically significant at 5%), while the

bias in relative recommendations for rival firms as compared to neutral ones becomes negative (0.07-

0.11 = -0.04 statistically significant at 1%). Within this setting, the gap between the two biases continues

to exist in the aftermath of the regulation, so we cannot reject our second hypothesis. In other words,

even if affiliated brokers comply with the regulation by reducing the over-optimism for their clients, they

nevertheless continue to keep their clients on significant higher ratings compared to their rivals. We

remark that the coefficient on SOX is negative and significant, suggesting that affiliated brokers become

on average more pessimistic for neutral firms. Overall, the control variables display signs that are in line

with related studies; see Ljungqvist et al. (2007); Kadan et al. (2009).

[Insert Table 4 about here]

To verify if our results are robust, we run several alternative specifications15. In column (2), we

require at least five outstanding recommendations to compute the consensus. One potential concern is

that with a small number of recommendations, the timing of their releases can substantially affect the

relative nature of our dependent variable. A broker can also issue a relatively higher recommendation

compared to her own recommendations on the rest of the covered firms. Therefore, we modify the

benchmark of the relative measure from firm and focus on the broker level in specification (3). The

dependent variable is the current recommendation minus the mean recommendation of the rest of the

15 As detailed in Section 4.1., we fit these models with firm and time fixed effects. Although fitting the models with

broker and time fixed effects does not comply partly with our assumptions, the majority of the inferences are not

sensitive to this alternative specification.

20

covered firms by the same broker in the previous twelve months. In column (4), we run a logit model

where the dependent variable is the probability that the current recommendation is higher than the

consensus. In column (5), we restrict the affiliation relationship to recommendations issued in the next

six months (instead of a one year window) after the deal. In column (6), Rival is the TNIC competitor

with which the affiliated firm has the highest daily return correlation over the year (instead of the top five

firms). Overall, the results are not affected by these modifications16.

We run the similar set of specifications on price targets and present the results in Panel B. The

findings correspond in great part to our previous results. It appears that conflicted research is not only

first and foremost present in recommendations, but also in price targets. To illustrate, relative price

targets on affiliated (rival) firms are 0.04 (0.01) times higher than those on neutral firms before SOX.

Furthermore, the affiliated bias in price targets is four times bigger than the one in rival firms (statistically

and economically significant at 1%). As such, we reject again the first hypothesis. While affiliated

brokers continue to provide higher relative price targets for their clients in the aftermath of the

regulations (0.04 - 0.02 = 0.02 significant at 1%), the relative price targets for rivals are now not different

from those on neutral firms (F-test of Rival + Rival x SOX of 0.26). Similar to recommendations, both

biases decrease, and the gap between the relative price targets for affiliated and rival firms, with respect

to neutral firms, does not change after the introduction of SOX (p-value 0.22). We note that the

magnitude of the gap between affiliated and rival bias is not as consistent as in the case of

recommendations. Indeed, in two out of the six specifications (i.e., columns 3 and 6) we reject the

second hypothesis at 1%.

Figure 1, Panel A (B) displays the affiliation bias in relative recommendations (price targets) for

affiliated and rival firms to the neutral ones over the entire period. For each panel, we run quarterly

regressions of the baseline model and plot the evolution of the gap between affiliated and rival relative

recommendations (price targets). First, we remark that the drop in bias corresponds to the enactment of

SOX, suggesting that affiliated brokers became more pessimistic. Though less obvious for

recommendations in the first years, the parallel evolution of the two biases confirms our results so far.

16 In unreported results, we also check whether the documented change in rating scale is likely to introduce an error

to the interpretation of our findings. Generally, the rating scale moved from a five- to a three-notch distribution, such

that it continued to be centered on the middle point (i.e., from 1 to 5, to 2 to 4). Since the rating does not change

(i.e., favorable (unfavorable) recommendations continue to be coded as above (below) 3), we run two logit models

where the dependent variable is the probability that the recommendation is above (below) 3; see Kadan et al.

(2009). The results do not change qualitatively.

21

Interestingly, right after 2002 this parallel trend slowly diverges for as long as two years, showing that

affiliated brokers downgraded the rival firms in the short-run. Likewise, the positive bias for rival firms,

as compared to neutral ones, is always positive (and below the affiliated bias) before SOX, whereas

after SOX it turns negative in recommendations and with some delay (i.e., starting 2004) for price

targets.

[Insert Figure 1 about here]

To provide a different perspective on the real impact of SOX on conflicted research, we assess the

effect of SOX on the over-optimism bias for affiliated firms as compared separately with rival and then

with neutral firms. The results are presented in Table 5. The coefficient on Affiliated x SOX is

significantly negative in both recommendations and price targets in specification 2 (t-stat -4.37 and -3.51

respectively), so that affiliated brokers decreased their over-optimism compared to neutral firms after

SOX. When comparing this result with specification (1), we clearly observe that SOX had nevertheless

no impact in curbing the existing gap in ratings between affiliated and rival firms. Indeed, the coefficient

on Affiliated x SOX is not statistically different from zero in neither recommendations nor price targets.

These findings highlight a striking aspect of the consequences of regulations aiming to curb conflicts of

interest in investment banking. Although in all appearance necessary, but only partly effective, this

regulation left the door open for the affiliated brokers to continue favoring their clients through another

channel.

