Broker Trading Ahead of
Stock Recommendations
AndeRS AndeRSon JoSé VicenTe MARTinez
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Institute for Financial Research, SIFR, Drottninggatan 89, SE‐113 60 Stockholm, Sweden Phone: +46 (8) 728 51 20, Fax +46 (8) 728 51 30, E‐mail: info@sifr.org, Web: www.sifr.org
Broker
Trading
Ahead
of
Stock
Recommendations
Anders
Anderson
and
José
Vicente
Martinez
Broker Trading Ahead of Stock Recommendations
∗
Anders Anderson
†Jos´e Vicente Martinez
‡First draft: 2007-04-14, this draft: 2010-11-22
Abstract
This paper documents the aggregate positions and profits derived from the trades that pass through brokers surrounding the date of their recommendation revisions. Using ten years of detailed trading data from Sweden, we find significant evidence of pre-recommendation trading related to all stock recommendation revisions, but of abnormal profits only in the case of upgrades. The corresponding return reaction can be linked to the trades of the recommending brokerage firms, and it is brokerage firms not supplying equity analysis that stand on the other side of these trades. Almost half the total profits come from transactions occurring before the official recommendation date. Reasonable estimates of trading volumes and fees would suggest that most of these profits are captured by the recommending brokerage.
Keywords:stock recommendations; performance evaluation; tipping.
JEL codes:G14; G24; J44.
∗We are grateful to Ulf Axelson, Lauren Cohen, Magnus Dahlquist, Campbell Harvey, G ¨oran
Roberts-son, Per Str ¨omberg and seminar participants at BI Norwegian School of Management, Copenhagen Busi-ness School, HEC Paris, RSM Erasmus University, Sa¨ıd BusiBusi-ness School (Oxford), SIFR, Stockholm School of Economics, Stockholm University and University of Amsterdam for helpful comments and suggestions. Special thanks to Petter Dahlstr ¨om and Mattias Olausson at Nasdaq OMX for providing us with the data.
†Institute for Financial Research (SIFR), Drottninggatan 89, SE-113 60 Stockholm, Sweden, Phone:
+46-8-728 5123, E-mail: anders.anderson@sifr.org. Anderson is grateful for financial support from the OMX Group Foundation.
‡Sa¨ıd Business School, University of Oxford, Oxford-Man Institute of Quantitative Finance, and Institute
for Financial Research (SIFR), Park End Street, Oxford, OX1 1HP, United Kingdom, Phone: +44-(0)1865-288937, E-mail: jose.martinez@sbs.ox.uk. Martinez is grateful for financial support from the Jan Wallanders and Tom Hedelius Foundation.
Introduction
Several recent studies provide evidence of abnormal returns in the direction implied by analysts’ stock recommendations, but still relatively little is known about just how profitable such recommendations are for the customers of the brokerages issuing them.1 Brokerage firms, which are the main suppliers of sell-side equity research, have a long standing tradition of folding research costs into trading commissions, making the cost benefit analysis of investment advice difficult. On a more general level, sell-side commis-sions would have to be covered by buy-side profits to sustain a Grossman and Stiglitz (1980) equilibrium. Given a business model in which sell-side research is valuable, and its production rewarded by trading commissions, brokerage firms have no incentives to share this private information with the market instantly, but instead gradually exploit the opportunity to trade against uninformed investors (e.g., Kyle, 1985). In what is widely known as “tipping”, brokerage firms inform a small group of privileged clients about up-coming equity analysis. According to anecdotal evidence from practitioners, tipping is an important way to reward clients and appears to be fairly common practice.2 Policy-makers around the world have recently shown interest in regulating this behavior, which is often regarded as treating investors unequally.
This paper measures the magnitude of pre-recommendation trading as well as the profits accruing to recommending brokers’ customers, using a comprehensive daily trad-ing data set for all Swedish firms and brokers that spans almost ten years. The Stockholm Stock Exchange (SSE), a pure limit order book market with brokerage firms as members, seems a particularly suitable exchange on which to measure broker trading flows. As client trades are executed between members without passing through dealers or special-ists, we can establish a direct link between the trades handled by a brokerage firm and the stock recommendations it issues.
1See, for example, Stickel (1995), Womack (1996), Barber, Lehavy, McNichols, and Trueman (2001),
Je-gadeesh, Kim, Krische, and Lee (2004).
2In one recent case cited in theWall Street Journalon 24 August 2009 (Craig and Corkery (2009)),
Gold-man Sachs’ researchers called 50 top clients to inform them of the firm’s bullish views on Janus Capital Group on 3 April 2009, but did not release their research note saying they had changed from neutral to positive on the stock until six days later. By that point, the stock already had jumped 5.8%.
We exploit the simple fact that as markets clear, the net purchases and profits of all brokers are zero in expectation. Obviously, if some broker clients possess exclusive in-formation and choose to trade systematically in any particular direction, this does not hold. Net buying, defined as the difference between the values of purchases and sales, is our measure of when information contained in recommendations reaches its users. By combining the aggregate positions implied by net buying with returns, we think that we realistically capture the value that recommendations add to the main recipients of equity research: the broker client base. Schultz (2003) and Madureira and Underwood (2008) argue that there may be informational spillovers between equity research and trading in brokerage firms trading in which such brokers and their clients may have an advantage. In addition to recommending brokers, we define a group we label informed brokers. In-formed brokers provide equity research into the relevant stocks, but do not issue recom-mendation revisions within the same time frame. We use informed brokers as a control group when assessing both net buying and profits related to recommendations in order to address such issues.
Apart from assessing timing and magnitudes, profits have the added appeal of being robust to several pervasive problems concerning abnormal returns that frequently affect studies of analyst recommendations. The first limitation of using abnormal returns is that it ignores market frictions, such as bid-ask spreads and the price impact of trading, which may hamper investor ability to act on a recommendation. Such lack of investability is most notorious among smaller, less liquid stocks - precisely those stocks where analyst ability to detect mispricings seems greatest.3 Second, stock recommendations often co-incide with public announcements, leaving little room to trade before subsequent price changes. Third, investors may actually be able to perform better than simple mechanical rules would suggest.4 Matching trading data to returns allows us to address these more general issues in the previous literature surrounding stock recommendations.
3See Stickel (1985), Ivkovic and Jegadeesh (2004) and Green (2006).
4Recommendations typically contain more information than standardized categories, such as buy, hold,
or sell, can convey. This additional information could presumably be exploited by investors in their trading strategies; se, for example, Asquith, Mikhail, and Au (2005).
