Do Affiliated Analysts Mean What They Say?

Do Affiliated Analysts Mean What They Say?Author(s): Michael T. CliffSource: Financial Management, Vol. 36, No. 4 (Winter, 2007), pp. 5-29Published by: Wiley on behalf of the Financial Management Association InternationalStable URL: http://www.jstor.org/stable/30129810 .

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Do Affiliated Analysts Mean What They Say?

Michael T. Cliff*

Many investors were upset with the losses they experienced by following the recommendations of stock analysts during the recent market downturn. Allegations that these recommendations were often tainted by investment banking relationships fueled their anger This study examines the investment performance of stock recommendations made by analysts employed by lead underwriters as compared to analysts independent of investment banking. The results indicate that during the 1994-2005 time period, Buy or Hold recommendations from affiliated analysts underperform stocks recommended by independent analysts. On the other hand, shorting their Sells earns significant abnormal returns. Announcement period returns suggest that the market over-reacts to Buys from affiliated analysts, but under-reacts to their Holds or Sells. Further analysis indicates that affiliated analyst recommendations are viewed as more credible following recent regulatory reforms.

Considerable attention has been paid in recent years to "independent" stock analysts - that is, those analysts that are not affiliated with investment banks. It is widely assumed (at least by much of the media and regulatory community) that the conflict of interest between the investment banking and research arms of financial services firms causes the investment performance of stock recommendations from affiliated analysts to lag behind that of independent analysts. However, evidence on the comparative performance of independent stock research is mixed. This paper provides comprehensive evidence on the investment performance of stock recommendations from affiliated and independent analysts.

The profession of stock analyst has traveled a meteoric path the past decade. Before the mid- 1990's, stock analysts were largely unknown to the general public. During the spectacular run-up of the late 1990's, many analysts achieved celebrity status. The best-known analysts generally worked at Wall Street's leading investment banks and leveraged their stature by landing lucrative banking deals for their firms and multi-million dollar pay packages for themselves. However, the public's love affair with these financial superstars faded quickly as share prices tumbled. From the market peak in 2000 to the end of 2002, investors in U.S. equities lost over $7 trillion, or about 40% of their value at the peak. Despite stratospheric valuation levels near the market peak, analysts were generally bullish and remained so as stocks plummeted.

In the wake of such devastating losses, why did the recommendations serve investors so poorly? New York State Attorney General Eliot Spitzer was a prominent figure in the quest to find an answer

The author thanks Mukesh Bajaj, Dan Bradley, Dave Denis, Michael Eames, Huseyin Gulen, John McConnell, Atulya Sarin, Vijay Singal, an anonymous referee, Bill Christie (the editor), and seminar participants at the University ofArizona, Penn State, and Virginia Tech for their comments. Ken French kindly provided data on factor returns and Thomson Financial provided the I/B/E/S recommendations data. Don Autore and Ajay Bhootra provided valuable research assistance. Any remaining errors are mine.

*Michael T Cliff is an Assistant Professor at the Pamplin College of Business at Virginia Tech in Blacksburg, VA. A substan- tial part of the work on this paper was completed while the author was at Purdue University.

Financial Manacaement * Winter 2007 * oaaes 5 - 29



6 Financial Management * Winter 2007

(see Spitzer, 2002). A series of well-publicized episodes of alleged misconduct served as the impetus for the $1.4 billion Global Settlement in April 2003 between ten leading Wall Street firms and a collection of regulators. A total of $450 million of this is earmarked for "independent research." Each bank is required to fund research by at least three government-approved independent firms and distribute reports from these firms along with their own research reports. The settlement and related regulations such as NASD rule 2711 and NYSE rule 472 also include other provisions about disclosing conflicts, providing recommendations histories, and severing the links between the research and investment banking groups within the banks.

A basic premise of the settlement is that the conflicts of interest between investment banking and research cause analysts to issue misleading recommendations. In other words, what these analysts said was not necessarily what they meant. Sophisticated investors may be aware of this conflict and interpret the recommendations accordingly. In support of this view, Boni and Womack (2002) report that 87% of the buy-side professionals they survey believe that banking conflicts are an important motivation for analysts to provide optimistic recommendations and that 79% interpret a Hold to mean Sell. However, the focus of the settlement is on retail investors who may be unable to decode the recommendations on their own. In the words of Spitzer, his probe "... has been about one thing. It has been about ensuring that retail investors get a fair shake" (Spitzer, 2002). This paper examines whether access to independent research may help these investors make better decisions.

Spitzer's critics question whether investors are likely to benefit from these independent recommendations. The literature does not provide a clear answer to this question. A number of papers (e.g., Dugar and Nathan, 1995; Lin and McNichols, 1998; Iskoz, 2003; and Agrawal and Chen, 2005b) find insignificant differences between the investment performance of recommendations from affiliated and unaffiliated analysts. Though the papers differ in sample periods and several methodological details, there are three common elements that may explain their results.

One critical issue is the definition of the unaffiliated benchmark. None of these papers uses a control group that is free from investment banking conflicts. In most cases, the control group consists of underwriters who were not lead managers for the covered firm. Of course those banks may have an interest in becoming the lead underwriter on future banking deals. Agrawal and Chen (2005b) do not include the banking relationship between the broker and covered firm. Instead, they use the fraction of each broker's revenues derived from banking as a proxy for the conflict of interest. This implicitly assumes that a given bank faces an identical conflict with each firm it covers.

A second methodological issue common to these studies is the use of arbitrary holding periods for measuring abnormal returns. None allow for holding the position more than one year beyond the recommendation. This might mask poor investment performance associated with lead analysts' alleged reluctance to downgrade banking clients and willingness to provide "booster shots" for floundering clients. Also, some of these studies maintain positions beyond the life of the recommendation which may further misstate actual investment performance.

A third methodological issue is the model used to measure abnormal returns. Dugar and Nathan (1995) use simple market-model residuals while Lin and McNichols (1998) use size-matched portfolios. Iskoz (2003) and Agrawal and Chen (2005b) use the Fama and French (1993) model, but given the evidence in Jegadeesh, Kim, Krische, and Lee (2004) that recommendations are related to past returns, it would be useful to include the momentum factor as well.

In contrast to these papers, a few studies provide evidence that affiliated analysts do indeed provide inferior investment recommendations. Michaely and Womack (1999) use a methodology that runs into the three issues raised above (contaminated unaffiliated benchmark, arbitrary holding periods, and size-adjusted abnormal returns). Their sample of IPOs from 1990-1991 is also



Cliff * Do Affiliated Analysts Mean What They Say? 7

rather limited, which may be driving their results. In fact, McNichols, O'Brien, and Pamukcu (2006) update their study for the 1994-2001 time period and find no significant difference between independent and affiliated analyst recommendations. In addition, Dunbar, Hwang, and Shastri (1999) provide evidence that most of the poor performance from underwriter recommendations comes from their initial coverage and that subsequent underwriter Buy recommendations perform slightly better than those from unaffiliated analysts.

In a contemporaneous paper, Barber, Lehavy, and Trueman (2007) avoid the three methodological issues plaguing other papers. Their evidence indicates investment bank Buy recommendations do underperform, largely due to coverage of recent equity issuers in the period following the market peak in early 2000. However, they do not directly link the broker making the recommendation to a banking relationship with the covered firm. In addition, they examine Hold and Sell recommendations together. They find underperformance for these recommendations as well, but it is unclear how to interpret the results. It makes a difference whether the underperformance arises from Holds or from Sells. For example, underperformance from both Holds and Sells is consistent with the conflict of interest hypothesis. However, neutral Hold performance coupled with negative Sell performance is more consistent with investment banks having superior information.

This paper adds to the existing literature by providing the most comprehensive comparison to date of the investment performance of affiliated and independent recommendations. The relatively long sample period of 1994-2005 allows more reliable analysis of the period following the market peak in 2000 and the regulatory reforms in 2002. The sample is broad in that it includes issuers of debt and convertibles in addition to IPOs or SEOs. The research design is careful to avoid the contamination of the comparison groups. Investment banks that were not lead managers are not included in either group as their conflict is unclear. Relative to other papers, I have the most comprehensive battery of tests for risk adjustment, including use of a factor to control for the issuer-oriented nature of the sample. I also separately examine Sell recommendations since NASD rule 2711 emphasizes that they should be distinct from Holds. Finally, I supplement the analysis of investment performance with an examination of the announcement period returns.

