The impact of reputation on analysts’ conflicts of interest: Hot versus cold markets

Journal of Banking & Finance 36 (2012) 2190–2202

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The impact of reputation on analysts’ conflicts of interest: Hot versus cold markets Daniel Bradley a, Jonathan Clarke b, John Cooney Jr. c,⇑ a

Department of Finance, University of South Florida, Tampa, FL 33620, USA College of Management, Georgia Institute of Technology, Atlanta, GA 30308, USA c Rawls College of Business, Texas Tech University, Lubbock, TX 79409, USA b

a r t i c l e

i n f o

Article history: Received 20 May 2010 Accepted 26 March 2012 Available online 20 April 2012 JEL classification: G24 Keywords: Analysts’ recommendations Investment banking Conflicts of interest

a b s t r a c t During periods of high IPO underpricing, unaffiliated all-star analysts from high reputation banks issue fewer strong-buy recommendations while unaffiliated all-star analysts from low reputation banks do not change their level of optimism. In contrast, unaffiliated non-star analysts from both high and low reputation banks issue more strong-buy recommendations. Consistent with the results on analyst optimism, the market reacts more favorably to strong-buy recommendations by unaffiliated all-star analysts from high reputation banks than other unaffiliated analysts during high IPO underpricing periods. Finally, we find that unaffiliated non-star analysts from low reputation banks reduce their coverage following an SEO if they are not selected as a part of the managing syndicate. Collectively, our results indicate that during periods of high IPO underpricing unaffiliated analysts face conflicts of interest, but personal-level reputation, and to a lesser extent bank-level reputation, plays a role in reducing this bias. Ó 2012 Elsevier B.V. All rights reserved.

1. Introduction On October 8, 2009, the Wall Street Journal reported that more than a dozen major investment banks settled a $586 million class-action lawsuit for allegedly engaging in unethical behavior related to firms conducting initial public offerings (IPOs) during the 1999–2000 ‘‘bubble period’’ (Smith and Bray, 2009). The allegations revolved around inflating stock prices of several hundred IPO firms through biased analyst research and forcing IPO investors to buy more shares of the IPO firm in the aftermarket in return for future IPO allocations (a practice known as ‘‘laddering,’’ Hao, 2007). Favorable IPO allocations were also given to CEOs and other executives who had the power to return the favor by awarding lucrative future investment banking mandates and to institutions that pay abnormally high commissions (i.e., Liu and Ritter, 2010). In this paper, we focus on one aspect of the above allegations— biased analyst research—and explore the role that personal- and bank-level reputation play in mitigating this problem. We make two main contributions to existing work by Fang and Yasuda (2009). First, we focus on IPOs, where conflicts of interest have been shown to be severe. Second, we focus on unaffiliated analysts. While most regulatory, academic, and popular press scrutiny has focused on analysts affiliated with IPO firms, Bradley et al. (2008) argue that unaffiliated analysts may be more biased than affiliated ⇑ Corresponding author. Tel.: +1 806 834 1536; fax: +1 806 742 3197. E-mail addresses: [email protected] (D. Bradley), [email protected]. edu (J. Clarke), [email protected] (J. Cooney Jr.). 0378-4266/$ - see front matter Ó 2012 Elsevier B.V. All rights reserved. http://dx.doi.org/10.1016/j.jbankfin.2012.03.022

analysts because they do not possess the incumbent’s relationship advantage and thus may have to issue more optimistic research to catch management’s attention. We hypothesize that unaffiliated analysts have at least two incentives to issue biased, optimistic recommendations. First, optimistic recommendations may be used to curry favor with a firm that may need future banking services. Second, and perhaps more importantly, optimistic recommendations may be used to advertise to the rest of the market that the bank is willing to issue optimistic research. These incentives should increase during hot markets when industry-wide investment banking profits are high and the value of analyst coverage increases. Potentially offsetting these incentives are the reputational costs to the bank and the analyst if this bias is revealed.1 Our initial set of tests examines analysts’ incentives to curry favor with the market in general (and particularly with firms planning an IPO). If IPO firms are attracted to analysts that demonstrate a propensity to issue optimistic recommendations and if analyst incentives for biased behavior increase in hot markets, then we should observe analysts becoming more biased during hot markets. We use both aggregate IPO proceeds and average IPO 1 The possibility of regulatory sanctions and penalties provide further incentives to refrain from issuing biased recommendations, especially after the ‘‘Global Analyst Research Settlement’’ was finalized in April of 2003. Under the terms of the Global Settlement, 10 of the top investment banks agreed to a landmark $1.4 billion settlement as restitution, without admitting blame. In addition, the Global Settlement prohibited certain behavior among financial analysts as a means of preventing future abuses.

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underpricing to proxy for hot markets. Direct income from underwriting fees is high during high proceeds periods. Loughran and Ritter (2004), and others, point out that an underwriter’s ability to allocate underpriced IPO shares generates additional indirect profits associated with quid-pro-quo arrangements with the recipients of these underpriced shares. Logically, these indirect profits increase during high underpricing periods. Potential damage to personal or bank reputation could offset the incentive to curry favor. We proxy for the magnitude of personal-level reputation by whether the analyst is on the Institutional Investor All-American team (i.e., an ‘‘all-star’’ analyst) and bank-level reputation by the reputation of the investment bank that employs the analyst. Using a sample of 3334 IPO firms that went public between 1994 and 2009 and 14,553 analyst initiations of coverage up to 1 year following the IPO, our results suggest both personal- and bank-level reputation play a role in mitigating analyst conflicts of interest for unaffiliated analysts. However, personal-level reputation appears to be the more important effect. Specifically, we find that all-star analysts at high reputation banks reduce their optimism during high IPO underpricing periods whereas non-star analysts at high reputation banks become more optimistic. Non-star analysts at low reputation banks also display a similar increase in optimism during high IPO underpricing periods, while all-star analysts at low reputation banks show no significant change in their optimism. In contrast to our findings with average IPO underpricing, our other hot market variable, aggregate IPO proceeds, has no relation to analyst optimism, suggesting that indirect profits associated with being able to allocate underpriced shares provides greater incentives for biased behavior than direct profits from fee income. We next examine how the market reacts to initiations of coverage with a strong-buy recommendation by unaffiliated analysts. We find that during periods of high underpricing, strong-buy initiations issued by all-star analysts from high reputation banks generate significantly higher abnormal returns when compared to other types of analysts. This suggests that market participants value both personal- and bank-level reputation. Similar to the analysis of unaffiliated analysts’ optimism, market reactions are generally unrelated to the amount of aggregate IPO proceeds, again indicating that indirect profits are the more important factor in motivating biased behavior. Our second set of tests examines the incentives of unaffiliated analysts to curry favor with the issuing firm. In this analysis we focus on the subset of IPO firms that have a seasoned equity offering (SEO) within 3 years of their IPO. We compute unaffiliated analyst coverage in the year before the filing date and the year after the offer date of the SEO. Unaffiliated analysts at the IPO stage can either become affiliated if their investment bank is hired as one of the managing underwriters of the SEO or they can remain unaffiliated. If the main purpose for unaffiliated analyst coverage of a particular firm is to provide information to investors about that firm, we would expect an increase in coverage as equity offerings are important events which should generate interest from both analysts and investors. However, if the main purpose of providing coverage is to curry favor, unaffiliated analysts might be expected to decrease coverage as their efforts have now proven to be unsuccessful for two consecutive offerings and switch coverage to another firm where currying favor could yield better results.2

First, we show that all-star analysts at high reputation banks are unlikely to provide coverage unless they are affiliated at either the IPO or SEO stage. That is, a firm is unlikely to get all-star coverage from a high reputation bank unless it hires the bank to provide investment banking services. We next examine coverage decisions for analysts that are unaffiliated at both the IPO and SEO stage and find that high reputation banks do not decrease their coverage following the SEO. For low reputation banks, all-star analysts are less likely to decrease their coverage while non-star analysts are more likely to decrease their coverage. Throughout this paper, we compare the results from the full sample to two subsamples—one that eliminates the 1999–2000 bubble period and another that focuses only on the post-2003 period corresponding to the implementation of regulatory reforms of the Global Settlement. When we eliminate the bubble period, the results tend to become insignificant. This suggests that a severe hot market may be necessary to induce the type of behavior that leads to noticeable conflicts. We also generally find insignificant results when we restrict our analysis to the post-Global Settlement period. While the lack of significance is consistent with the Global Settlement reducing analyst conflicts, there has not been an extreme hot market period since its passage. Consequently, another period of extreme IPO underpricing may be necessary to conclusively test whether the settlement has had the desired effect. Overall, our results suggest that unaffiliated analysts face conflicts of interest when profits from investment banking activities are high, like the extreme hot market observed during 1999– 2000. However, personal-level reputation, and to a lesser extent bank-level reputation, is effective in mitigating the potential bias in analyst behavior. Our evidence is broadly consistent with the results in Fang and Yasuda (2009), who find that personal-level reputation aids in eliminating bias in analyst earnings forecasts. The remainder of this paper will proceed of follows. Section 2 discusses the previous literature and develops the hypotheses tested in this paper. Section 3 describes the data and gives sample descriptive statistics. Section 4 provides empirical results, while Section 5 concludes.

2 As an example, Deutsche Bank Alex Brown was not included in the managing syndicate for Primus Telecom’s IPO on November 7, 1996. However, starting in May of 1998, a non-star analyst from Deutsche Bank Alex Brown began covering Primus Telecom and issued a total of three recommendations in the year prior to the September 10, 1999 filing date for Primus Telecom’s SEO. Deutsche Bank Alex Brown was not included in the underwriting syndicate for Primus Telecom’s SEO and issued no recommendations in the year after the offering.