Our work is also related to the recently emerging literature concerning tipping. Irvine, Lipson, and Puckett (2007) find that institutional buying increases in the presence of pos-itive initiations. Using Nasdaq trading data, Juergens and Lindsey (2009) find that af-filiated market makers increase selling volume in the two days preceding a downgrade, and Christophe, Ferri, and Hsieh (2010) find that short selling increases in anticipation of downgrades. We depart from this literature in three ways. First, we explore the profitabil-ity of this practice taking the investors’ perspective. As pointed out by Barber, Lehavy, McNichols, and Trueman (2001), traditional event studies can, at best, indicate that prof-itable investment strategies could potentially be designed in connection with recommen-dations. In our calendar profit approach, we implicitly take the price impact of trading into account, since we are able to analyze the outcome of actual trades. Second, we use our data to identify trades through groups of brokers based on the production of equity research. This adds a new dimension to the literature concerning how various classes of investors react to investment advice.5 Third, we compare aggregate profits obtained by the brokers’ customer base with the abnormal trading that stock recommendations generate. We examine how the surplus created by equity analysis is split between the brokers and their customers, which relates to the literature concerning trade-generation and incentives.6
We have four key findings. First, we find that aggregate abnormal returns and net buying by recommending brokers increase (decrease) two weeks before the release of an upgrade (downgrade), continuing up to one month afterwards. Almost half the broker clients’ aggregate net positions are taken before the official recommendation date. These results are valid for both small and big firms, but the volume for the top decile of biggest firms is about ten times larger than the volume of the remaining deciles. We take this as evidence of tipping, and also as confirmation that lack of investability in small stocks is a real concern when trading on recommendations. We find that it is investors trading through brokers that do not provide equity analysis that take the opposite side of the
5This literature is predominantly concerned with investor size, as in Lee (1992), Mikhail, Walther, and
Willis (2007), and Malmendier and Shanthikumar (2010).
trades. In line with our tipping hypothesis, uninformed investors are also more likely to be on the opposite side in the pre-recommendation window compared with after the information is released.
Second, we investigate the price impact of trading to establish that it is indeed the rec-ommending broker flows that are associated with the aggregate price changes. We find confirming evidence in the case of upgrades, and that other brokers providing analyst coverage of the recommended stocks generally trade in the same direction as do the rec-ommending brokers in the case of downgrades. This asymmetry in our results is open to multiple explanations. The group of informed broker clients may either possess the same information as do the recommending broker clients, or both groups react to public bad news symmetrically. Earnings announcements do not explain this difference.
Third, abnormal profits accruing from trading on upgrades approximately five days preceding and after the recommendation date amount to SEK 467 thousand (USD 58 thou-sand) per day, or SEK 117 million (USD 15 million) annualized. These profits are net of costs derived from the bid–ask spread and the market impact of trading, although not of brokerage commissions. Our results therefore support the idea that recommendations are a valuable source of investment information for which brokers should be compensated. Approximately half the profits measured are associated with transactions that occur be-fore the recorded recommendation dates, and as much as 80% of the profits are generated by the trades in the largest firm decile. Consistent with the results of the price impact of trading, we find no evidence that broker clients profit from negative recommendations. The corresponding annualized profit for trading on downgrades is only SEK 9 million (USD 1 million), and insignificant. Since our evidence indicates that recommending bro-kers significantly reduce positions before downgrades, we conclude that they are unable to capture any profits in this process.
Fourth and finally, we also find that almost 50% of the opened positions are closed be-fore the end of the first month after an upgrade. That is not the case, however, with down-grades, where most recommendation–motivated sales are never reversed in the windows
we analyze. This suggests that recommendation upgrades trigger a larger number of round-trip transactions, and consequently, larger trading revenues for brokers. Overall estimated round-trip brokerage commissions triggered by stock recommendations seem to be of the same magnitude as abnormal profits, suggesting that brokers are able to cap-ture almost all the surplus created by their research.
Tipping has in recent years attracted interest of both American and European poli-cymakers, who have attempted to limit the practice. Our results establish not only its occurrence, but quantify the associated re-distribution of wealth. A large part of the prof-its are earned by the recommending brokers’ clients who have the opportunity to trade early, but in the end the profits are mostly appropriated by the research–producing bro-ker. Apart from deadweight losses incurred in this process, it is not clear that tipping is inefficient from a welfare perspective. Equity research may provide markets with impor-tant externalities, such as lower bid–ask spreads and higher liquidity, although this is in many ways still an unresolved issue.7 The question remains whether it is possible to find better and more efficient ways to pay for, and consume, equity research.
The rest of the paper is organized as follows. Section I describes the legal framework with respect to tipping, as well as the institutional framework in Europe, in Sweden in particular. Section II summarizes and explains the data we use. Section III presents the results in four parts. First, we establish the occurrence of abnormal net buying and returns close to recommendation events. Second, we investigate the extent to which this price and flow relationship can be associated with the trading of the recommending broker. Third, we estimate the profits associated with these trades to assess the economic value of stock recommendation revisions. Fourth and finally, we estimate how much more trading activity these revisions generate. Section IV summarizes the main findings and concludes the paper.
7See the discussion in Brennan and Subrahmanyam (1995), Easley, O’Hara, and Paperman (1998), and
I
The legal framework of tipping
The legal status of tipping in Sweden and the rest of the European Union is not entirely clear. The overall view is that there are restraints on tipping, but it is not settled law as to whether tipping can be considered an illegal practice. The Swedish Financial Services Authority (FSA) acknowledges that there are no guidelines with respect to the timing of the release of investment recommendations. Nowhere it is required that stock recom-mendations be released at the same time to all customers. What is required, following Commission Directive 2003/125/EC of the European Union, is that “the date at which the recommendation was first released for distribution be indicated clearly and promi-nently” in the research report. The Swedish financial industry has imposed some self-regulatory restraints on trading related to recommendations from 2002 and onwards that go further. The Swedish Securities Dealers Association (SSDA) recommends members to impose measures to “ensure public confidence in the financial market”. Among these measures, there is a general prohibition for employees in brokerage firms to sell any fi-nancial instruments acquired within one month for profit. Specifically, analysts are not allowed to buy stocks before the issuance of recommendations, and are only allowed to trade in the same direction as a recommendation revision after its release.8 The SSDA reg-ulation, effective after 1 July 2002, is an interpretation and extension to the act concerning reporting obligations for financial instruments (SOU 2000:1087), and rules of market con-duct described in the Swedish FSA Directive 2002:7. Breach of these rules can result in the FSA (SSDA) to revoke the brokerage firms’ (individuals’) licence.