[Insert Table 5 about here]

4.3. Evaluating alternative hypotheses

The issuer may select for underwriting or advisory mandates those brokers that have the most

positive views on their business. This process may therefore lead to more optimistic brokers for affiliated

firms but only because they were already more optimistic before (the selection hypothesis); see Lin and

McNichols (1998). We compare in Table 4 the coefficients on Affiliated in the baseline specification and

column (3). When the affiliation relationship is considered solely over the next six months from the deal

date, the coefficient is 30% higher compared to the one obtained with a one year window around the

date at which the broker acted as an underwriter, advisor, lender or co-lender for the issuer. As such,

brokers seem to be the most aggressive in issuing over-positive ratings especially in the periods

following the deals.

22

Our results also suggest that brokers were significantly more positive on rival than on neutral firms

before SOX, and that this situation is reversed subsequently. According to the selection channel, we

should therefore observe a significantly greater proportion of rival firms that became subsequently

clients for the issuing broker before SOX (and conversely after SOX). We check for future investment

banking ties for each rival and neutral firm that became a client for the same broker in the next year (six

to eighteen months) or three years (six to forty-two months). We exclude the six months before the deal

since during that period the investment bank is likely to have already access to private information

related to the firm. For recommendations, on average 1.26% (2.91%) of all firms that were rivals at

some point in time before SOX become clients for the same broker in the next year (three years), while

the corresponding percentages for neutral firms are 1.01% (1.89%). The difference between the two

groups is statistically significant at 5% (1%) for deals in the next year (three years). After SOX, the

situation does not change much, with 1.33% (2.59%) of rival, and 0.96% (1.53%) of neutral firms

becoming clients in the next year (three years). Likewise, the difference between the two groups is

statistically significant at 1% irrespective of the period for future deals17. Accordingly, the selection

channel could at most explain the more positive views on rival than neutral firms before the enactment

of the law. However, this hypothesis is unable to explain the changing behavior of affiliated brokers

after SOX, with no more positive views on rival than neutral firms.

Another potential explanation for the over-optimism in brokers’ ratings is that they have a privileged

access to the firm-specific information. If this information gets incorporated into stock recommendations

and price targets, we could systematically observe over-optimism for affiliated firms even in the absence

of conflicts of interest (the information hypothesis; see, e.g., Michaely and Womach, 1999; Allen and

Faulhaber, 1989). Therefore, if the information channel was at play, we should observe significant and

highest abnormal performance for affiliated firms in both periods.

Following this conjecture, and the common characteristics of the business for affiliated and rival

firms, the positive information on affiliated firms should at least partially spread into positive information

on rival firms (but to a lesser extent, since brokers do not have an equal access to these firms). This is

in line with what we find before SOX. After SOX, however, although affiliated firms continue to enjoy the

same positive bias in ratings, rival firms are downgraded below (to the same level as) neutral firms in

recommendations (price targets). As such, we extend the information hypothesis to the possibility that 17 To conserve space, we do not report the corresponding percentages on price targets. The results are

qualitatively similar with the ones on recommendations.

23

affiliated brokers have additional information on the long-term prospects of rival and neutral firms. If this

was the case, we should observe that rival firms perform significantly better (equal to or significantly

worse) than neutral firms before (after) SOX.

To examine whether our results reflect the information channel, we analyze the abnormal

performance of recommendations and price targets for affiliated, rival and neutral before and after SOX.

We follow Womack (1996) and form an added-to-buy portfolio. The cumulated market abnormal returns

over the next six months are 4.1% for affiliated, 4.8% for neutral and 2% for rivals before SOX, and

7.4%, 10.9% and 7.6% respectively after SOX. However, the differences among firms are not

statistically significant. Before (after) SOX, the abnormal performance of affiliated stocks is not

significantly different from the one of rival stocks (p-value 0.83 and 0.21 respectively). Likewise, the

abnormal performance of rival and neutral stocks is not significantly different (p-value 0.19 and 0.91

respectively). We conclude that our results do not support the information hypothesis.

5. Market reaction to the affiliated brokers’ bias

In this section, we investigate whether, and to what extent, the market understands the bias

previously documented. Following our results, the conflicts of interest appear in two forms. First, brokers

issue higher ratings on affiliated firms over all periods, although this bias decreases after the adoption of

SOX; see Ertimur et al. (2006). Second, brokers issue significantly less favorable ratings on rival firms.

We measure to what extent investors recognize the positive (negative) bias for affiliated (rival) firms,

and whether SOX had an impact in bringing more awareness of such conflicts. To do so, we analyze

the short-term market reaction to recommendations for affiliated, rival and neutral firms, both before and

after the enactment of SOX. We focus on recommendations for two reasons. First, the magnitude of the

gap between affiliated and rival ratings that we identify in the two periods is larger for recommendations

than price targets. Second, the thresholds that we defined in Section 4 could be interpreted differently

by market participants. We test the following hypothesis.

Hypothesis 3. Before SOX, investors recognize the brokers’ bias on affiliated (rival) firms and

discount (assign a premium to) firms that are added-to-buy accordingly; for firms added-to-sell, the

discount is higher for affiliated firms.

24

Since the regulation’s main objective is to limit such conflicts, we are interested to check whether the

market accounts accordingly for its effective implementation, i.e., whether investors recognize the

limitation of both types of bias. Hence, our next hypothesis is the following.