3 Retail investors might not be aware of these conflicts. Malmendier and Shanthikumar (2007) find that large institutions do not exhibit buying pressure to affiliated buy recommendations, but sell on hold recommendations. Conversely, individual investors exhibit buying pressure to buy recommendations, but no similar sell response for hold recommendations. This suggests that institutions discount buy recommendations of affiliated analysts, and interpret holds as sell recommendations, whereas individual investors take these recommendations at face value.

2. Previous literature and hypotheses development Several academic papers study the extent of analysts’ conflicts of interests by comparing the behavior of analysts affiliated with a firm through an investment banking relationship to those that have no affiliation. Michaely and Womack (1999) use this approach to investigate a sample of IPOs from 1990 to 1991 and find that affiliated analysts are more optimistic than unaffiliated analysts. Moreover, they find that the market discounts affiliated recommendations, consistent with the view that market participants recognize the inherent bias. Finally, they show that the long-run performance following affiliated analyst ‘‘buy’’ recommendations is significantly worse than following unaffiliated analyst ‘‘buy’’ recommendations. All three aspects of their study (ratings, market reactions, and long-run performance) support the conflicts of interest hypothesis.3 Recent work by Fang and Yasuda (2009) examines the extent to which personal- and bank-level reputation can reduce analysts’ conflicts. Specifically, they examine a large, unrestricted sample of earnings forecasts over the period from 1983 to 2002 and argue

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that analysts’ conflicts of interest should be highest during periods of high issuance activity. For a subsample of analysts employed by top-tier investment banks, they find that all-star analysts tend to produce higher quality earnings forecasts than non-star analysts during high-proceeds periods; however, no difference in the quality of earnings forecasts for all-stars versus non-stars in high-proceeds periods is found for a sample of forecasts by analysts employed by lower-tier investment banks. They contend that allstar analysts at top-tier banks ‘‘are able to resist pressures from conflicts of interest better than other analysts, which is consistent with the notion that personal reputation mitigates conflicts of interest.’’ In contrast, non-star analysts working for top-tier banks ‘‘become significantly less accurate during boom years of the new issues market, when the attraction of large year-end bonuses and the temptation to liquidate one’s reputation for profit is high compared to normal years.’’ They conclude that the combination of non-star analysts at top-tier banks produces the lowest quality research in high-proceeds periods. They do not separately examine affiliated versus unaffiliated analysts. In this paper we conjecture that unaffiliated analysts are not unbiased, but have incentives similar to affiliated analysts. We also hypothesize that unaffiliated analyst bias should increase during hot IPO markets (proxied by high average IPO underpricing and high aggregate IPO proceeds) as investment banks seek to capture some of the profits associated with taking firms public. According to Fang and Yasuda (2009) personal-level reputation should be more important than bank-level reputation and conflicts of interest should be most acute for non-star analysts working for highreputation banks. This leads to our first hypothesis: H1. Unaffiliated non-star analysts (and particularly unaffiliated non-star analysts from high reputation banks) are likely to increase their optimism during periods of high IPO underpricing and issuance activity. It is plausible that differences in optimism are a result of selfselection. That is, some analysts may systematically cover firms with better prospects. However, if the optimism is due to bias, the market should discount the recommendation. Based on the arguments presented for the first hypothesis, biased recommendations should increase during hot markets and should be concentrated in the set of non-star analysts, and in particular non-stars from high reputation banks. This leads to our second hypothesis: H2. The market will discount strong-buy recommendations from unaffiliated non-star analysts (and particularly unaffiliated non-star analysts from high reputation banks) during periods of high IPO underpricing and issuance activity. Finally, if unaffiliated analysts curry favor with firms with the hope of getting investment banking business, what happens if they are unsuccessful in the next deal? Do they continue to allocate resources to the firm or do they shift their focus to another firm where currying favor might lead to more favorable results? Like our previous hypotheses, we believe that reputation plays an important role in resource allocation of analysts. H3. Unaffiliated non-star analysts (and particularly unaffiliated non-star analysts from high reputation banks) are likely to reduce their coverage of firms when they are not chosen to be included in the managing syndicate of a subsequent SEO. Our study complements and expands on Fang and Yasuda (2009). First, as mentioned above, we specifically examine unaffiliated analysts rather than combining affiliated and unaffiliated analysts together. Combining the two makes it harder to interpret

the results as the existence of (or lack of) an existing relationship with a firm could create different incentives for affiliated and unaffiliated analysts. Second, we examine recommendations rather than earnings forecasts. The controversy over biased research leading to the Global Settlement and October 2009 class action settlement is based on analysts’ recommendations, not earnings forecasts. Also, with earnings forecasts, analysts might lack the incentive to express optimism. In fact, Chan et al. (2007) argue that during hot periods analysts have an incentive to be more pessimistic so the firm can more easily meet or exceed earnings estimates and thereby manage investors’ expectations regarding its earnings. Thus, the sign of the bias is unclear for earnings forecasts. This is not true with recommendations. Consistent with this view, Malmendier and Shanthikumar (2009) find that earnings forecasts from affiliated analysts are more pessimistic than from unaffiliated analysts, but affiliated analysts’ recommendations are more optimistic than unaffiliated analysts’ recommendations. Third, we examine new initiations of coverage around IPOs – a period when analyst coverage is thought to be most important and when relationships between underwriters and issuing firms are less established. This should increase the relevance of the recommendation for new firms considering IPOs (i.e., the willingness of the analyst to give strong ratings to IPO firms) and also increase the likelihood that an unaffiliated underwriter can win a job as a manager in an upcoming offering of the firm they are covering (i.e., breaking into a managing syndicate may be harder if the issuing firm has had a long relationship with a set of managing underwriters).4 IPO firms were also the focus of the Global Settlement and related scandals. Fourth, we explore different definitions of hot markets by examining periods of high versus low IPO underpricing and high versus low IPO proceeds. Fang and Yasuda (2009) only examine how high versus low proceeds affect analyst behavior. In the case of IPOs, however, the incentive to realize indirect profits from underpricing might outweigh direct profits from fee income. Fifth, Fang and Yasuda (2009) do not examine the market’s reaction to analysts’ recommendations, thus preventing a direct comparison to Michaely and Womack (1999) and subsequent papers. Sixth, since our sample ends with IPOs in 2009 (and analysts’ recommendations in 2010), this allows us to investigate whether regulatory reforms associated with the Global Settlement were effective in reducing analyst bias. Finally, to the best of our knowledge, we are the first to examine analyst coverage decisions when their employing investment bank is not selected for a managing role in a firm’s IPO nor in its first SEO following the IPO.

3. Data and descriptive statistics Our initial sample of IPOs is identified through Thomson’s SDC New Issues database over the period 1994–2009. We delete unit offers, spinoffs, ADRs, closed-end funds, REITs, foreign firms, limited partnerships, non-underwritten offerings or those where we could not identify the lead underwriter(s), IPOs with an offer price at or below $5, IPO firms not on CRSP, and IPO firms that do not receive analyst coverage within 1 year of the IPO date. These filters result in a final sample of 3334 IPO firms. Our sample of 4 Gasparino (2005) suggests that Mary Meeker of Morgan Stanley Dean Witter initiated coverage the day of the EBAY IPO explicitly to beat coverage by Goldman Sachs. In 1998, Morgan Stanley was competing for the IPO mandate, but lost to Goldman. Meeker was quoted as saying, ‘‘When we miss a winning IPO, we should work like crazy (with tons of ideas) to secure a spot as book running manager on follow-on offerings.’’

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D. Bradley et al. / Journal of Banking & Finance 36 (2012) 2190–2202 Table 1 Descriptive statistics. Variable

Mean

Median

Minimum

Maximum

Panel A. IPO characteristics Underpricing IPO proceeds ($ millions) CMrank All-star coverage

26.7% 101.9 7.7 35.1%

11.1% 50.4 8.0 0.0

32.8% 4.7 1.0 0.0

697.5% 17,864 9.0 1.0

Affiliation Panel B. Rating by affiliation Lead Co-manager Affiliated Unaffiliated All obs. Status

Strong-buy (1)

Buy (2)

Hold (3)

Neutral (4)

Sell (5)

N

Average

p-value

1615 2265 3880 2878 6758

1089 1945 3034 2337 5371

409 616 1025 1232 2257

5 11 16 76 92

3 4 7 68 75

3121 4841 7962 6591 14,553

1.62 1.67 1.65 1.80 1.72

.001 .001 .001

Buy

Hold

Neutral

Sell

N

Average

p-value

Panel C. Rating by investment bank reputation High rep 1491 Low rep 5267

Strong-buy

1517 3854

643 1614

23 69

11 64

3735 10,868

1.79 1.69

.001

Panel D. Rating by all-star status All-star Non-star

693 4678

288 1969

8 84

5 70

12,870 1683

1.78 1.71

.001

689 6069

Notes: This table presents descriptive statistics. Panel A presents IPO characteristics. Underpricing is the return from the offer price to the closing price on the first day of trading. IPO proceeds is the amount raised in the IPO in $millions. CMrank is the Carter and Manaster (1990) underwriter reputation rank for the IPO’s lead underwriter(s), as updated by Loughran and Ritter (2004). All-star coverage is a dummy variable equal to one if the IPO receives coverage by an all-star analyst within 1 year of the IPO date. Allstar analysts are identified by Institutional Investor. Panel B presents information on Rating by affiliation. Rating is the recommendation level based on a five-point numerical scale with strong-buy = 1, buy = 2, hold = 3, neutral = 4, and sell = 5. The affiliation is Lead if the recommendation is from an analyst employed by the IPO’s lead underwriter(s) and Co-manager if the recommendation is from an analyst employed by one of the IPO’s co-managers. In the next row, we combine Lead and Co-manager recommendations and designate these as Affiliated analysts. All other recommendations are from Unaffiliated analysts. The last column of Panel B provides the p-values from t-tests of difference of means for Rating between Lead and Unaffiliated, between Co-manager and Unaffiliated, and between Affiliated and Unaffiliated assuming either equal or unequal variances based on equality of variance tests. Panels C and D are similar to Panel B, but provide information on Ratings by investment bank reputation and all-star status, respectively. High rep analysts are employed by investment banks with a Carter and Manaster (1990) underwriter reputation rank (as updated by Loughran and Ritter (2004)) of 9. Similar to Panel B, the last column in Panels C and D gives the p-value from a t-test of the difference of means between High rep and Low rep recommendations, and All-star and Nonstar analysts, assuming either equal or unequal variances based on equality of variance tests. The sample consists of 3334 IPO firms and 14,553 analysts’ recommendations. IPOs are during the period 1994–2009 and are identified through Thomson’s SDC new issues database. Unit offers, spinoffs, ADRs, closed-end funds, REITs, foreign firms, limited partnerships, non-underwritten offerings or those where we cannot identify the lead underwriter(s), issues with offer prices at or below $5, IPO firms not on CRSP, and IPO firms that do not receive analyst coverage within 1 year of the IPO date are excluded from our IPO sample. Analysts’ recommendations are for new initiations of coverage within 1 year of the IPO and are from I/B/E/S.