In legal and regulatory circles, insider trading laws are frequently cited as a con-straint on tipping. Insider trading laws make it illegal to use, pass on to others, or en-ter into transactions while in possession of maen-terial, non-public information. Although the Swedish FSA takes the view that tipping about impending stock recommendations cannot be regarded as passing insider information, since the information on which rec-ommendations are based is public, a source from the Swedish National Economic Crimes
Bureau told us that tipped stock recommendations could well be regarded as inside infor-mation. This could in principle be the case if the analyst issuing the recommendation is well known and his recommendation has the potential to affect stock prices significantly. The view that tipping stock recommendations could be regarded as passing insider information is shared by some securities lawyers we approached. There is, however, no jurisprudence in this area, and the rules have not been tested in court. In fact we do not know of any instance in which prosecutors have successfully convicted financial analysts for insider trading violations related to tipping in Sweden or the rest of the European Union.9
In the few cases in which financial analysts, or their employers, have been punished for leaking information it has not been for insider trading violations but for offenses re-lated to conflicts of interest or market misconduct. In a relevant case in the UK, the British FSA fined Roberto Casoni for disclosing his views on Banca Italease to certain clients ahead of initiation of coverage. The British FSA considered that “it is improper market conduct for an analyst to selectively disseminate valuations (including drafts), recom-mendations or target prices to clients ahead of publication of that research.” By selectively disseminating such information to clients ahead of publication, “an analyst allows those clients the opportunity to pre-empt the conclusions of the published research and thereby potentially influence their investment decisions ahead of the rest of the market.”10
There are no similar examples in Sweden but applicable laws, regulations and admin-istrative provisions tend to be similar to those of the UK and other European countries under the Lamfalussy model. The exact application of these directives may however dif-fer from country to country, to the extent that the Swedish FSA has expressed to us that
9The U.S. Attorney’s office recently sentenced Mitchel S. Guttenberg to 78 months in prison, and ordered
him to forfeit almost $16 million in connection with insider trading charges. Guttenberg was previously an executive director in the equity research department of UBS, who allegedly illegally sold nonpublic infor-mation concerning upcoming UBS analyst upgrades and downgrades to at least two Wall Street traders.
10In its notice, the FSA stated that Mr Casoni had breached Principle 3 of the FSA’s Statements of
Princi-ple for Approved Persons which makes it clear that it is: “Inappropriate for an employee (whether or not an investment analyst) to communicate the substance of any investment research, except as set out in the pol-icy.” Citigroup had policies in place that made it clear that draft research was not to be disseminated outside of the Research Department. These policies are similar to those of other brokerage institutions/investment banks we have talked to in Sweden.
it does not necessarily interpret conflict of interest rules as applicable to tipping and that these issues should be addressed on a case by case basis.
Recently, the European Union enacted Commission Directive 2006/73/EC, which was implemented in Sweden by November 1, 2007 (after the end of the period covered by this study). This European Commission directive seems to impose clearer boundaries on tipping. Among other things it requires that:
a) “Financial analysts and other relevant persons must not undertake personal trans-actions or trade, other than as market makers acting in good faith and in the ordinary course of market making or in the execution of an unsolicited client order, on behalf of any other person, including the investment firm, in financial instruments to which in-vestment research relates, or in any related financial instruments, with knowledge of the likely timing or content of that investment research which is not publicly available or available to clients and cannot readily be inferred from information that is so available, until the recipients of the investment research have had a reasonable opportunity to act on it.”
b) “Issuers, relevant persons other than financial analysts, and any other persons must not before the dissemination of investment research be permitted to review a draft of the investment research for the purpose of verifying the accuracy of factual statements made in that research, or for any other purpose other than verifying compliance with the firm’s legal obligations, if the draft includes a recommendation or a target price.”
The first of these points clearly limits the ability of analysts and their employers to act on impending stock recommendations whereas the second point seems to restrict their ability to disseminate their research report to selected customers ahead of publication, but it may be too early to draw definitive conclusions.
The overall legal status concerning tipping therefore suggests that there was very little regulation at the beginning of our sample in the late 90’s, but it has attracted increased interest in more recent years. The gradual tightening of laws surrounding the handling and release of investment research is proof that the legislators acknowledge this activity
as a potential problem. With almost no jurisprudence in the area, it is too early to judge whether these efforts have been effective.
II
Data Description
The Stockholm Stock Exchange is a fully electronic market where members (brokerage firms) pay both fixed and transaction based fees for matching of order flow. Swedish banks and full-service brokerage firms compete with their foreign counterparts on the market for broker services and investment advice. Members of the exchange include sev-eral of the major brokerage firms present in U.S. and the rest of the European markets. There is some evidence that the competition among brokers on the SSE has increased in later years due to the entry of foreign competitors. In 1997, the exchange had 50 unique members, where the top 10 brokers accounted for 73% of the total value of share trad-ing.11 In 2006, there were 70 members, of which the top 10 had only 58% of the value traded. This development was primarily driven by a higher degree of foreign compe-tition. By 2006, Morgan Stanley, Goldman Sachs, Lehman Brothers, and Merrill Lynch, together with Icelandic Glitnir bank, had broken into the top 10; none of these interna-tional brokerage firms was among the top 10 in 1997. The foreign brokers have to comply with Swedish regulation, and the majority of them also have extensive local representa-tion (the same way major Swedish brokers have foreign representarepresenta-tion). We therefore do not find it necessary to distinguish brokerage firm origin in our analysis.
We believe that the Swedish stock market has at least three features that make it in-teresting to study from an international perspective. First, we find that the legal frame-work with respect to the practice of tipping is very similar in Sweden compared to other European countries, and even the U.S. We have been unable to detect any important dif-ferences in regulation of the main stock markets in the world, and have found very few examples of actual legal actions. Second, Sweden has a well developed and competitive stock market. International brokerage firms compete for trades in several large
compa-11Several members of the exchange also have foreign subsidiaries registered as members. We define
unique members by identifying the brokers who belong to the same company or group, and treating the group as one unit.
nies such as Ericsson, Nokia, Volvo, Astra Zeneca, and H&M. Many companies have cross-listings on foreign stock exchanges, and are therefore of interest to a wide group of international investors. Third and last, the stock market is sizable. At the end of 2005, the total market capitalization was SEK 3,507 (USD 438) billion, making Sweden the 12th largest stock market in the world according to the World Federation of Exchanges. The key advantage of using Swedish trading data is that we can identify all trades that be-long to each and every individual broker. For each trading date, stock, and member of the exchange, we have access to the number of trades executed, the number of shares traded (volume) and the value of those trades, measured in SEK, all of them broken up into purchases, sales, and internal trading.
