Deal Making and Stock Picking: Better Be Optimistic than Good? Abstract

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1 Deal Making and Stock Picking: Better Be Optimistic than Good? Michel Fleuriet and Jinghua Yan* First draft: November 2005 This version: December 1, 2006 Abstract The common wisdom about sell-side analysts is that their recommendations tend to be overly optimistic under the pressure from their investment banking colleagues. This paper answers the question whether it is the optimistic analyst or the good analyst (an analyst with superior stock-picking ability) who increases the chance of winning equity mandates for investment banks. We find that analysts recommendation performance can explain their investment banks chance of winning future mandates. There is a positive relation between the performance of an analyst s recent buy and strong buy recommendations and his bank s chance of winning future underwriting mandates. The effect is significant for both raw stock performance as well as industry-adjusted stock performance. In contrast, the relation between an analyst s optimism and his bank s chance of winning future mandates is weakly negative. The results are robust after controlling for analyst and investment bank characteristics as well as past investment banking relationship. We conjecture that stock-picking earns analysts credibility among investors and this reputation plays an integral role in investment banks underwriting process. And we document anecdotal evidence to support our conjecture. The key finding of this paper identifies a countervailing force to the well-documented incentives for analysts to be optimistically biased. The current organization structure of investment banks, i.e., the affiliation between equity research and investment banking, has been blamed for analysts optimism due to the conflict of interest problem. The feedback effect identified in this paper suggests that the affiliation also provide analysts with incentive to be good rather than optimistic. *Both authors are from the Wharton School. fleuriet@wharton.upenn.edu and jyan@wharton.upenn.edu. We thank Gary Gorton, Andrew Metrick, Ayako Yasuda, Paul Zurek, and seminar participants at Wharton, Journal of Banking and Finance Conference (Beijing), and CRSP Forum (Chicago) for helpful comments. All errors are our own. 1

2 I. Introduction There are two well known facts about sell-side analysts: their recommendations predict stock returns; 1 their recommendations tend to be optimistic due to pressure from investment banking colleagues. 2 This paper links the two strands of literature together by asking the question whether being a good stock picker increases an analyst and its bank s chance of winning future mandates. We find a positive relation between the performance of an analyst s buy and strong buy recommendations and his bank s chance of winning future mandates. In contrast, the relation between analyst optimism and deal making is weakly negative. We conjecture that stock-picking earns analysts credibility among investors and this reputation plays an integral role in investment banks underwriting process. And we document anecdotal evidence to support our conjecture. Since the recent stock market bubble burst, the well-documented conflict of interest problem for sell-side analysts has triggered a series of policy debates. Some blame the current organization structure of investment banks, i.e., the affiliation of analysts with underwriting banks, for leading analysts to be overly optimistic. This paper argues that the feedback effect, i.e., good but not optimistic analysts help increase chance of winning future mandates, serves as a countervailing force that provides sell-side analysts with incentive to issue valuable recommendations. The findings of this paper are best exemplified by the career of Jack Grubman at Citigroup (formerly Salomon Smith Barney). In 1990s, Grubman built a good reputation among investors for his ability to pick stocks. 3 In 1999, Salomon s retail force ranked Grubman the 4 th among all equity research analysts. During 1999 and 2000, Salomon 1 See, for example, Barber et al (2001) and Fang and Yasuda (2005a). 2 See, for example, Lin and McNichols (1998) and Michaely and Womack (1999). 3 Wall Street Meat Andy Kessler, Harper Business, 2003 p

3 was awarded 35 mandates in the telecommunication sector that Grubman covers. In early 2000, Grubman issued a number of Buy and Strong Buy recommendations on AT&T, Global Crossing, WorldCom, and Qwest that performed poorly. In 2000 and 2001, as the performance of Grubman s recommendations suffered, his internal rank among Salomon Smith Barney s retail brokers dropped to the last. 4 Salomon Smith Barney was not selected as manager or co-manager in any of the 14 telecommunication stock offerings in Grubman s optimism not only costs investors money, but also costs his bank opportunities of winning future mandates. The incentives for a sell-side analyst to issue optimistic recommendations have been well documented in recent literature: Lin and McNichols (1998) and Michaely and Womack (1999) both found that analysts tend to be more optimistic on stocks underwritten by their affiliated investment banks. These overly optimistic recommendations are followed by worse stock performance vis-à-vis other analyst recommendations. More recent literature has shown that investment banking is not the sole incentive provider for analyst optimism: Lim (2001) shows that analysts can be optimistically biased so as to gain access to company management; Jackson (2005) shows that analyst optimism leads to increased trading volume, hence higher brokerage profits for investment banks. This paper sides with another recent paper by Fang and Yasuda (2005b) in pointing out there are also incentives for analysts not to be optimistic. Fang and Yasuda (2005b) found that reputation (measured by All American status) keeps analyst optimism in check during market booming years. In contrast, this paper shows that being a good stock picker significantly increases affiliated bank s chance of winning 4 See SEC s litigation against Jack Grubman dated April 28, 2003 at 3