recommendations is from I/B/E/S. The beginning of our sample in 1994 corresponds to the first full year of coverage by I/B/E/S. We limit our sample of recommendations to new initiations within 1 year of the IPO. Combining initiations with reiterations, upgrades, and downgrades might lead to erroneous inferences. For instance, Irvine (2003) shows that new initiations of coverage generate an incremental market reaction over recommendations on firms already covered by an analyst and Bradley et al. (2008) show that market reactions to reiterations, upgrades, and downgrades are not symmetric. The main advantage of using the I/B/E/S database is that it identifies (1) the specific analyst making the recommendation, (2) the brokerage house that employs the recommending analyst, (3) the date of the recommendation, and (4) the numerical code representing the strength of the recommendation. With this information we can determine the level and frequency of affiliated versus unaffiliated analyst coverage and whether the analyst in question is an all-star or non-star. We define affiliated analysts as those employed by either the IPO’s lead underwriter(s) or one of the comanagers of the IPO; all other analysts are defined as unaffiliated (Bradley et al., 2008). Thus, an analyst that is employed by an underwriter that participates as a non-managing syndicate member of the IPO is coded as unaffiliated. Consistent with Clarke et al. (2007), Cliff and Denis (2004), Fang and Yasuda (2009), and others, we use Institutional Investor’s All-American Research Team to identify all-star analysts defined as the top three analysts plus

runner-ups in the industry. All other analysts are defined as nonstars. Our analysts’ recommendation sample consists of 14,553 initiations (7962 affiliated and 6591 unaffiliated) between 1994 and 2010. With 3334 firms in our sample, the average firm has 4.4 analysts providing coverage within 1 year of the IPO date. Table 1 presents descriptive statistics of our sample. Descriptive statistics about the IPO are given in Panel A. The average for Underpricing, defined as the return from the offer price to the closing price on the first day of trading, is 26.7% and the average IPO raises $101.9 million. This is generally consistent with studies using data over a similar period. We measure the prestige of the IPO’s lead underwriter(s) with CMrank, the Carter and Manaster (1990) underwriter reputation rank, as updated by Loughran and Ritter (2004). The average CMrank for the IPOs’ lead underwriter(s) is 7.7 on a 0 to 9 point scale indicating that our sample of issuing firms was brought to the market by relatively prestigious underwriters. About one-third of our sample receives all-star coverage. The next set of characteristics presented in Panel B of Table 1 relate to analyst affiliation and ratings. 3121 (or 21.4%) of the 14,553 recommendations are from analysts employed by the lead underwriter(s), whereas 4841 (or 33.3%) of the recommendations come from analysts employed by co-managing underwriters. (For the remainder of our analysis, we combine recommendations from lead and co-managing underwriters and denote these as ‘‘affiliated’’ recommendations.) The remaining 6591 (or 45.3%) are from unaffiliated analysts. The frequency distribution for each

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recommendation is based on Rating. Rating takes on a numerical value of 1 to 5, with strong-buy recommendations = 1, buy = 2, hold = 3, neutral = 4, and sell = 5. The average Rating across all analysts is 1.72. This relatively high level of optimism is consistent with Bradley et al. (2003). Affiliated analysts have an average rating of 1.65, significantly more optimistic than the average rating of 1.80 for unaffiliated analysts. In addition, only 23 affiliated underwriter recommendations are either a neutral or a sell (a rating of 4 or 5). The frequency distributions of ratings by the reputation of the analyst’s firm and by the analyst’s all-star status are provided in Panels C and D, respectively. We define high reputation banks as those that have a Carter and Manaster (1990) rank of 9 according to the rankings in Loughran and Ritter (2004); all other analysts are employed by low reputation banks.5 Both high reputation banks and all-star analysts provide less optimistic ratings, on average. This result is consistent with the view that both highly reputable banks and all-star analysts issue less optimistic ratings, potentially due to bank-level and personal-level reputational concerns. Fig. 1 plots a time series of our proxies for hot markets across our sample period: average IPO underpricing and aggregate gross proceeds for IPOs. To calculate these measures, for each calendar day we compute the average underpricing and aggregate gross proceeds during the previous 30 calendar days.6 As argued in Section 2, investment banks and analysts may be more willing to sacrifice their reputation to win banking mandates during high underpricing and high proceeds periods. Thus, the premise of these measures is to proxy for the degree of potential conflicts of interest. Panel A provides a graph of average IPO underpricing whereas Panel B presents aggregate proceeds. IPO underpricing and IPO proceeds both peak during the bubble period, but there are periods of relatively low underpricing and high proceeds (e.g., IPO underpricing is relatively low in 1996, but proceeds are quite high). The correlation between the IPO underpricing level and aggregate IPO proceeds level for the 14,553 recommendations used in our analysis is 0.47. In Table 2, we tabulate for each year in our sample the number of IPOs, total recommendations for these IPO firms, average number of recommendations received per IPO firm, percentage of recommendations from affiliated analysts, the average rating for affiliated and unaffiliated analysts, and the p-value from a t-test of the difference of mean ratings for affiliated and unaffiliated analysts. Since our sample consists of analysts’ recommendations within 1 year of a firm’s IPO, the number of recommendations is closely correlated with IPO activity and peaks in 1999 with 2450 recommendations for 400 firms. The average number of recommendations per firm is 2.7 in 1994 and between 4.2 and 8.7 after 1999. The percentage of recommendations from affiliated analysts averages 54.7% over the full sample period and is highest in 2003 (65.2%) and lowest in 2008 (45.8%). In most years, affiliated analysts are significantly more optimistic than unaffiliated analysts.

5 According to the rankings on Jay Ritter’s website, Alex Brown and Dean Witter have ranks of 8 during the 1992–2000 period while Deutsche Banc Alex Brown and Morgan Stanley Dean Witter have ranks of 9 during this same period. (Alex Brown merged with Deutsche Bank in 1999 and Dean Witter merged with Morgan Stanley in 1997.) We code both Alex Brown and Dean Witter as high reputation banks. If we code Alex Brown and Dean Witter as low reputation banks it does not quantitatively impact our results. 6 The sample of IPOs used to calculate average underpricing and aggregate gross proceeds must meet the following criteria: the firm’s IPO is listed on Thomson’s SDC new issues database, is identified on CRSP, and is underwritten (with the name of the lead underwriter(s) provided by SDC or found through other sources). The firm/ offering must not be a unit offer, spinoff, ADR, closed-end fund, REIT, foreign firm, or a limited partnership. Aggregate proceeds are CPI-adjusted using a 1982–1984 base year. If there are no offerings in the previous 30 days, the value is set equal to the previous valid value.

4. Empirical results 4.1. Analysis of recommendation levels As an initial test of our hypotheses, we examine the impact of bank reputation and all-star status on the likelihood of an analyst’s initiation of coverage with a strong-buy recommendation. We use the following probit regression model:

Strong-Buy ¼ a0 þ a1 ðIPO underpricing lev elÞ þ a2 ðIPO proceeds lev elÞ þ a3 ðLog IPO proceedsÞ þ a4 ðVC dummyÞ þ a5 ðInternet dummyÞ þ a6 ðPrior month returnÞ þ a7 ðPartial adjustmentÞ þ 

ð1Þ

where Strong-Buy takes a value of one if the recommendation is a Strong-Buy, zero otherwise.7 IPO underpricing level (IPO proceeds level) is the average IPO underpricing (CPI-adjusted aggregate IPO proceeds) over the 30 days before the recommendation. Log IPO proceeds is the natural log of the IPO proceeds for the IPO firm. VC dummy and Internet dummy are dummy variables equal to one if the IPO firm had venture capital-backing or is internet-related, respectively. Prior month return is the market-adjusted stock return for the 20 trading days ending three trading days before the recommendation. We use the CRSP value-weighted index as a proxy for the market. In the event the 20 trading day period extends back to the IPO date, we shorten the period by starting on the second day of trading. Partial adjustment is the percentage difference between the offer price and midpoint of the initial file range. We estimate separate regressions for affiliated and unaffiliated analysts, analysts employed by high and low reputation banks (based on the Carter and Manaster (1990) underwriter reputation ranks), and all-star and non-star analysts. The first two variables are those of primary interest in this paper, with the remaining included as control variables. Bhushan (1989) and others find that analysts are attracted to larger firms. So, to control for the potential impact size has on analyst ratings, we include the natural log of IPO proceeds. Because venture capitalists provide a constant pipeline of potential banking business, investment banks may have incentives to issue optimistic reports for VC-backed issuers. Thus, we include VC dummy as a control variable. We also control for internet-related deals because analysts may be optimistic (or pessimistic) about the growth prospects of these firms. We include prior performance as the literature suggests that analysts frequently follow momentum (Jegadeesh et al., 2004) and to control for changing market conditions since the IPO date. Finally, we include Partial adjustment, which measures the demand of the IPO during the bookbuilding period and is a gauge of expected underpricing. Rajan and Servaes (1997) argue that analysts are attracted to firms with high underpricing. Results are presented in Table 3. Panel A presents results for the full sample. Focusing on affiliated analysts (columns 1–4), high reputation all-stars (column 1) become less optimistic in high IPO underpricing periods, but IPO underpricing level is not significant for the other three analyst categories. In addition, IPO proceeds level is insignificant for three of the four analyst types. This indicates that affiliated analysts do not appear to increase their optimism in hot markets. Generally, strong-buy recommendations 7 Starting in 2002, a number of investment banks moved from a five-point rating scale to a three-point scale. I/B/E/S is not consistent in coding the three-point scale. In some cases, recommendations are assigned a 1, 3, or 5 rating, while in other cases, a 2, 3, or 4 rating scheme is used. To address these cases, we code either a 1 or 2 as a strong-buy recommendation if it is the bank’s highest recommendation.