We combine our detailed trading data with two other data sets containing stock rec-ommendations and stock prices. We obtain data on financial analysts’ stock recommenda-tions of stocks listed on the SSE from the Institutional Brokers Estimate System (I/B/E/S) database for the period January 1997 to June 2006.12 We concentrate on recommendation revisions, as opposed to recommendation levels. Revisions are discrete and salient events and previous research generally finds that they have significant information content.13 To construct the recommendation revision variable we rely on I/B/E/S recommendation classification. I/B/E/S classifies recommendations into five categories, from 1 to 5, which are usually interpreted along the following lines: (1) strong buy, (2) buy, (3) hold, (4) sell and (5) strong sell. We concentrate on two types of recommendation revisions: positive recommendation revisions (also labeled “upgrades”) and negative recommendation re-visions (labeled “downgrades”). An upgrade (downgrade) is defined as a buy (sell) or strong buy (strong sell) recommendation issued by an analyst whose previous recom-mendation on the stock was not as positive (negative) as the current recomrecom-mendation. Unlike many studies on financial analysts’ recommendations we do not make a distinc-tion based on the strength of the recommendadistinc-tion, that is we do not distinguish between buy and strong buy or sell and strong sell revisions. This is because many of the larger
12We work with a recent download of I/B/E/S to avoid the issues raised by Ljungqvist, Malloy, and
Marston (2009).
domestic brokers in Sweden use a three point scale incompatible with that distinction, or changed to it at some point during the sample period.
[Table I here]
Our original sample consists of 10,935 recommendations of which 4,936 are revisions: 2,924 upgrades and 2,012 downgrades. These revisions cover 296 firms and are more or less evenly distributed in the 10 year period we study.14 The sample includes recom-mendations by 824 analysts or teams of analysts and 46 brokerage firms. The 10 largest brokers in the sample, defined according to trading volume in the SSE, are responsible for slightly more than 50% of all recommendation revisions in our sample.
We match the I/B/E/S recommendations to our trading data using I/B/E/S tickers. When a company has more than one share class traded at the exchange, the matching is to the most broadly traded security (typically B shares), as identified in the trade data. After matching both data sets we are left with 2,507 upgrades and 1,730 downgrades across 270 firms. Table I reports features of these revisions, which tend to be concentrated among the largest most liquid firms. Many previous studies focusing on the level of stock recommendations find a strong bias towards buy recommendations. We only find weak evidence of this, since upgrades constitute about 60% of our total recommendation sample. Market capitalization is heavily skewed, where firms in the top decile on average are over ten times as large as the firms in the second decile.
In addition to information on recommendations and trades we also collect stock prices (adjusted and unadjusted), returns, market values and complementary information from Datastream. We match this to the trade data from the Stockholm Stock Exchange using securities’ ISIN codes.15 Researchers are usually careful about excluding recommenda-tion changes issued in the proximity of company earnings announcement dates, to avoid confounding public information. In addition to that, it is also common to eliminate obser-vations with current share prices lower than a certain threshold (less than U$S 5 or U$S
14At the end of 2006 the SSE had 417 listed companies
15The use of Datastream as a provider of individual stock returns may raise a number of concerns (see
Ince and Porter (2006)). We compare the prices in Datastream to transaction prices from the SSE, and uncover only one case in which the information in both samples is clearly conflicting. We opt to exclude that observation (recommendation) from our sample.
2) to avoid penny stocks, since outliers are usually concentrated among them. Since our approach explicitly takes these problems into account, we choose to keep all observations in our sample.16
Although the focus of our paper is the trading behavior of recommending brokers, our complete dataset enables us to make some further classifications based on brokers’ recommending behavior in each stock. We label brokers covering or issuing recommen-dations on the same stock as the recommending broker “informed”, and brokers not is-suing recommendations on the same stock “uninformed”. Our classification is motivated by the fact that financial analysts not only issue stock recommendations, but they also produce other types of research and maintain communication with clients, which could result in additional information being transferred to customers. We define the coverage period as beginning two months before the first recommendation release in our dataset and ending twelve months after the final observation. Our definition implies that some informed brokers would have recommendations within the window. We exclude these observations from the informed group, amounting to 1,336 and 918 cases for upgrades and downgrades.17 Informed and recommending brokers are therefore likely to be sim-ilar with respect to the size and scope of their firm’s business, as well as their customer base. The remaining group, uninformed brokers, contains full service brokers who hap-pen to lack coverage of the specific stock, but also some regular brokers without local research departments, and discount (online) brokers. We report the percentage of aggre-gate volume and number of trades initiated by the brokers who cover stocks according to our definition above (the recommending and informed) in Table I. This group is respon-sible for 66% of the volume and 45% of all trades in the market during the ten year period we study. The high share of volume reflects that they are among the larger brokerage houses, and the smaller share of trades that they on average execute larger transactions
16As a robustness check, we excluded all observations which had earnings announcements within a
10-day window surrounding the recommendation. The key results regarding the timing of trades and profits were virtually unchanged.
17Cases in which we exclude broker trades are equally common for buy and sell revisions, and occur in
about 26% of the recommendation sample. It will be clear in the following analysis of net buying that these exclusions are relevant, but do not drive any of our main results.
compared to other market participants. The volume and trades by those covering stocks in our sample falls with firm size along with overall coverage.
III
Results
Our approach relies on the simple fact that markets clear such that the expectation of brokers’ net trades in any particular stock is zero. We use this framework for measuring broker trade imbalances in both the value of trades and net buying (N B). For each broker
b, stocki, and dayt, net buying is defined as follows:
N Bb,i,t =Bb,i,t−Sb,i,t, (1)
whereBb,i,t and Sb,i,t are the values of purchases and sales of stockiexecuted by broker
b on day t. The definition of trade imbalance is similar, but where B and S reflect the number of purchases and sales. This measure provides us with a natural benchmark for detecting abnormal trading activity, since market clearance implies that the unconditional expected value of net buying (or trade imbalance), for any broker, stock and time is zero, i.e.,
E(N Bb,i,t) = 0 ∀b, i, t. (2)
Our results are divided into four subsections. First, we compute cumulative net buy for each day for each recommendation and day in our sample across a window of 20 trading days surrounding the recommendation date (τ). The cumulative net buy is our measure of the aggregate position taken by the recommending brokers’ customers, and we find a significant build-up before the official recommendation release that is strikingly similar to the aggregate return reaction. We contrast the group of recommending brokers with our defined groups of informed and uninformed brokers. We find that it is generally investors in the uninformed broker category that take the opposite side of the trades. In the second step, we analyze the return reaction to the recommendations in more detail, in order to verify that the measured returns are in fact associated with the recommending broker flows, as opposed to informed flows in general. In the third step, we use
cumu-lative positions and returns to compute profits implied by the trading data. We regard these profits as a measure of the aggregate benefit to the broker’s clients (for those who have early access). Finally, we estimate the abnormal turnover associated with our rec-ommending sample, thereby obtaining a crude measure of the benefit to the brokerage firms.