4 investment banking mandates. It provides direct evidence for an important incentive for analyst to be good rather than optimistic. This paper s methodology is closest to two papers that identify the determinants of the choice of underwriting investment bank, Dunbar (2000) and Ljungqvist et al (2005). The former found that analyst reputation explains investment bank s IPO market share, whereas the latter found that analyst s optimism does not increase bank s chance of winning future mandates. Like Ljungqvist et al (2005), this paper examines the determinants of underwriter choice at the deal level but with a focus on the effect of the stock performance following recent analyst recommendations. In light of previous paper s findings, this paper controls for bank characteristics as well as past investment banking relationship. This paper s finding implies a potential analyst reputation effect in the choice of underwriter, as anecdotal evidences suggest that analyst reputation is heavily influenced by the performance of his recommendations. The importance of analyst reputation in the selection of underwriter has been argued in the academic literature. 5 But this paper is the first that uses deal-level data to argue the significance of analyst reputation (in addition to bank reputation) on the choice of underwriter. How do we reconcile this paper s findings with earlier research on the conflict of interest for sell-side analysts? The conflict of interest problem leads analysts to be optimistic to please past or potential future investment banking clients. This paper s findings indicate that such a favor comes at a cost on analyst reputation, hence reducing the bank s chance of winning future mandates. Put together, an analyst may well be influenced by the conflict of interest at certain time on certain stocks when the benefit of 5 See, for example, Dunbar (2000). 4

5 optimism is high, but this paper shows that such blind optimism may in the end hurt the analyst and the investment bank themselves. The rest of the paper is structured as follows: Section II discusses various interests of a sell-side analyst. It entails anecdotal support for the positive relation between stock picking and deal making that this paper finds. Section III describes the data, sample selection, and methodology. Section IV discusses the results. Section V concludes. II. The various interests of an analyst A. Analysts work in a world with built-in conflicts of interest As the British Bankers Association states: The whole reason for having research analysts has always been as a means of selling stocks in the primary market, and generating transactions, whether buy or sell, in the secondary market. The origin of the research analyst within securities firms has been succinctly described as follows: Charles Merrill [of Merrill Lynch] and his imitators soon adopted security analysis as a selling tool and established large research departments - quite unknown until the 1940s - to turn out stock recommendations and market letters. 6 If the whole reason for having research analysts has always been as a means of selling stocks, it should not come as a surprise that analysts work in a world with builtin conflicts of interest. The SEC explained clearly this conflict of interest in On the one hand, sell-side firms want their individual investor clients to be successful over time because satisfied long-term investors are a key to a firm's long-term reputation and success. A well-respected investment research team is an important service to customers. At the same time, however, several factors can create pressure on an analyst's independence and objectivity. The existence of these factors does not necessarily mean that the research analyst is biased. But investors should take them into account before making an investment decision. The SEC stress among these factors the Investment Banking Relationships, and what it may mean for the fairness of the report. -The analyst's firm may be underwriting the offering 6 British Bankers Association, Response to CP 171: Conflicts of Interest: Investment Research and Issues of Securities 18/04/2003 5

6 -Client companies prefer favorable research reports -Positive reports attract new clients -A positive-sounding analyst report can help firms make money indirectly by generating more purchases and sales of covered securities -Analyst Compensation Brokerage firms' compensation arrangements can put pressure on analysts to issue positive research reports and recommendations. -Ownership Interests in the Company an analyst, other employees, and the firm itself may own significant positions in the companies an analyst covers. 7 All these factors may explain why research analysts might be unduly optimistic in their reports. And, indeed, we do not pretend that analysts have never been pressured to issue rosy reports by investment bankers. But the events of 2001 and after led to a more damning observation, that, in essence, the banks gave companies favorable research coverage in exchange for investment banking business. B. What investors really want? If security analysis is a selling tool for banks, it begs the question: what investors really want? For institutional investors, the answer appears in the survey published by the Institutional Investor magazine. Most large institutional investment managers employ portfolio managers who invest their clients capital in diversified baskets of assets, such as open-end mutual funds. Given the wide scope of their mandate, fund managers are supported by their firms internal research analysts who follow companies that compose a handful of broadly classified sectors. These buy-side analysts turn to sell-side analysts for their in-depth knowledge of the various industries within the sectors they cover. Each year, buy-side investors rank sell-side Analysts in the All-America Research Team poll published in the October edition of Institutional Investor magazine. The voting universe and sector classifications are determined by the editors of the magazine. The October 2002 survey included responses from 3,492 individuals from 605 firms representing $7.7 trillion in U.S. equity assets. Analysts primary clients are institutional investors (I.I. 7 Analyzing Analyst Recommendations, SEC Modified: 06/20/2002 6