To evaluate the performance of analysts' recommendations I consider two hypothetical investors. One investor follows the recommendations of the analysts at "Independent" brokers while the other listens to analysts working at investment banks serving as lead underwriter for a securities issuance by the covered firm.' I refer to the recommendations from the latter as coming from "Lead" brokers. Each investor tracks three portfolios corresponding to the level of the recommendation: Buy, Hold, or Sell. The two broker classes and three recommendation levels yield six groups into which the recommendations are classified. I then examine the performance of portfolios for each of these six categories, much like mutual fund performance evaluation.

To interpret the performance of these portfolios it is necessary to take a stand on the meaning of the recommendation categories. The position of this paper is to take the recommendations at face value. That is, a Buy should signal "good" anticipated performance, "Hold" neutral performance, and "Sell" poor performance. Though some may view this literal interpretation as naive, it is consistent with the spirit of the Global Settlement and the plain-meaning mandate in NASD rule 2711(h)(4) and the associated interpretation articulated by the NASD and NYSE (2002, page 375). This interpretation is also in line with many of the brokers' own definitions of their ratings. For example, Merrill Lynch's top rating (on a five point scale) meant "20% or more of price

'I use the term "broker" to refer to the organization employing the analyst. In some cases these organizations may be pure research firms that do not have brokerage operations.




growth expected" while their middle rating was for expected returns of + 10% (see New York State Attorney General, 2002, page 8).

A related issue is the time horizon over which the recommendation is followed. This paper considers the returns earned over the life of the recommendation. As noted earlier, many aca- demic papers ignore recommendations after one year as they may be "stale."2 However, this arbitrary treatment is inconsistent with the complaints that brokers were too slow to downgrade stocks following the market collapse in 2000 and with a plain-meaning standard for recommendations. If an analyst no longer feels a stock is likely to deliver favorable performance after several months as a Buy, it is incumbent upon them to downgrade the stock rather than leaving the Buy in place. In addition, many investors do hold positions for more than one year, if for no other reason to qualify for lower tax rates on capital gains. Furthermore, NASD 2711 requires brokers to include recommendation histories over the prior three years in their research reports.

Given the literal interpretation of recommendations, this paper does not address whether the recommendations can be used to identify some profitable trading rule. For example, a broker's recommendations would be a useful (contrarian) trading signal if Buys precede poor returns and Sells precede good returns. This broker is providing a useful public signal, but only if the investor knows how to decode the stated advice. It is also worth emphasizing that the analysis is based only on the analysts' public reports. One of the allegations raised about the investment banks' recommendations is that the analysts write a report with one rating but then call their important clients to provide a different recommendation verbally. In fact, analysts at Credit Suisse First Boston (CSFB) refer to this process as the "Agilent Two-Step."3 The analysts' verbal recommendation may also be useful investment advice, but the typical brokerage customer does not have access to it.

The evidence in the paper indicates that Buy or Hold recommendations from Lead analysts perform poorly, losing roughly 25 basis points per month. The corresponding Independent portfolios have neutral performance. This evidence is similar to that in Barber, Lehavy, and Trueman (2007). The picture for Sells is much different. Lead Sells have large negative abnormal returns so shorting those stocks would deliver good performance. Performance of the Independent portfolio is again neutral so the spread between the two portfolios of 70 basis points per month or more is consistent with the notion that a conflict of interest makes Lead Sells a particularly strong signal. These results are robust to alternative risk adjustments and a host of other methodological choices.

I then extend the analysis to subsamples to shed light on the sources of differential performance. Splitting the sample into Bull and Bear subsamples based on the March 2000 market peak does not yield significant subsample differences except for Lead Sells. This result differs from Barber, Lehavy, and Trueman (2007), due to my longer sample period. Analyzing each issuance type separately shows that the poor Lead performance is not driven by IPOs. Indeed, underperformance of Lead Buys for SEOs or debt offerings is even larger than that for IPOs.

Though the portfolio performance analysis excludes the announcement effect, a separate analysis shows that the announcement effect does not drive the results. Over the full sample, Lead Buys earn a three-day announcement abnormal return of 1.2% percent, versus 0.6% for Inde- pendents. Holds or Sells from either group have negative announcement returns, but the price

2See, for example, Dugar and Nathan (1995), Lin and McNichols (1998), Michaely and Womack (1999), Iskoz (2003), Agrawal and Chen (2005b), and McNichols, O'Brien, and Pamukcu (2006).

3This and other examples are cited in support of the allegation that "Analysts disseminated biased, subjective, and com-

promised research favorable to CSFB investment banking clients, which resulted in ... millions of dollars of investment

banking fees for CSFB." (Massachusetts Securities Division, 2002, p. 1-2).




drop for Leads is four or more percentage points larger. Considering the CAR results and negative Lead portfolio performance together, the CARs indicate that the market over-reacts to their Buys but under-reacts to their Holds or Sells. When splitting the CAR analysis into subsamples the results suggest that NASD Rule 2711 may have improved the credibility of Lead recommendations.

The rest of the paper proceeds as follows. Discussion of the recommendations data and some methodological issues are in Section I. Section II evaluates the performance of calendar-time portfolios based on the recommendations using a variety of factor models. Section III examines the announcement period returns. Finally, concluding remarks are offered in Section IV. A number of robustness checks and additional details are provided in a separate Appendix.4

I. Construction of Lead and Independent Portfolios

There are two broad steps in preparing the data for analysis. The first step is to construct a sample of recommendations made by independent and affiliated analysts. The second step is to use these recommendations to form calendar time portfolios. This section describes the main issues in this process.

A. Sample Construction

The sample selection begins by obtaining the list of firms issuing securities between 1992 and 2005 from the Securities Data Corporation (SDC) New Issues database. I include all non-financial issuers regardless of security type (e.g., IPOs, SEOs, preferred stock, debt, convertibles, and other securities such as asset-backeds).5 For each issue, I identify the investment bank(s) serving as the Lead underwriter (described below).

I then retrieve from I/B/E/S the recommendations made by Lead and Independent analysts for each issuing firm. I merge the recommendations and stopped coverage files to construct a history of recommendations for a given firm from each broker. Since the adoption of NASD rule 2711, many brokers now report recommendations on a three-point scale. I recode "Strong Buy" recommendations as "Buys" and similarly combine "Strong Sells" and "Sells."6 Reiterations are dropped from the database as they would have no effect on the portfolio holdings of my hypothetical investors (described in more detail in Section D).

4The Appendix is available at http://www.fma.org/FinMgmt/Appendices/CliffAppendix.pdf.

5Much of the focus of the Spitzer investigation was on IPO underwriting. Yet it is possible that subsequent equity or debt issuances create even more important conflicts. For example, Jack Grubman faced Congressional scrutiny over his involvement in securing Salomon Smith Barney as lead underwriter on WorldCom's $17 billion bond offerings. NASD 2711 requires disclosure of all underwriting relationships.

6The rule permits brokers to use whatever rating system they choose so long as it is clearly defined, but requires brokers to report the distribution of their outstanding recommendations on a three-point scale. I/B/E/S does continue to classify recommendations into a five-point scale following the September 9, 2002 effective date of NASD 2711. Some broker- ages (e.g., Goldman Sachs) use a 2-3-4 scale while others (e.g., Merrill Lynch) use 1-3-5. However, there is no meaning- ful distinction between 1 and 2, or 4 and 5. Inspection of the "Broker Text" field shows that some brokers have literally adopted the simple three-point scale (e.g., JP Morgan, Merrill Lynch, Smith Barney) while others (e.g., First Boston, Goldman Sachs, Lehman, Morgan Stanley) have more elaborate systems. Also, rule 2711 triggered a wave of stoppages which, in many cases, were resumed within a few days. Consequently I delete all Stops which are resumed within seven days.




B. Independence Class

To interpret the influence of a banking conflict as cleanly as possible, I consider two groups of analysts on opposite ends of the spectrum in terms of their conflict of interest. I refer to this grouping variable as Class, which is defined as:

Lead- The recommendation originates from the broker (or a member of that corporate family at that point in time) who was lead or joint-lead underwriter on the covered firm's issuance.7 As the strength of the relationship between issuer and underwriter may wane over time, only recommendations made between one year prior to and two years after the issuance are included.8

Independent - These recommendations are made by organizations that do not perform a material amount of underwriting.9 To facilitate comparison with the Leads, I limit Independent coverage to the same set of issuing firms for the same three year window around issuance.

