analyst behavior surrounding tender offer announcements

The Journal of Financial Research • Vol. XXX, No. 1 • Pages 1–19 • Spring 2007

ANALYST BEHAVIORSURROUNDING TENDER OFFER ANNOUNCEMENTS

Daniel J. Bradley, Angela G. Morgan, and Jack G. WolfClemson University

Abstract

We examine the usefulness and credibility of analyst recommendations by focus-ing on their behavior surrounding tender offer announcements. For our 1998–2001sample, we find analysts did not identify takeover targets through their recommen-dations nor did they distinguish between wealth-increasing and wealth-decreasingtender offers. We find some evidence of conflicts of interest in analyst recom-mendations, but it is confined to the 1999–2000 dot-com period. However, thelong-run performance following recommendations suggests that these conflictshave little ultimate cost to investors.

JEL Classification: G11, G14, G24, G34

I. Introduction

It is generally believed that sell-side analysts provide a valuable service to thefinancial markets, but their credibility has been under increased scrutiny. The land-mark $1.4 billion settlement by securities firms in April 2003 motivated severalresearchers to investigate whether affiliated analysts issue more optimistic rec-ommendations. However, nearly all researchers focus on companies issuing newsecurities. Whether these potential conflicts affect other advisory affiliations re-mains answered.

Aside from potential biases, the usefulness of analyst recommendations hasalways been an interesting topic of debate among practitioners and academicians.What value, if any, do analysts provide? In an efficient market, analysts’ stock picksshould not perform any better than a relevant market index, but casual observationand empirical studies show a significant effect on stocks recommended by analysts,indicative of the perceived value of these recommendations.

We thank William T. Moore (the former editor), an anonymous referee, and participants at the FinancialManagement Association 2004 meeting for extremely helpful comments. The authors acknowledge thecontribution of Thomson Financial for providing analyst recommendations data, available through theInstitutional Brokers Estimate System. These data have been provided as part of a broad academics programto encourage earnings expectations research.

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2 The Journal of Financial Research

Several studies show that following the advice of analysts would haveproven to be profitable (see Barber et al. 2001; Jegadeesh et al. 2004). However,Barber et al. (2003) document that adhering to the recommendations of analysts in2000 and 2001 would have been “disastrous.” Specifically, they show that stocksleast favored by analysts outperformed those most preferred by more than 20 per-centage points. Although they do not test whether this severe underperformancewas motivated in part by the potential conflicts faced by investment banks, they dooffer it as a possible explanation.

We identify a unique type of corporate event that enables us to study boththe usefulness and credibility issues facing analysts—mergers and acquisitions(M&As). As Kolasinski and Kothari (2004) note, the total value of M&A dealsin 1999 exceeded equity offerings by an order of magnitude. Although the fees(relative to the size of the transaction) are typically smaller for M&As than for equityofferings, aggregate fees from M&As have exceeded fees from equity issuancesevery year for the past decade. This creates a potential for conflict of interest asthe close relationship that exists with the client may sway an affiliated analyst toissue a favorable, and perhaps biased, recommendation to increase the likelihoodof repeat business.

Additionally, M&As generally provide abnormally positive returns to targetfirms. Analysts could create value for the investors following their recommenda-tions by identifying these targets ex ante. Numerous practitioner and academicarticles suggest that merger targets are predictable (see Hasbrouck 1985; Song andWalking 1993); therefore, we might expect analysts to incorporate this predictabilitycomponent into their recommendations.

Another way analysts could create value would be to discern wealth-increasing versus wealth-decreasing tender offers. Upon the announcement of atakeover, there is much uncertainty as to whether the takeover will be good foracquiring shareholders. These events have the potential to substantially change thefirms’ future prospects. Financial analysts who cover the stock following this an-nouncement must incorporate this information into their recommendations. Thus,if analysts have superior forecasting skills, we would expect them to be ableto determine which tender offers will add value and which will destroy firmvalue.

We address two primary areas relating to analysts and tender offers: (1)whether there is systematic evidence of a conflict of interest in analyst behaviorsurrounding tender offer announcements, and (2) whether financial analysts’ opin-ions are useful relating to these tender offer transactions. By considering a typeof event that has not been previously studied, we examine the pervasiveness ofpotential conflicts of interest. The conflicts inherent to M&A advisors may dif-fer from initial public offering (IPO) underwriters because of the different feestructures and the higher frequencies of M&A transactions. Because the shares ofM&A firms are already publicly traded, we can examine recommendation levels

Analyst Behavior 3

before the announcement, which is not possible with IPOs. Finally, tender offersforce analysts and other investors to reevaluate the buyer to consider the effect ofthe business combination. Inspection of long-run performance allows us to gaugethe value of recommendations with respect to tender offer firms as well as to deter-mine how much it would hurt investors to follow the advice of affiliated analystsif the recommendations are biased.

Kolasinski and Kothari (2004) is the closest in spirit to this work. Theyexamine earnings forecasts and recommendations for both cash- and stock-basedM&As between 1993 and 2001. They consider two main hypotheses. The briberyhypothesis posits that analysts issue biased research to attract investment bankingand M&A businesses. The execution-related hypothesis posits that analysts haveincentives to act strategically with their recommendations to either increase thelikelihood of completing the deal or completing it at a more favorable price. Be-cause these two hypotheses may conflict at times, Kolasinski and Kothari devoteconsiderable energy addressing whether their tests have sufficient power.

In our article, we focus on tender offers. Using a sample consisting solely oftender offers allows us to consider the potential bias in analysts’ recommendationsonly in terms of the bribery hypothesis because the strategic incentives to manipu-late a stock-based deal are not relevant in our tender offer setting. Eliminating thisconfounding hypothesis alleviates much of the power issues with which Kolasin-ski and Kothari (2004) must contend. We also focus on analyst recommendationsbecause these were at the core of the conflict-of-interest allegations motivatingour study, whereas Kolasinski and Kothari primarily focus on earnings per shareestimates.