A
Returns and positions during recommendation revisions
Previous studies of recommendation performance have entertained the possibility that some investors could have access to recommendations, and trade on them, before the recorded recommendation date.18 We show how recommendations’ investment value changes depending on the date investors can first trade on them by computing buy and hold abnormal returns (BHARs) to recommendation revisions in the (-20; +20) event win-dow of the recommendation change. These returns try to capture the return that can be attained by investing in the recommendation, in excess of what could have been ob-tained by investing in a portfolio of firms of similar risk (proxied by the Swedish market index). The twin solid lines in Figure 1 depicts the return reactions to the recommen-dations revisions in the same window (right scale). As most of the literature, we find positive (negative) and significant abnormal returns following and fundamentally pre-ceding upgrades (downgrades). Depending on the window chosen abnormal returns can be as large as 3.78% for upgrades and -2.14% for downgrades. Most of these abnormal returns, however, take place in the pre-recommendation window (approximately 75% to 80% of the documented abnormal returns, both for positive and negative recommenda-tion revisions), with only a small fracrecommenda-tion of them clearly set in the post-event period (approximately 20% to 25% of the abnormal returns, a value that further shrinks if we value-weight abnormal returns). A conservative estimate of recommendations’ invest-ment value would clearly ignore pre-event returns and thus most of the total potential profitability. It is natural to entertain the possibility that at least part of those abnormal
18See Womack (1996), Mikhail, Walther, and Willis (2004), Asquith, Mikhail, and Au (2005), and Sorescu
returns could be captured by investors who get early access to financial analysts’ advice. We begin our analysis by comparing returns to the cumulative net buying of our three groups of brokerage firms; the recommending, informed, and uninformed. We measure broker’s aggregate net positions in the event window by summing net buying over event time, (τ +x), and over the firms,i, they recommend across each broker’s categorybC,
CN BbC,t|τ = τ+20 X t=τ−20 X i N BbC,i,t, bC ∈(bR, bI, bU), (3)
which we hereafter is referred to as cumulative net buy for the three categories.
Figure 1 shows the results of cumulative net buy normalized by the number of recom-mendations in event time for buy and sell recomrecom-mendations separately. The solid line in Panel A depicts the average aggregate net position of the recommending broker. During the first two weeks of the window, days -20 to -10, there is no noticeable sign of recom-mending brokers taking a position on the recommended stock. From day -10, net buying slowly starts to diverge from zero in the direction implied by the recommendation, to then accelerate a few days in advance of the release. By the end of the window, cumulative net buying represents a position of around SEK 25 million (USD 3 million) per recommen-dation, on average. This represents almost 4% of the cumulative value of shares bought by all brokerage firms in the same period around upgrades. The dashed line depicts the aggregate net buying of the group of informed brokers, excluding any concurrent obser-vations in the same window, who maintain a slightly positive net position throughout the time period. Since all positions add up to zero (apart from our exclusions of concurrent recommendations), we find that it is mainly the investors trading through uninformed brokers who sells their stock to investors trading through the recommending brokers.19
[Figure 1 here]
Panel B shows the corresponding results for downgrades, with qualitatively similar results, but also with some notable differences. We find that net buying of the recom-mending brokers tend to decrease sharply before the recommendation date, beginning
19The excluded broker flows account for the whole difference of approximately SEK 4.1 million per
around five trading days in advance. Cumulative net selling by the end of the window is around SEK 17 million, which represents about 2% of the value of all shares sold. Even if the overall pattern is similar to upgrades, there are three main differences. First, the average aggregate position taken by the recommending brokers is considerably smaller than that of the upgrades. Second, most of the build up of total net positions at the end of the window occurs five days prior to the recommendation date. Third, although the recommending brokers have a slightly positive aggregate position in the window prior to the recommendation, they also reduce it by a significant amount during the five trading days prior to the recommendation release. The common reduction of aggregate positions of both informed and recommending brokers may reflect leakage across informed bro-kers or the release of some common information prior to the recommending revision. We will address these issues when we assess the price impact of trading and the magnitude of trading profits.
We assess the statistical significance of net buying as follows. First, since our vari-able net buying contain considervari-able noise, we choose to collapse daily observations to weekly:
N Bb,i,w =
X
t∈w
N Bb,i,t w = (−4,−3,−2,−1,1,2,3,4) (4)
such that we form eight weekly buy observations from our daily window for each broker in sample (week 1 includes the recommendation date). Next, we regress this variable on a set of weekly dummy variables for each broker category (C),
N Bb,i,w = X bC X w DbC,w+eb,i,w bC = (bR, bI, bU), (5)
which means that we obtain eight regression coefficients for each of the three categories: recommending (bR), informed (bI), and uninformed (bU). The regression in (5) is com-pletely specified. The coefficients correspond to the sample means of weekly net buying for the broker categories, but we cluster errors on the broker level. The results are pre-sented in Table II.
bro-kers in the graphical analysis, but reveal more detailed information of individual broker trading. On average, net buying is SEK 7.1 million in the first week in which a buy rec-ommendation is issued, and is about half the size but still significantly positive, in the following three weeks after the event. For the purpose of documenting leakage, it is more notable that SEK 5.7 million of the build-up in the total position occurs in the week prior to the recommendation date. This, we believe, indicates that some investors trading with the recommending broker anticipate the upcoming event. We do not find that the average informed broker follows this pattern, as suggested by the negative, but insignificant co-efficients for this group during the weeks immediately surrounding the revision date. In the regression for downgrades, we find even stronger evidence of trading prior to the rec-ommendation date. Recommending brokers take a negative position of SEK 7.2 million in the week prior to the revision, which represents about half of the cumulative position at the end of the window. It therefore seems that pre-recommendation trading is more prevalent for downgrades compared to upgrades, which has also been documented in other studies.20 We also find that informed brokers trade in the same direction as the rec-ommending brokers during downgrades, but the coefficients for their average position are insignificant.
We further partition our sample based on recommended firm’s size, reported at the bottom rows of Table II. We find significant and large imbalances in the week preceding the recommendation for big firms (decile 1), and some weaker evidence of even earlier build-up of positions for smaller firms (in deciles 2 to 10). Purchases exceeds sales by SEK 9.9 (-15.2) million for upgrades (downgrades) in large firms on average in the week preceding the revision. The corresponding average positions for the remaining sample of smaller firms is SEK 1.9 (-1.6). The partition therefore highlights the economic significance of trading in large firms. Recommending brokers’ weekly net positions in the top ten percent of firms are all in the range of five to ten times as large as the remaining sample firms. As the number of recommendation revisions are fairly evenly distributed between these two groups of firms, the overall profitability is bound to be dependent on the ability
to trade successfully in the largest firms. We also divided the sample into two, equally spaced, time periods, but since we found similar results, we choose not to report them.
[Table II here]
The strong relation between cumulative net buying and returns does not only suggest that some investors get access to information early, but also that they are able to make profitable trades. In general, the presence of measured average abnormal returns coupled with average abnormal net buys in the pre-event window is not enough to guarantee that investors are in possession of valuable information at that point in time. For that we would also need to know the correlation between the two variables. It could be that pre-recommendation abnormal returns are measured for pre-recommendations for which there is no pre-recommendation abnormal trading. Another possibility is that the information contained in the recommendation is revealed, making it difficult to trade profitably.