The classification excludes the recommendations made by co-lead underwriters or general syndicate members. In some cases, these firms may face conflicts of interest that are as strong, or perhaps even stronger, as those of the Lead underwriters. For instance, a bank who served as a co-lead on the IPO may aggressively pursue the lead role in a SEO or debt issuance. Due to the difficulty in determining the conflict for these firms, I simply focus on the two extreme cases of Lead underwriters and Independent brokers. Indeed, a distinguishing feature of this paper is the clean separation of investment banking conflicts of interest. One problem is that I/B/E/S may not include a number of important Independent brokers.10 To the extent that I miss these recommendations, I will understate the value of independent research if the missing Independents are better than those for which I have data. It is not clear that the missing Independents should be systematically better than the ones in I/B/E/S."

C. Summary Statistics

Table I provides summary statistics for the recommendations data. There are a total of 214 institutions providing recommendations, of which 144 are classified as Leads and 80 as Independents.12 Overall, there are a total of 24,010 recommendations (not counting the 4,351 instances of stopped coverage). Of these, 13,794 recommendations are made by Lead brokers

7SDC codes 'BM', 'JB', or 'JL'. Note that a joint-lead is a more prestigious role than a co-lead.

8Though pre-issuance recommendations are included, those stocks are not added into the portfolios until the issuance date so there is no look-ahead bias. Robustness checks in the separate Appendix show this choice does not drive the results.

9This classification is based on banking activity in SDC and information from sources including company websites, Nelson's directory, McGinn Smith and Company, Investars, Integrity Research Associates (2004), and Factiva searches. Additional details on the classification are available in the separate Appendix.

'0oFor example, S&P, the largest of the Independent brokers, has recently been removed from I/B/E/S and is therefore no longer part of my sample.

"Many of the organizations featured in press articles on independent research are relatively new so their short track record makes it difficult to distinguish luck from skill.

'2The count of brokers is based on I/B/E/S broker code and somewhat overstates the number of actual brokers as they assign multiple codes to a particular broker. Similarly, issuers are counted by the I/B/E/S ticker.




Table I: Description of Recommendations Data

This table shows summary information on the sample of recommendations. The sample contains recommendations made between 1994 and 2005 on firms issuing securities between 1992 and 2005. Recommendations are separated into Classes based on the degree of independence between the covering analyst firm and the issuing firm. "Lead" means the broker was also the lead underwriter for the covered firm while "Ind" means the broker is classified as being independent of investment banking. The table reports the count of recommendations for each class, along with the number of unique issuing firms (defined by I/B/E/S ticker), unique brokers (defined by I/B/E/S broker codes), the median duration ofa recommendation (in days), the median time from IPO to initiation of coverage, and the average level of initial and ongoing recommendations (1 is Buy, 3 is Hold, and 5 is Sell). Strong Buys and Buys are consolidated together, as are Strong Sells and Sells. The table does not count stopped coverage events as recommendations but does use stoppage of coverage to calculate the duration of recommendations. Reiterations are deleted from the dataset.

Class Recs Issuers Brokers Rec Init Average Rec Life Time Init Ongoing

Lead 13,794 5,132 144 278 41 1.18 2.53 Ind 10,216 2,269 80 190 466 1.58 2.40

All 24,010 5,451 214 240 51 1.26 2.47

and 10,216 are made by Independent brokers. There are a total of 5,451 issuers covered, with the Lead tracking 5,132 and the Independents following 2,269. The table also shows that the median recommendation life is about nine months for Lead brokers and six months for the Independents.13 The analyst groups differ dramatically in the timeliness of their initial coverage. Leads tend to begin coverage almost immediately, with a median time between issuance and first coverage of 41 days, slightly longer than the quiet period.14 Independent analysts take much longer to begin their coverage, with a median initiation time of about fifteen months. The final two columns show the average initial and ongoing recommendations. Leads are more optimistic than Independents, consistent with the literature. Though each group's initial coverage is more favorable than its ongoing coverage, this initial optimism is especially strong for Leads. Both sets of differences in average recommendation are significant at the 1% level based on a t-test assuming independent samples with unequal variances (not reported in the table).

Table II examines the distribution of recommendations for the Lead (Panel A) and Independent (Panel B) brokers. The unconditional distributions are shown in the "Total" rows. Consistent with the average recommendations shown in Table I, Lead recommendations are more optimistic. A test for the equality of the overall distributions (not reported in the table) has a X2 statistic of 10.83 with three degrees of freedom, which is significant at the 1% level. Buy recommendations com- prise 64% of Lead's active recommendations as compared to 52% for Independents. Just 3.3% of active Leads recommendations are Sells, and Figure 1 shows that most of these came after NASD

13This calculation excludes recommendations still active as of 12/31/2005 since right-censoring makes the recommendation life unobservable.

'4This calculation excludes coverage where either 1) the issuer's IPO date precedes the broker's first coverage of any firm in I/B/E/S, or 2) the IPO date follows the broker's first coverage of any firm in I/B/E/S and this recommendation is within one year of the broker's appearance in I/B/E/S. The intent of these filters is to avoid incorrectly measuring initiation times for recommendations that are left-censored by when the broker enters I/B/E/S. For example, Merrill Lynch does not appear in I/B/E/S until 8/19/1998 but they are certainly making recommendations prior to that time. Without the filters, initial coverage for a firm underwritten by Merrill in 1993 would appear to be five years.




Table II: Recommendations Transition Matrices

This table tabulates the recommendations according to the prior recommendation and the new recommendation. Panel A is for recommendations made by brokers who served as lead underwriter for the covered firm between two years prior to and one year after the recommendation. Panel B is for recommendations made by analysts who are classified as being independent from investment banking. "From" and "To" refer to the prior and new recommendations, respectively. "Init" refers to recommendations that were definitively iterations while "Trunc Init" are those whose status is indeterminate due to truncated I/B/E/S coverage. Recommendations are from 1994 through 2005 for firms issuing securities between 1992 and 2005.

PanelA: LeadAnalysts

From To Total

Buy Hold Sell Stop

Buy 3,055 95 2,301 5,451 56.0% 1.7% 42.2%

Hold 1,568 292 610 2,470 63.5% 11.8% 24.7%

Sell 36 170 50 256 14.1% 66.4% 19.5%

Stop 1,992 579 52 2,623 75.9% 22.1% 2.0%

Init 3,146 300 2 0 3,448 91.2% 8.7% 0.1% 0.0%

Trunc Init 2,082 409 16 4 2,511 82.9% 16.3% 0.6% 0.2%

Total 8,824 4,513 457 2,965 16,759 %Total 52.7% 26.9% 2.7% 17.7% %Active 64.0% 32.7% 3.3%

Panel B: Independent Analysts

From To Total

Buy Hold Sell Stop

Buy 2,163 140 797 3,100 69.8% 4.5% 25.7%

Hold 1,822 326 510 2,658 68.5% 12.3% 19.2%

Sell 103 295 73 471 21.9% 62.6% 15.5%

Stop 705 601 106 1,412 49.9% 42.6% 7.5%

Init 712 199 37 0 948 75.1% 21.0% 3.9% 0.0%

Trunc Init 1,972 886 149 6 3,013 65.4% 29.4% 4.9% 0.2%

Total 5,314 4,144 758 1,386 11,602 %Total 45.8% 35.7% 6.5% 11.9% %Active 52.0% 40.6% 7.4%




Figure 1: Distribution of Active Recommendations

These graphs show the distribution of recommendations made by each broker class. Stop coverage events are not counted in the total number of recommendations for the purposes of the graph, although stoppage is used in determining when to remove a firm from a portfolio. Portfolios are defined based on the level of recommendation and the link between the covered firm and the broker providing the recommendation. "Lead" means the broker was also the lead underwriter for the covered firm while "Ind" means the broker is classified as being independent of investment banking. The portfolios are formed based on recommendations between 1994 and 2005 for firms issuing securities between 1992 and 2005. Portfolios are rebalanced daily to reflect recommendation changes. The solid vertical line marks the deadline for most of the substan- tive requirements of NASD rule 2711.

Panel A: Buy 80%

60%

40%

20%, '94 '95 '96 '97 '98 '99 '00 '01 '02 '03 '04 '05 '06

Panel B: Hold

40%

20% '94 '95 '96 '97 '98 '99 '00 '01 '02 '03 '04 '05 '06

PanelC: Sell 20%

15% -

0

5%

'94 '95 '96 '97 '98 '99 '00 '01 '02 '03 '04 '05 '06

Lead -.... .

Ind

rule 2711 was introduced in September 2002. Though Independents are slightly more willing to issue non-favorable recommendations, there is still a pronounced lack of negative recommendations (just 7.4% Sells over the full sample). The low incidence of Sell recommendations is consistent with Chung (2000) who argues that brokers focus their attention on "high quality" firms. The figure shows that their distribution of recommendations did not change around NASD 2711 and that subsequent to the rule, Leads and Independents have very similar recommendations distributions of about 55% Buy, 40% Hold, and 5% Sell.

