Calculators, lemmings or frame-makers? The intermediary role of securities analysts

Daniel Beunza and Raghu Garud

Introduction
As Wall Street specialists in valuation, sell-side securities analysts constitute a particularly important class of market actor.1 Analysts produce the reports, recommendations and price targets that professional investors utilize to inform their buy and sell decisions, which means that understanding analysts’ work can provide crucial insights on the determinants of value in the capital markets. Yet our knowledge of analysts is limited by insufficient attention to Knightian uncertainty. Analysts estimate the value of stocks by calculating their net present value or by folding the future back into the present. In so doing, they are faced with the fundamental challenge identified by Frank Knight, that is, with the difficulty of making decisions that entail a future that is unknown. These decisions, as Knight wrote, are characterized by ‘neither entire ignorance nor complete . . . information, but partial knowledge’ of the world (Knight, [1921] 1971: 199). The finance literature has not examined the Knightian challenge faced by analysts. Indeed, existing treatments circumvent the problem by adopting one of two extreme positions. In the first, put forward by orthodox economists, it is assumed that Knightian uncertainty is non-existent and that calculative decision-making is straightforward. Analysts are presented as mere calculators in a probabilistic world of risk (Cowles, 1933; Lin and McNichols, 1998; Lim, 2001). In the second, put forward by neo-institutional sociologists and behavioural finance scholars, analysts face too much uncertainty to engage in individual calculation. Analysts confront this uncertainty by resorting to a lemming-like imitation of their colleagues’ opinions (see respectively Rao, Greve and Davis, 2001; Scharfstein and Stein, 1990; Hong, Kubik and Solomon, 2000). None of these views, however, examines the Knightian challenge that analysts confront, namely, the imperative to decide with a significant but limited knowledge of the world. In recent years, an emerging sociological literature has begun to redress this neglect of Knightian uncertainty by viewing analysts as critics. According to the
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Daniel Beunza and Raghu Garud

analysts-as-critics approach, the function performed by these professionals is to assess the value of securities whose value is uncertain (cf. Zuckerman, 1999, 2004; Zuckerman and Rao, 2004). Like film reviewers, food experts or wine connoisseurs, analysts bring a social dimension back into decision-making. They do so, even if buyer and seller do not have an ongoing social relation, because they tend to reproduce the prevailing social order in their critiques (Hirsh, 1972, 1975; Rao, 1998; Benjamin and Podolny, 1999). For instance, Zuckerman (1999) argues that analysts reinforce existing industry categories by engaging in selective company coverage. Analysts exclude companies that lie outside established industry categories, depressing their value and creating a ‘categorical discount.’ In this manner, the analysts-as-critics approach restores Knightian uncertainty to the centre of our understanding of securities analysts. While notably advancing our knowledge of analysts, the critics approach leaves several questions unanswered. First, if security analysts do little more than classify stocks into categories, it is unclear how their work could offer added value to the users of the reports, the portfolio managers. Portfolio managers, as any other participant in a speculative market, are less interested in accurate valuations than in finding profit opportunities. Second, passive classification cannot explain the rise of new categories, yet new categories have presided over almost all major valuation episodes within the post-war decades: to name a few, the ’tronics bubble, the emergent markets bubble, the biotech bubble or the Internet bubble (Malkiel, 1973). Finally, the critics approach does not explain how unknown analysts can rise to star positions. In short, the view of analysts as critics does not fully explain how these professionals add value, innovate, rise to fame, or fall into oblivion. Seeking a detailed understanding of the activity performed by analysts, we ask: What is the meaning of analysis under Knightian uncertainty? We address this question with a grounded-theory, qualitative content analysis of selected analyst reports. These documents have the unique advantage of providing a window into the cognitive processes followed by analysts in real time, a window not obscured by retrospective reconstruction. In developing our grounded theory methodology, we rely on the constant comparative method advocated by Glaser and Strauss (1967). We centre on the financially volatile period of the Internet ‘bubble’ of 1998–2000 because it best captures the problem of analysis under extreme