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Fig. 1. Average IPO underpricing and aggregate IPO proceeds through time. The figure plots average lagged 30-day IPO underpricing and proceeds for IPOs from 1994–2010. Panel A presents a plot of the 30-day lagged average for IPO underpricing (the return from the offer price to the closing price on the first day of trading after the offering) and Panel B graphs 30-day lagged aggregate IPO proceeds (the amount raised in the offering in millions adjusted by the CPI index, base-year = 1982–84). When there are no offerings for the previous 30 days, underpricing and aggregate proceeds is set equal to the previous valid value. IPOs are identified through Thomson’s SDC new issues database. Firms must be identified on CRSP and be underwritten (with the name of the lead underwriter(s) provided by SDC or found through other sources). The firm / offering must not be a unit offer, spinoff, ADR, closed-end fund, REIT, foreign firm, or limited partnership.

Table 2 Descriptive statistics by year. Year

# IPOs

Average per firm

% Affiliated

Affiliated rating

Unaffiliated rating

p-value

1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009

270 356 525 381 237 400 310 58 54 60 166 154 153 151 21 38

# Recommendations 741 1136 1645 1335 987 2450 1510 242 310 293 932 785 807 896 153 331

2.74 3.19 3.13 3.50 4.16 6.13 4.87 4.17 5.74 4.88 5.61 5.10 5.27 5.93 7.28 8.71

53.8 51.3 61.5 56.6 54.1 49.4 56.8 61.6 56.1 65.2 59.1 51.2 53.4 52.9 45.8 51.5

1.52 1.56 1.50 1.48 1.54 1.68 1.59 1.61 1.74 1.69 1.92 1.95 1.87 1.69 1.80 1.73

1.73 1.73 1.68 1.63 1.67 1.71 1.80 1.90 1.97 1.96 1.99 2.00 2.10 1.94 1.96 2.07

.01 .01 .01 .01 .01 .29 .01 .01 .01 .02 .24 .48 .01 .01 .26 .01

Total

3334

14,553

4.36

54.7

1.65

1.80

.01

Notes: This table provides the annual number of IPOs, total number of recommendations for these IPO firms, average number of recommendations received per firm, the percentage of affiliated recommendations, and the average Rating for affiliated and unaffiliated analysts. An analyst’s recommendation for a firm is Affiliated if the analyst is employed by an investment bank that was a managing underwriter (lead or co-manager) in the firm’s IPO; otherwise, the analyst’s recommendation is Unaffiliated. Rating is the recommendation level based on a five-point numerical scale with strong-buy = 1, buy = 2, hold = 3, neutral = 4, and sell = 5. The last column provides the p-value from a ttest of difference of means for Rating for affiliated and unaffiliated analysts, assuming either equal or unequal variances based on an equality of variance test. The sample consists of 3334 IPO firms and 14,553 analysts’ recommendations. IPOs are during the period 1994–2009 and are identified through Thomson’s SDC new issues database. Unit offers, spinoffs, ADRs, closed-end funds, REITs, foreign firms, limited partnerships, non-underwritten offerings or those where we cannot identify the lead underwriter(s), issues with offer prices at or below $5, IPO firms not on CRSP, and IPO firms that do not receive analyst coverage within 1 year of the IPO date are excluded from our IPO sample. Analysts’ recommendations are for new initiations of coverage within 1 year of the IPO and are from I/B/E/S.

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are more likely for issues that have had worse recent performance and smaller partial adjustments. The next four columns examine unaffiliated analysts, the main focus of our paper. Similar to the results for affiliated analysts, IPO underpricing level is negative and significant for high reputation all-star analysts. IPO underpricing level is also negative, albeit insignificant, for low reputation all-stars. However, unlike affiliated analysts, IPO underpricing level is positive and significant for both high reputation non-stars and low reputation non-stars. Thus, regardless of bank reputation, non-star analysts become more optimistic during periods of high IPO underpricing. IPO proceeds level is insignificant in all four regressions and no consistent pattern is seen with the control variables. Overall, these results are consistent with the notion that periods of high IPO underpricing generate potential conflicts of interest for unaffiliated analysts and personallevel incentives mitigate these conflicts of interest while bank-level reputation does not. In Panel B, we analyze the sensitivity of our results to two different sub-periods. We first exclude recommendations during the bubble period of 1999–2000 and then consider the post-Global Settlement period (i.e., recommendations from 2003 to 2010).8 To conserve space, we only report the results for unaffiliated analysts. We find that after excluding 1999–2000 recommendations, IPO underpricing level is insignificant. As with the full sample, IPO proceeds level is insignificant for all analyst types. The results for the post-Global Settlement period indicate an insignificant relation between the underpricing level and strong-buy ratings, with the exception of for high reputation non-star analysts. IPO proceeds level is significantly positive for high reputation all-stars and insignificant for the other analyst types. The full sample results support our first hypothesis (H1) that unaffiliated non-stars increase their optimism during hot markets (as defined by average IPO underpricing), but star analysts refrain from issuing overly favorable recommendations under such conditions. Hypothesis H1 also states that the increase in optimism should be especially apparent in recommendations issued by high reputation non-stars. For the full sample we find that both high reputation non-stars and low reputation non-stars increase their optimism in high underpricing periods. We find no significant relation between aggregate IPO proceeds and unaffiliated analyst optimism, consistent with the notion that unaffiliated analyst conflicts of interest are more influenced by the indirect profits associated with the ability to allocated underpriced shares in IPO firms than the direct profits associated with fee income. These results are not robust to the elimination of the bubble period suggesting that extreme hot markets may be needed to induce biased behavior by unaffiliated non-stars. Further, the generally insignificant relation between average IPO underpricing and unaffiliated analyst optimism for the post-Global Settlement period indicate that the associated reforms may have been effective in reducing analyst bias. We provide a full discussion of these sub-period results in Section 4.5.

star analysts (without regard to the reputation of the employing bank) sacrificing reputation during high IPO underpricing periods in an attempt to attract investment banking business while all-star analysts (and especially all-stars from high reputation banks) working to protect reputation. However, it is possible that the difference in optimism could be driven by the quality of firms each type of analyst covers. For example, non-star analysts could be more optimistic in high IPO underpricing periods because they cover better firms during these periods. In this section, we test whether the market recognizes the increased level of optimism by non-star analysts as evidence of bias or from the selection of better firms. If the increased optimism is evidence of bias, the market should discount these strong-buy recommendations. We measure the cumulative market-adjusted return (CAR) to initiations of coverage over a 5-day (2, +2) window centered on the recommendation date following Bradley et al. (2003). We limit our analysis to investigating the market reaction to strong-buy recommendations because it is unlikely that analysts will attempt to curry favor with less than a strong-buy. We use the CRSP valueweighted index as a proxy for the market. As a first look at market reactions, we separate high and low reputation banks and all-star and non-star analysts. Univariate results are presented in Table 4. Panel A of Table 4 examines affiliated recommendations. The market reacts positively to strong-buy recommendations across all groups of analysts. There is no evidence that all-star analysts generate a higher CAR surrounding their initiations of coverage. Panel B examines announcement period CARs for unaffiliated analysts. We again find positive market reactions across all groups of analysts. All-star analysts working for high reputation banks generate the highest CAR of 5.2% when they release their strong-buy recommendations, which is statistically different than the 1.9% corresponding CAR from non-star analysts from high reputation banks. We find no statistical difference in the CARs between all-star and non-star analysts from low reputation banks. Although the results in Table 4 indicate that for unaffiliated analysts, the combination of being an all-star and being employed by a high reputation bank may be important determinants of the market response to recommendations, we are interested in how reputation might influence the market’s assessment during different market conditions. Thus, we introduce the following regression model that includes variables to capture market conditions and other controls:

CAR ¼ a0 þ a1 ðHigh rep no starÞ þ a2 ðLow rep all-starÞ þ a3 ðLow rep no starÞ þ a4 ðIPO underpricing lev elÞ þ a57 ðIPO underpricing lev el interactionsÞ þ a8 ðIPO proceeds lev elÞ þ a911 ðIPO proceeds lev el interactionsÞ þ a12 ðQuiet periodÞ þ a13 ðLog IPO proceedsÞ þ a14 ðVC dummyÞ þ a15 ðInternet dummyÞ þ a16 ðPrior month returnÞ þ a17 ðPartial adjustmentÞ þ 

4.2. Market reaction to strong-buy initiations of analyst coverage The analysis in Table 3 suggests that for unaffiliated analysts, all-stars from high reputation banks reduce their optimism in high IPO underpricing periods and all-stars from low reputation banks do not change their level of optimism. However, non-star analysts from both high and low reputation banks become more optimistic when IPO underpricing is high. This result is consistent with non8 Although the Global Settlement was not finalized until April 2003, analyst scrutiny in the popular press was intense and regulatory reforms were anticipated, so we elected to use the beginning of 2003 as our cutoff of the post-Global Settlement period.