In addition to comparing the recommendations of affiliated analysts withthose made by unaffiliated analysts, we examine the stock market reaction to theserecommendations. If the market perceived a conflict, the reaction to affiliated rec-ommendations should be discounted relative to unaffiliated recommendations. Wepartition our sample into subperiods to see whether the results are consistent acrosstime. We also examine whether affiliated analysts incorporate information aboutthe transaction into their recommendations more effectively, such as by makingBuy recommendations on targets before the announcement of the deal or timingupgrades and downgrades to create value for the investors that follow their recom-mendations.

Our full-sample results do not provide evidence of systematic conflictsof interest; however, subperiod analysis reveals that a conflict may have existedduring the 1999–2000 Internet bubble period, which coincides with the periodunder scrutiny by regulators and the investing public. We also find no evidence thatanalysts can discern which firms are “in play” and which are not. For acquiringfirms, analysts’ recommendations do not appear to reflect whether deals will besuccessful or unsuccessful as measured by long-run stock returns. Instead, we findthat stocks with recommendations rated below Buy outperform those with Buy


recommendations. Therefore, although investors that follow the recommendationsof affiliated analysts are not significantly worse off than investors that followunaffiliated analysts, neither investor group does better than investors who do notuse analysts’ recommendations at all.

II. Research Design and Data

M&As in general, including tender offers, represent potential turning points in theperformance of a firm. The combination and integration of two (or more) sets ofoperations can radically alter the future prospects of the company. The initial marketreaction is positive on the target side (e.g., Andrade, Mitchell, and Stafford 2001).Mitchell and Stafford (2000) find that the robust average three-year post-mergerabnormal return is 3.6%, but insignificantly different from zero.

We focus on tender offers rather than M&As in general because the useof stock financing reveals additional information about the bidder. Shleifer andVishny (2003), among others, argue that the use of stock indicates that the acquiringfirm’s equity is likely overvalued. Thus, analysts may revise their estimates andrecommendations based on this signal as well as on the expectations of the combinedcompany’s prospects. Also, if the merger consideration includes stock, the analyst’srecommendation could be used strategically to influence the outcome of the dealin question. By restricting our sample to tender offers, we eliminate these possibleconfounding influences.

To investigate whether analyst recommendations are systematically biased,we focus on three areas. First, we compare the ratings strength of analysts from theinvestment bank hired as advisors (affiliated analysts) relative to those that arenot (unaffiliated analysts). If a conflict of interest does exist, we would expect toobserve more optimistic ratings from affiliated analysts than from unaffiliated an-alysts. Studying equity offerings, Bradshaw, Richardson, and Sloan (2003), Dugarand Nathan (1995), and Michaely and Womack (1999) find that affiliated analystsare more optimistic than unaffiliated analysts, which they interpret as consistentwith conflict of interest. Bradley, Jordan, and Ritter (2003) find no such evidencewhen examining IPOs. Lin and McNichols (1998) find that short-term earningsforecasts are the same for affiliated and unaffiliated analysts, but longer term fore-casts by affiliated analysts tend to be more optimistic. However, Kolasinski andKothari (2004) find little difference in the earnings forecasts of unaffiliated an-alysts and analysts affiliated with bidding firms in M&As. Finally, Clarke et al.(2006) find that analyst recommendations remain optimistic even as firms approachbankruptcy; they find no difference in ratings strength between affiliated and unaf-filiated analysts. Collectively, the evidence is mixed as to whether investment banksprovide biased advice as a result of their relationships.

Analyst Behavior 5

Next, we examine the initial market response between affiliated and unaffil-iated analyst recommendations. If a conflict of interest exists, affiliated underwriterrecommendations should be discounted relative to their unaffiliated counterpartsas market participants should recognize the inherent bias included in these recom-mendations. Consistent with this view, Michaely and Womack (1999) find supportfor this conjecture, but Bradley, Jordan, and Ritter (2003) find no difference inmarket reactions between the two groups.

Finally, we compare the long-run performance of affiliated versus unaf-filiated analyst recommendations. For analysts in general, Barber et al. (2001),Jegadeesh et al. (2004), and Womack (1996) present evidence of long-run valuestemming from analyst picks. However, a recent study by Barber et al. (2003) in-dicates that following the advice of analysts in 2000 and 2001 would have resultedin very poor stock return performance. Inferior long-run performance by affiliatedanalyst recommendations would be consistent with a conflict of interest. Michaelyand Womack (1999) find support for this conjecture for IPOs.

We also examine whether analysts add value for investors that follow theirrecommendations. If analysts add value by correctly identifying target firms, wewould expect their recommendations for these firms to become more optimisticbefore the announcement of a takeover.1 Hence, we can analyze the recommenda-tion patterns of analysts for target firms leading up to the announcement date. Atrend toward more favorable opinions could suggest a tender offer forecast. An-alysts could also add value by determining which mergers will perform better.Although the average acquirer long-run stock return performance following tenderoffers may be neutral, there is cross-sectional variation with some combinationsperforming better than others. If analysts can distinguish between successful andunsuccessful mergers, we should observe evidence of this in the performance oftheir recommendations following the announcement.