Hypothesis 4. After the adoption of SOX, there is no significant difference in the short-term

performance of affiliated, rival and neutral firms.

We follow Womack (1996) and construct added-to-buy (i.e., upgrades to or above 4) and added-to-

sell (i.e., downgrades to or below 2) lists starting from our sample. By definition, initiations (no previous

recommendation) and reiterations (no change) are excluded. We also exclude recommendation

changes that are issued in the three-day window around earnings announcements to filter the cases in

which recommendations merely repeat the information contained in firm-specific news releases; see,

e.g., Loh and Stulz (2011); Salva and Sonney (2011). Finally, a recommendation change is added to the

buy (sell) list if no other downgrade (upgrade) is issued on the same firm during the window.

Next, we calculate daily excess returns as the difference between the stock’s return and the CRSP

value-weighted daily index. The event date is the announcement date of the recommendation change or

the next trading day (for recommendations on non-trading days or between 4:30 PM and 11:59 PM on a

trading day). The cumulated excess returns are computed from the prior trading date to the next one,

five, and ten trading days following the recommendation change. Since we work with three different

groups of firms, it is possible that a firm is classified under different groups at different points in time (for

the same or for different brokers). As such, it is possible that a firm enters more than one group during

the event window. To isolate these cases, we require that no other recommendation change occurs on

the same firm during the event window.

[Insert Table 6 about here]

Table 6 details the number and the proportion of added-to-buy and added-to-sell recommendation

changes for each group of firms. The results correspond largely to the ones in levels in Section 4. Not

surprisingly, over the entire period, affiliated brokers are the most positive on their clients, with 39.2% of

all prior recommendations transforming to strong buy and buy, opposite to 37.1% and 35.9% in the case

of neutral and rival firms, respectively. In line with our previous results, after SOX, affiliated firms have

the highest (lowest) proportion of recommendation changes to the added-to-buy (added-to-sell) list. The

opposite is true for rival firms. To illustrate, the proportion of added-to-sell recommendation change for

25

rival, affiliated and neutral firms goes from 4%, 3.6% and 5.5% respectively before SOX to 18.3%,

12.6% and 16.7% respectively after SOX. Upgrades occur five times more often than downgrades,

suggesting that, on average, brokers are reluctant to issue unfavorable recommendations.

[Insert Table 7 about here]

Table 7, Panel A (B) presents the cumulated excess returns for affiliated, neutral and rival firms that

enter the added-to-buy (added-to-sell) list. Following hypothesis 3, we should observe the weakest

(strongest) market reaction for added-to-buy affiliated (rival) firms and the converse for

added-to-sell firms. According to our results, affiliated (rival) firms in the added-to-buy list have the

highest (lowest) abnormal performance before SOX, irrespective of the event window. In the short run

(i.e., three to seven days event), affiliated firms perform significantly better than the rest of the firms. For

example, added-to-buy firms abnormal performance is 2.78%, 2.19% and 2.13% respectively for

affiliated, neutral and rival firms in the three-day window event (p-value of pairwise tests is 0.06 for

affiliated vs. neutral and 0.04 for affiliated vs. rival). During the same period, there is no significant

difference between the performance of rival and neutral firms (p-values 0.79 for the three-day window).

Before SOX, the abnormal performance associated with added-to-sell recommendations are not

significantly different (p-value pairwise test affiliated firms vs. neutral = 0.60 and affiliated firms vs.

rival = 0.79). These results provide evidence that, before SOX, investors do not account for the

presence of conflicted research in the investment banking. Therefore, hypothesis 3 is rejected.

After SOX, the market discounts the positive bias in affiliated recommendations since affiliated firms

do not perform significantly better than neutral ones in the added-to-buy list. To illustrate, the

corresponding performance for added-to-buy list firms on the three-day window is 3.37% and 2.93%

respectively for affiliated and neutral firms (p-value = 0.11). Strikingly, the rival firms’ performance is

significantly lower (significant at 1%) than the one of affiliated and neutral firms and persists in all event

windows. Taken together, these results suggest that investors translated the current regulation as an

ex-post decrease in brokers’ positive bias on affiliated firms. Nevertheless, the downward bias on rival

firms is not recognized in the short-run. As such, investors are less likely to be aware of the existence of

such gap in the relative nature of recommendations. We therefore reject hypothesis 4.

Overall, our findings depict two interesting aspects of the market reaction to brokers’ bias. First,

before SOX, market participants are not aware of the existence of a positive bias on affiliated, nor of the

negative bias on rivals. Second, after the enactment of SOX, the market recognizes the positive bias for

26

affiliated firms, while no sign of recognition is associated with the brokers’ negatively biased

recommendations of the rivals. Indeed, in the post-regulation period, the added-to-buy

recommendations for rival firms translate into significant lower performance than the rest of the firms in

the short-run.

6. Conclusions

We document empirical evidence that affiliated brokers issue persistently higher recommendations

and price targets on affiliated firms compared to their rivals. To the best of our knowledge, this form of

conflict was not investigated previously in the literature. We identify a significant gap between affiliated

and rival ratings both before and after the enactment of the regulations aiming to curb the conflicts of

interest in investment banking research. While affiliated brokers become, on average, more pessimistic

after the enactment of SOX, they nevertheless continue to favor their clients by keeping their rivals on

significantly lower ratings. Our findings are robust to several alternative specifications and definitions of

the variables of interest. Additionally, we find no consistent support for other potential hypotheses

(selection bais and positive information), other than the existence of conflicts of interests, which could

mislead the interpretation of our results.