The table also provides information on the change in recommendations. Each row corresponds to the prior recommendation by the broker for the firm in question ("From") and the columns show the new recommendation ("To"). Initial coverage is shown on either the "Init" or "Trunc Init" row, depending on whether there is likely to be earlier coverage truncated from the sample.




The "Stop" row is for resumption of coverage after a stoppage. The sample excludes reiterations so the cells along the diagonal are empty. The table shows that Leads are more likely than Inde- pendents to stop coverage, especially for Buys. When resuming coverage after a Stop, Leads are quite optimistic relative to either their overall distribution or the resumed coverage of Independ- ents. This pattern of behavior is consistent with Leads using Stop in lieu of Sell. Finally, the table shows that initiations are quite favorable for both groups as previewed in Table I. Over 90% of Lead initiations are Buy, versus about 75% for Independents. This observed optimism of affiliated analysts is consistent with many other papers in the literature."5

D. Portfolio Formation

I consider portfolios formed by two hypothetical investors. One follows the advice of the Lead brokers and the other listens to the Independent brokers. Each investor maintains three portfolios corresponding to the possible recommendations: Buy, Hold, and Sell. Each time one of these investors receives a new recommendation he adds that stock to the appropriate portfolio. For example, if a Lead broker issues a Buy on Amazon, the investor tracking the Lead recommendations adds Amazon to the "Buy" portfolio. If that Buy was an upgrade from Hold, the investor would also remove Amazon from the "Hold" portfolio. Each new recommendation is assumed to trigger a $1 initial investment. Subsequent portfolio weights evolve based on the compound return of each stock and future portfolio additions and deletions.

Portfolio holdings are rebalanced daily as new recommendations are provided. Since the specific timing of the recommendation within the day is not known, trading is done at the closing prices on the day of the recommendation. Thus a purchase earns a first-day return equal to the return in the CRSP database on the trading day following the recommendation date.16 The investor does not earn the announcement-effect return (if any) from the recommendation, reflecting the experience of a real-world investor without early access to the recommendation.'7 Stocks that delist use the CRSP delisting return if available, otherwise -30% following Shumway (1997). Daily portfolio returns are compounded to monthly returns for use in the subsequent analysis.

Note that a particular stock can be in multiple portfolios at once. For example, the Lead broker and an Independent broker may both have recommendations on the same stock. Or one Inde- pendent broker may have a Buy and another may have a Hold. In the case where multiple brokers of a given Class have the same recommendation, that stock receives proportional weighting in the portfolio.

'5SThe general consensus is that pre-NASD 2711, recommendations from affiliated analysts are optimistic (e.g., Dugar and Nathan, 1995; Michaely and Womack, 1999; O'Brien, Lin, and McNichols, 2005; Barber, Lehavy, and Trueman, 2007; Kolasinski and Kothari, 2007; Clarke, Khorana, Patel, and Rau, 2006; and Kadan, Madureira, Wang, and Zach, 2006). Though several of these papers also report optimism in earnings, Lin and McNichols (1998) and Malmendier and Shanthikumar (2005) emphasize there can be a distinction between optimism in recommendations and earnings forecasts. The literature is mixed on the latter issue with papers such as Cowen, Groysberg, and Healy (2006), Agrawal and Chen (2005a), Clarke, Khorana, Patel, and Rau (2006), and Bradshaw, Richardson, and Sloan (2006) finding either pessimism or no bias on the part of affiliated analysts.

16It is my understanding that the recommendations are typically released prior to closing, often in the morning. To the extent that they may be issued after the market is closed I am giving credit to the investor for any price impact that recommendation may contain.

'7Announcement period returns are analyzed in Section III. Robustness checks show that the calendar time portfolio results continue to hold when allowing the investor to earn the announcement day return.




II. Analysis of Portfolio Performance

The main question of interest in this paper is whether investors, taking brokers' recommendations at their face value, would be better off listening to the advice of brokers free from the conflicts of interest created by investment banking. Though this comparison naturally implies a focus on relative performance, some attention must also be paid to absolute performance. For example, even if Independent recommendations outperform Leads, much of the basis for the Spitzer settlement evaporates if it is also true that Leads deliver positive abnormal returns.

One way to make this performance comparison is to form portfolios based on the recommendations of the two groups of brokers, then conduct performance evaluation much like with mutual funds (e.g., Carhart, 1997). I begin by examining the raw performance of the portfolios before turning to factor pricing models to assess risk-adjusted performance. To allow for possible model misspecification I consider the CAPM, Fama and French (1993) three-factor model, and the Carhart (1997) four-factor model adding a momentum factor. In addition, I consider two other models which are reported in a separate Appendix since they are similar to the main findings. One is a five-factor model which adds an IMN factor (issuer minus non-issuer) to control for the unique nature of the issuer sample. The second set of models in the Appendix are condi- tional factor models that allow for time variation in expected returns.

A. Raw Performance

The summary statistics in Table III provide a preliminary understanding of the investment performance of the various portfolios. The table contains annualized average returns on the portfolios in excess of the one month T-bill (Panel A), annualized standard deviations (Panel B), Sharpe ratios (Panel C), and the ending value of a one dollar investment in each portfolio (Panel D). For a point of reference, the market has an annualized average excess return over the sample period of 7.4%, standard deviation of 15.2%, Sharpe ratio of 0.48, and a dollar invested initially would grow to $3.28. The basic results can be seen quite clearly in Figure 2, which shows the value over time of a $1 investment in each of the portfolios. For all but Sells, the Lead portfolios have the worst performance.

The poor performance of the Lead Buy or Hold portfolios is clear in the figure. Even during the strong bull market of the late 1990's the Lead portfolios trail the Independent portfolios and the market. The Lead Buy portfolio did close the gap during 1999, but lost all that ground and then some in the few years following the market peak. As reported in Table III, the average monthly returns on these portfolios are well below the market, yet they are slightly more volatile so their Sharpe ratios are about half that of the market. The low Sharpe ratios are consistent with Carleton, Chen, and Steiner (1998) who find that Buys from sell-side analysts generate a lower Sharpe ratio than those from buy-side analysts. Relative to the corresponding Independent portfolios, the Leads give up more than 200 basis points per year in average return. Investing a dollar in these portfolios at the start of 1994 translates into ending values of $2.33 and $2.35, well behind the market. In comparison, the Independent Buy or Hold portfolios beat the market in terms of average return or ending value. They also have higher volatility so the Sharpe ratios are comparable to the market.

The story is much different for Sell portfolios. Sells from Independents do not seem to be particularly informative as those stocks perform very similarly to those with Buy or Hold recommendations. Shorting these stocks would therefore not have been profitable. Leads, on the other hand, are better able to identify stocks to short. The long version of the portfolio has an average return of -2.2% per year and a $1 initial investment has an ending value of $0.80. However, it is




Table III: Calendar Time Portfolio Summary Statistics

This table shows summary statistics for portfolio retumrns. The portfolios are formed on the basis of independence class and recommendation level. "Lead" means the broker was also the lead underwriter for the covered firm while "Ind" means the broker is classified as being independent of investment banking. Rows labeled "Ind-Lead" are zero-cost portfolios long the Independent portfolios and short the Lead portfolios. For all other rows in Panels A and B the portfolio returns are in excess of the one month T-bill. The sample period covers recommendations made between 1994 and 2005 on firms issuing securities between 1992 and 2005. Portfolios are rebalanced each day to reflect the recommendations changes. A stock is added or dropped from a portfolio at the close on the recommendation day. A stock can enter into a portfolio multiple times, in which case it receives weight proportional to the number of separate analysts recommending the firm. A stock can also enter into multiple portfolios on the same day. Each new recommendation receives a $1 initial investment in a portfolio. Daily portfolio returns are compounded into monthly returns for subsequent analysis. Summary statistics shown in this table are expressed as annualized percentages for convenience.

Panel A: Mean Excess Returns (Mkt= 7.36)

Class Buy Hold Sell Lead 6.38 5.41 -2.19 Ind 8.54 7.82 7.90 Ind - Lead 2.16 2.42 10.09

Panel B: Standard Deviation (Mkt=15.20)

Lead 24.61 20.21 26.04 Ind 19.64 15.95 15.50 Ind - Lead 7.85 8.59 17.66

Panel C: Sharpe Ratio (Mkt=0.48)

Lead 0.26 0.27 -0.08 Ind 0.43 0.49 0.51

Panel D: Wealth Relative (Mkt=3.28)

Lead 2.33 2.35 0.80 Ind 3.45 3.43 3.49

important to note from the figure that while shorting this portfolio would generate a gain by the end of the sample, the investor would have faced significant margin calls during the late 1990's.