Our tender offer sample is drawn from the M&A Roster included in Merg-ers and Acquisitions Journal for deals announced between 1998 and 2001. Weselect this period because it is timely, it overlaps with the period under scrutiny byregulators and the investing public, and it allows for an analysis of long-run returns.We first identify all tender offers during this period and delete firms in deals witheither a private target or acquirer, firms not listed on the Center for Research inSecurity Prices (CRSP), and firms with no recommendation data available. Ad-visors are identified from the prospectus filed with the Securities and ExchangeCommission. We do not require a matched set of targets and acquirers (requiring

1Several studies investigate pre-announcement target firm price run-ups (see Denis and McConnell1986; Halpern 1976; Keown and Pinkerton 1981). Arnold et al. (2000) investigate activity in option marketsbefore tender offer announcements and find both price run-ups and increased volume. With such signals,analysts should be able to determine which firms are likely to become “in play.”


this does not significantly change our results but does decrease sample size). Thisscreening process leaves 257 acquiring firms and 263 targets.2

Analyst recommendations are collected from two sources. We use Institu-tional Brokers Estimate System (IBES) and Briefing.com to maximize the compre-hensiveness of our sample (recommendations on IBES start in January 1997 andthose on Briefing.com start in January 1998). We focus on recommendations thatoccur −365 days to +365 days relative to the announcement date. We believe thisperiod (four quarters before and after the announcement) is sufficient to answerour questions. Stock price data are from CRSP and fundamental characteristics arefrom Compustat.

Analyst ratings traditionally fall on a five-point scale, where Strong Buy isthe best (coded 1) and Sell is the worst (coded 5). Although not all banks use thisscale or wording, we follow IBES in their ranking scheme. For instance, MerrillLynch’s top rating is Buy whereas Morgan Stanley’s is Strong Buy; we code both topratings as a 1. We follow this ranking system for banks listed only on Briefing.com.There are 8,154 acquirer recommendations, or an average of 31.7 recommendationsper acquiring firm. This compares to a total of 2,198 recommendations for targets,or an average of 8.4 recommendations per firm.3 We attribute the large differencein the number of recommendations between the groups to two factors. First, thedistribution is truncated for target firms because they cease to exist after the tenderoffer is completed (usually within one year of the announcement). Second, analystfollowing is highly correlated with firm size (Bhushan 1989), and our acquirerstend to be much larger than our targets.

Consistent with other studies, analyst ratings tend to be optimistic. Nearly75% of the acquiring firm’s receive recommendations of either Buy or Strong Buy,and only 1% receive Sell recommendations. For the targets, 63% are labeled Buy orStrong Buy, and only 3% receive Sell or Strong Sell recommendations. Our data areconsistent with the conventional view that analysts refrain from issuing pessimisticratings. Several explanations have been offered for this overoptimism. We morefully analyze analyst recommendations in the next section.

2Because our sample contains a relatively small number of companies and multiple recommendationsfrom the same analyst for the same company (albeit at different points in time), there may be some concernsabout the power of our tests and the effect of cross-sectional dependence. Power does not appear to bean issue because we find significant results when examining subperiods. We repeat our analyses examin-ing recommendation levels and short-run market reactions using reduced samples without cross-sectionaldependence; the results remain unchanged. We note these concerns in the empirical analysis sections.

3Analysts that may work for the other side of the transaction are included in our sample set. Forexample, if Merrill Lynch was the advisor for the target firm, we do not exclude recommendations it mayhave made for the acquiring firm. Excluding these recommendations provides similar results.

Analyst Behavior 7

1

1.5

2

2.5

3

-4 -3 -2 -1 1 2 3 4

Quarter

Rat

ing

Acquirer Target

Figure I. Analyst Ratings for Target Firms by Time. This figure plots analyst ratings for acquirer andtarget firms for the two-year period centered around the announcement of the tender offer.

III. Empirical Results

We should be able to discern whether analysts can predict tender offer participantsby observing their recommendation patterns leading up to the announcement. Ifanalysts are effective in identifying target firms, we should see a profound patternof recommendations becoming more optimistic. We examine this issue by firstplotting the recommendation patterns of analysts for both targets and acquirers byquarter in Figure I.

As shown in Figure I, analyst recommendations are almost identical for ac-quirers and targets in quarters −4 and −3, but in quarter −2 target recommendationsbecome more pessimistic and acquirer ratings become slightly more optimistic. Ifanalysts made the appropriate forecast, we would expect to see the opposite pattern.The most notable pattern in this plot occurs for targets in quarters +1 and +2. Inquarter +1, the average target rating takes a profound pessimistic turn. This is mostlikely because analysts are reacting to the tender offer news and believe that thestock has correctly incorporated the merger premium. In quarter +2, the averagerating returns to previous levels. Although the preliminary evidence suggests thatanalysts are ineffective in identifying takeover targets, we revisit this issue in thenext section.4

4The patterns observed in Figure I are qualitatively unchanged if we use recommendation changesrather than recommendation levels. A change is defined as the difference between an analyst’s current ratingand his or her previous rating. For example, if an analyst previously issued a Buy rating (which is typicallycoded 2) but later upgraded the stock to a Strong Buy, the change would equal −1. By definition, new


Are Analyst Recommendations Biased?

To gauge potential conflicts of interest, we look primarily at the acquiring firm be-cause the severity of a conflict is much stronger relative to the target. The acquiringfirm will continue as a going concern whereas the target firm will not; therefore,the probability of repeat business is near zero for target firms.5 Note that more opti-mistic ratings for the targets by advisors in the period preceding the announcementwould benefit investors because they could capture the premium if they acted onthe recommendations.

Analyst Ratings

Table 1 provides average recommendations by quarter and affiliation for targetsand acquirers. We find no evidence of a bias by affiliated analysts. Panel A showsacquirer recommendations. For the full sample, advisor and nonadvisor ratings areidentical at 1.91. Some significant differences emerge in quarters −4, +3, and +4,but the signs flip-flop and these differences are observed only at the extreme endsof the period.