We find no evidence that investors account for the positive (negative) bias on their affiliated (rival)

firms before SOX. In the post-regulation period, affiliated firms do not perform significantly better than

neutral firms, suggesting that investors integrate, to some extent, the conflicts. However, the negative

bias on rival firms is not perceived in the short-run.

The main message of this paper is that the inherently subjective nature of affiliated research

continues to exist in spite of the tremendous amount of regulatory initiatives aiming to curb conflicts of

interest in the investment banking industry. Despite regulatory reforms, the conflicts relapse in a

different form. As such, brokers continue to favor their clients by issuing negatively biased ratings on

their clients’ rivals.

This study is important for financial regulators and contributes to the ongoing debate on the effect of

the current regulations. It is now a befitting question to ask whether regulators are able to account in the

future for the various innovations that can sidestep regulatory restrictions, and more importantly, at what

costs. Understanding the channels through which financial analysts’ incentives change after the

regulations is both theoretically and practically important and awaits future empirical examination. We

27

look forward to research clarifying whether shaping efficient regulations is indeed achievable, or the

conflicts of interests in the investment banking industry are stronger and bound to prevail.

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30

Appendix

Table A1 Determining the rival firms: an example

This table presents the top five rivals for Pfizer between 2006 and 2008. Each year, firms are classified as rivals according to the TNIC pairwise score, and if Pfizer’s daily returns correlation with the firm’s is among the five highest for the year. The numbers in boldface mark the yearly rivals for Pfizer. For example, in 2006 (2007) Pfizer has the greatest (third greatest) daily return correlation with Merck and Co. Inc. In 2008, the firm does not enter in the top five list. By the same token, Baxter International Inc. is not in the top five list in 2006 and 2007, but is in the top five list in 2008.

TNIC competitor Rank 2006 2007 2008

Abbott Laboratories 30 4 3 Allied Healthcare Products Inc. 3 7 7 Baxter International Inc. 16 9 5 Bristol-Myers Squibb Company 22 2 6 Eli Lilly and Company 4 1 2 Johnson & Johnson 5 5 1 Merck and Co. Inc. 1 3 8 Onyx Pharmaceuticals Inc. 2 201 90 Western Potash Corp. 44 23 4 Table A2 Variables definition and source

This table provides details on the definition and source of the variables used in the models.

Variable Definition Source

, ,b i tRelRec The recommendation for firm i issued by broker b at time t minus

the mean recommendation of the rest of the brokers covering

the same firm during the last twelve months.

I/B/E/S

, ,b i tRelPtg P The difference between the price target for firm i issued by

broker b and the consensus price target, scaled by the current

stock price. Consensus price target is computed as the mean

price target of the rest of the analysts covering the same firm

during the last twelve months. Alternatively, consensus is

computed as the mean price target of the rest of the firms

covered by the same broker during the last twelve months.

I/B/E/S

CRSP

, ,b i tRival Dummy variable equal to one if during the year of the

recommendation (price target) issue firm i is classified as a

competitor for at least one of the broker b’s clients according to

the TNIC classification and ii). firm i is among the top five firms

with which the client firm has the highest correlation in daily

returns during the current year, zero otherwise.

CRSP

I/B/E/S

SDC

LPC DealScan

, ,b i tAffiliated Dummy variable equal to one if broker b had at least one

business deal (IPO, SEO, debt raising, M&A, syndicated loan)

I/B/E/S

SDC

31

with firm i one year prior and one year post the recommendation

(price target) issue, zero otherwise.

LPC DealScan

tSOX Dummy variable equal to one if the recommendation (price

target) is issued before July 31th 2002, zero otherwise.

,b tBrokerSize Number of analysts working for broker b during the current

quarter.

I/B/E/S

, ,(# )b i tLn brk Logarithm of the number of brokers covering firm i during the

current quarter.

I/B/E/S

,i tPastFirmPerf Cumulative market-adjusted return over the preceding six

months (months -6 through -1) for firm i.

CRSP

,i tPastMktPerf Cumulative market return over the preceding six months

(months -6 through -1).

CRSP

, ,b i tInitiation Dummy variable equal to one if broker b initiates the coverage

for firm i at time t, zero otherwise.

I/B/E/S

32

Table 1 Descriptive statistics

This table reports the proportion of stock recommendations by rating (Panel A), the general distributional statistics on the price target to current price ratio (Panel B), the number of recommendations (price target to current price ratio), the number of brokers, and the number firms covered by year.