While the discussion thus far has ignored any risk adjustments, it is quite conceivable that the analysts tend to follow riskier, more exciting companies. If so, the risk-adjusted performance will be even worse than what we see in Table III. Furthermore, the risk of the portfolios may differ systematically across the broker Classes and/or recommendation levels. To control for this I now estimate the risk-adjusted performance of the portfolios using factor pricing models.

B. Abnormal Performance

To measure calendar time abnormal performance I estimate the intercept and factor loadings from a time series regression of the portfolio excess returns on the factors from a parametric asset pricing model

R,~, = a;,+jjpFt+ epf 1 (1)




Figure 2: Portfolio Performance Over Time

These graphs show the value of a dollar investment in each portfolio over time. Portfolios are defined based on the level of recommendation and the link between the covered firm and the broker providing the recommendation. "Lead" means the broker was also the lead underwriter for the covered firm while "Ind" means the broker is classified as being independent of investment banking. The portfolios are formed based on recommendations between 1994 and 2005 for firms issuing securities between 1992 and 2002. Portfolios are rebalanced daily to reflect recommendation changes. When stocks are added to a portfolio, they enter with a $1 initial investment. The thin line in Panels A and B is the value-weighted market portfolio.

Panel A: Buy $4.00

$2.00 $1.00

$0.50

$0.25 '94 '95 '96 '97 98 '99 '00 '01 '02 '03 '04 '05 '06

Lead nd Market

Panel B: Hold $4.00

$2.00

$1.00

$0.50 $0.25

'94 '95 '96 '97 '98 '99 '00 '01 '02 '03 '04 '05 '06

- Lead Ind Market

Panel C: Sell

$1.00

$0.50

$0.25 '94 '95 '96 '97 '98 '99 '00 '01 '02 '03 '04 '05 '06 - Lead Ind

where R, is the excess return on portfolio p at time t and Ft is the vector of factors. To allow for possible model misspecification, I use the CAPM, the three-factor Fama and French (1993) model, and the four-factor model including momentum as in Carhart (1997).18 In addition to estimating the models for the six portfolios, I also estimate a for portfolios long the Independent portfolio and short the Lead portfolio of a given recommendation level.

The main results are presented in Table IV. The table contains columns for the excess return, alpha measures of abnormal performance from the three models, and for the four-factor model only, the factor loadings and regression R2. Separate panels show the results for Buy, Hold, and Sell recommendations and each panel contains rows corresponding to the Lead, Independent, and Independent minus Lead spread. Statistical significance to test the hypothesis that the coefficient is zero (or, in the case of the market beta, one) is marked with superscripts.

Panel A of Table IV shows that stocks with Buy recommendations tend to be riskier than the market, and are tilted toward small growth firms (positive SMB loading, negative HML loading).

'8Thanks are due to Ken French who provides the factors on his website.



Table

IV:

Calendar

Time

Portfolio

Performance

Regressions

This

table

summarizes

the

results

of monthly

time

series

regressions

Rp,t

= ap

+ pFt

+ep,t

where

R,

is the

excess

return

on portfolio

p at time

t (measured

as a monthly

percentage)

and

F, is the

vector

of benchmark

factors.

The

first

column

shows

the

monthly

average

return

in excess

of the

one

month

T-bill.

The

next

three

columns

are

the

estimates

of (1)

from

the

CAPM,

Fama-French

three-factor

model

(excess

market,

SMB

and

HML),

and

the

four-factor

model

(adding

the

momentum

factor,

UMD).

The

table

also

shows

the

factor

loadings

and

R2 for the

four-

factor

model.

Each

panel

corresponds

to a different

recommendation

level.

Within

a panel

the

rows

correspond

to independence

class.

"Lead"

means

the

broker

was

also

the

lead

underwriter

for the

covered

firm

while

"Ind"

means

the

broker

is classified

as being

independent

of investment

banking.

The

rows

"Ind-Lead"

are

long

the

Independent

portfolios

and

short

the

Lead

portfolios.

Regressions

for all other

rows

use

portfolio

returns

in excess

of the

one

month

Tbill.

The

null

for these

tests

is a coefficient

of zero,

except

in the

case

of the

market

factor

loading

where

the

null

is one.

Regressions

use

Newey-West

standard

errors

with

four

lags.

The

portfolio

returns

are

monthly

from

1994

through

2005.

(1)

Alpha

Factor

Loadings

Class

Excess

CAPM

FF

4-factor

ExMkt

SMB

HML

UMD

R2

Return

Panel

A: Buy

Lead

0.53

-0.31

-0.23*

-0.27**

1.15***

0.73***

-0.22***

0.04

0.9530

Ind

0.71

-0.03

0.02

0.03

1.08***

0.37***

-0.12***

0.01

0.9551

Ind-

Lead

0.18

0.29

0.25**

0.31***

-0.08**

-0.36***

0.10"**

-0.05*

0.6841

Panel

B: Hold

Lead

0.45

-0.24

-0.41***

-0.25*

1.00

0.76***

0.13"**

-0.14***

0.9390

Ind

0.65*

0.04

-0.13***

-0.08

1.03

0.30***

0.20***

-0.04

0.9583

Ind-

Lead

0.20

0.27

0.28**

0.17

0.03

-0.46***

0.08

0.10**

0.6487

Panel

C: Sell

Lead

-0.18

-0.97b

-1.06**

-0.72*

1.09

0.61***

-0.01

-0.30***

0.6818

Ind

0.66*

0.09

-0.09

-0.02

0.99

0.16***

0.25***

-0.07

0.8591

Ind-

Lead

0.84**

1.06b

0.96**

0.70*

-0 -0.-

0.45***

0.26

0.23***

0.3029

***

Significant

at the 0.01

level.

** Significant

at the 0.05

level.

* Significant

at the 0.10

level.





Each of these three coefficients is larger in magnitude for Leads than Independents. Neither Buy portfolio has a significant loading on the momentum factor, but the spread portfolio shows that Leads are marginally more related to past winners.

Given the significant factor loadings, the alphas measuring abnormal performance will differ from the raw excess return. The Lead Buy alphas are around -25 basis points per month for each of the models, significant at the 5% level for the four-factor model. The Independent Buy portfolio alphas are insignificantly different from zero. The spread portfolio alphas are about 30 basis points and are in fact larger than the difference in raw returns. The four-factor alpha is significant at the 1% level. These results are consistent with what Barber, Lehavy, and Trueman (2007) find in their IPO/SEO sample, though they also find evidence that both groups have positive alphas for their non-IPO/SEO sample.

Moving to Panel B, the basic story for Hold portfolios is very similar. The Lead portfolio alphas indicate losses of a 0.25% per month or more. The four-factor estimate is significant at the 10% level while the Fama-French estimate of-41 basis points is significant at the 1% level. Inde- pendent alphas are generally slightly negative but insignificant and alphas on the spread portfolio are between 17 and 28 basis points. There are two noteworthy differences in the factor loadings as compared to the Buy portfolios. Both Lead and Independent portfolios load positively on HML, indicating a tilt toward value stocks. They both also load negatively on UMD indicating that the stocks in these portfolios tend to be past losers. The momentum loading is more negative for the Lead portfolio so the spread portfolio has a positive loading, significant at the 5% level.

Finally, Panel C documents the results for the Sells. Alphas for the Lead portfolio are large and negative at -72 basis point or lower. The large point estimate is estimated imprecisely due to the relatively large unexplained variation (R2 Of 0.68 leads to a standard error of 39 basis points) but is significant at the 10% level for the four-factor model and at the 5% level for the other models. The Independent Sell alphas are economically small and insignificant so the alphas on the spread portfolio are similar to those on the Lead portfolio. As with the Buy and Hold portfolios, Sells load positively on SMB and more so for Leads than Independents. Stocks rated Sell by Independ- ents have a value tilt while the Lead Sells have a neutral HML loading. As may be expected, loadings on the momentum factor are negative and more so for the Leads. Given the momentum premium of about 0.85% per month over the sample period, the loading of -.3 for the Lead portfolio translates into a reduction in expected return of about 3% per year.

Though the results in Table IV differ slightly across the three models of risk adjustment, the overall message is quite consistent. Independents generate neutral performance. Lead Buys or Holds generate inferior performance both in absolute terms and relative to the Independents. The only glimpse of any good performance is for Lead Sells.19

C. Abnormal Performance in Subsamples

The results thus far estimate portfolio performance over the full sample period of 1994 through 2005. It is of interest to examine subperiods to see whether the results differ in bull versus bear markets or after the implementation of the new regulations.