7 voters) and arguably the equity salespersons (who ultimately sell analysts recommendations to the same group of investors) of their firms. Research-rating is crucial for the career of an analyst. Since at least the October 1999 issue Institutional Investor, industry knowledge has been rated as the single most important attribute of an equity analyst. In the October 2002 survey, Institutional Investor poll voters rank integrity/professionalism second behind industry knowledge, much higher than stock selection and earnings estimates which come only in 11 th and 12 th position, well after Independence from corporate finance (8th position) 8. Analysts should focus on delivering compelling new investment ideas to investors. Looking at What Investors Really Want, it is clear to see the 80/20 rule in play; 20% of work analysts undertake represents 80% of the value for their clients. Written reports, earnings estimates, financial models, and even stock selection are nowhere as important as industry knowledge and useful calls/visits. As a recent author, Charles Gasparino points out, when he mentions the Institutional Investor magazine s beauty contest of Wall Street analyst : The voting was telling; it didn t measure quality of stock research, but it did measure an analyst s name recognition 9 This data gives an inside view on how the buy-side actually uses the sell-side. Why don t institutional investors pay more attention to the recommendations? Information has value to the degree that it is not already impounded in the price. Published recommendations and written reports are disseminated simultaneously to scores, if not hundreds, of investors, and unless there is a lagged market reaction, any price impact should occur immediately. An analyst provides value to a money manager by answering questions and supplying information in individual telephone calls before the information is fully reflected in market prices See Exhibit 4 from Institutional Investor 2003 below. 9 Blood on the Street, Charles Gasparino, Free Press 2005 p Bradley, Daniel, Bradford D. Jordan and Jay R. Ritter, Analyst Behavior Following IPOs: The Bubble Period Evidence,

8 To the best of our knowledge, there is no public survey of what the individual investors want. However, circumstantial evidence is provided in the SEC s complaint against Grubman, when the SEC reviewed scathing written evaluations of Grubman by the SSB retail sales force in 2000 and Hundreds of retail sales people sent negative written evaluations of Grubman in both years. Many claimed Grubman had a conflict of interest between his role as an analyst and his role assisting investment banking: "poster child for conspicuous conflicts of interest"; "I hope Smith Barney enjoyed the investment banking fees he generated, because they come at the expense of retail clients"; "Let him be a banker, not a research analyst"; "His opinions are completely tainted by `investment banking' relationships (padding his business)"; "Investment banker or research analyst? He should be fired"; "Grubman has made a fortune for himself personally and for the investment banking division. However, his investment recommendations have impoverished the portfolio of my clients and I have had to spend endless hours with my clients discussing the losses Grubman has caused them." Many criticized his support of companies that were SSB investment banking clients: 8

9 "Grubman's analysis and recommendations to buy (1 Ranking) WCOM [WorldCom], GX [Global Crossing], Q [Qwest] is/was careless"; "His ridiculously bullish calls on WCOM and GX cost our clients a lot of money"; "How can an analyst be so wrong and still keep his job? RTHM [Rhythm NetConnections], WCOM, etc., etc."; "Downgrading a stock at $1/sh is useless to us. "How many bombs do we tolerate before we totally lose credibility with clients?" After a while, analysts lose the trust of investors by making wrong recommendations. In an unusual arbitration claim filed Feb. 22, two former Salomon Smith Barney stockbrokers are targeting one of their own: Salomon telecom analyst Jack Grubman. The brokers, Philip Spartis and Amy Elias, claim Grubman's buy ratings and bullish reports on WorldCom stock from early 1999 ($80 a share) to mid-2001 ($13 a share) led their clients--a group of former and current WorldCom employees--to lose millions. The WorldCom employees had sued Salomon and its brokers. Spartis and Elias were later dismissed. 11 When the analyst loses his or her reputation, analysts should prove useless for investment banking in securing new mandates. In fact, an analyst who issues a positive call to curry favor with clients could ultimately lose his firm business. On Oct. 9, Jack Grubman, Salomon Smith Barney's telecom analyst, dropped AT&T from a buy to an outperform, a lesser rating in Wall Street's lexicon, after news reports claimed he issued the better recommendation to win Salomon an underwriting role in the IPO for AT&T Wireless Group. Some observers say Grubman's buy rating so angered Wall Street that Salomon has been kept out of other upcoming telecom deals. 12 Grubman represented short-term gain but long-term pain as investors turned away from the firm in droves Going After A Super Analyst By Marcia Vickers, Business Week 11 March Investment banks: let analysts do their jobs By Heather Timmons, Business Week 30 October Blood on the Street, Charles Gasparino, Free Press 2005 p.185 9