We focus on target firms in Panel B of Table 1. Although advisor analystsappear to be more optimistic, these results are more significant for quarters −4and −3, which explains the significant difference for the full sample. Advisorratings become more pessimistic in magnitude (albeit not significantly different)in quarters −2 and −1. This is the reverse of what would be expected if affiliatedanalysts are biased.

Multivariate Analysis of Ratings

To more formally evaluate analyst ratings and to condition for joint effects, we usea multivariate regression framework.6 Our models are:

Acquirer Ratingi = a0 + a1−3(Quartern) + a4(Affiliated) + a5(Nobank)

+ a6(Logtrval) + a7(Relsi ze) + a8(Logcap) + a9(Samesic)

+ ei ,(1)

initiations of coverage are not included in this analysis. Similar to reported results, we find no evidence thatanalysts can predict tender offers based on their recommendations.

5It could also be argued that the conflict would be more severe for firms that had a prior relationshipwith the analyst’s investment bank. Therefore, we identify all equity-related deals with any recommendingbank done by the firm for three years before the merger announcement and repeat all tests conditioning forthis possibility. Our conclusions remain unchanged.

6To ensure that our results are not driven by cross-sectional dependence, we repeat our analysiseliminating all recommendations made by the same analyst on the same company in the same quarter. Wefind similar results.

Analyst Behavior 9

TABLE 1. Analyst Ratings Surrounding Merger Announcements by Affiliation and Time.

Quarter

Affiliation −4 −3 −2 −1 +1 +2 +3 +4 Total

Panel A. Acquirer Sample

Advisor 1.67 1.74 2.05 1.84 1.94 2.06 2.16 1.74 1.91(43) (38) (38) (43) (52) (47) (57) (57) (375)

Nonadvisor 1.95 1.94 1.85 1.88 1.94 1.91 1.89 1.92 1.91(878) (916) (884) (894) (983) (1,032) (1,059) (1,133) (7,779)

t-statistic 2.24∗∗ 1.56 −1.52 0.32 −0.02 −1.30 −2.51∗∗∗ 1.71∗ 0.10

Total 1.94 1.93 1.86 1.88 1.94 1.92 1.90 1.92 1.91

Panel B. Target Sample

Advisor 1.75 1.61 2.11 2.15 2.71 2.00 2.00 NA 2.00(40) (26) (35) (38) (14) (1) (2) (156)

Nonadvisor 1.89 1.94 2.02 2.11 2.77 2.27 2.23 2.38 2.17(376) (378) (364) (378) (366) (77) (55) (48) (2,042)

t-statistic 1.71∗ 2.04∗∗ −0.63 −0.36 0.26 0.33 0.55 NA 2.40∗∗

Total 1.95 1.92 2.03 2.11 2.77 2.27 2.23 2.36 2.16

Note: This table provides analyst ratings surrounding merger announcements by affiliation and time.Quarter represents the quarter a recommendation occurred in relation to the announcement day. Forexample, quarter +1 contains all recommendations that occurred during the first quarter following themerger announcement. Panel A presents analyst recommendations for the acquirer sample and Panel Bpresents analyst recommendations for the target sample. Analyst ratings are on a five-point scale with 1being the best and 5 the worst. The number of observations are in parentheses. The t-statistic tests thedifference between advisor and nonadvisor recommendations.

∗∗∗Significant at the 1% level.∗∗Significant at the 5% level.∗Significant at the 10% level.

Target Ratingi = a0 + a1−3(Quartern) + a4(Affiliated) + a5(Logtrval) + ei , (2)

whereQuartern = series of dummy variables representing the quarter when the

announcement took place relative to the announcement date;Affiliated = dummy variable equal to 1 if the recommendation comes from

an investment bank that serves as the advisor for the merger,and 0 otherwise;

Nobank = dummy variable equal to 1 if the firm chose not to use an advisor,and 0 otherwise;

Logtrval = the natural log of the value of the merger consideration, that is,the aggregate purchase price;

Relsize = the value of the merger transaction scaled by the market value ofthe acquiring firm;


Logcap = the natural log of the acquiring firm’s market capitalization30 calendar days relative to the recommendation date; and

Samesic = dummy variable equal to 1 if the acquirer and target are withinthe same four-digit Standard Industrial Classification code.

In Table 2, the variables, Quartern, reflect the period the recommendationoccurred relative to the announcement date. We separate recommendations thatoccur before and after the announcement date because (1) this will allow us todetermine whether analysts can identify target firms ex ante after controlling forother attributes, and (2) the severity of a conflict may be different relative to the an-nouncement time for acquiring firms. If the recommendation occurs before (after)the announcement, quarter −1 (+1) is the omitted category. For the acquiring firms,none of the quarters is significantly different from the omitted quarter (quarter −1 or+1), suggesting that analyst recommendations are not more or less optimistic in anyparticular quarter. For the target firms, the “before” regressions are of primary inter-est because of the truncation of data following the tender offer. A quick glance at thiscolumn shows that, relative to quarter −1, recommendations in quarters −2 to −4are significantly more optimistic after conditioning for other factors. This resultconfirms our univariate findings that securities analysts do not do a good job ofidentifying takeover targets.

Affiliated, the other primary variable of interest, is included to gauge con-flicts of interest. The Affiliated coefficient estimate for acquirers is small in mag-nitude and not statistically significant. Our multivariate findings of analyst ratingsdo not provide evidence of a conflict. We note, however, that affiliated analystrecommendations are significantly more positive for target firms preceding theannouncement. One may be inclined to interpret this as evidence consistent withbiased research; however, as discussed previously, investors acting on this informa-tion would be better, not worse, off. Also, these results are most likely the effect ofquarters −4 and −3, as we documented previously.