Panel A. Recommendations

Year Strong Sell (%) Sell (%) Hold

(%) Buy (%)

Strong Buy (%) No. obs. No.

firms No.

brokers 1997 1.99 1.89 31.06 37.46 27.61 11,328 2,739 153 1998 0.97 1.78 31.91 40.53 24.80 13,120 2,920 164 1999 0.80 2.10 29.98 40.25 26.86 13,964 2,973 162 2000 0.61 1.46 27.81 40.02 30.10 13,950 2,773 158 2001 0.90 1.93 35.14 38.61 23.42 15,629 2,712 145 2002 1.83 5.47 40.63 31.36 20.71 20,677 2,900 136 2003 3.13 9.46 46.48 25.36 15.58 15,649 2,651 141 2004 3.89 7.87 45.54 24.89 17.81 12,541 2,520 147 2005 3.73 7.34 47.07 23.18 18.68 12,044 2,645 145 2006 3.80 7.94 47.65 23.19 17.42 12,286 2,747 141 2007 3.49 7.07 49.34 24.47 15.63 12,761 2,818 136 2008 4.09 9.18 46.69 22.72 17.32 12,687 2,576 129 Total 2.36 5.26 39.84 31.22 21.32 166,636 7,378 246

Panel B. Price targets

Year Mean Stdev p25 Median p75 No. obs. No. firms

No. brokers

2000 1.431 0.436 1.164 1.303 1.538 24,258 2,811 127 2001 1.379 0.438 1.127 1.245 1.463 27,512 2,709 129 2002 1.324 0.377 1.115 1.223 1.406 30,608 2,580 121 2003 1.170 0.243 1.040 1.141 1.251 29,217 2,501 132 2004 1.163 0.211 1.047 1.139 1.240 31,695 2,592 137 2005 1.161 0.199 1.051 1.139 1.236 33,461 2,808 139 2006 1.161 0.203 1.049 1.136 1.235 35,845 2,958 132 2007 1.164 0.208 1.053 1.138 1.235 37,883 3,002 129 2008 1.298 0.368 1.084 1.209 1.404 41,608 2,814 123 Total 1.242 0.321 1.072 1.172 1.313 292,087 5,335 196

Table 2 Translation of the recommendations scale to price targets cut-off points.

This table displays the corresponding percentiles between favorable and unfavorable recommendations and price targets. We consider only the set of brokers that provide both measures in I/B/E/S. The 0.95 (1.22) cut-off point represents the threshold below (above) which a price target/current price is classified as unfavorable (favorable). Category Unfavorable Hold Favorable Percentile p2 p7 p40 p60 p80 Rec Strong sell Sell Hold Buy Strong buy Ptg/P 0.8 0.95 1.13 1.22 1.38

33

Table 3 Percentage of favorable and unfavorable recommendations and price targets

This table reports the percentages of favorable and unfavorable advices for rival, affiliated and neutral firms. Panel A displays the proportion of stock recommendations. Panel B displays the proportion of price targets. Price targets are classified as favorable (unfavorable) if the price target scaled by the current price ratio is above 1.22 (below 0.95). Affiliated recommendations (price targets) represent recommendations (price targets) on firms that have at least one business deal (IPO, SEO, public debt raising, M&A, syndicated loan) with the broker one year prior and one year post the recommendation (price target) issue. Rival recommendations (price targets) represent recommendations (price targets) on firms that are classified as competitors for at least one affiliated firm for the broker issuing the recommendation (price target) and that are among the top five firms with which the affiliated firm had the highest daily return correlation during the current year. Neutral recommendations (price targets) are defined as neither affiliated nor rival.

Panel A. Recommendations Favorable (%) Unfavorable (%)

Year Rival (i) Affiliated (ii)

Neutral (iii)

p-val. (ii) - (i)

p-val. (iii) - (i)

p-val. (ii) - (iii) Rival (i) Affiliated

(ii) Neutral

(iii) p-val. (ii) - (i)

p-val. (iii) - (i)

p-val. (ii) - (iii)

Before (a) 66.91 73.07 63.52 0.00 0.00 0.00 2.35 1.82 3.35 0.02 0.00 0.00 After (b) 37.71 44.24 41.52 0.00 0.00 0.00 13.61 8.58 11.79 0.00 0.00 0.00

p-val. (b) - (a) 0.00 0.00 0.00 - - - 0.00 0.00 0.00 - - - 1997 67.48 78.60 63.33 0.00 0.00 0.00 3.21 1.52 4.22 0.01 0.06 0.00 1998 65.64 71.76 64.72 0.00 0.46 0.00 2.08 1.76 2.95 0.57 0.04 0.04 1999 70.48 73.34 66.12 0.13 0.00 0.00 1.51 1.78 3.20 0.60 0.00 0.02 2000 74.53 75.08 68.87 0.75 0.00 0.00 1.16 1.40 2.30 0.57 0.00 0.07 2001 65.40 70.29 60.63 0.01 0.00 0.00 1.75 1.24 3.19 0.28 0.00 0.00 2002 51.66 61.38 51.34 0.00 0.73 0.00 7.35 5.29 7.46 0.01 0.81 0.00 2003 37.60 46.86 41.29 0.00 0.00 0.00 13.11 8.50 12.80 0.00 0.65 0.00 2004 39.31 47.16 43.22 0.00 0.00 0.04 14.22 6.66 11.53 0.00 0.00 0.00 2005 36.85 42.34 42.86 0.01 0.00 0.79 13.04 8.61 10.84 0.00 0.01 0.07 2006 36.97 46.43 40.88 0.00 0.00 0.00 15.14 6.43 11.48 0.00 0.00 0.00 2007 38.94 42.00 40.22 0.15 0.27 0.35 11.53 7.79 10.55 0.00 0.18 0.02 2008 36.22 38.40 40.99 0.30 0.00 0.18 15.63 13.32 12.75 0.14 0.00 0.66 Total 50.89 59.19 52.35 0.00 0.00 0.00 8.53 5.07 7.63 0.00 0.00 0.00