10 C. The lawsuits from investors claiming they had been harmed by fraudulent research advice. After the stock market crash of 2000, many investors introduced lawsuits claiming that their losses were caused by biased research reports. The typical claim is that research analysts at the large securities firms were merely shills for the investment bankers. Rather than providing objective research and recommendations, plaintiffs allege, the research analysts ignored their true and often-negative opinions of the companies they covered and became cheerleaders for the companies to whom their securities firms provided investment banking services. 14 Plaintiffs have essentially alleged a quid pro quo - the Banks gave companies favorable research coverage in exchange for investment banking business. By June 2001, New York attorney general Eliot Spitzer initiated an investigation into the causes of the failure of Merrill Lynch s research. His conclusions were that the research analysts were acting as quasi-investment bankers for the companies at issue, often initiating, continuing, and/or manipulating research coverage for the purpose of attracting and keeping investment banking clients. Since late 1999, the internet research analysts (the internet group ) at Merrill Lynch have published on a regular basis ratings for internet stocks that were misleading because: (1) the ratings in many cases did not reflect the analysts true opinions of the companies; (2) as a matter of undisclosed, internal policy, no reduce or sell recommendations were issued, thereby converting a published five-point rating scale into a de facto three-point system; and (3) Merrill Lynch failed to disclose to the public that Merrill Lynch s ratings were tarnished by an undisclosed conflict of interest: the research analysts were acting as quasi-investment bankers for the companies at issue, often initiating, continuing, and/or manipulating research coverage for the purpose of attracting and keeping investment banking clients, thereby producing misleading ratings that were neither objective nor independent, as they purported to be Loss Causation: Who Was Responsible for the Market Bubble? By John F.X. Peloso and Francis S. Chlapowski; New York Law Journal, August 21, Affidavit in Support of Application for an Order, filed with Supreme Court of New York in the matter of an inquiry by Eliot Spitzer, Attorney General of the State of New York, Petitioner, Pursuant to Article 23-10

11 In Fogarazzo v. Lehman Brothers, Inc 16, plaintiffs, investors in RSL Communications, Inc., allege that Lehman Brothers, Inc., Goldman Sachs & Co., and Morgan Stanley & Co., Inc. issued false and misleading research reports pertaining to RSL. Specifically, plaintiffs allege that the reports were fraudulently optimistic due to conflicts of interest between the research and investment banking departments of the defendant Banks. The complaint alleged that the banks had misrepresented to the public material facts about the corporation's performance in order to gain lucrative investment banking business. 1. [The Banks] regularly used analyst research to obtain and/or maintain lucrative investment banking business from companies it covered, including RSL; 2. [The Banks] caused the analysts covering RSL to assign and maintain bullish ratings for RSL stock, in order to pursue and win and/or maintain investment banking business from RSL 5. [A]s a result of the foregoing, [the Banks'] analysts issued more positive reports and ratings, and avoided downgrades or negative reports regarding investment banking clients, including RSL, and thus caused the stock price of RSL to be artificially inflated, throughout the Class Period. 17 In this case, the Court concluded that the plaintiffs have made specific allegations - based on, among other things, s from the Banks' own analysts - suggesting that the Banks received investment banking business in return for favorable analyst coverage, as part of a standard industry practice. Moreover, they have alleged that the Banks' relationship with RSL was the same as with these other companies - the Banks issued positive research reports on the heels of new investment banking business and in the face of overwhelming information calling into question RSL's financial health. Taken together, I can easily infer that the Banks' relationships with RSL were subject to the same conditions as with other companies. That being so, plaintiffs have amply pleaded A of the General Business Law of the State of New York with regard to the acts and practices of Merrill Lynch & Co., Inc., Henry Blodget, et alii, April Fogarazzo v. Lehman Brothers Inc. Goldman Sachs & Co. and Morgan Stanley & Co., New York Law Journal, May 28, RSL Communications Shareholders Group - comprising the Fogarazzos, Hopkins, and Engel v. Lehman Brothers, Inc., Goldman Sachs & Co., and Morgan Stanley & Co., New York Law Journal May 28, 2004, Vol. 103; Pg