We include Nobank to see whether acquiring firms that do not use anadvisor have different ratings from those that do. It is plausible that investmentbanks may feel slighted if a potential client chooses not to use an advisor and couldconsequently issue a negative recommendation. This does not appear to be the case.In fact, once the tender offer announcement occurs (and the realization is knownby all investment banks), acquiring firms that do not use an advisor have moreoptimistic ratings.7

7Typically, firms not using advisors are large firms that have participated in acquisition-related activityin the past. They also tend to acquire small firms relative to their size. In our sample, 11% of acquirers donot use an advisor, whereas only about 1% of targets do not. Given the small percentage of targets that donot use a bank and the fact that conflicts are likely to be less severe for target firms, we do not includeNobank in the target regressions.

Analyst Behavior 11

TABLE 2. Regression Results: Analyst Ratings.

Panel A. Full Sample Period

Acquirer Target

Variable Before After Before After

Intercept 1.85 2.62 2.27 2.79(.0001) (.0001) (.0001) (.0001)

Quarter–4 0.06 −0.25(.1340) (.0001)

Quarter–3 0.05 −0.28(.1839) (.0001)

Quarter–2 −0.02 −0.17(.5019) (.0028)

Quarter 2 −0.02 −0.46(.6328) (.0001)

Quarter 3 −0.02 −0.50(.5327) (.0001)

Quarter 4 −0.01 −0.36(.8589) (.0064)

Affiliated −0.09 −0.01 −0.12 −0.07(.1799) (.9181) (.1039) (.7447)

Nobank 0.02 −0.13(.5329) (.0005)

Logtrval 0.00 −0.01 0.01 −0.01(.9682) (.5257) (.4809) (.7408)

Relsize 0.01 0.04(.5055) (.1579)

Logcap 0.00 −0.03(.9612) (.0014)

Samesic 0.07 0.06(.0287) (.0534)

N 3,767 4,381 1,673 525Adj. R2 0.0017 0.0161 0.0167 0.0535

Panel B. Time Trend Analysis: Affiliated Dummy Coefficient

Acquirer

Years Before After

1997–1998 −0.02 −0.24(.9060) (.1700)

1999–2000 −0.24 −0.21(.0049) (.0460)

2001–2002 0.44 0.14(.0001) (.0570)

Note: This table provides regression results with analyst ratings as the dependent variable. Before andafter columns partition the sample based on whether the recommendation occurred before or after theannouncement of the tender offer. Quarter represents the quarter a recommendation occurred in relationto the announcement day. Affiliated is a dummy variable equal to 1 if the recommendation was made byan investment bank that was providing merger advisory services for the firm, and 0 otherwise. Nobankis a dummy variable equal to 1 if there is no affiliated investment bank, and 0 otherwise. Logtrvalis the natural log of the value of the transaction. Relsize is the value of the transaction scaled by themarket value of the acquiring firm. Logcap is the market capitalization of the firm 30 calendar daysbefore the merger announcement. Samesic is a dummy variable equal to 1 if the merger partners are inthe same four-digit Standard Industrial Classification code, and 0 otherwise. The p-values are in parentheses.


The remaining explanatory variables are included as means to condition forjoint effects.8 Three of the four variables are size related, and Samesic controls forfocus-increasing tender offers. Although the size-related variables do not explainanalyst ratings, Samesic appears to influence analyst opinion for the acquiring firms.The regression models do not explain a significant portion of analyst ratings, asjudged by the relatively low adjusted R2s.

Because regulators suggest that conflicts of interest were exacerbated dur-ing the 1999–2000 Internet bubble period, we investigate subperiods to determinewhether any discrepancies emerge. Panel B of Table 2 reports the coefficient forAffiliated when the acquirer sample is divided into several periods. We find thatduring 1999–2000, affiliated analysts were statistically and economically more op-timistic. During 2001–2002, however, this pattern reversed and affiliated analystsbecame less optimistic than unaffiliated analysts.

Our univariate results, combined with the multivariate analysis of analystratings, indicate that: (1) analysts do not possess a superior ability to identify targetfirms of tender offers, and (2) some evidence of a conflict of interest exists, butit is solely concentrated during 1999–2000. We turn to initial market reactions tofurther analyze this finding.

Initial Market Reaction to Analyst Recommendations

We rely on standard event-study methods to investigate the market reactions toanalyst recommendations.9 The average cumulative abnormal return (CAR) dur-ing the day (−1,+1) window is −0.81% for acquirer recommendations, which issignificantly different from zero.

For Buy and Strong Buy recommendations, the average stock price reactionfor acquirers is 0.1% whereas the stock price drops 0.8% on average for sub-Buy recommendations. The reactions are actually more positive for affiliated thanfor nonaffiliated analysts after the announcement of the tender offer when themarket has become aware of the potential conflict of interest. Under the conflict ofinterest hypothesis, market participants should discount advisor recommendations,but this does not appear to be the case. We analyze this finding in a multivariatesetting later. Additionally, if we expand the announcement window to (−10,+10),we find that advisor Buy recommendations are outperformed by nonadvisor Buyrecommendations by about 2.4%.

8Because it is possible that the affiliated and unaffiliated analyst recommendations are on differentdistributions of firms, we reestimate our model and find that the results are robust to several measures usedby Jegadeesh et al. (2004) to control for firm fundamentals.

9Reported results are based on the CRSP value-weighted index as the market proxy. Using the equallyweighted index instead does not qualitatively change the results. When we eliminate all recommendationsoccurring within three trading days of another recommendation by the same analyst on the same company,we find similar results, thereby alleviating concerns of cross-sectional dependence.