Table V splits the sample at the NASDAQ peak in March 2000. For brevity the table shows only the excess return and alphas from the four-factor model. Before highlighting the details, it

9Note that none of the performance results include any transactions costs. Based on the work of Keim and Madhavan (1998), Barber, Lehavy, McNichols, and Trueman (2001) use a round-trip transactions cost of 1.3%. As a rough approxi- mation, I estimate monthly portfolio turnover as 31/n where n is the median recommendation life (not reported). This implies costs for Lead portfolios ranging from about 12 basis points per month for Buys up to 25 basis points for Sells. Corresponding estimates for Independent portfolios are about five basis points higher.




Table V: Abnormal Performance in Bull and Bear Markets

This table reports the abnormal performance for the four-factor model when splitting the sample at the peak of the bull market. The Bull period is January 1994 through March 2000 (75 observations) and the Bear period is April 2000 through December 2005 (69 observations). The time series regressions are estimated separately in the subperiods. For comparison, the excess market return was 1.32% per month in the Bull period and 0.16% in the Bear period. The factors in the four-factor model are the excess market, SMB (size), HML (value), and UMD (momentum). Within a panel the rows correspond to independence class. "Lead" means the broker was also the lead underwriter for the covered firm while "Ind" means the broker is classified as being independent of investment banking. The rows "Ind-Lead" are long the Independent portfolios and short the Lead portfolios. Regressions for all other rows use portfolio returns in excess of the one month T-bill. Regressions use Newey-West standard errors with four lags. The portfolio returns are monthly from 1994 through 2005 and are measured in percent.

Panel A: Buy

Bull Period: Bear Period: 1/94-3100 4/00-12/05

Class Excess 4-factor Excess 4-factor Return Alpha Return Alpha

Lead 1.30" -0.13 -0.30 -0.22 Ind 1.39** 0.04 -0.02 0.02 Ind - Lead 0.09 0.17 0.28 0.24**

Panel B: Hold

Lead 0.68 -0.22 0.20 -0.24 Ind 1.05** -0.10 0.22 -0.09 Ind - Lead 0.37 0.12 0.01 0.15

Panel C: Sell

Lead 0.64 -0.12 -1.08 -1.37** Ind 1.05** 0.08 0.24 -0.07 Ind - Lead 0.41 0.20 1.31** 1.30** ** Significant at the 0.05 level. * Significant at the 0.10 level.

is worth noting that the reduced sample sizes alone cause standard errors to increase roughly 40%. As a result, many of the subsample estimates are insignificant even though the point estimates are often comparable to the full-sample results.

Most of the alphas are similar across subperiods. As before, the Independent alphas are generally below ten basis points in magnitude and are never significant. The Lead Buy or Hold portfolios are also fairly similar in the two subperiods. The main difference is with Lead Sells. The abnormal return to shorting this portfolio during the Bear period is 1.37% per month (significant at the 5% level) versus only -12 basis point in the Bull period. On the surface, the subsample results differ considerably from Barber, Lehavy, and Trueman (2007) who find a large improvement in Independent performance in the Bear period and a decline in performance for investment bank coverage of recent equity issuers. Untabulated results suggest that much of this difference is due to my extended sample period. Over their subsamples I too find a decline in performance for Buys from Leads and an improvement for Independents. But in the additional 30 months after June 2003, Lead performance improved considerably, yielding overall post-peak performance




fairly similar to the pre-peak period.20 They do not analyze Holds and Sells separately so it is a bit difficult to make direct comparisons with my results, but the large decline in their point estimate for investment bank Hold/Sell recommendations in the Bear period is consistent with my results.

In addition to splitting the sample based on calendar periods, it is also important to examine whether the results differ by issuance type. Recall that the results presented thus far include IPOs, SEOs, debt offerings, and other miscellaneous issuances. Table VI addresses this issue. As with the subperiod results, the issuance-type stratification yields lower statistical significance than the overall results because the issuer-specific portfolios have higher volatility. This is particularly true for IPOs. For example, the Lead Sell IPO portfolio often contains five or fewer stocks and as a result has annual volatility of 54%. Since the portfolios for Sells are so sparsely populated I focus only on Buys and Holds.

The results in Table VI are very similar to the overall results. There are no subsamples in which Leads display positive performance in absolute terms. Relative to Independents, the Lead portfolios lag behind in all cases except for Holds on Debt issuers. The fact that Lead performance for IPOs is fairly neutral but very poor for SEOs does not support the notion that a rush to judgment adversely affects the performance of Lead recommendations as suggested by Dunbar, Hwang, and Shastri (1999). SEOs seem to be the largest source of underperformance, where the Buy spread portfolio earns about 6% per year (significant at the 1% level). The Lead Hold portfolio for SEOs is nearly identical to their Buy portfolio, though Independent SEO Holds also exhibit negative performance so the spread portfolio abnormal return is cut in half. The somewhat surprising result that IPOs do not seem to play a large role in the overall poor performance of Lead Buys is consistent with McNichols, O'Brien, and Pamukcu (2006). Further investigation reveals that the Lead IPO portfolio performance varies considerably by the subperiod. Performance is actually positive (either in absolute terms or relative to Independents) during the bull period, but negative in the bear market period (results untabulated). This helps reconcile the subsample results in Table V with Barber, Lehavy, and Trueman (2007).

D. Robustness Checks

To address the possibility that the key results are driven by methodological issues, I conduct a battery of robustness tests. These range from altering the formation of the Lead and Independ- ent samples to alternative risk adjustment procedures including an effort to account for the issuer-related nature of the sample. As a general statement, alphas for the Lead Buy or Hold portfolios remain negative and below the corresponding Independent alphas. Interested readers can find the details in the separate Appendix.

III. Announcement Returns

The results thus far show that Lead Buys or Holds perform poorly, while their Sells are poten- tially useful trading signals. Independents, on the other hand, have neutral performance across all recommendation categories. This section examines the market reaction to the recommendation announcement to facilitate the interpretation of the results. Subsample analysis also provides some insights on the effectiveness of recent regulatory reforms.

For each recommendation I construct the cumulative abnormal return (CAR) for the three-day window centered on the recommendation date. I report the raw return over this period along with

20This analysis runs the full-sample regression then averages the intercept plus residual over various subsamples.




Table VI: Portfolio Performance by Issuance Type

This table reports the abnormal performance for the four-factor model when splitting the sample by type of securities issuance. The time series regressions are estimated separately in the subsamples. The factors in the four-factor model are the excess market, SMB (size), HML (value), and UMD (momentum). Each panel corresponds to a different recommendation level (Sells are not shown due to insufficiently populated portfolios). Within a panel the rows correspond to independence class. "Lead" means the broker was also the lead underwriter for the covered firm while "Ind" means the broker is classified as being independent of investment banking. The rows "Ind-Lead" are long the Independent portfolios and short the Lead portfolios. Regressions for all other rows use portfolio returns in excess of the one month T-bill. Regressions use Newey-West standard errors with four lags. The portfolio returns are monthly from 1994 through 2005 and are measured in percent.

Panel A: Buy

Class IPOs SEOs Debt Other All

Lead -0.09 -0.42*** -0.12 -0.26 -0.27** Ind 0.11 0.05 0.11 -0.20 0.03 Ind - Lead 0.20 0.47*** 0.23 0.06 0.31***

Panel B: Hold

Lead -0.15 -0.39*** -0.04 -0.23 -0.25" Ind 0.23 -0.16 -0.11 -0.17 -0.08 Ind - Lead 0.38* 0.23 -0.07 0.06 0.17

*** Significant at the 0.01 level. ** Significant at the 0.05 level. * Significant at the 0.10 level.

the abnormal return from the four-factor model.21 As before, the recommendations are separated into groups based on recommendation level and for each recommendation level compared across independence class.

Table VII contains the basic results. Though I later control for other determinants of announcement returns and examine subperiod results, I begin with a discussion of the simple averages over the full sample since they convey the main point. As would be expected, Buy recommendations are greeted by a positive market reaction (about 1.2% for Leads and 0.6% for Independents) and Sells a strong negative reaction (about -6.25% for Leads and -2.1% for Independents). The magnitudes are larger in either case for Leads. The market reacts negatively to Holds, especially when made by Leads (-7.1% versus -1.4%). This negative reaction is consistent with the Boni and Womack (2002) survey in which sophisticated investors agree that "Hold means Sell."