34

Table 3 (continued) Panel B. Price targets Favorable (%) Unfavorable (%)

Year Rival (i) Affiliated (ii)

Neutral (iii)

p-val. (ii) - (i)

p-val. (iii) - (i)

p-val. (ii) - (iii) Rival (i) Affiliated

(ii) Neutral

(iii) p-val. (ii) - (i)

p-val. (iii) - (i)

p-val. (ii) - (iii)

Before (a) 49.91 59.67 57.98 0.00 0.00 0.01 3.50 1.93 4.17 0.00 0.00 0.00 After (b) 26.35 34.83 35.63 0.00 0.00 0.03 9.81 6.05 9.11 0.00 0.00 0.00

p-val. (b) - (a) 0.00 0.00 0.00 0.00 0.00 0.00 2000 58.53 69.69 65.61 0.00 0.00 0.00 2.67 1.98 2.88 0.10 0.46 0.02 2001 48.80 56.44 56.10 0.00 0.00 0.76 3.60 2.30 4.80 0.00 0.00 0.00 2002 43.64 54.63 52.19 0.00 0.00 0.01 4.79 2.32 5.68 0.00 0.01 0.00 2003 24.38 35.55 31.95 0.00 0.00 0.00 12.76 7.77 12.17 0.00 0.21 0.00 2004 21.85 32.91 31.79 0.00 0.00 0.23 11.44 6.21 9.48 0.00 0.00 0.00 2005 20.36 31.18 30.38 0.00 0.00 0.38 9.59 5.84 8.80 0.00 0.05 0.00 2006 23.45 28.14 29.69 0.00 0.00 0.06 9.72 5.38 9.06 0.00 0.09 0.00 2007 19.75 28.64 29.90 0.00 0.00 0.15 8.42 5.27 8.48 0.00 0.87 0.00 2008 40.33 44.78 50.65 0.00 0.00 0.00 8.64 6.96 8.18 0.01 0.21 0.02 Total 32.14 41.01 40.82 0.00 0.00 0.55 8.26 5.02 7.96 0.00 0.02 0.00

35

Table 4 Brokers’ bias in recommendations and price targets and the impact of SOX

This table presents the results of regressions examining the brokers’ bias in recommendations and price targets by affiliation and the impact of SOX. In column (1), the dependent variable is the relative recommendation (price target/current price), defined as the difference between the current recommendation (price target/current price) and the consensus (mean recommendation (price target/current price) of the rest of the brokers covering the same firm during the last twelve months). In column (2), we require at least five outstanding recommendations (price targets) to compute the consensus. In column (3), we modify the definition of the consensus as the mean recommendation (price target/current price) of the rest of the firms covered by the same broker during the last twelve months. In column (4) models a logit regression in which the dependent variable is the probability that the relative recommendation (price target) is higher than the consensus. In column (5) we define the affiliation relationship over the next six months from the deal date. In column (6), we restrict the definition of rival only to the first TNIC competitor with which the affiliated firm has the highest daily return correlation during the year (instead of the top five firms). Affiliated recommendations (price targets) represent recommendations (price targets) on firms that have at least one business deal (IPO, SEO, public debt offering, M&A, syndicated loan) with the broker one year prior and one year post the recommendation (price target) issue. Rival recommendations (price targets) represent recommendations (price targets) on firms that are classified as competitors based on TNIC for at least one affiliated and that are among the top five firms with which the affiliated firm had the highest daily return correlation during the current year. All specifications include with firm and quarter fixed effects. Robust standard errors (in italics) are clustered at the firm level. ***, ** and * denote statistical significance at 1%, 5% and 10% respectively.

Panel A. Recommendations

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

Rival (i) 0.0739*** 0.0862*** 0.0575*** 0.1475*** 0.0667*** 0.0640***

0.0106 0.0149 0.0094 0.0223 0.0137 0.0171

Affiliated (ii) 0.1406*** 0.1632*** 0.1763*** 0.1970*** 0.1857*** 0.1416***

0.0155 0.0259 0.0122 0.0332 0.0203 0.0132

SOX -0.1459*** -0.1212*** -0.1339*** -0.3530*** -0.1730*** -0.1550***

0.0321 0.0426 0.028 0.0652 0.0311 0.0316

Rival x SOX (iii) -0.1180*** -0.1259*** 0.001 -0.2271*** -0.0875*** -0.0739***

0.0152 0.0213 0.0134 0.03 0.0188 0.0245

Affiliated x SOX (iv) -0.1004*** -0.1630*** -0.0052 -0.1403*** -0.1212*** -0.1020***

0.0241 0.0417 0.0184 0.0479 0.0326 0.0202

BrokerSize -0.0016*** -0.0013*** 0.0002** -0.0029*** -0.0015*** -0.0015***

0.00010 0.00010 0.00010 0.00020 0.00010 0.00010

Ln(#brk) -0.0245*** -0.0232*** -0.0185*** -0.0319*** -0.0242*** -0.0239***

0.0013 0.0017 0.0013 0.0027 0.0012 0.0013

PastFirmRet 0.0075 0.0280*** 0.1070*** 0.0067 0.0077 0.0077

0.0063 0.0101 0.0068 0.0141 0.0061 0.0061

PastMktRet 0.1963*** 0.12 0.0726 0.2798** 0.2592*** 0.2230***

0.0584 0.0884 0.0477 0.1184 0.0562 0.0569

Initiation 0.1880*** 0.1216*** 0.2408*** 0.2690*** 0.1852*** 0.1848*** 0.007 0.0108 0.006 0.0138 0.0069 0.0068 Obs. 166,636 61,784 190,495 163,244 172,781 171,259 Adj. R-sq./Pseudo R-sq. 0.02 0.02 0.03 0.01 0.02 0.02 Difference-in-Differences