B
Price impact of broker trading
We have documented average net buying and corresponding return reactions to our rec-ommendation revisions, but have not yet showed how these two are related in the cross-section. Under our tipping hypothesis, we expect that it is mainly the positions taken by the recommending broker that are responsible for the return reaction. Ruling out intra-day feedback trading, there are two main reasons why concurrent changes in positions should be correlated with price changes: due to price pressure or informed trading. Price pressure may arise if changes in positions by the recommending broker customers are not completely offset by some other group of investors. This could for instance be the case if providers of liquidity are pushed away from their preferred portfolio positions.21 An al-ternative explanation is that information is incorporated into prices as it is revealed in the trading process.22 Even if we believe that the information story is more consistent with our data, it is difficult to conclusively rule out the price pressure explanation. Our main
21The demand for immediacy as in Demsetz (1968) and Grossman and Miller (1988), or downward
slop-ing demand as in Shleifer (1986).
22As suggested by French and Roll (1986), and in line with microstructure models such as Kyle (1985),
objective is, however not to characterize the microstructure of the market, but rather to determine the source of the price reaction in the pre-recommendation window.
We analyze cumulative contemporaneous returns and net purchases in a window of five trading days before and after the recommendation change is issued. Based on the previous results presented in Figure 1, it is clear that most of the return reaction for both upgrades and downgrades occur within this window.23 As we do not know the exact time at which leakage occurs, we choose to analyze the relation between net buying and returns on a cumulative basis. The pre-recommendation window starts the day prior to the revision date and goes backwards in event time (t|τ). The post-recommendation window is constructed in a similar fashion, but goes forward from the day of the revision (τ|t). For each chosen time frame, we regress cumulative market-adjusted returns onto corresponding cumulative net buying, normalized with the average value of total daily turnover in the month preceding the window. The normalization is motivated by the fact that we here wish to compare relative price impact across brokers. The regression takes the form
CARb,i,t|τ =α+βbc,t|τCN Bb,i,t|τ/T Oi,τ−20+δM OMi,τ−20+γM CAPi,τ−20+b,i,t|τ, (6)
where CN B/T O is normalized cumulative net buying for each broker category, (bC),
M OM is a momentum factor defined as the previous month return on the stock, and
M CAP is the log of market capitalization of the firm at the beginning of the window.24 As before, we divide brokers into three groups: recommending, informed, and uninformed, where we would expect that the coefficients for recommending brokers are significantly positive for the relevant time period for which we suspect tipping. It is reasonable to be-lieve that the second group also have firm-specific information or could exert some price pressure, as they have very similar operations. The net buying results imply that it is the group of uninformed brokers that should stand on the other side of these trades. We test
23In line with our hypothesis, we find only weak results as the window is expanded and so choose not to
report them.
24We tried several different specifications: without or normalizing net buy with market capitalization, or
both hypotheses with a Wald test of coefficients to determine if they are different. [Table III here]
Table III displays the results of these regressions. The first and last column of Panel A shows the joint effect of upgrades for the five trading day cumulative net buying on abnormal returns for the same period. The coefficient for the recommending broker is not significant in any of these specifications, meaning that we are unable to establish a relation between net buying and returns when the window is wide. As we narrow the window size towards the recommendation date, the coefficient for the recommending broker becomes increasingly larger, and strongly significant. The coefficient for the in-formed broker category shows the reverse pattern, and is always positive throughout the window sizes. We interpret this as evidence that the observed return pattern is mainly attributed to the net position acquired by the investors at the recommending broker, and not through informed brokers in general. The strong negative coefficients for the larger group of uninformed brokers again show that they are trading against the two former groups throughout the whole period. The Wald test of differences in coefficients reveals that this last proposition has strong statistical support, but cannot be rejected in the test of differences between informed and recommending brokers. The latter result can be ex-plained by the lack of power due to considerable noise in our regression model, but is nevertheless somewhat unsatisfying.25 The coefficients for market capitalization gener-ally load negatively, which means that the return reaction on average is smaller for larger firms. We find that our momentum factor has a negligible impact on recommendations’ adjusted returns, and is never significant for downgrades. This may not be so surprising, given that momentum seems conspicuously absent in the Swedish stock market.26
Turning to the results of the added to sell recommendations of Panel B in Table III, we find even stronger evidence of price impact stemming from the recommending broker, but also very similar and significant estimates for the informed group of brokers. We see
25In unreported regressions for the top decile of firms with respect to market cap, we do find statistical
differences between the informed and recommending broker coefficients for upgrades, but not for down-grades, the day prior to the recommendation date.
two main explanations for this result. Either there is some leakage of information from the recommending broker to the informed group, or the information contained in the rec-ommendations are of broader content, such that the downgrade merely reflects this com-monly shared information. We think that the latter explanation is more sensible, since it is difficult to see why there should be an asymmetry of leakage between buy and sell rec-ommendations. Altinkilic¸ and Hansen (2007) and Ivkovic and Jegadeesh (2004) provide evidence that recommendations tend to chase past returns, and that they are frequently revised on earnings announcement dates, management’s earnings forecast dates and after other newsy events. We used earnings announcements to proxy for public information and as a possible source of the correlated trading in downgrades by informed brokers. We re-estimated the regressions of net buying and price impact specified in equations (5) and (6), after deleting all recommendations where there was an earnings announcement within the trading window. As we found that the estimates were almost identical to those reported, we choose not to report them and conclude that earnings announcements are not driving this difference.27 Even if it is difficult to infer if the information contained in downgrades is less useful because it is of poor content, or just more widely known in the market, we do expect this to be reflected in the profitability of trading in downgrades compared to upgrades.
C
Profits
When the exact date, and time, in which recommendations become available is not known with certainty, abnormal returns can provide at best an approximation to the real expected returns of investing in recommended stocks. Choosing a conservative window when computing returns, i.e. assuming recommendations become available to their users the day they appear in most databases, will result in this measure missing part of the profits obtained by investors who benefit from early tips and leakages. On the other hand, trying to avoid the problem by choosing a wide pre-recommendation window instead faces the
27We also found that earnings announcements are twice as common in the ten days prior to the
rec-ommendation revision compared to the rest of the window, but these proportions are almost identical for downgrades and upgrades.
risk of overestimating the real benefits of recommendations. This overestimation can be especially severe if recommendations are issued after public events, or if analysts tend to recommend stocks that have recently appreciated.