Taken alone, these results are perhaps not surprising, as the direction of the reactions is plausi- ble and the relative magnitudes are consistent with Leads having an informational advantage. However, the market reaction to Lead recommendations is puzzling in light of the investment performance documented earlier. The market evidently over-reacts to their Buys, since the positive announcement effect is followed by negative abnormal returns. On the other hand,

21For each recommendation "event" i, the parameters of the four-factor model are estimated from a sample of up to 255 1

trading days ending 45 days prior to the recommendation date t. The CAR for recommendation i is AR i,t+r where

aRi.t = R, -(Lhi + PiRM,, + siSMB, + hiHML, + fniUMot) where ii, bi, si, hi, and ,0; are the estimated parameters for event i.



cliff do Affiliated Analysts Mean What They Say? 23

Table VII: Announcement Period Abnormal Returns

This table contains the percentage cumulative abnormal returns (CAR) for the three day window centered on the date of recommendation. "Raw" is the raw return without any risk adjustment and CARs use the four-factor model to adjust for risk. The sample period covers recommendations made between 1994 and 2005 on firms issuing securities between 1992 and 2005. Subsamples are 1/1/04-3/10/00 (I), 3/11/00-9/8/02 (II), and 9/9/02- 12/31/05 (III). The subsamples mark the peak of the NASDAQ market (March 10, 2000) and the effective date of NASD rule 2711 (September 9, 2002). Recommendations are grouped based on independence class and recommendation level. "Lead" means the broker was also the lead underwriter for the covered firm while "Ind" means the broker is classified as being independent of investment banking. Average abnormal returns are obtained by regressing abnormal returns on dummy variables. Regressions for each panel are run separately. The final two columns are p-values from Wald tests of the regression coefficients.

Panel A: Buy

Class Full Sample Subsample CAR p-value for I II III subsamples

Raw CAR 1/1194- 3/11/00- 9/9/02- All III 0

3/10/00 9/8/02 12/31/05 Equal I, II Lead 1.52*** 1.20*** 0.95*** 0.54 2.95*** 0.0000 0.0000 Ind 0.94*** 0.62*** 0.39*** 0.37 1.37*** 0.0000 0.0000 Ind- Lead -0.57*** 0.58*** -0.56*** -0.17 -1.58*** 0.0123 0.0031

Panel B: Hold

Lead -7.20*** -7.06*** -7.68*** -9.91*** -3.44*** 0.0000 0.0000 Ind -1.12*** -1.40*** -0.81*"** -2.63*** -1.59*** 0.0000 0.2694 Ind- Lead 6.08*** 5.67*** 6.87*** 7.28*** 1.84*** 0.0000 0.0000

Panel C: Sell

Lead -6.40*** -6.25*** -4.17** -8.02*** -6.25*** 0.3543 0.9966 Ind -1.87"*** -2.14*** -1.17*** -4.08*** -2.43*** 0.0223 0.5092 Ind- Lead 4.53*** 4.11*** 3.01 3.94* 3.82*** 0.9205 0.7986

*** Significant at the 0.01 level. ** Significant at the 0.05 level. * Significant at the 0.10 level.

the market under-reacts to their Holds or Sells since the negative announcement returns are followed by negative abnormal returns. Since there are no significant abnormal returns following the announcement of Independent recommendations, the market appears to react correctly to their announcements, at least on average.

The table also reports the four-factor CARs over three distinct subperiods: before the market peak in March 2000 (Period I), between the peak and the implementation of NASD 2711 on September 9, 2002 (Period II), and following NASD 2711 (Period III). The estimates are obtained by regressing abnormal returns on dummy variables for time period and independence class. A few interesting patterns emerge. First, the regulatory changes seem to lead to a stronger market reaction to Buys, especially for Leads. The CAR for Lead Buys increases by about 2% while Independent Buys rise by 1%. Following the rule the market reacts more favorably to Lead Buys than Independents. The same basic pattern is true for Holds. Following the rule, the decline for a Hold was less severe, especially for Leads. Thus, the rule seems to have enhanced the credibility of Lead recommendations. Notice also that the Hold CARs were especially negative in the




middle period, consistent with the notion that the market distrusted analysts, especially Leads, following the collapse of the "bubble." The Sell results indicate that the market took those reports more seriously following the market peak.

Overall, the results in Table VII are similar to Kadan, Madureira, Wang, and Zach (2006). However, a problem with interpreting either their results or those in Table VII is that other factors may affect the announcement returns, and these factors may differ systematically over time or between Lead and Independent recommendations. For example, Barber, Lehavy, McNichols, and Trueman (2006) report that the magnitude of the announcement effect is positively related to the brokers' optimism. Since Leads tend to be more optimistic than Independents, it is important to control for this when comparing the two groups.

Several variables aim to capture the information structure of the covered firm. The amount of analyst coverage can proxy for information asymmetries (D'Mello and Ferris, 2000 and Dou- kas, Kim, and Pantzalis, 2005). Firm size captures the general volume of information on the covered firm. Similarly, reports that arrive shortly after other reports on the same firm are likely to be relatively less informative so I include the days since last report. Other variables measure the credibility of the brokerage providing coverage. Large brokers (proxied by the number of active recommendations) may be viewed as more sophisticated and therefore presumably more informative. As emphasized by Barber, Lehavy, McNichols, and Trueman (2006), the broker's overall optimism (measured here as the percentage of their recommendations that are Buys) should cause the market to react less favorably to their Buys and more negatively to their Holds or Sells. Finally, a number of variables capture particular features of the recommendation. Firm optimism (percentage of active recommendations on the covered firm that are Buys) should be negatively related to the market reaction. A Buy on a firm with lots of existing Buys should translate into a smaller upward price revision, while a Sell on a firm with mostly Buys is much more informative than a Sell on a firm with few existing Buys. I also include dummy variables for initiations and resumptions of coverage (other recommendations are either upgrades or downgrades of active recommendations). The Hold regression includes dummy variables for upgrades or downgrades. For the Buy (Sell) regression I include a dummy variable for Strong Buys (Strong Sells) for recommendations made prior to 9/9/2002.

Table VIII reports the results from adding control variables to the regression of the announcement period abnormal return on the subsample and Class dummy variables. The results indicate that NASD 2711 seems to mark a break in the market reaction to recommendations. Prior to the rule there is no significant difference between Leads and Independents for either Buys or Sells but there is a significant difference for Holds. After the rule the pattern flips: the differential reaction is insignificant for Holds but significant for either Buys or Sells. The evidence from the pre- rule period is consistent with the findings in Dugar and Nathan (1995), Michaely and Womack (1999), and Lin and McNichols (1998). Combining the evidence following NASD 2711 with the change in recommendation distributions shown in Figure 1, the results paint the picture that prior to the rule change a Lead Hold often meant Sell, and the market now interprets their recommendations somewhat more literally.

Turning now to some of the details of the results in Table VIII, Panel A contains the regression coefficients and t-statistics while Panel B contains p-values from several Wald tests on the coefficients. In the interest of space, the control variables are not discussed other than to say they are generally significant and of the expected sign.22 Their inclusion in some cases has a marked

220ne result worth mentioning is broker optimism. One would expect a more negative reaction to recommendation from an optimistic broker but the results indicate a positive and significant coefficient in the Buy regression. Though I do not have an explanation, this puzzling result is also noted by Barber, Lehavy, McNichols, and Trueman (2006).




Table VIII: Multivariate Analysis of Announcement Period Abnormal Returns

This table reports the results of three cross-sectional regressions of the announcement period returns on dummy variables for time period and independence status as well as a number of control variables. The dependent variable is the three-day cumulative abnormal return (measured in percent) centered on the recommendation date. The CAR is based on the four-factor model. Subsamples are 1/1/04-3/10/00 (I), 3/11/00-9/8/02 (II), and 9/9/02-12/31/05 (III). "Lead" and "Ind" refer to the independence status between the covered firm and the broker making the recommendation. In(ME) is the natural log of the covered firm's market value of equity, as of the recommendation month. In(# analysts) is the natural log of the number of active recommendations from all analysts on the stock. In(broker size) is the natural log of the number of the broker's active recommendations. Broker optimism is the percentage of the broker's active recommendations that are Buys. Firm optimism is the percentage of all active recommendations on the stock that are Buys. Each of the prior four variables is measured as of the month-end prior to the recommendation. In(time) is the natural log of the time (in days) since the last active recommendation on the stock (by any analyst). The remaining variables Initiation, Resumption, Upgrade, Downgrade, and Strong Buy or Strong Sell are self-explanatory dummy variables. Note that the explanatory variables are determined based on the full I/B/E/S universe, not just the issuer sample. All explanatory variables other then the Class and Period dummies are demeaned so that the coefficients on these dummy variables represent average CARs. Panel A reports the regression results while Panel B contains p-values for Wald tests on the coefficients.