Analyst Behavior 13

Multivariate Analysis of CARs

We regress the day (−1,+1) CARs on the same explanatory variables used pre-viously and incorporate two new variables to condition for the strength of therecommendation. Strong Buy and Sub-Buy are dummy variables equal to 1 if therecommendation is Strong Buy or sub-Buy, respectively, and 0 otherwise. (For thisanalysis, Buy recommendations are the omitted category; therefore, Strong Buyand sub-Buy capture the incremental abnormal return relative to Buy.) For com-pleteness, we provide the reactions for target recommendations even though ourfocus is on the acquiring firm after the announcement of a tender offer is released.

In Table 3, Strong Buy (sub-Buy) recommendations are generally greetedpositively (negatively) by market participants. However, Affiliated, the primaryvariable of interest, is not statistically significant in either period for acquirers (ortargets), which does not support the conflict of interest hypothesis.

We provide a summary by subperiod for acquiring firms in Panel B. Sim-ilar to Table 2, the affiliated coefficient estimate is statistically significant during1999–2000, consistent with the conflict of interest hypothesis. The market discountsaffiliated underwriter recommendations by 3.6% during the dot-com period, sug-gesting that market participants understood the conflicts during this period, whichcoincided with heavy scrutiny from regulators. As a final test of the conflict ofinterest hypothesis, we evaluate long-run returns.

Long-Run Returns

We examine acquirer long-run performance following analyst recommendationsfor two reasons. First, we investigate potential conflicts of interest. If conflictsare present, we would expect affiliated analyst recommendations to underperformthose made by unaffiliated analysts. Second, we examine whether analysts’ recom-mendations are valuable by determining whether analysts are able to distinguishbetween wealth-increasing and wealth-decreasing tender offers.

Because analysts’ recommendations are (at least nominally) based on howthe company’s stock is expected to perform over the next 12 months relative to themarket, we compute one-year buy-and-hold returns from each recommendationdate. We rely on recommendations occurring after the tender offer announcementbecause pre-announcement recommendations would include the announcement pe-riod and could bias our results against finding that analysts add value. We calculateraw returns and market-adjusted returns (using the CRSP value-weighted index).We also use a size/book-to-market matched-firm approach as in Barber and Lyon(1997).10

10For each firm, we first find a matching firm that is closest in market capitalization and book-to-market ratio. We then subtract the matching firm’s holding-period return from our firm’s holding-periodreturn to arrive at the abnormal return.


For the full sample of acquiring firms, analyst one-year abnormal perfor-mance is not significantly different from zero (2.0% for market-adjusted abnormalperformance and −1.8% for Barber and Lyon 1997 abnormal performance). How-ever, the full sample includes both favorable and unfavorable recommendations,which need to be evaluated separately.

In Table 4 we evaluate the long-run performance of analyst recommenda-tions based on ratings strength. Focusing on the size/book-to-market control firm,stocks that received a rating of 3 are the best performers (in magnitude). From a

TABLE 3. Regression Results: Cumulative Abnormal Returns (CARs) Around RecommendationDates.


Acquirer Target

Variable Before After Before After

Intercept 10.21 3.34 −0.77 9.97(.0001) (.1430) (.6800) (.0109)

Quarter–4 0.04 −3.37(.9207) (.0006)

Quarter–3 −0.14 −4.44(.7449) (.0001)

Quarter–2 −0.27 −4.45(.5366) (.0001)

Quarter 2 −0.00 −9.60(.9965) (.0001)

Quarter 3 −0.44 −7.28(.2877) (.0037)

Quarter 4 0.81 −7.09(.0472) (.0083)

Strong Buy 1.57 1.80 3.04 3.04(.0001) (.0001) (.0004) (.2347)

Sub-Buy −3.15 −3.92 −2.00 5.69(.0001) (.0001) (.0191) (.0012)

Affiliated −0.41 −0.26 −1.53 1.01(.5794) (.7020) (.2324) (.8118)

Nobank 0.94 0.46(.0373) (.2998)

Logtrval 0.41 −0.34 0.38 −0.71(.0001) (.0021) (.1219) (.1466)

Relsize −0.04 0.52(.8458) (.1414)

Logcap −0.54 −0.10(.0001) (.3532)

Samesic −2.45 −0.18(.0001) (.6001)

N 3,767 4,381 1,673 525Adj. R2 0.0531 0.0550 0.0289 0.0792

(Continued)

Analyst Behavior 15

TABLE 3. Continued.


Year Coefficient N

1997–1998 0.10 412(.6242)

1999–2000 −3.60 2,157(.0079)

2001–2002 0.63 1,812(.4385)

Note: This table provides regression results with the day (−1, +1) CAR centered around the recom-mendation date as the dependent variable. Panel A provides full-sample results, and Panel B presentsthe coefficient and p-value for the affiliated dummy variable in each period. Before and after columnspartition the sample based on whether the recommendation occurred before or after the announcementof the tender offer. Quarter represents the quarter a recommendation occurred in relation to theannouncement day. Strong Buy is a dummy variable equal to 1 if the recommendation is the analyst’sbest rating, and 0 otherwise. Sub-Buy is a dummy variable equal to 1 if the recommendation is belowbuy, and 0 otherwise. Affiliated is a dummy variable equal to 1 if the recommendation was made byan investment bank that was providing merger advisory services for the firm, and 0 otherwise. Nobankis a dummy variable equal to 1 if there is no affiliated investment bank, and 0 otherwise. Logtrvalis the natural log of the value of the transaction. Relsize is the value of the transaction scaled by themarket value of the acquiring firm. Logcap is the market capitalization of the firm 30 calendar daysbefore the merger announcement. Samesic is a dummy variable equal to 1 if the merger partners arein the same four-digit Standard Industrial Classification code, and 0 otherwise. The p-values are inparentheses.

market-adjusted and raw return standpoint, stocks that received a rating of 4 (theworst rating) are the best performers (once again in magnitude) This is consistentwith Barber et al. (2003), who find that stocks rated the least favorable significantlyoutperform the top-rated stocks, and it again questions the usefulness of analystrecommendations. These results suggest that analysts add little, if any, value toinvestors who follow their recommendations around a tender offer.