uncertainty. We further focus on a single well-known company, Amazon.com, and compare the reports written about it by the top Amazon analyst at the time, Henry Blodget, against those of maximally different rival analysts. Our findings point to an insight that has been underdeveloped in economic sociology. Underlying the assessments made by securities analysts, we find internally consistent associations between categorizations, analogies and key metrics. We label these ‘calculative frames’. For example, one particular calculative frame for Amazon categorized the company as an Internet company, presented it as analogous to Dell Computers, and appraised its prospects in terms of revenue growth. Analysts who used this frame typically had a buy recommendation for 14
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Calculators, lemmings or frame-makers? The intermediary role of securities analysts

the firm. A contrasting frame viewed Amazon as a book retailer, analogous to Barnes & Noble, and valued it on the basis of its profits at the time. Analysts who espoused this alternative frame tended to have a more pessimistic ‘sell’ or ‘hold’ recommendation for Amazon. We find, moreover, that these frames were robust over time, leading to sustained controversies among analysts over the value of Amazon. We suggest that there is utility in viewing analysts as framemakers, that is, as specialized intermediaries that help investors value stocks in contexts of extreme uncertainty. The chapter is structured as follows. After reviewing the academic literature on analysts, we outline the guiding principles of our grounded theory research design. Next, we examine three episodes in the financial controversy over Amazon, located in December 1998, May 1999 and June 2000. Each of these moments yields rich theoretical insights that build up to a rounded perspective on analysts as frame-makers. We conclude by examining the implications of this perspective for an understanding of analysts as market intermediaries and of the social determinants of value in the capital markets.

Perspectives on analysts
Despite the extensive academic attention bestowed upon analysts, existing treatments provide a limited account of their intermediary role. Extant work is best understood as three broad streams. One approach, rooted in the finance and accounting literatures, views analysts as information processors and stresses their activeness in searching, assembling and communicating information. Another approach, based on neo-institutional sociology and behavioural finance, documents the tendency of analysts to mimic each other. We refer to this as the imitation perspective. Finally, a more recent sociological approach has started to outline the role of analysts as critics. Analysts as information processors The information-processing literature on analysts rests on a remarkable finding: securities analysts, long regarded as valuation experts, are unable to provide accurate forecasts of stock prices. Beginning with Cowles’ (1933) seminal piece, titled ‘Can Stock Market Forecasters Forecast?’ numerous finance and accounting theorists have documented the failure of analysts’ recommendations to produce abnormal returns and accurate forecasts of earnings and price targets (Lin and McNichols, 1998; Hong and Kubick, 2002; Michaely and Womack, 1999; Lim, 2001; Boni and Womack, 2002; Schack, 2001). Two complementary explanations have been put forward to account for this failure. One view, based on the efficient market hypothesis (EMH), argues that accurate financial forecasting is simply impossible in an efficient capital market (Samuelson, 1965; Malkiel, 1973). According to the EMH, stock prices in a competitive capital market capture all of the relevant information about the
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value of a security, following a random walk. There are no mispricings, no possibilities for any actor to find extraordinary profit opportunities, and indeed, no scope for financial intermediaries to help their clients to do so (Fama, 1965, 1991; Samuelson, 1965; Malkiel, 1973). The bleak implication for analysts is that accurate forecasting and lucrative advice giving are impossible. An additional explanation for analysts’ inaccuracies, based on agency theory, is that the fiduciary relationship between analyst and investor is distorted by a variety of conflicts of interest, producing dysfunctional biases in their forecasts and recommendations. These distortions include investment banking ties (Lin and McNichols, 1998; Hong and Kubick, 2002; Michaely and Womack, 1999), access to company information (Lim, 2001), brokerage interests of the bank employing the analyst (Boni and Womack, 2002), investment interests of the clients of the bank (Sargent, 2000), or the investment interests of the analysts themselves (Schack, 2001). In short, in this literature analysts come across as conflict-ridden intermediaries. The aforementioned conflicts have