12 facts giving rise to a strong inference that the Banks made the alleged misstatements with scienter. 18 In Bernard v. UBS Warburg LLC Plaintiffs have alleged, inter alias, that Defendants concealed their true opinion of Interspeed "with the express intention of inflating Interspeed's stock price to secure investment banking business from Interspeed;" 19 III. Data Our data are compiled from several sources. Details on analyst recommendations company information, analyst and broker codes, recommendations and dates of recommendations are obtained from I/B/E/S Detailed History file. Since comprehensive data coverage by I/B/E/S started in 1993, our sample period is from 1993 to Stock performance data comes from CRSP. Stock offering data offering date, company information, and underwriters are obtained from SDC public stock offering. We focus on the 50 largest underwriters (ranked by total number of mandates as manager or co-manager during the sample period). These underwriters combined account for over 90% of the mandates awarded over the sample period. The results are similar if we use top 25 underwriters. We analyzed all equity offerings (IPO s and Seasoned Equity Offerings) between 1993 and For each offering, we identified the analysts who issued a recommendation on the stock offered. We consider that a bank which has issued a recommendation on the stock offered either before or after the issue, was eligible for mandate consideration. 18 RSL Communications Shareholders Group - comprising the Fogarazzos, Hopkins, and Engel v. Lehman Brothers, Inc., Goldman Sachs & Co., and Morgan Stanley & Co., New York Law Journal May 28, 2004, Vol. 103; Pg Bernard v. UBS Warburg LLC, New York Law Journal, October 1,

13 For each buy recommendation, we measure its performance by examining the cumulative buy and hold return over 100 days starting from the recommendation date. We then measure the reputation of each afore-identified analyst with investors by examining the average stock performance following all his buy or strong buy recommendations in the last but one year preceding the offering (see Figure 1). We associate each analyst with the bank he works for and we identify the underwriting banks for each offering. The research question of this paper is whether there is a relation between the average performance of the analyst s recommendations and the choice of underwriter. More precisely, our sample is constructed in the following steps: 1) For each stock offering in the SDC dataset, we identify the set of banks which are eligible for the mandate (thereafter, eligible banks) in the following way. We search for all analysts who issued at least one recommendation on the stock being offered during a period going from a year before (-365 days) to two years after (730 days) the stockoffering date. The bank employing such an analyst was deemed eligible for the mandate. 2) Among the eligible banks, the SDC dataset identifies the mandate winners. 3) For each afore-identified eligible bank/analyst, we identify all his Buy and Strong Buy recommendations 20 on any stock he followed during a period of 365 days ending a year before the offering. Then, we measure the stock performance over the 100 days following his recommendation. 20 Recommendations are typically placed on this 5-point scale, but many investment banks use different terminology, such as market outperform. For instance, Bear Stearn s highest recommendation is Buy whereas CS First Boston and many others use the term Strong Buy. In all cases, a bank s highest recommendation is coded as a 1, consistent with the practice of IBES and First Call. Here, we include all recommendations coded as 1 or 2 in IBES. 13

14 4) For each deal-analyst match identified in Step (1), we examine the relation between the mandate flag, i.e., whether or not a mandate was awarded to the analyst s bank, and average performance of the analyst s recommendations during the window from 500 days prior to the offering date to 200 days prior to the offering date. Note that in step (3), we examine the performance of all recommendations by the analyst. This is in contrast to the approach in Ljungqvist et al (2005), who focused on analyst s behavior, i.e., optimism, on the stock to be issued only. Because of the way we assess the analysts recommendations, the last year for offerings that we consider is This is a significant date, as it includes all relevant offerings not only during years of rising stock prices (until 2000) but also during the downside of the years 2001 and At the same time, the observations end just before the date of the Global Analyst Research Settlement announced by the New York District Attorney in April Table 1 provides descriptive statistics for our final sample ( ) of dealanalyst matches. Consistent with previous research 21, analysts on average are good stock pickers. And their stock picking ability varies significantly over time due to changes in market condition. Also consistent with earlier findings, analyst recommendations are on average optimistic, i.e., more than 80% of the recommendations were Buy s and Strong Buy s. IV. Results and Discussions It is common knowledge as Gasparino said, that a positive recommendation from one of the superstar analysts was like money in the bank when underwriting business was 21 See, for example, Barber et al (2001) and Fang and Yasuda (2005a). 14