Panel A: Regression Results

Buy Hold Sell

Coef (t-stat) Coef (t-stat) Coef (t-stat) Lead x PdI 0.91*** (5.28) -5.55*** (-15.67) -2.90* (-1.67) Lead x Pd II 0.49 (0.93) -6.58*** (-10.12) -5.25** (-2.45) Lead x Pd III 2.46*** (7.99) -2.78*** (-7.49) -4.75*** (-4.60) Ind x Pd I 0.54*** (3.16) -2.97*** (-11.54) -2.58*** (-3.11) Ind x Pd II 0.68*** (2.23) -3.64*** (-7.38) -5.13*** (-3.81) Ind x Pd III 1.19*** (5.41) -3.37*** (-12.85) -2.68*** (-3.81) In(ME) -0.13*** (-1.53) 1.09*** (8.70) 1.66*** (5.01) In(# analysts) 0.08 (0.37) -0.55 (-1.63) -2.01"* (-2.32) In(broker size) 0.26*** (2.98) -0.79*** (-6.47) -0.62* (-1.80) broker optimism 1.22** (2.38) -1.21 (-1.34) 0.30 (0.11) firm optimism -1.88*** (-4.66) -6.62*** (-10.17) -3.65* (-1.79) In(time) 0.18** (2.48) 1.09*** (8.28) 0.49 (1.37) Initiation -1.64*** (-7.64) -0.97 (-1.41) 0.48 (0.55) Resumption -2.17*** (-9.33) -0.42 (-0.60) 2.60*** (3.24) Upgrade 1.01 (1.24) Downgrade -3.60*** (-5.33) Strong Buy or

Strong Sell 0.31 (1.51) 0.03 (0.02) R2 0.0259 0.1408 0.0915 N 9,601 7,681 1,136 *** Significant at the 0.01 level. ** Significant at the 0.05 level. * Significant at the 0.10 level.




Table VIII: Multivariate Analysis of Announcement Period Abnormal Returns (Continued)

Panel B: p-values for Hypothesis Tests

Ind = Lead: Pd I 0.1794 0.0000 0.8647 Ind = Lead: Pd II 0.7655 0.0004 0.9662 Ind = Lead: Pd III 0.0002 0.1959 0.0649 All Pds Equal: Lead 0.0000 0.0000 0.6499 All Pds Equal: Ind 0.0653 0.2911 0.1526 All Pds Equal: Ind - Lead 0.0230 0.0000 0.5888 All Pds = 0: Lead 0.0000 0.0000 0.0000 All Pds = 0: Ind 0.0000 0.0000 0.0000 All Pds = 0: Ind - Lead 0.0022 0.0000 0.3182

effect on the magnitude of the announcement returns (all control variables are demeaned so that the class and time period dummies can be compared to Table VII). After controlling for other determinants of the announcement period return, the market reaction to Buys from either group is positive and statistically significant but fairly small economically, especially before NASD 2711. Reaction to Sells is universally negative, ranging from about -2.5% to -5% and larger in magnitude in the subperiod between the market peak and the regulatory reforms. After the regulatory reforms the market reacts more strongly to Buys or Sells from Leads than from Independents (p-values of 0.0002 and 0.0649, respectively). This is consistent with an increase in credibility of Leads after adoption of the rules. For Holds, the pattern is opposite. In all cases the reaction to Holds is negative, but prior to the rule change Holds from Leads were interpreted as significantly worse news than Holds from Independents. Following the rule change the market reaction to Holds is not statistically different between the two groups (p-value of 0.1959).

IV. Concluding Remarks

This paper addresses whether the investment performance of recommendations made by a firm's Lead underwriter differs from that of Independent analysts. This question is of interest because of the recent regulatory changes such as NASD 2711 and the Global Settlement that seek to address conflicts of interest due to investment banking and to provide investors with independent research. This study is careful to create a control group of Independents that is free from investment banking conflicts and examines the 1994-2005 time period which includes both bull and bear markets. The bottom line answer is that the investment performance of Lead Buy or Hold recommendations is poor while there is some evidence of attractive returns from shorting their Sells. Evidence on the announcement effect over the full sample period suggests that the market does not properly react to recommendations from Lead analysts. There seems to be an over-reaction to their Buy recommendations and an under-reaction to their Holds or Sells. The overall message from the paper is that investors who make use of analyst recommendations would be wise to pay attention to the banking relationships between the brokerage firm and the subject of the report.

Examination of the investment performance over different subsamples yields further insights into the relative performance of Lead recommendations. Although much attention has been paid to coverage of recent IPO firms, a comparison by type of issuance reveals that this segment is not the source of poor performance of Buys over the sample period. Instead, SEOs and debt offerings seem




to be a central part of the story. It is perhaps fortunate then that NASD 2711 requires disclosure of any investment banking relationship. It is too early to tell whether this rule will improve investment performance of Lead recommendations, but a comparison of announcement period returns before and after the effective date of the rule suggests that it has shaped the behavior of Lead analysts and the market reaction to their recommendations. The distribution of Lead recommendations shifted dramatically around the September 9, 2002 effective date, becoming less optimistic. Following this rule the distribution of Lead and Independent recommendations on the sample of firms in this study was almost identical. Announcement period returns suggest that the market now views Lead recommendations as more credible and consistent with their plain meaning.

Though the results in this paper are consistent with a banking conflict of interest adversely affect- ing the investment value of Lead recommendations, it is worth noting an alternative explanation. The selection bias hypothesis maintains that the observed poor performance is an artifact of an innocent mistake on the part of the underwriter. The issuing firm will tend to select from the more optimistic of available underwriters, so on average the bank winning the Lead role is over-optimistic. In other words, banking is correlated with underperformance but does not cause it. Distinguishing between these hypotheses is important because if underperformance merely reflects a correlation then the regulatory reforms severing the ties between banking and research are likely to be ineffective.

A number of results in this and other papers cast doubt on the selection bias explanation. How can a selection bias account for the usefulness of Lead Sell recommendations? Why are Leads relatively more likely to Stop coverage? Why are Leads slow to downgrade but quick to upgrade their clients as reported in O'Brien, Lin, and McNichols (2005)? Why is earnings forecast optimism positively related to banking fees as documented by Dechow, Hutton, and Sloan (2000)? Why do affiliated analysts provide optimistic recommendations but neutral earnings forecasts (Malmendier and Shanthikumar, 2005)? Why do active issuers ofextemrnal financing receive higher consensus recommendations despite earning low future returns (Bradshaw, Richardson, and Sloan, 2006)? Why do analysts affiliated with M&A targets upgrade the acquirer's stock shortly after terms of stock deals are set (Kolasinski and Kothari, 2007)? All of these facts are hard to reconcile with a selection bias but are consistent with the conflict of interest. Supplementing this empirical evidence is survey work of Michaely and Womack (1999) and Boni and Womack (2002) that finds almost 90% of investment professionals believe conflicts of interest are consequential. Thus, the banking conflict of interest hypothesis appears most consistent with the data, though other factors such as trading volume may also be at play (see Jegadeesh, Kim, Krische, and Lee, 2004; Jackson, 2005; Agrawal and Chen, 2005b; and Cowen, Groysberg, and Healy, 2006).

As to the question of whether Spitzer's settlement will help "retail investors get a fair shake," the results at least point in the right direction. Sells aside, the independent research does appear to deliver better investment performance than the recommendations provided by the underwriters. As documented in the separate Appendix, Lead recommendations also underperform Inde- pendents in a sample of non-issuing firms. In addition, Lead recommendations on the issuing firms perform poorly relative to their recommendations on non-issuing firms. Thus, the required disclosures of banking relationships will likely help investors identify situations where they should be especially skeptical of a recommendation. However, several implementation details will be important. When the underwriter gives an investor a research report, it will also provide a report from an independent analyst. But if the underwriter can pick and choose from a set of independent reports it is reasonable to suppose they will choose the one most concordant with their own report. Moreover, in my sample, Independents simply weren't covering firms as early as the underwriters. Finally, there is the practical issue of determining which organizations will receive the government's certification as an independent organization. Many of these




organizations have rather short track records. Some have posted impressive performance, but it is very difficult to distinguish luck from skill over a short period.E

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Do Affiliated Analysts Mean What They Say?