To overcome the problems that affect abnormal returns when dating is imprecise we use an alternative measure of recommendation performance: abnormal profits. Abnor-mal profits capture the excess profits made by investors who channel their recommendation-motivated trades through the broker making the recommendation. They are defined as the product of trades on the recommended stock executed by the recommending broker and the abnormal return obtained by that broker on these trades. Formally, for trades executed by brokerb, on stocki, on any given dayt:
Πb,i,t =
Bb,i,t·ARBb,i,t−Sb,i,t·ARSb,i,t
, (7)
whereBb,i,t is the amount the broker issuing the recommendation purchased in the rec-ommended stock measured in SEK,Sb,i,t is the amount the broker issuing the recommen-dation sold in the recommended stock, ARBb,i,t is a broker-specific abnormal return for purchases andARS
b,i,tis a broker-specific abnormal return for sells. With transaction level
data, or if average transaction price for sells equal average transaction price for purchases (7), conveniently reduces to:
Πb,i,t =N Bb,i,t·ARb,i,t. (8)
Abnormal profits are calculated in excess of what could have been obtained by in-vesting in a pre-defined benchmark (in our case the value-weighted Swedish SIX index return). Again, our methodology exploit the fact that, at any given time, brokers’ ex-pected net purchases on any stock are zero. We measure exex-pected net profits triggered by recommendations by associating net purchases with the appropriate stock prices.28 By ex-ploiting information from broker trades abnormal profits overcomes the dating problem that affects abnormal return measures. They are also free from the investability problems that may affect abnormal returns, especially in illiquid stocks. Abnormal profits are, after
all, computed using actual transaction quantities and prices.
In order to accommodate for overlapping recommendation windows, we study abnor-mal profits associated to recommendations in calendar time. For this purpose we build two portfolios, an “upgrade” and a “downgrade portfolio”, based on recommending bro-kers’ actual daily trades around those recommendation revision dates. Each time a firm receives an upgrade (downgrade), all trades executed by the recommending broker on the recommended stock are added to the corresponding portfolio within a window ofx
days of the recommendation change. By adjusting the window of trading days,x, in the portfolio, we are able to capture and compare profits from trades that originates both be-fore, during, and after, the revision is issued. The portfolio therefore properly reflects how investors actually responded to the recommendations, without having to assume when, or how much, they were able and willing to trade. Trades (purchases and sales) on each stock are kept in the portfolio for a fixed investment horizon, which we setT trading days after the recorded release date of the recommendation that motivated its inclusion in the portfolio. Our fixed investment horizon means that all positions opened in relation with that recommendation are liquidated atT, regardless of the choice of trading window, x. At the end of any given dayt, the upgrade portfolio will be invested in all stocks recom-mended in anx-day window of that trading date, and the amounts invested in each stock will be equal to the cumulative net buying at datet. We choose our investment horizon,
T = 20 such that we facilitate comparisons between different windows, but a relatively distant window also helps avoid the effect of price pressure in our measures.
Formally, for each stockiand brokerb, we calculate daily individual abnormal profits (AP) for upgrades in the following way:
APb,i,t =CN Bb,i,t−1 ·ARi,t+λb,i,t, (9)
whereCN Bb,i,t−1is brokerb’s net position in stockiat the end of the previous day, defined
as
and λb,i,t = Pi,t −Pb,i,tB PB b,i,t Bb,i,t− Pi,t −Pb,i,tS PS b,i,t Sb,i,t. (11)
ARi,tis daytdaily abnormal return on stockicomputed from closing prices andλb,i,t is an intraday adjustment that corrects for the fact that transactions may be carried out at prices that differ from closing prices. The numeratorPi,t−Pb,i,tB in the first term of equation (11) is the difference between stock’si closing price on dayt and the weighted average transaction price on that same stock for purchases (B) by brokerbon that same day. The price-adjustment for sales (S) corresponds to the second term in the same equation.
To obtain a time series of aggregate daily abnormal profits we sum individual abnor-mal profits across all stocks and brokers in each calendar day. We then calculate average daily abnormal profits and assess their statistical significance using Newey-West stan-dard errors. Annualized abnormal profits are computed simply by multiplying daily abnormal profits times 250 trading dates. An identical procedure is followed with down-graded stocks in the added to sell portfolio. It is important to keep in mind that the calendar time strategy we pursue here is actually implemented (at least on the aggregate) by broker clients or the broker itself.
We report daily and annualized abnormal profits calculated using this procedure in Table IV. One result that is evident from this table is that broker trades around posi-tive recommendation revision dates are on average profitable. Daily abnormal profits obtained by recommending brokers are estimated to be between SEK 466,971 and SEK 535,652 depending on the window used for measurement (that is between 116 and 133 million SEK once annualized) and are matched by negative profits obtained by the rest of the brokers (see Table V for a contrast between scaled profits obtained by recommending brokers and the profitability of other brokers’ transactions).29 The results are similar in all three windows analyzed, suggesting that abnormal profits are concentrated in a short window of the recommendation change, although their statistical significance decreases as we widen the observation period. We find this to be reasonable, as recommendations
29The abnormal profits measure we use here is a zero-sum measure; this means that any positive profits
tend to be more valuable, and trades based on them more profitable, at the moment of their release or shortly after it, but their value quickly recedes as investors act on them and their information gets impounded into prices. Expanding the window therefore only results in additional non-event days that dilute the statistical significance without sig-nificantly affecting the estimate. This also indicates that the differential performance is most likely related to analysts’ recommendations and is not just the result of preexistent differences in broker clients’ ability or information. In addition, the profits suggest that broker clients possess an informational advantage on those days, and that they make use of it. This is something beyond what can be inferred just by looking at abnormal returns, and coupled with the finding of significant broker-specific net buying in the vicinity of the recommendation release dates, confirms that recommendations are more than a mere sideshow.
In untabulated results we find that most of these profits are obtained by trading in rela-tively large firms. More than 80% of the documented abnormal profits comes from trades on revisions in firms ranked in the first decile of the size distribution, whereas the rest is obtained by trading in firms classified in deciles 2 to 10, even when less than half of the recommendations are issued on decile 1 firms. Perhaps more interestingly, table IV also reveals that pre-recommendation profits, defined as those associated with transactions that take place before the reported recommendation date are also positive and significant (when we look at narrow windows) and amount to almost half of the total recommen-dation profits. Pre- and post-recommenrecommen-dation profits are computed by narrowing the trading window to(t−x;t−1)and(t+ 1;t+x), respectively. This result provides further evidence of informed activity taking place before the recorded recommendation date.
From the results of downgrades in panel B we deduct that negative revisions either do not contain any valuable information or for some reason investors fail to capitalize on them. This result may seem surprising given the evidence of substantial selling ac-tivity around these recommendations coupled with negative average abnormal returns. Most of those returns, however, are pre-recommendation returns and the previous
sec-tion showed that informed brokers in general trade in the same direcsec-tion, making them difficult to exploit. Average post-event returns go from−0.3%to−0.5% for the average sell recommendation and are concentrated in small, illiquid stocks.30 Overall, we do not find any evidence of profits from trading in downgrades. As a robustness check, we par-tition our sample in two, equally long, time periods. We find significant evidence of net buying before the recorded recommendation date in both subsamples, and still that only upgrades are profitable. Since the results are similar to the complete sample, we choose not to report them.