15 on the line. 22 But then one has to ask the question: how can an analyst be both highly ranked and highly wrong? Investment banks want their investment research to be well-respected and their individual investor clients to be successful over time. Analysts lose their reputation when they make too many wrong calls. As the author Andy Kessler, himself a former analyst, put it: I had figured out long ago you work on Wall Street to get paid. But my clients were institutions for which I felt an obligation, to make smart and right stock calls. All you have on Wall Street is your reputation. If you lose that, you are toast. 23 The question therefore is the following: if the banks gave companies favorable research coverage in exchange for investment banking business, how long can this last? The answer this paper provides is somewhat different than the existing literature: While the analysts give in general favorable research coverage which was on average perfectly exact, when the bank gave favorable research that is followed by with poor stock performance, they reduced their chance of getting investment banking business. How much does analysts blind optimism hurt banks chance of winning mandates? In Table 2, we show the percentage of time an eligible analyst s bank is awarded a stock underwriting mandate. It is interesting noting that the more an analyst is accurate (the more his optimism is justified) the more he increases the chance of the banks to land a mandate for an offering. From the worst performance decile (#1) to the best decile (#10), the chance of getting a mandate as an underwriter increases from 28% to 40%. The results in Table 2 can be driven by market timing: Stock offerings are often preceded by a period of high returns in the industry, hence good performance for analysts 22 Blood on the Street, Charles Gasparino, Free Press 2005 P Wall Street Meat Andy Kessler, Harper Business, 2003 p

16 eligible for the upcoming mandates. To address this alternative interpretation, we use industry adjusted return instead of raw returns to measure the performance of analysts recommendations. As results in Table 3 shows, the increasing trend in chance of winning a mandate from worst-performance decile to the best-performance decile remains strong (from 30% for decile #1 to 38% for decile #10). Moreover, we show in Table 4 the difference in performance between mandate winners and mandate losers are positive for all years. It is interesting noting that the difference is the highest during the hot market period from In other words, analysts are rewarded by remaining honest in their buy recommendations even during the hot market periods. Overall, Table 4 shows that there is a significant difference between analysts who win the mandates and those who lose. Over the entire sample period, mandate winning analysts overperform mandate losers by 2.7% per recommendation over 100%. This is statistically and economically significant. 24 In addition to univariate analysis, we also use regression approach to examine the effect of recommendation performance on winning mandates. Model (1) through (4) in Table 5 employs a conditional logit model (group by deal, or offering) The results confirm our univariate results that better performance leads to higher chance of winning the mandate. A 1% increase in raw (adjusted) performance increases the odds ratio, i.e., the probability of winning over the probability of losing, by 0.9% (4%). The results are 24 Ljungqvist et al (2006) documents that analyst names of nearly twenty thousand recommendations were removed in I/B/E/S from 2002 to This is unlikely to be driving the results, because the by year results show that the difference between winner bank and loser bank s analyst recommendations is substantial in all years in our sample. 25 Conditional logit grouping by deal is the best model as far as we are aware of that tackles the problem. However, it is problematic because when multiple mandates are awarded for the same deal, the model treats all mandates as mutually independent. But to derive an appropriate model for our problem involves multidimensional integration which drastically complicates the computation. Therefore, we show our results as a first pass. 26 The results are qualitatively similar if we run a multinomial probit regression. 16

17 robust to controlling for investment bank fixed effect (see model (4)). In addition, being optimistic, measured by the percentage of recommendations that are buys and strong buys (Buy%), does not increase the probability of winning mandates: the coefficient is negative and insignificant across all four models. Finally, analysts who issue more recommendations have higher likelihood of winning mandates. This is consistent with findings by Ljungqvist et al (2005). The results remain the same if we run a fixed effect regression on mandate flag adjusted by the ratio of number of mandates awarded to the number of eligible banks (see models (5) through (8)). Finally, for the seasoned equity offerings in our sample, we control for the past investment banking relationship between the eligible bank and the company. Table 6 shows the conditional logit regression results. The positive and significant coefficient on the past investment banking relationship dummy is consistent with Ljungqvist et al (2005). Nonetheless, after controlling for the past investment banking relationship dummy variable, the relation between recommendation performance and probability of winning mandates remains significant. V. Conclusions Claims against analysts allege that they ignored their true and often-negative opinions of the companies they covered and became cheerleaders for the companies to whom their securities firms provided investment banking services. What this research shows is that being wrongly optimistic reduces an analyst and its affiliated bank s chance of winning a future mandate, probably due to loss of credibility. 17