become particularly prominent following the Wall Street scandals of 2000–2001. During these years, top-ranked Internet analysts (including Henry Blodget) resisted downgrading their recommendations even as prices fell from record highs to zero (Boni and Womack, 2002). Other analysts were recorded privately criticizing companies that they publicly recommended (Gasparino, 2005). Such was the public uproar against analysts, that the Securities and Exchange Commission issued explicit guidelines that advised investors to use analyst reports with caution (Securities and Exchange Commission, 2002). Whether in the form of market efficiency or conflicts of interest, the two approaches to analysts presented so far share a common premise: both assume that the core intermediary function performed by security analysts is to forecast the future and to provide recommendations. Analysts are accordingly presented as engaged in the search, assembly and diffusion of information. To highlight this common focus on information, we refer to this literature as the ‘information processing approach’. Analysts as imitators The information processing literature outlined above has been challenged by work in behavioural finance. In an important attack on the neoclassic emphasis on processing, assembling and calculating data, behavioural theorists have documented the tendency of analysts to imitate each other or to herd (Scharfstein and Stein, 1990; Banerjee, 1992; Trueman, 1994; Prendergast and Stole, 1996; Hong, Kubick and Solomon, 2000). According to the seminal work of Scharfstein and Stein (1990), overly comparative compensation schemes such as firing and promoting analysts based on their performance relative to one another; pressure them to herd, that is, to copy each other to the extreme of ignoring their own private information when the latter is inconsistent with the view of the majority. The concept has received important empirical support. For 16
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instance, Hong, Kubick and Solomon (2000) found that the career paths of securities analysts make imitation a worthwhile strategy. ‘Analysts,’ the authors conclude, ‘are more likely to be terminated and less likely to be promoted when they make relatively bold forecasts,’ which suggests that the pressures for herding are indeed present (Hong, Kubick and Solomon, 2000: 123). In a related challenge to information processing, the literature in neoinstitutional sociology argues that the search for legitimacy among analysts promotes imitation and conformity (Phillips and Zuckerman, 2001; Rao, Greve and David, 2001). For instance, Rao, Greve and Davis (2001) argue that the work of analysts is characterized by imitation, and that one reason they do so is that their work conforms to a well-studied decision-making pattern known as ‘informational cascades.’ An informational cascade arises when economic actors face a decision in a context of risk. Decisions are made in a sequential pattern, and the last actors to decide see the decisions that were made by the first. In these circumstances, the emerging consensus among the first decision-makers creates pressure for subsequent actors to swing in their favour, thereby adding to the consensus and reinforcing this pressure for the following ones. As a result, the last actors to decide invariably end up agreeing with the consensus, even if their private information should make them disagree (Bikchandani, Hirshleifer and Welch, 1992). Rao, Greve and Davis argue that this cascading dynamic characterizes the coverage decisions made by analysts. Where is the analysis? The information processing and imitation perspectives provide complementary perspectives on the shortcomings of the analyst profession. Our overall assessment of this literature, however, remains mixed: while purporting to examine the intermediary role played by analysts, existing treatments have glossed over the content of the analytic work itself, that is, over the arguments, tables, charts and figures that make up analysts’ reports. As a result, these treatments overlook the social, cognitive and material processes that make forecasting possible. In this section we present empirical and theoretical arguments suggesting that a proper understanding of analysis needs to address the content of the reports. Analysis is more than forecasting One important reason why we reject equating analysis with forecasting is that the latter is not critically important for institutional investors, the actual users of analysts’ reports. This surfaces clearly from the ‘All-American’ rankings complied by Institutional Investor magazine, the most widely-used source of data about the impact of analysts’ work. For instance, in the 2003 Institutional Investor rankings, the magazine asked its readers to rank in importance eight different dimensions of analyst merit: industry knowledge, written reports, special services, servicing, stock selection, earnings estimates, market making and quality of sales force. Among these, investment recommendations and earnings estimates were ranked sixth and seventh out of a total of eight criteria. The