Hypothesis (p-value) (ii) - (i) = 0 0.00 0.01 0.00 0.19 0.00 0.00

(iv + ii) - (iii + i) = 0 0.00 0.24 0.00 0.00 0.00 0.03 (iv) - (iii) = 0 0.52 0.41 0.77 0.11 0.36 0.36

36

Table 4 (continued) Panel B. Price targets

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

Rival (i) 0.0155*** 0.0148*** -0.0247*** 0.0753*** 0.0167*** 0.0165***

0.0036 0.0039 0.0039 0.024 0.0039 0.0058

Affiliated (ii) 0.0432*** 0.0433*** 0.0179*** 0.2048*** 0.0662*** 0.0437***

0.0062 0.0073 0.0061 0.0358 0.0076 0.0049

SOX -0.0566*** -0.0699*** -0.0696*** -0.3963*** -0.0561*** -0.0596***

0.0096 0.0109 0.0095 0.0526 0.0093 0.0095

Rival x SOX (iii) -0.0145*** -0.0140*** 0.0348*** -0.0928*** -0.0109*** -0.0109*

0.0038 0.0042 0.0043 0.0274 0.0041 0.0061

Affiliated x SOX (iv) -0.0232*** -0.0294*** 0.0039 -0.1000** -0.0434*** -0.0237***

0.0066 0.0076 0.0064 0.0417 0.0081 0.0052

BrokerSize -0.0006*** -0.0005*** 0.0003*** -0.0040*** -0.0005*** -0.0006***

0.00002 0.00003 0.00026 0.00020 0.00002 0.00002

Ln(#brk) 0.0022*** 0.0018*** 0.0023*** 0.0046*** 0.0021*** 0.0021***

0.0002 0.0003 0.0004 0.0017 0.0002 0.0002

PastFirmRet -0.0285*** -0.0199*** -0.1151*** -0.0491*** -0.0286*** -0.0275***

0.0026 0.0035 0.005 0.0122 0.0025 0.0025

PastMktRet 0.0715*** 0.0840*** 0.2006*** 0.5681*** 0.0820*** 0.0793***

0.0179 0.0204 0.0179 0.1027 0.0173 0.0174

Initiation -0.0111*** -0.0161*** -0.0278*** 0.0313** -0.0117*** -0.0114*** 0.0019 0.0022 0.0019 0.0127 0.0019 0.0019 Obs. 292,087 189,292 305,453 291,240 307,499 303,677 Adj. R-sq./Pseudo R-sq. 0.03 0.03 0.06 0.02 0.03 0.03 Difference-in-Differences

Hypothesis (p-value) (ii) - (i) = 0 0.00 0.00 0.00 0.00 0.00 0.00

(iv + ii) - (iii + i) = 0 0.00 0.00 0.00 0.00 0.00 0.00 (iv) - (iii) = 0 0.22 0.06 0.00 0.87 0.00 0.09

37

Table 5 The affiliation bias relative to rival and neutral recommendations (price targets)

This table presents the results of regressions examining the brokers’ affiliation bias for their clients in recommendations and price targets and the impact of SOX. The dependent variable is the relative recommendation (price target/current price), defined as the distance between the current recommendation (price target/current price) and the consensus (mean recommendation (price target/current price) of the rest of the brokers covering the same firm during the last twelve months). In column (1) the sample is restricted to affiliated and rival recommendations (price targets). In column (2) represents the sample is restricted to affiliated and neutral recommendations (price targets). Affiliated recommendations (price targets) represent recommendations (price targets) on firms that have at least one business deal (IPO, SEO, public debt raising, M&A, syndicated loan) with the broker one year prior and one year post the recommendation (price target) issue. Rival recommendations (price targets) represent recommendations (price targets) on firms that are classified as competitors for at least one affiliated firm and that are among the top five firms with which the affiliated firm had the highest daily return correlation during the current year. Neutral recommendations (price targets) are defined as neither affiliated nor rival. All specifications are run with firm and quarter fixed effects. Robust standard errors (in italics) are clustered at the firm level. *** and ** denote statistical significance at 1% and 5% respectively.