18 We show that the more in the wrong analysts are, the less are the chances of their banks to win equity offering. It does not pay to ignore the truth for an analyst. Moreover, while analysts may be pressured to be optimistic as conflict-of-interest literature documents, optimism does not increase the bank-analyst s chance of winning a future mandate. In April 2003, a settlement known as the Global Analyst Research Settlement was reached between the State of New York together with regulator authorities and ten of the top US investment banks. The settlement has mainly focused on insulating research from investment banking. In addition, the banks had to finance independent research during the next five years and this service started in July, It is not obvious that this separation between research and investment banking divisions was needed. This is exactly what our research shows. 18

19 Figure 1: Timeline of Sample Collection Performance measure: postrecommendation stock return over 100 days Assessment period (-500d, -200d) Offering Date -500d -365d -200d 730d A bank/analyst is eligible if a recommendation is issued during (-365d, 730d) 19

20 Table 1 Summary Statistics Number of deals is the total number of stock offerings. Each deal-analyst observation is a match of offering with an eligible investment bank (and its analyst). Average performance of an analyst s recommendations is the average 100-day stock return for all his recommendations during [t-500d, t-200d], where t is the offering date. Buy % is the percentage of total recommendations that are buys and strong buys. Year Number of Deals Number of Deal-Analyst Observations Average Performance of Analysts' Recommendations Average Number of Recommendations per Analyst % % % % % % % %

21 Table 2 Probability of Winning a Mandate as an Underwriter vs. Performance of Recent Recommendations For each deal, we identify the set of analysts who are eligible for the mandate in the following way: we search for all analysts who issued at least one recommendation on the stock from -365 days to 730 days relative to the stock offering date. For each analystdeal match, we measure the performance of an analyst by the post-recommendation return of his buy recommendations over the assessment period, which dates from 500 (calendar) days prior to the stock offering date to 200 (calendar) days prior to the stock offering date. We sort the entire sample of analyst-deal s into 10 equal-sized deciles by performance detailed above. For each decile, we show the percentage of time the analysts banks were selected as underwriters, the percentage of time the analysts banks were selected as underwriters subtracted by 1/(number of analysts eligible), the average performance of the analysts, and average number of recommendations over the assessment period. Accuracy (Decile) Average performance of recommendation s issued over the assessment period Average industryadjusted performance of recommendations issued over the assessment period Average of mandate flag (as an underwriter) (1=Win) Average number Average of of mandate flag recommendations adjusted by during the the number of assessment eligible banks period % -0.4% % 0.1% % 0.1% % 0.2% % 0.3% % 0.5% % 0.5% % 0.6% % 1.0% % 1.9%

22 Table 3 Probability of Winning a Mandate as an Underwriter vs. Industryadjusted Performance of Recent Recommendations For each deal, we identify the set of analysts who are eligible for the mandate in the following way: we search for all analysts who issued at least one recommendation on the stock from -365 days to 730 days relative to the stock offering date. For each analystdeal match, we measure the performance of an analyst by the post-recommendation return of his buy recommendations over the assessment period, which dates from 500 (calendar) days prior to the stock offering date to 200 (calendar) days prior to the stock offering date. We sort the entire sample of analyst-deal s into 10 equal-sized deciles by performance detailed above. For each decile, we show the percentage of time the analysts banks were selected as underwriters, the percentage of time the analysts banks were selected as underwriters subtracted by 1/(number of analysts eligible), the average performance of the analysts, and average number of recommendations over the assessment period. Accuracy (Decile) Average performance of recommendations issued over the assessment period Average industryadjusted performance of recommendations issued over the assessment period Average of mandate flag (as an underwriter) (1=Win) Average of mandate flag adjusted by the number of eligible banks Average number of recommendations during the assessment period 1 3.5% -3.2% % -0.8% % -0.3% % -0.1% % 0.1% % 0.4% % 0.7% % 1.2% % 2.0% % 4.9%