ð2Þ

The dependent variable is CAR. High rep no star is a dummy variable equal to one if the analyst is a non-star employed by a high reputation bank. Low rep all-star, and low rep no star are determined in a similar manner. We include High rep no star, Low rep all-star, and Low rep no star to pick up the incremental market reaction relative to recommendations from all-star analysts from high reputation banks. IPO underpricing level and IPO proceeds level are previously defined. To capture the incremental market reaction effects of each analyst category in different market conditions, we interact each analyst category with the underpricing and proceeds level variables. We also include a dummy for the quiet period as Bradley et al. (2008) find a different market reaction for recommendations at

Table 3 Probit regressions modeling the probability of receiving analyst’s highest rating. Panel A. Full sample Affiliated Analyst type Intercept IPO underpricing level IPO proceeds level Log IPO proceeds VC dummy Internet dummy Prior month return Partial adjustment Pr>ChiSq N

High rep all-star 0.37 0.82 0.02 0.08 0.08 0.24 0.38 0.88

(0.58) (0.04) (0.81) (0.39) (0.62) (0.33) (0.29) (0.00)

<.0001 940

Unaffiliated High rep no star 0.90 0.02 0.04 0.21 0.05 0.22 0.89 1.56

(0.07) (0.94) (0.41) (0.00) (0.67) (0.18) (0.00) (0.00)

Low rep all-star 2.40 0.02 0.19 0.12 0.11 0.67 1.19 1.39

(0.01) (0.97) (0.10) (0.40) (0.67) (0.11) (0.04) (0.01)

Low rep no star 0.21 0.22 0.01 0.04 0.18 0.28 0.28 0.58

(0.44) (0.19) (0.79) (0.32) (0.01) (0.03) (0.03) (0.00)

0.001 371

<.0001 4,803

High rep no star

Low rep all-star

Low rep no star

1.77 1.21 0.12 0.19 0.49 0.90 0.98 0.25

(0.20) (0.08) (0.52) (0.27) (0.20) (0.07) (0.14) (0.62)

0.006 222

High rep no star 0.27 0.85 0.02 0.08 0.08 0.01 0.31 0.67

(0.75) (0.03) (0.87) (0.47) (0.66) (0.96) (0.38) (0.02)

Low rep all-star 1.37 1.63 0.16 0.06 0.40 0.36 1.26 0.06

(0.45) (0.23) (0.50) (0.78) (0.37) (0.66) (0.22) (0.95)

Low rep no star 0.41 0.23 0.05 0.13 0.03 0.28 0.06 0.39

(0.12) (0.07) (0.86) (0.00) (0.61) (0.00) (0.61) (0.00)

<.0001 687

0.077 144

<.0001 5,469

High rep no star

Low rep all-star

Low rep no star

Panel B. Subsample results for unaffiliated analysts Excluding bubble period Analyst type Intercept IPO underpricing level IPO proceeds level Log IPO proceeds VC dummy Internet dummy Prior month return Partial adjustment Pr>ChiSq N

High rep all-star 0.18 0.46 0.06 0.23 0.21 0.55 0.54 0.56

(0.91) (0.74) (0.76) (0.22) (0.60) (0.41) (0.57) (0.41)

0.823 159

Post-Global Settlement

0.56 0.99 0.11 0.19 0.44 0.20 0.80 0.61

(0.53) (0.16) (0.21) (0.14) (0.06) (0.47) (0.15) (0.11)

0.001 448

0.21 0.24 0.16 0.21 0.55 11.66 0.18 1.52

(0.91) (0.91) (0.44) (0.36) (0.22) (0.98) (0.89) (0.18)

0.104 102

0.41 0.09 0.02 -0.16 -0.03 -0.38 0.11 -0.52

(0.10) (0.69) (0.44) (0.01) (0.63) (0.01) (0.55) (0.01)

<.0001 3,925

High rep all-star 27.32 16.04 2.06 0.07 3.30 1.99 5.61 2.44

(0.90) (0.14) (0.09) (0.89) (0.11) (0.31) (0.34) (0.41)

0.116 37

2.08 4.69 0.27 0.35 0.68 0.24 2.35 1.18

(0.29) (0.05) (0.23) (0.13) (0.27) (0.70) (0.14) (0.25)

0.001 123

44.65 17.07 1.12 2.40 21.88 6.08 24.08 2.12

(0.79) (0.26) (0.35) (0.10) (0.68) (0.94) (0.13) (0.57)

0.140 30

0.45 0.01 0.06 0.17 0.25 0.16 0.87 0.75

(0.26) (0.98) (0.17) (0.01) (0.04) (0.30) (0.01) (0.01)

<.0001 1,658

Notes: This table shows estimates from probit regressions modeling the probability of receiving a strong-buy recommendation by affiliation, investment bank reputation, and all-star status. An analyst’s recommendation for a firm is Affiliated if the analyst is employed by an investment bank that was a managing underwriter (lead or co-manager) in the firm’s IPO; otherwise, the analyst’s recommendation is Unaffiliated. High reputation (High rep) analysts are employed by investment banks with a Carter and Manaster (1990) underwriter reputation rank (as updated by Loughran and Ritter (2004)) of 9. All-star analysts are identified by Institutional Investor. IPO underpricing level is the average IPO underpricing over the 30 days before the recommendation. IPO proceeds level is the aggregate IPO proceeds over the 30 days before the recommendation adjusted by the CPI index (base-year = 1982–1984). Log IPO proceeds is the natural log of the IPO proceeds of the issuing firm. VC dummy and Internet dummy are dummy variables equal to one if the IPO firm had venture capital-backing or is internet-related, respectively. Prior month return is the market-adjusted stock return for the 20 trading days (i.e., 1 month) ending three trading days before the recommendation. We use the CRSP value-weighted index as a proxy for the market. In the event the 20 trading day period extends back to the IPO date, we shorten the 20 trading day period by starting on the second day of trading. Partial adjustment is the percentage difference between the offer price and the midpoint of the initial file range. pvalues are reported in parentheses. The sample consists of 3334 IPO firms and 14,553 analysts’ recommendations. IPOs are during the period 1994–2009 and are identified through Thomson’s SDC new issues database. Unit offers, spinoffs, ADRs, closed-end funds, REITs, foreign firms, limited partnerships, non-underwritten offerings or those where we cannot identify the lead underwriter(s), issues with offer prices at or below $5, IPO firms not on CRSP, and IPO firms that do not receive analyst coverage within 1 year of the IPO date are excluded from our IPO sample. Analysts’ recommendations are for new initiations of coverage within 1 year of the IPO and are from I/B/E/S. Panel A presents results for the full sample and Panel B reports the results for unaffiliated analysts in two subsamples: (1) excluding recommendations in the 1999–2000 bubble period, and (2) only including recommendations from the 2003–2010 post-Global Settlement period.

D. Bradley et al. / Journal of Banking & Finance 36 (2012) 2190–2202

<.0001 1,827

High rep all-star

2197

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Table 4 Market reaction to recommendations. Star status

Reputation High

p-value Low

Panel A. Affiliated recommendations All-star 2.30%*** (365) No star 2.83%*** (794) p-value .54

2.17%** (182) 1.77%*** (2539) .67

.90

Panel B. Unaffiliated recommendations All-star 5.23%*** (81) No star 1.93%** (251) p-value .10

3.34%*** (61) 2.30%*** (2485) .47

.39

.04

.64

Notes: This table presents market reactions to a strong-buy recommendation by affiliation, investment bank reputation, and all-star status. We calculate the cumulative market adjusted return (CAR) over 5 days (2, +2) centered on the recommendation date. We use the CRSP value-weighted index as a proxy for the market. An analyst’s recommendation for a firm is Affiliated if the analyst is employed by an investment bank that was a managing underwriter (lead or comanager) in the firm’s IPO; otherwise, the analyst’s recommendation is Unaffiliated. High reputation analysts are employed by investment banks with a Carter and Manaster (1990) underwriter reputation rank (as updated by Loughran and Ritter (2004)) of 9. All-star analysts are identified by Institutional Investor. The number of observations is in parentheses. The last column (and bottom row in each panel) provides p-values from a t-test of difference in means for CAR for high and low reputation analysts (and all-star and non-star analysts), assuming either equal for unequal variances based on an equality of variance test. The sample consists of 3334 IPO firms and 14,553 analysts’ recommendations. IPOs are during the period 1994–2009 and are identified through Thomson’s SDC new issues database. Unit offers, spinoffs, ADRs, closed-end funds, REITs, foreign firms, limited partnerships, non-underwritten offerings or those where we cannot identify the lead underwriter(s), issues with offer prices at or below $5, IPO firms not on CRSP, and IPO firms that do not receive analyst coverage within 1 year of the IPO date are excluded from our IPO sample. Analysts’ recommendations are for new initiations of coverage within 1 year of the IPO and are from I/B/E/S. ** Statistically different than zero at the 5% level. *** Statistically different than zero at the 1% level.