Recommendations Price targets Affiliated+Rival Affiliated+Neutral Affiliated+Rival Affiliated+Neutral (1) (2) (1) (2) Affiliated (i) 0.0747*** 0.1467*** 0.0248*** 0.0443***

0.0218 0.0154 0.0069 0.0062

SOX -0.1626*** -0.1690*** -0.0710*** -0.0557***

0.0577 0.0347 0.0147 0.0107

Affiliated x SOX (ii) 0.0027 -0.1035*** -0.0034 -0.0231***

0.0314 0.0237 0.0073 0.0066

BrokerSize -0.0010*** -0.0017*** -0.0004*** -0.0006***

0.00020 0.00010 0.00003 0.00003

Ln(#brk) -0.0215*** -0.0250*** 0.0020*** 0.0024***

0.0024 0.0014 0.0004 0.0003

PastFirmRet 0.0247** 0.0052 -0.0299*** -0.0294***

0.0126 0.0069 0.0047 0.0029

PastMktRet 0.4308*** 0.1349** 0.0832*** 0.0670***

0.1191 0.0622 0.0287 0.02

Initiation 0.1179*** 0.1996*** -0.0195*** -0.0104*** 0.0163 0.0072 0.0038 0.0021 Obs. 37,627 139,417 77,520 238,655 Adj. R-sq. 0.02 0.02 0.03 0.03 Hypothesis (p-val.)

(i) + (ii) = 0 0.00 0.02 0.00 0.00

38

Table 6 Descriptive statistics of added-to-buy (added-to-sell) list This table describes the number and the frequency of recommendation changes. Added-to-buy (added-to-sell) recommendation changes are defined as positive (negative) recommendation changes that fall into either Buy or Strong Buy (Sell and Strong Sell) categories. Affiliated recommendations (price targets) represent recommendations (price targets) on firms that have at least one business deal (IPO, SEO, public debt offering, M&A, syndicated loan) with the broker one year prior and one year post the recommendation issue. Rival recommendations represent recommendations on firms that are classified as competitors for at least one affiliated and that are among the top five firms with which the affiliated firm had the highest daily return correlation during the current year. Neutral recommendations are defined as neither affiliated nor rival.

Rival Affiliated Neutral Rival Affiliated Neutral Rival Affiliated Neutral Before SOX After SOX 1997-2008 Added-to-buy 2,380 1,046 11,741 2,482 863 10,872 4,862 1,909 22,613

44.97% 44.42% 39.82% 31.81% 34.40% 32.55% 37.13% 39.25% 35.96%

Added-to-sell 211 86 1,660 1,435 316 5,596 1,646 402 7,256

3.99% 3.65% 5.63% 18.39% 12.59% 16.75% 12.57% 8.26% 11.54%

Total 5,292 2,355 29,482 7,803 2,509 33,404 13,095 4,864 62,886

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Table 7 Cumulative abnormal returns associated with added-to-buy and added-to-sell recommendation change events

This table reports the cumulative abnormal returns for various periods surrounding recommendation changes and the associated p-values by type of affiliation. Panel A (B) reports the cumulative abnormal returns for added-to-buy (added-to-sell) recommendation changes. Daily excess return is computed as the difference between stock’s return and the CRSP value-weighted index. Added-to-buy (added-to-sell) recommendation changes are defined as positive (negative) recommendation changes that fall into either Buy or Strong Buy (Sell and Strong Sell) categories. Affiliated recommendations represent recommendations on firms that have at least one business deal (IPO, SEO, public debt offering, M&A, syndicated loan) with the broker one year prior and one year post the recommendation issue. Rival recommendations represent recommendations on firms that are classified as competitors based on TNIC for at least one affiliated and that are among the top five firms with which the affiliated firm had the highest daily return correlation during the current year. Neutral recommendations are defined as neither affiliated nor rival.

Rival (i) Affiliated (ii)

Neutral (iii)

p-val (ii) - (i)

p-val (iii) - (i)

p-val (ii) - (iii) Rival (i) Affiliated

(ii) Neutral

(iii) p-val

(ii) - (i) p-val

(iii) - (i) p-val

(ii) - (iii)

Before SOX After SOX

Panel A. Added-to-buy [-1, 1] 2.13% 2.78% 2.19% 0.04 0.79 0.06 2.30% 3.37% 2.93% 0.00 0.00 0.11

N 2,109 935 10,541

2,173 792 9,639 [-1, 5] 2.37% 3.26% 2.53% 0.04 0.57 0.08 2.34% 3.87% 3.06% 0.00 0.00 0.02

N 1,987 902 10,115

1,999 750 9,114 [-1, 10] 2.53% 3.67% 2.92% 0.03 0.27 0.13 2.54% 4.26% 3.36% 0.00 0.00 0.03

N 1,850 861 9,642 1,833 714 8,502 Panel B. Added-to-sell

[-1, 1] -4.75% -5.26% -4.22% 0.79 0.67 0.60 -2.78% -3.46% -3.65% 0.17 0.01 0.78 N 190 73 1,453

1,252 278 4,717

[-1, 5] -4.36% -4.56% -3.88% 0.93 0.74 0.77 -2.82% -3.68% -3.53% 0.21 0.07 0.85 N 180 70 1,393

1,167 266 4,404

[-1, 10] -4.57% -2.15% -4.33% 0.29 0.89 0.41 -2.48% -3.83% -3.31% 0.11 0.08 0.58 N 172 67 1,333

1,054 249 4,089

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Figure 1 The affiliation bias over time

This figure displays the affiliation bias and the 95% confidence bounds. Panel A (B) reports the affiliation bias for recommendations (price target to current price ratio). The affiliation bias is obtained from quarterly regressions on the effect of brokers’ affiliation (Affiliated and Rival) on their relative recommendation or price target to current price ratio. All specifications include the set of variables defined in Table A2 as well as firm fixed effects. The vertical line corresponds to the enactment of SOX.