To determine whether analyst conflicts are at least partially responsiblefor this unusual result, we implement virtually the same multivariate model usedin evaluating initial market reactions (CARs) in Table 3, except that we use theone-year buy-and-hold return as the dependent variable. The results are presentedin Table 5.

Affiliated, the primary variable of interest, is negative in both models, but itis not significantly different from zero. Hence, we do not find evidence that perfor-mance following affiliated analyst recommendations is inferior to that following un-affiliated recommendations. Similar to previous work, such as Megginson, Morgan,and Nail (2004), we find that the long-run performance of focus-increasing tenderoffers is higher (as evidenced by the positive coefficient estimate on Samesic).Nobank is positive in the size/book-to-market model but not in the market-adjustedmodel.


TABLE 4. Long-Run Returns for Acquiring Firms by Analyst Rating Strength.

Size/BM MarketRating Matched Adjusted Raw

1 −1.28 2.62 −6.21(1,533) (1,533) (1,533)

2 −3.31 1.71 −2.99(1,739) (1,739) (1,739)

3 0.00 1.54 −2.58(1,056) (1,056) (1,056)

4 −1.49 6.25 −0.01(55) (55) (55)

5 N/A N/A N/A

Note: This table presents average one-year buy-and-hold returns (in percent) following analyst recommen-dations for firms performing a tender offer partitioned by analyst rating strength, where analyst ratingsare based on a five-point scale with 1 being the best and 5 the worst. We use a size and book-to-market(BM) measure based on Barber and Lyon (1997), a market-adjusted measure using the CRSP value-weighted index as a market proxy, and the raw buy-and-hold return. The number of observations are inparentheses.

We include Strong Buy and Sub-Buy to examine the value provided byanalyst recommendations. The estimates on both coefficients are positive, indicat-ing that both Strong Buy and sub-Buy recommendations perform better than Buyrecommendations, although only Sub-Buy is statistically different from zero at the10% level (and only using the size/book-to-market approach).11 Thus, followingthe recommendations of analysts in the period surrounding a tender offer does notappear to result in better investment performance. The remaining variables appearto be significantly related to the long-run performance of analyst recommendations,but these are primarily conditioning variables, and for the sake of brevity, we donot report these results.12

Finally, consistent with our previous tables, we investigate time-series pat-terns in Panel B of Table 5, focusing on the Affiliated coefficient. The dependent

11Given that there is some degree of cross-sectional dependence because different analysts makerecommendations on the same firm (and, to a lesser extent, time-series dependence because the same an-alyst sometimes makes multiple recommendations on the same firm), our test statistics may be overstatedas shown by Mitchell and Stafford (2000). However, there is no evidence that performance after affili-ated recommendations is different from performance after unaffiliated recommendations, or that strongerrecommendations are associated with higher long-run performance than are weaker recommendations.

12We also analyze long-run performance (one year from the announcement date) at the firm level.We find no evidence of underperformance (Barber and Lyon 1997 returns and market-adjusted returns of−1.2% and −1.0%, respectively). Similarly, we find no difference in the long-run returns between firmsthat have an affiliated analyst following the acquiring firm within one year of the announcement and thosethat do not.

Analyst Behavior 17

TABLE 5. Regression Results: Long-Run Returns for Acquiring Firms Following AnalystRecommendations.


Size/BM MarketVariable Matched Adjusted

Intercept 28.23 28.75(.0001) (.0001)

Strong Buy 3.79 2.54(.1206) (.2208)

Sub-Buy 4.95 1.11(.0639) (.6240)

Affiliated −0.01 −5.52(.8722) (.1920)

Nobank 7.19 2.48(.0108) (.3022)

Logtrval −6.30 −5.49(.0001) (.0001)

Relsize 4.85 4.35(.0817) (.0664)

Samesic 10.09 12.57(.0001) (.0001)

N 4,389 4,389Adjusted R2 0.0244 0.0269


Year Coefficient N

1997–1998 −5.47(.6287) 412

1999–2000 −6.20(.5980) 2,157

2001–2002 1.79(.6615) 1,812

Note: This table provides regression results for firms performing tender offers with the one-yearbuy-and-hold return as the dependent variable. Panel A provides full-sample results, and Panel B presentsthe coefficient and p-value for the affiliated dummy variable in each period. We calculate both a sizeand book-to-market (BM) measure similar to Barber and Lyon (1997) and a market-adjusted measureusing the CRSP value-weighted index as a market proxy. Strong Buy is a dummy variable equal to 1 ifthe recommendation is the analyst’s best rating, and 0 otherwise. Sub-Buy is a dummy variable equal to1 if the recommendation is below buy, and 0 otherwise. Affiliated is a dummy variable equal to 1 if therecommendation was made by an investment bank that was providing merger advisory services for thefirm, and 0 otherwise. Nobank is a dummy variable equal to 1 if there is no affiliated investment bank, and0 otherwise. Logtrval is the natural log of the value of the transaction. Relsize is the value of the transactionscaled by the market value of the acquiring firm. Samesic is a dummy variable equal to 1 if the mergerpartners are in the same four-digit Standard Industrial Classification, and 0 otherwise. The p-values are inparentheses.

variable is the matched firm-adjusted return, although the market-adjusted re-turn provides similar results. Although affiliated underwriter recommendationsunderperform those of unaffiliated analysts during 1999–2000, the difference inreturns is not statistically different from zero.