the end of the quiet period (which are mostly from affiliated analysts) compared to other recommendations.9 Other variables are previously defined and generally follow the model in Bradley et al. (2008). In the first two columns of Table 5, we present results for the full sample. In the first model, we consider affiliated analysts. The coefficients on High rep no star and Low rep no star are both equal to 0.02 and are significant at the 7% level indicating that the market reaction is 2 percentage points lower for these analysts compared to high reputation all-star analysts. IPO underpricing level and IPO proceeds level are not significant. The interaction term between the underpricing level and high reputation non-stars is positive and significant. Consistent with Bradley et al. (2008) the quiet period dummy is positive and significant. We also find a negative relation between CAR and the partial adjustment and a positive relation between CAR and venture capital-backing and internet firms. In the next column we examine unaffiliated recommendations. We find that the dummy variables for all three analyst categories are insignificant. However, when interacted with the underpricing level, they are all significantly negative. This indicates that during high underpricing periods, the market discounts strong-buy recommendations from high reputation non-stars, low reputation all-stars, and low reputation non-stars compared to strong-buy 9 For recommendations before (after) 2003, the quiet period is defined as 30 (45) calendar days after the IPO. This change is related to regulatory reforms that extended the quiet period from 25 to 40 days.

recommendations from all-star analysts at high reputation banks, presumably because market participants perceive these recommendations as biased. The interaction of IPO proceeds level and High rep no star is positive and significant. Similar to the regression examining affiliated analysts, the market greets recommendations on internet firms more positively and CAR is negatively related to the prior 1-month return and the partial adjustment. According to hypothesis H2, the market should discount strongbuy recommendations from non-star analysts (and particularly non-star analysts from high reputation banks) during periods of high IPO underpricing and issuance activity. As predicted, we find that strong-buy recommendations from unaffiliated non-star analysts (from both high reputation and low reputation banks) are discounted by the market during high IPO underpricing periods. However, we find that strong-buy recommendations from unaffiliated all-star analysts at low reputation banks are also discounted. We do not find that the market especially discounts strong-buy recommendations from unaffiliated high reputation non-star analysts. As in Table 3, there are generally insignificant results when a hot market is defined by high IPO proceeds, again indicating that analyst bias is more induced by the indirect income associated with the ability to allocate underpriced shares than by direct fee income. In sum, the full sample results show that both bank- and personal-level reputation are important determinants of the market reaction to unaffiliated analysts’ recommendations. In the next four columns we exclude the bubble period and then consider the post-Global Settlement period. For unaffiliated analysts, the interaction terms of IPO underpricing level and the three analyst categories result in insignificant coefficients. Thus, similar to the results in Table 3, the lack of significance when excluding the bubble period may be consistent with the necessity of extreme underpricing to inducing biasness in unaffiliated analysts. The lack of significance for the post-Global Settlement period may be consistent with post-Global Settlement reforms reducing analyst bias. With the exception of the interaction with low reputation nonstars in the subsample excluding the bubble, we also find no relation between IPO proceeds level and the three analyst categories during the two sub periods, consistent with IPO proceeds have little effect on analyst behavior. 4.3. The likelihood of receiving all-star coverage from reputable banks Holding all else constant, IPO firms value coverage from reputable analysts. However, as our descriptive statistics in Table 1 indicate, only about one-third of IPO firms receive coverage from allstar analysts. In the next analysis, we examine the subset of IPO firms that have an SEO within 3 years of the IPO date to see what types of analysts provide coverage for the firm. (If there is more than one SEO, we only examine the first SEO in this 3-year period. The SEO offer date must be on or before 2009. We also require an analyst to have issued at least one recommendation in the year prior to the SEO filing date, i.e., the filing date of the firm’s registration statement with the SEC. We impose this filter since analysts that are currying favor with the issuing firm are most likely to be providing coverage prior to the SEO. Unlike the rest of the analyses in this paper, we examine all recommendations, not just initiations. There are 1119 firms out of our total sample of 3334 IPO firms that meet these requirements.) Of interest is how many firms can attract coverage from reputable analysts if they do not hire the analyst’s bank at the IPO or SEO stage. In Table 6, we present unique bank-firm pairs for each analyst category. For the full sample, there are 677 all-stars from high reputation banks providing recommendations for 447 firms (in parenthesis). In the next row, we eliminate analysts that were not affiliated at the IPO stage. For high reputation all-star analysts, this drops the sample size to 286 bank-firm pairings for 217 firms. If we

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D. Bradley et al. / Journal of Banking & Finance 36 (2012) 2190–2202 Table 5 Regression results for market reactions to strong-buy recommendations. Full sample

Excluding bubble

Affiliated

Unaffiliated

Affiliated

Intercept High rep no star Low rep all-star Low rep no star IPO underpricing level IPO underpricing level x High rep no star IPO underpricing level x Low rep all-star IPO underpricing level x Low rep no star IPO proceeds level IPO proceeds level x High rep no star IPO proceeds level x Low rep all-star IPO proceeds level x Low rep no star Quiet period Log IPO proceeds VC dummy Internet dummy Prior month return Partial adjustment

0.05 0.02 0.02 0.02 0.03 0.07 0.04 0.02 0.00 0.00 0.00 0.00 0.02 0.00 0.01 0.02 0.00 0.05

0.08 0.02 0.03 0.01 0.11 0.11 0.22 0.11 0.01 0.00 0.00 0.00 0.00 0.00 0.00 0.02 0.04 0.02

0.01 0.01 0.01 0.02 0.07 0.06 0.08 0.08 0.00 0.00 0.00 0.00 0.01 0.00 0.01 0.01 0.01 0.04

Adjusted R2 N

0.026 3,867

(0.04) (0.07) (0.25) (0.05) (0.29) (0.03) (0.42) (0.46) (0.38) (0.36) (0.01) (0.06) (0.01) (0.11) (0.01) (0.04) (0.65) (0.01)

(0.01) (0.49) (0.42) (0.80) (0.01) (0.02) (0.01) (0.01) (0.03) (0.01) (0.50) (0.40) (0.89) (0.27) (0.41) (0.02) (0.01) (0.01)

0.026 2,849

(0.63) (0.29) (0.78) (0.19) (0.29) (0.38) (0.29) (0.21) (0.59) (0.01) (0.85) (0.01) (0.01) (0.84) (0.02) (0.49) (0.33) (0.01)

0.014 3,068

Post-Global Settlement Unaffiliated

Affiliated

Unaffiliated

0.03 0.02 0.01 0.01 0.03 0.05 0.00 0.05 0.00 0.00 0.00 0.00 0.00 0.00 0.01 0.01 0.04 0.03

0.05 0.03 0.02 0.02 0.17 0.22 0.26 0.18 0.00 0.00 0.00 0.00 0.00 0.01 0.00 0.01 0.01 0.05

0.05 0.01 0.02 0.00 0.10 0.11 0.04 0.12 0.00 0.00 0.00 0.00 0.00 0.00 0.01 0.00 0.05 0.04

(0.44) (0.52) (0.74) (0.74) (0.86) (0.77) (0.99) (0.74) (0.57) (0.21) (0.99) (0.03) (0.76) (0.41) (0.15) (0.26) (0.01) (0.01)

0.022 2,135

(0.14) (0.10) (0.54) (0.54) (0.05) (0.03) (0.09) (0.05) (0.25) (0.53) (0.29) (0.50) (0.97) (0.07) (0.98) (0.39) (0.74) (0.01)

0.033 1,244

(0.20) (0.86) (0.69) (0.98) (0.64) (0.62) (0.92) (0.57) (0.31) (0.66) (0.48) (0.90) (0.71) (0.31) (0.12) (0.66) (0.01) (0.01)

0.019 857

Notes: This table shows estimates from regressions of market reactions to a strong-buy recommendation. The dependent variable is the cumulative market adjusted return (CAR) over a 5-day (2, +2) window centered on the recommendation date. We use the CRSP value-weighted index as a proxy for the market. An analyst’s recommendation for a firm is Affiliated if the analyst is employed by an investment bank that was a managing underwriter (lead or co-manager) in the firm’s IPO; otherwise, the analyst’s recommendation is Unaffiliated. High rep analysts are employed by investment banks with a Carter and Manaster (1990) underwriter reputation rank (as updated by Loughran and Ritter (2004)) of 9. All-star analysts are identified by Institutional Investor. The three dummy variables High rep no star, Low rep all-star, and Low rep no star represent bank reputation and all-star status. IPO underpricing level is the average IPO underpricing over the 30 days before the recommendation. IPO proceeds level is the aggregate IPO proceeds over the 30 days before the recommendation adjusted by the CPI index (base-year = 1982–84). Each of the three analyst categories is interacted with IPO underpricing level and IPO proceeds level. Quiet period is a dummy variable equal to one if a recommendation occurs within 30 calendar days of the IPO for recommendations before 2003 and 45 days after 2003. Log IPO proceeds is the natural log of the IPO proceeds. VC dummy and Internet dummy are dummy variables equal to one if the IPO firm had venture capital-backing or is internet-related, respectively. Prior month return is the market-adjusted stock return for the 20 trading days (i.e., 1 month) ending three trading days before the recommendation. We use the CRSP value-weighted index as a proxy for the market. In the event the 20 trading day period extends back to the IPO date, we shorten the 20 trading day period by starting on the second day of trading. Partial adjustment is the percentage difference between the offer price and the midpoint of the initial file range. p-values are reported in parentheses. The sample consists of 3334 IPO firms and 14,553 analysts’ recommendations. IPOs are during the period 1994– 2009 and are identified through Thomson’s SDC new issues database. Unit offers, spinoffs, ADRs, closed-end funds, REITs, foreign firms, limited partnerships, non-underwritten offerings or those where we cannot identify the lead underwriter(s), issues with offer prices at or below $5, IPO firms not on CRSP, and IPO firms that do not receive analyst coverage within 1 year of the IPO date are excluded from our IPO sample. Analysts’ recommendations are for new initiations of coverage within 1 year of the IPO and are from I/B/E/S. Columns 1 and 2 show regression results for the full sample. Columns 3 and 4 show regression results excluding recommendations in the 1999–2000 bubble period. Columns 5 and 6 show regression results only including recommendations from the 2003–2010 post-Global Settlement period.