23 Table 4 Difference in Performance Between Mandate Winners and Losers For each deal, we identify the set of analysts who are eligible for the mandate in the following way: we search for all analysts who issued at least one recommendation on the stock from -365 days to 730 days relative to the stock offering date. For each analyst-deal match, we measure the performance of an analyst by the post-recommendation return of his buy recommendations over the assessment period, which dates from 500 (calendar) days prior to the stock offering date to 200 (calendar) days prior to the stock offering date. For each year, we show the average performance and industry adjusted performance among all mandate winners and losers. Z-statistics of the difference are shown in brackets. # of eligible banks # of winning banks # of losing banks Winner's avearge performance Loser's avearge performance Diff in performance between winners and losers Winner's average industryadjusted performance Loser's average industryadjusted performance Diff in ind-adj performance between winners and losers % 3.6% 0.5% [1.3] 0.26% 0.21% 0.0% [1.2] % 6.9% 1.1% [2.9] 0.47% 0.31% 0.2% [3.3] % 5.4% 0.5% [1.4] 0.60% 0.44% 0.2% [2.3] % 7.0% 1.9% [4.2] 0.60% 0.49% 0.1% [1.4] % 5.8% 5.6% [6.6] 0.68% 0.34% 0.3% [3.3] % 16.6% 7.9% [5.7] 1.62% 1.06% 0.6% [4.6] % 2.9% 3.6% [3.6] 0.50% 0.52% 0.0% [0.1] % 0.4% 1.4% [2.4] 0.23% 0.01% 0.2% [2.0] Overall: % 6.4% 2.7% [10.1] 0.62% 0.43% 0.2% [6.1] 23

24 Table 5 Regression Results Models (1) through (4) are conditional logit models grouping by deal. Models (5) through (8) are regressions with deal fixed effects. Mandate flag is 1 if a mandate is awarded. Average performance is the average performance of an eligible investment bank s analysts recommendations over the assessment period. Average adjusted performance is the average industry-adjusted performance of an eligible investment bank s analysts recommendations over the assessment period. %Buy is the percentage of the eligible bank/analyst s recommendations that are buys and strong buys. Number of recommendations is the total number of recommendation by the eligible bank/analyst over the assessment period. Absolute value of z-statistics or t-statistics is shown in parentheses. (1) (2) (3) (4) (5) (6) (7) (8) Mandate Mandate Mandate Mandate Adjusted Adjusted Adjusted Adjusted flag flag flag flag mandate flag mandate flag mandate flag mandate flag Average performance (8.47)*** (7.42)*** (4.65)*** (8.47)*** (7.52)*** (4.88)*** Average adjusted performance (5.99)*** (4.27)*** (2.78)*** (6.02)*** (4.55)*** (3.27)*** % Buy (0.95) (1.08) (1.06) (0.22) (0.97) (1.09) (1.07) (0.14) Number of recommendation (4.14)*** (4.03)*** (4.18)*** (2.22)** (4.20)*** (4.09)*** (4.21)*** (2.28)** Investment bank fixed effect N N N Y N N N Y Constant (1.22) (1.73)* (1.21) (1.25) Observations R-squared Absolute value of z statistics in parentheses * significant at 10%; ** significant at 5%; *** significant at 1% 24

25 Table 6 Regression Results Models (1) through (7) are conditional logit models grouping by deal for all seasoned equity offerings. Mandate flag is 1 if a mandate is awarded. Average performance is the average performance of an eligible investment bank s analysts recommendations over the assessment period. Average adjusted performance is the average industry-adjusted performance of an eligible investment bank s analysts recommendations over the assessment period. %Buy is the percentage of the eligible bank/analyst s recommendations that are buys and strong buys. Number of recommendations is the total number of recommendation by the eligible bank/analyst over the assessment period. Past banking relationship equals 1 if the eligible investment bank is the underwriter for a former equity underwriter for the company. Absolute value of z-statistics or t-statistics is shown in parentheses. (1) (2) (3) (4) (5) (6) (7) Mandate flag Mandate flag Mandate flag Mandate flag Mandate flag Mandate flag Mandate flag Average performance (6.47)*** (5.75)*** (4.96)*** (4.35)*** Average adjusted performance (3.93)*** (2.48)** (2.37)** (2.24)** % Buy (1.66)* (1.74)* (1.64) (1.03) (1.14) (1.28) (1.34) Number of recommendation (3.77)*** (3.72)*** (3.84)*** (1.93)* (1.85)* (1.41) (1.33) Past banking relationship (13.60)*** (13.78)*** Observations Absolute value of z statistics in parentheses * significant at 10%; ** significant at 5%; *** significant at 1% 25

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