IV. Concluding Remarks

We examine analyst behavior surrounding tender offer announcements. A conflictof interest may exist for analysts associated with the firm’s merger advisor becausethe bank may promise superior recommendations if chosen to perform the advisoryactivities. Because the fee structure for tender offer advisors is different from thatof other investment bank services, examining analyst behavior around tender offerspresents the opportunity to examine the scope of analyst conflicts of interest. Weexamine the market reaction to and level of recommendations made between 1997and 2002. Although we do not find evidence of biased recommendations in ourfull-sample results, we do find that affiliated advisor recommendations are moreoptimistic than those made by unaffiliated analysts during 1999–2000, the periodunder the most scrutiny by regulators. During this period, the market also discountedrecommendations made by affiliated analysts, consistent with the view that marketparticipants realized the recommendations were potentially biased.

Although our results show that conflicts of interest are not limited to un-derwriting, it is not clear whether these conflicts are more or less severe with M&Aadvisors. Although M&A advisory fees are smaller than underwriting fees on aper deal basis, there are typically many more M&A deals in a given period thansecurity issuances. If providing optimistic ratings is related to winning mandates,unfavorable ratings may be related to switching M&A advisors.

We find little evidence to suggest that affiliated analysts enhance investors’returns following their recommendations. Analysts do not appear to add value byrecommending the purchase of takeover targets before announcement or by differ-entiating between merger combinations that will or will not ultimately create valuefor shareholders. We do not observe a significant difference between the long-runperformance of affiliated and unaffiliated recommendations. Additionally, stockswith favorable recommendations perform no better than stocks with unfavorablerecommendations. In other words, it does not appear that analysts, regardless oftheir affiliation, can discern which acquisitions will create value. Investors follow-ing the advice of affiliated analysts are not significantly worse off than investorswho follow the advice of unaffiliated analysts. Therefore, although affiliated an-alysts may issue biased recommendations, there is little ultimate cost to investorswho follow their advice.

References

Andrade, G., M. Mitchell, and E. Stafford, 2001, New evidence and perspectives on mergers, Journal ofEconomic Perspectives 15, 103–20.

Arnold, T., G. Erwin, L. Nail, and T. Bos, 2000, Speculation or insider trading: Informed trading in op-tions markets preceding tender offer announcements, Working paper, University of Alabama-Birmingham.

Analyst Behavior 19

Barber, B., R. Lehavy, M. McNichols, and B. Trueman, 2001, Can investors profit from the prophets?Consensus analyst recommendations and stock returns, Journal of Finance 56, 531–63.

Barber, B., R. Lehavy, M. McNichols, and B. Trueman, 2003, Reassessing the returns to analysts’ stockrecommendations, Financial Analysts Journal 59, 88–96.

Barber, B. and J. Lyon, 1997, Detecting long-run abnormal stock returns: The empirical power and speci-fication of test statistics, Journal of Financial Economics 43, 341–72.

Bhushan, R. 1989, Firm characteristics and analyst following, Journal of Accounting and Economics 11,255–74.

Bradley, D., B. Jordan, and J. Ritter, 2003, The quiet period goes out with a bang, Journal of Finance 58,1–36.

Bradshaw, M., S. Richardson, and R. Sloan. 2003, Pump and dump: An empirical analysis of the relationbetween corporate financing activities and sell-side analyst research, Working paper, HarvardUniversity, University of Pennsylvania, and University of Michigan.

Clarke, J., S. Ferris, N. Jayaraman, and J. Lee, 2006, Are analyst recommendations biased? Evidence fromcorporate bankruptcies, Journal of Financial and Quantitative Analysis, 41, 169–96.

Denis, D. and J. McConnell, 1986, Corporate mergers and security returns, Journal of Financial Economics16, 143–87.

Dugar, A., and S. Nathan, 1995, The effect of investment banking relationships on financial analysts’earnings forecasts and investment recommendations, Contemporary Accounting Research 12,131–60.

Halpern, J., 1976, Empirical estimates of the amount and distribution of gains to companies in mergers,Journal of Business 49, 554–75.

Hasbrouck, J. 1985, The characteristics of takeover targets: Q and other measures, Journal of Banking andFinance 9, 351–62.

Jegadeesh, N., J. Kim, S. Krische, and C. Lee, 2004, Analyzing the analysts: When do recommendationsadd value? Journal of Finance 59, 1083–1124.

Keown, A. and J. Pinkerton, 1981, Merger announcements and insider trading activity: An empirical inves-tigation, Journal of Finance 36, 855–69.

Kolasinski, A. and S. Kothari, 2004, Investment banking and analyst objectivity: Evidence from forecastsand recommendations of analysts affiliated with M&A advisors, Working paper, MassachusettsInstitute of Technology.

Krigman, L., W. Shaw, and K. Womack, 2001, Why do firms switch underwriters? Journal of FinancialEconomics 60, 245–84.

Lin, H. and M. McNichols, 1998, Underwriting relationships, analysts’ earnings forecasts and investmentrecommendations, Journal of Accounting and Economics 25, 101–27.

Megginson, W., A. Morgan, and L. Nail, 2004, Changes in corporate focus, ownership structure, and long-run merger returns, Journal of Banking and Finance 28, 523–52.

Michaely, R. and K. Womack, 1999, Conflict of interest and the credibility of underwriter analyst recom-mendations, Review of Financial Studies 12, 653–86.

Mitchell, M. and E. Stafford, 2000, Managerial decisions and long term stock price performance, Journalof Business 73, 287–329.

Shleifer, A. and R. Vishny, 2003, Stock market drive acquisitions, Journal of Financial Economics 70,295–311.

Song, M. and R. Walkling, 1993, The impact of managerial ownership on acquisition attempts and targetshareholder wealth, Journal of Financial and Quantitative Analysis 28, 439–57.

Womack, K., 1996, Do brokerage analysts’ recommendations have investment value, Journal of Finance51, 137–67.

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