Table 6 Who gets coverage from reputable analysts and banks?

Full sample No IPO affiliation No IPO or SEO affiliation No affiliation/total coverage

High rep all-star

High rep no star

Low rep all-star

Low rep no star

677 (447) 286 (217) 149 (128) 22.0%

1299 (692) 813 (509) 531 (372) 40.9%

325 (269) 178 (151) 119 (99) 36.6%

6716 (1157) 5314 (1106) 4386 (1030) 65.3%

Notes: This table presents the number and types of analysts that cover sample firms based on their affiliation status. High reputation (High rep) analysts are employed by investment banks with a Carter and Manaster (1990) underwriter reputation rank (as updated by Loughran and Ritter (2004)) of 9. All-star analysts are identified by Institutional Investor. The sample consists of 1119 firms (out of the 3334 IPO firms in our sample) that conducted an SEO with an offer date within 3 years of the IPO date. The SEO offer date must be on or before 2009. If the firm has more than one SEO in this period, we use the first SEO. The firm conducting the SEO must have received at least one analyst recommendation in the year before the SEO filing date. IPOs are during the period 1994–2009 and are identified through Thomson’s SDC new issues database. Unit offers, spinoffs, ADRs, closed-end funds, REITs, foreign firms, limited partnerships, non-underwritten offerings or those where we cannot identify the lead underwriter(s), issues with offer prices at or below $5, IPO firms not on CRSP, and IPO firms that do not receive analyst coverage within 1 year of the IPO date are excluded from our IPO sample. Analysts’ recommendations are from I/B/E/S. The first row gives the numbers of bank-firm pairings, with the numbers of total firms below in parentheses, across the four analyst categories for the full sample of 1119 firms. The second row gives the results for investment banks that were not managing underwriters (lead or co-manager) in the firm’s IPO. The third row gives the corresponding results for investment banks that were not managing underwriters in both the firm’s IPO and first SEO. The last row gives the number of bank-firm pairings in the third row (i.e., no IPO or SEO affiliation) as a percentage of the number of bank-firm pairings in the first row (i.e., the full sample).

further restrict the sample to analysts not affiliated with either the IPO or SEO, this further drops the size to 149 bank-firm pairings for 128 firms. Thus, out of 677 total bank-firm pairs, only 22% (149/

677) of all-stars from high reputation banks are unaffiliated for both the IPO and SEO. The corresponding percentages for high reputation non-stars, low reputation all-stars, and low reputation

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Table 7 Logistic regressions for coverage decisions around SEO. Analyst type Panel A. Full sample Intercept Stay unaffiliated dummy VC dummy Internet dummy Log SEO proceeds Log days from IPO # of observations Pr > Chisq

High rep all-star

High rep no star

Low rep all-star

Low rep no star

1.84 (0.12) 0.11 (0.73) 0.23 (0.26) 0.23 (0.39) 0.01 (0.95) 0.30 (0.08) 464 0.446

1.40 (0.17) 0.08 (0.69) 0.29 (0.09) 0.03 (0.89) 0.01 (0.93) 0.27 (0.06) 689 0.377

1.93 (0.27) 0.62 (0.09) 0.03 (0.92) 0.41 (0.35) 0.24 (0.12) 0.08 (0.78) 206 0.230

1.69 (0.00) 0.21 (0.01) 0.02 (0.83) 0.29 (0.00) 0.11 (0.01) 0.25 (0.00) 3145 <.0001

Excluding bubble Analyst type Panel B. Subsamples Intercept Stay unaffiliated dummy VC dummy Internet dummy Log SEO proceeds Log days from IPO # of observations Pr > Chisq

Post-Global Settlement

High rep allstar

High rep no star

Low rep allstar

Low rep no star

High rep allstar

High rep no star

Low rep allstar

Low rep no star

1.46 (0.26) 0.36 (0.32) 0.34 (0.13) 0.40 (0.35) 0.02 (0.84) 0.25 (0.19) 365 0.370

1.98 (0.09) 0.15 (0.53) 0.29 (0.12) 0.27 (0.40) 0.01 (0.94) 0.37 (0.03) 549 0.178

0.81 (0.67) 0.66 (0.09) 0.04 (0.91) 0.40 (0.59) 0.28 (0.10) 0.15 (0.63) 169 0.271

1.49 (0.00) 0.23 (0.01) 0.07 (0.42) 0.22 (0.14) 0.10 (0.02) 0.22 (0.00) 2446 <.0001

2.05 (0.38) 0.86 (0.17) 0.02 (0.96) 0.11 (0.87) 0.04 (0.84) 0.39 (0.22) 157 0.696

0.27 (0.90) 0.35 (0.40) 0.26 (0.45) 0.50 (0.32) 0.15 (0.39) 0.24 (0.38) 239 0.569

3.95 (0.27) 1.35 (0.10) 0.98 (0.19) 0.33 (0.77) 0.38 (0.27) 0.94 (0.08) 64 0.173

1.71 (0.03) 0.16 (0.23) 0.16 (0.31) 0.17 (0.43) 0.11 (0.11) 0.31 (0.00) 1151 0.010

Notes: This table shows estimates from a logistic regression on analyst coverage around the first SEO following the IPO. The dependent variable takes on a value of one if the number of recommendations made by a unique bank on a firm in the 1-year period following the SEO offer date (post-SEO period) is less than in the 1-year period preceding the SEO filing date (pre-SEO period), 0 otherwise. High rep analysts are employed by investment banks with a Carter and Manaster (1990) underwriter reputation rank (as updated by Loughran and Ritter (2004)) of 9. All-star analysts are identified by Institutional Investor. Stay unaffiliated dummy is a dummy variable equal to one if the analyst is unaffiliated with both the IPO and the SEO. VC dummy and Internet dummy are dummy variables equal to one if the IPO firm had venture capital-backing or is internet-related, respectively. Log SEO proceeds is the natural log of the SEO proceeds. Log days from IPO is the natural log of the number of calendar days between the IPO date and SEO file date. p-values are reported in parentheses. The sample consists of analysts’ recommendations before and after 1119 SEOs conducted by the 3334 IPO firms in our sample. The SEO offer date must be within 3 years of the IPO date and with the SEO offer date on or before 2009. If the firm has more than one SEO in this period, we use the first SEO. The firm conducting the SEO must have received at least one analyst recommendation in the year before the SEO filing date. IPOs are during the period 1994–2009 and are identified through Thomson’s SDC new issues database. Unit offers, spinoffs, ADRs, closed-end funds, REITs, foreign firms, limited partnerships, non-underwritten offerings or those where we cannot identify the lead underwriter(s), issues with offer prices at or below $5, IPO firms not on CRSP, and IPO firms that do not receive analyst coverage within 1 year of the IPO date are excluded from our IPO sample. Analysts’ recommendations are from I/B/E/S. Panel A presents results for the full sample and Panel B reports the results excluding SEOs in the 1999–2000 bubble period (columns 1–4) and only including SEOs from the 2003–2010 post-Global Settlement period (columns 5–8).

non-stars are 41%, 37%, and 65%, respectively. Thus, a firm is unlikely to attract coverage from an all-star employed by a high reputation bank unless the bank is affiliated with the firm at either the IPO or SEO stage. 4.4. Coverage decisions by unaffiliated analysts after SEO issuance The previous analysis suggests that unaffiliated analyst coverage from all-star analysts employed by high reputation banks is unlikely. A natural extension of the analysis is to examine analysts’ coverage decisions before and after the firm’s first SEO following their IPO. If the analyst covered the firm to curry favor prior to the SEO, but was unsuccessful (i.e., the analyst’s bank was not selected as a lead or co-managing underwriter in the SEO), does she reduce her effort as a result? Table 7 gives results from a logistic regression using the sample of recommendations from the analysis in Table 6. The dependent variable in each specification takes on a value of one if the number of recommendations made by a bank is

less in the year after the SEO offer date than in the year before the SEO filing date, zero otherwise. Independent variables include Stay unaffiliated dummy, a dummy variable indicating whether an unaffiliated bank at the IPO stage stays unaffiliated in the SEO, VC and internet dummies, the natural log of the proceeds of the SEO, and the natural log of the number of days between the IPO and SEO. VC dummy and Internet dummy are included as control variables since strong ties exist between investment banks and VC firms and investor interest in internet firms could influence whether analysts maintain coverage. We include Log SEO proceeds since larger issues could generate more interest from analysts and investors. We include Log days from IPO since more time between the IPO and SEO could affect the likelihood a bank is affiliated (or unaffiliated) for both the IPO and SEO. If the main purpose for unaffiliated analyst coverage of a firm is to provide information to investors, we would expect no change in coverage from before to after the SEO if the analyst remains unaffiliated. In fact, an increase in coverage may be expected as equity