We close this section with a word on risk adjustment. We have assumed that the risk of the average recommended stock is similar to the risk of the average stock in the market,β = 1, which we find to be a reasonable assumption given that analyst coverage has a clear tilt towards big firms. We also know from previous studies that multifactor models typically do not add much to this picture (see Green (2006)); size and book to market usually have no impact on recommendation-based strategies’ alphas, and neither do other variables that proxy for the state of the economy. In a previous version of this paper, we also constructed a portfolio based on trades by the recommending broker and measured its abnormal return by a conditional asset pricing model. The results of this procedure only confirms the ones obtained by profits in this section. We found that the return of the trade-weighted portfolio is lower than that obtained by equally-weighting, and similar to that of value-weighting returns. Abnormal returns were quite insensitive to our choice of factors.
[Table IV here]
D
Abnormal volume and trading costs
A large part of the cost of equity analysis is meant to be financed through increased com-mission revenues. In order to be able to compare estimated profits to trading commis-sions, we estimate the value of abnormal volume that can be attributed to
recommenda-30Abnormal returns are almost three times as large for small stocks compared to large ones. Research
by Zhang (2006) and others also show that price continuation tends to be exclusively concentrated in small stocks.
tion revisions. We estimate the value of abnormal buy volume with the following model for each broker-stock-year triplet,b, i, y:
BVbR,i,t =β0+β1BVbU,i,t+β2BU Yt+β3BVbU,i,t·BU Yt+bR,i,t, (12)
whereBVbR,i,tis the buy side SEK volume of recommending brokerbRon firmiand day
t; BVbU,i,t is the aggregate buy side SEK volume of uninformed brokers on stock i and
dayt andBU Ytis a dummy variable equal to one if brokerbRhas issued an upgrade on firmiless than 20 days away fromtand zero otherwise. We useβ2+β3BVbU,i,t+bR,i,tas
our estimate of the abnormal amount of purchases executed by the recommending broker
bR on firm ion each dayt in the upgrade recommendation window, and use a separate but identical method to estimate abnormal sales for downgrades. The cumulative sum of the average estimated abnormal SEK volume for recommending brokers is presented in Figure 2.
The estimated value of average cumulative abnormal buy volume per recommenda-tion 20 trading days after a recommendarecommenda-tion release is SEK 52 million. This figure is nat-urally lower than average cumulative buy volume per buy recommendation in the same window, SEK 650 million, but higher than cumulative net buys per recommendation at the end of the window, SEK 25 million (see Figure 1). We believe that this is a sensible result, as some positions are likely closed within the trading window. Our estimation sug-gests that approximately 50% of the positions taken during the recommendation window are closed after 20 trading days. For downgrades, the estimate for cumulative abnormal selling is much smaller (SEK 13 million after day 20), and is much closer to the net po-sition held throughout the investigated window. Differences in the levels of abnormal trading in upgrades and downgrades are consistent with findings of previous studies (see, for example, Irvine (2004) and Jackson (2005)), which have generally been attributed to individuals’ reticence to short sell stocks they do not possess. Our results add to this finding by documenting that there is also much less short-term trading for downgrades, meaning that repurchases (or the covering of short trades) are much less frequent.
At this point we can perform a simple back of the envelope calculation. According to our sources within the brokerage industry, broker customers usually pay full commis-sions on recommendation triggered trades, which until the end of the sample period were of around 0.5% of the value of the trade. If we multiply recommendation motivated trad-ing of SEK 51 million by a commission fee of 0.5% (1% roundtrip) we find that average commissions are of exactly the same magnitude as average profits, around SEK 510,000 per buy recommendation (SEK 135 million per year). This suggests that brokers appro-priate all the value of their recommendations, and at the same time, investors do not lose money. A 1% roundtrip commission fee seems excessive though, since there is no need to pay full commission to sell the stock. If we assume the roundtrip commissions to be less than 1%, the abnormal profits we have identified for upgrades would be split between brokers and investors. A roundtrip commission fee of 0.5%, for instance, would result in abnormal profits being equally divided between brokers and customers.
With negative recommendations we have already found no abnormal profits and there-fore no real service to investors. Still, a 1% commission schedule on the abnormal trading triggered by a negative recommendation would yield SEK 120,000 in commissions (SEK 22 million per year once we aggregate all negative recommendations).
[Figure 2 here]
Summing up buy and sell recommendations and assuming a roundtrip commission fee of 0.50% would result in investors making SEK 65 million per year in upgrades (ab-normal profits minus commissions) and losing SEK 50 million per year in downgrades (abnormal losses and commissions) for a net benefit of SEK 15 million per year. Brokers on the other side would perceive commission fees for a total of SEK 65 million per year for buy recommendations and SEK 11 million per year for sell recommendations, for a grand total of SEK 76 million per year (USD 10 million). These total revenues seem low com-pared to even conservative estimates of research costs.31 But there are important reasons why we are reluctant to generalize these results over the total market. First, we are only
31As an example, by hand-collecting tax data, we found that a senior (junior) analyst in our sample earns
around SEK 5 (1) million per year. We have five large research departments that have at least 5% market share in sample, and recommendations are issued by 46 distinct broker identities.
considering the direct monetary benefits of recommendation revisions, which is less than 40% of all recommendations we collected. Revisions represent salient events which have been found to be more powerful in explaining abnormal returns and trading, but it is very likely that the remaining recommendations provide some service to customers, and induces trading. Second, research by Irvine (2001) and Madureira and Underwood (2008), among others, also suggests that investors reward research production by brokers on a much more general level. Brokerage houses research departments have historically pro-vided services to other branches, such as retail sales, investment banking, and proprietary trading desks.32 Since we can only speculate around the magnitude of trade-generation outside our sample of revisions, we stress our result that brokers seem to capture most of the measured trading profits surrounding revisions.
IV
Conclusion
We have used an extensive data set containing detailed information about daily trad-ing activity in each firm by each broker on the Swedish stock market durtrad-ing almost ten years, in order to investigate to what extent broker clients take significant and profitable positions in firms prior to the official date on which the recommendation revision is re-leased. As much as 80% of the cumulative abnormal return reaction can be attributed to the period prior to the recommendation change. The aggregate net positions taken by the recommending broker reveal that a significant part of the build-up of positions oc-curs prior to recommendation date for both upgrades and downgrades. We interpret this as evidence of tipping, and then ask, from the investors’ point of view, the more impor-tant question: is this behavior profitable? We find that the return reaction can be associ-ated with the daily trades of recommending brokers for upgrades and that uninformed brokers (without equity research) generally stand on the other side of these trades. For downgrades, we find that informed brokers, who cover the same stock, tend to trade in the same direction as the recommending broker. This asymmetry directly carries over to
32This has started to change recently following legislatory changes brought upon by the corporate