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offerings are important events which should generate interest by both analysts and investors.10 However, if the main purpose for providing coverage is to curry favor with the issuing firm, we would expect analysts to decrease coverage if their bank remains unaffiliated. Since providing coverage with the issuing firm has proven to be unproductive (i.e., the bank was not selected as a lead or co-managing underwriter for the IPO and also for the firm’s first SEO, the bank may feel that the probability of being selected in any future offering is low), the analyst may switch coverage to another firm where currying favor could yield better results. We estimate the logistic regressions over four subsamples: recommendations by all-star analysts from high reputation banks (column 1), non-star analysts from high reputation banks (column 2), all-star analysts from low reputation banks (column 3), and non-star analysts from low reputation banks (column 4). Results for the full sample are presented in Panel A of Table 7. The main variable of interest is Stay unaffiliated dummy. All-star and non-star analysts at high reputation banks do not decrease their coverage intensity after the SEO if they remain unaffiliated. All-stars from low reputation banks are less likely to decrease coverage (i.e., they are more likely to maintain or increase coverage) if they remain unaffiliated following an SEO. However, low reputation non-stars are more likely to decrease coverage following the SEO if they remain unaffiliated. Providing coverage before the SEO, but decreasing/dropping coverage after the SEO is consistent with unaffiliated low reputation banks using their non-star analysts to curry favor with issuing firms prior to the SEO.11 When not selected as part of the managing syndicate of the firm’s first SEO, they decrease or drop coverage to reallocate resources elsewhere. Conversely, low reputation all-star analysts maintain or increase their coverage if they remain unaffiliated at the SEO stage, suggesting that providing information to clients is presumably more important than currying favor.12 In Panel B, we exclude SEOs during the bubble period and also examine the behavior of analysts for SEOs during the post-Global Settlement regime. The results are not affected by excluding the bubble period. Stay unaffiliated dummy is negative and significant for low reputation all-stars and positive and significant for low reputation non-stars. In the post-Global Settlement period, the coefficients have the same sign, but are generally insignificant. With respect to our third hypothesis (H3), it appears that personal-level reputation matters for low reputation underwriters, but not for high reputation underwriters. Moreover, unlike the results we find for analyst ratings and the market reactions to analysts’ recommendations, extreme underpricing does not appear to influence the coverage decisions of analysts. However, the lack of significance for the post-Global Settlement period indicates the analyst coverage decisions are more driven by incentives to provide information to investors rather than to curry favor with firms. 4.5. Impact of the bubble period and Global Settlement In the three main tests in our paper that focus on analyst optimism (Table 3), the market reaction to strong-buy recommendations (Table 5), and the propensity to reduce analyst coverage if 10 Consistent with this conjecture, we find that for all analyst groups combined, there is an overall increase in the number of recommendations in the year following the SEO offer date compared to the year before the SEO filing date. 11 Although it is unlikely that a low reputation bank would be chosen to lead an SEO, they may have an incentive to provide unaffiliated coverage to be invited into a comanaging position (Ljungqvist et al., 2009). 12 We also examine coverage within 6 months before and after the SEO. In this case, we find that both high reputation non-stars and low reputation non-stars are more likely to decrease coverage after the SEO if they remain unaffiliated. All-stars from both high and low reputation banks do not decrease their coverage frequency.

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the bank, which is unaffiliated with the firm’s IPO, is not chosen as a managing underwriter in the firm’s first subsequent SEO (Table 7), we find that the results are sensitive to the period chosen. When we exclude the bubble period from the sample or examine only the post-Global Settlement period, most of the statistical significance disappears. One interpretation of the evidence from the subsample in which the bubble period is excluded is that extreme hot markets are needed to induce the biasness that we document. When the post-Global Settlement period is considered, one may conclude that the regulatory changes enforced by the Global Settlement had the intended effect on conflicts of interest pertaining to analyst behavior. While the settlement directly related to affiliated analyst behavior, our results are consistent with the notion that it may have influenced unaffiliated analyst behavior as well. Presumably the scrutiny placed on the industry and passage of the Global Settlement at least indirectly influenced all analysts, regardless of their affiliation or reputation status. We however caution the reader from over-interpreting these results. First, excluding the bubble period eliminates a significant portion of the recommendation sample. These 2 years represent more than one quarter of the observations. Second, the time since the Global Settlement is relatively short and IPO underpricing levels have been relatively low, especially in comparison to the 1999– 2000 bubble period. To more definitively assess the effectiveness of the Global Settlement on analysts’ conflicts, a longer time period, and one that includes high levels of IPO underpricing, needs to be examined.

5. Conclusion Regulators, the popular press, and most academic research contends that conflicts of interest are isolated to analysts affiliated with an issuing firm through an investment banking relationship, while unaffiliated analysts are viewed as unbiased. We contend however that unaffiliated analysts are not unbiased but have incentives similar to affiliated analysts. The purpose of our study is to examine what factors mitigate this bias. Using a sample of analysts’ recommendations for firms conducting their IPO between 1994 and 2009, our results suggest that both personal- and banklevel reputation are important in explaining unaffiliated analyst bias, but personal-level reputation appears to be the more important of the two. Specifically, we find that unaffiliated all-star analysts from high reputation banks tend to reduce their optimism during high IPO underpricing periods while the level of optimism for unaffiliated all-star analysts from low reputation banks remains unchanged. In contrast, non-star analysts increase their optimism during high IPO underpricing periods with bank-level reputation having no discernible impact. We next examine how the market reacts to initiations of coverage with a strong-buy recommendation. For unaffiliated analysts during periods of high IPO underpricing, strong-buy initiations issued by all-star analysts from high reputation banks generate significantly higher abnormal returns when compared to other types of analysts. This suggests that market participants value both bank- and personal-level reputation. Finally, we examine unaffiliated analyst coverage decisions before and after the first SEO following the IPO. We find that non-star analysts from low reputation banks reduce coverage if they are not included in the managing syndicate of the SEO, consistent with these analysts using their coverage to curry favor with issuing firms. However, analysts from high reputation banks (both all-stars and non-stars) do not decrease the level of their coverage if they remain unaffiliated at the SEO stage and all-star analysts from low reputation banks maintain or increase their coverage. This is

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consistent with the provision of information to investors being the main reason for providing coverage around an SEO for these other three types of analysts. We find generally insignificant results when either excluding recommendations made during the 1999–2000 bubble period or limiting the analysis to the period after the Global Settlement. The lack of significance when excluding the bubble period supports the notion that extreme hot IPO markets may be needed to induce bias behavior in unaffiliated analysts. The lack of significance during the post-Global Settlement period is consistent with the view that the regulatory changes associated with the Global Settlement had the desired impact of reducing analyst conflicts. However, the amount of time since its passage is limited and the IPO market has been relatively weak. To make a definitive conclusion, more time, along with a more robust IPO market and more variation in underpricing is necessary. Acknowledgements We thank the editor, Ike Mathur, an anonymous referee, Brian Adams, Mike Cliff, François Derrien, Maureen McNichols, Sandra Mortal, Patricia O’Brien, Andy Puckett, Jay Ritter, Ajay Singh, Mike Stegemoller, Tjalling van der Goot and seminar participants at Clemson University, Mississippi State University, Texas Tech University, University of Missouri, the 2006 Financial Management Association meeting, and the 2007 Financial Management Association European Conference meeting for useful comments and suggestions. We also thank Jia Geng for helpful research assistance. An earlier version of this paper was titled ‘‘Do analysts curry favor with issuing firms?’’ All errors are our own. References Bhushan, R., 1989. Firm characteristics and analyst following. Journal of Accounting and Economics 11, 255–274.

Bradley, D.J., Jordan, B.D., Ritter, J.R., 2003. The quiet period goes out with a bang. Journal of Finance 58, 1–36. Bradley, D.J., Jordan, B.D., Ritter, J.R., 2008. Analyst behavior following IPOs: the ‘‘bubble period’’ evidence. Review of Financial Studies 21, 101–133. Carter, R.B., Manaster, S., 1990. Initial public offerings and underwriter reputation. Journal of Finance 45, 1045–1067. Chan, L.K.C., Karceski, J.J., Lakonishok, J., 2007. Analysts’ conflicts of interest and biases in earnings forecasts. Journal of Financial and Quantitative Analysis 42, 893–914. Clarke, J.E., Khorana, A., Patel, A., Rau, P.R., 2007. The impact of all-star analyst job changes on their coverage choices and investment banking deal flow. Journal of Financial Economics 84, 713–737. Cliff, M.T., Denis, D.J., 2004. Do initial public offering firms purchase analyst coverage with underpricing? Journal of Finance 59, 2871–2901. Fang, L., Yasuda, A., 2009. The effectiveness of reputation as a disciplinary mechanism in sell-side research. Review of Financial Studies 22, 3735–3777. Gasparino, C., 2005. Blood on the Street: The Sensational Inside Story of How Wall Street Analysts Duped a Generation of Investors. Free Press, New York, NY. Hao, G.Q., 2007. Laddering in initial public offerings. Journal of Financial Economics 85, 102–122. Irvine, P.J., 2003. The incremental impact of analyst initiation of coverage. Journal of Corporate Finance 9, 431–451. Jegadeesh, N., Kim, J., Krische, S.D., Lee, C.M.C., 2004. Analyzing the analysts: when do recommendations add value? Journal of Finance 59, 1083–1124. Liu, X., Ritter, J.R., 2010. The economic consequences of IPO spinning. Review of Financial Studies 23, 2024–2059. Ljungqvist, A., Marston, F., Wilhelm Jr., W.J., 2009. Scaling the hierarchy: how and why investment banks compete for syndicate co-management appointments. Review of Financial Studies 22, 3977–4007. Loughran, T., Ritter, J.R., 2004. Why has IPO underpricing changed over time? Financial Management 33 (3), 5–37. Malmendier, U., Shanthikumar, D., 2007. Are small investors naive about incentives? Journal of Financial Economics 85, 457–489. Malmendier, U., Shanthikumar, D., 2009. Do Security Analysts Speak in Two Tongues? Working Paper, University of California, Berkeley and Harvard University. Michaely, R., Womack, K.L., 1999. Conflict of interest and the credibility of underwriter analyst recommendations. Review of Financial Studies 12, 653– 686. Rajan, R.G., Servaes, H., 1997. Analyst following of initial public offerings. Journal of Finance 52, 507–529. Smith, R., Bray, C., 2009. IPO-abuses lawsuit is settled. The Wall Street Journal, M3, October 8.