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top two criteria, in contrast, were ‘written reports’ and ‘industry knowledge.’ This suggests that analysts’ arguments and ideas are far more helpful to investors than the brief numbers that the analysts attach to the reports in the form of recommendations and price targets. Our reading of investors’ responses is supported by anecdotal evidence from analysts. For instance, the analyst profession has never accepted the idea that their core intermediary function is forecasting prices or recommending stocks: as far back as 1933, analyst Robert Rhea replied to the charge that analysts provided unprofitable recommendations (first formulated by Cowles, 1933) by countering that research reports were intended ‘as educational pieces, not as investment advice’ (Bernstein, 1992: 35). More recently, a top-ranked securities analyst argued in the Wall Street Journal that ‘the analysts’ clients (. . .) could not care less if you say “buy, hold or sell.” They just want to know why’ (Kessler, 2001: A18). In a similar vein, a prominent analyst at investment bank Brown Brothers Harriman stated that,
One reason institutional investors continue to value the work of an analyst whose recommendations have been off-base is that they pay less attention to analysts’ recommendations than you might think (. . .) the institutional clients make their own buy or sell decisions. They want the analyst to add value to their decision-making (Brenner, 1991: 24)

Forecasts and recommendations, then, do not seem to be the key to analysts’ work. According to this analyst, investors want diversity in opinions: ‘an articulate case from both the bull and the bear’ (Brenner, 1991: 25, cited in Nanda and Groysberg, 2004). We conclude from this review of investor data that forecasting and investment advice are probably not the most important functions that analysts perform. We are led to inquire about the analyst functions that investors do value. Turning again to the survey results of Institutional Investor, we ask, what do the top-ranked survey responses, ‘written reports’ and ‘industry knowledge,’ actually mean? In particular, how do the ‘written reports’ produced by analysts help investors? What is the nature of the ‘industry knowledge’ that analysts convey? Limited treatment of uncertainty While the information processing literature assumes that the future is readily calculable, the imitation approach assumes that imitation replaces calculation. In both cases, the difficulties associated with calculating when the future is unknown are overlooked. Similarly, the final numbers provided by analysts are the only output that appears to matter in both approaches: both overlook the question of how those price targets were developed in the first place, how analysts decided between opposing scenarios, and where those scenarios came from. In the paragraphs below, we argue that this inattention to Knightian uncertainty leads to an unrealistic and unbalanced view of analysts. 18
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An under-calculative view of analysts As mentioned above, the imitation literature on analysts is characterized by a lack of interest in the ways in which analysts calculate value. Rao, Greve and Davis (2001), for example, view imitation as an economical alternative to calculation. According to the authors, analysts imitate their peers in contexts of uncertainty, just as a driver might imitate other drivers in deciding ‘how fast to drive on a certain stretch of highway’ (Rao, Greve and Davis, 2001: 504). Our position, by contrast, is that imitation does not fully account for the intermediary activity performed by analysts. Whereas imitation emphasizes similarity, we observe a great deal of heterogeneity among them: for instance, none of the Internet analysts ranking within the top five in 1998 retained their status by 2001 (Institutional Investor, 1998, 1999; Sargent and Kenney 2000a, 2000b, 2000c; Justin 2001; Abramowitz, Bloomenthal, Burke and D’Ambrosio, 2000). Another way in which analysts depart from Rao et al.’s imitators is that these professionals are Wall Street’s valuation specialists: unlike the occasional driver venturing in an unfamiliar highway, assessing companies is the core job of an analyst. Analysts are generously paid to do this and the positions they adopt can have career-altering consequences for them. Instead of taking shortcuts to avoid the costly work of calculating value, it seems more plausible to expect that they would devote most of their time and energy to valuation. Indeed, several prominent economic sociologists have recently emphasized the importance of understanding calculation rather than simply denying it exists (Callon, 1998; Stark, 2000; MacKenzie and Millo, 2003; Granovetter, 2004). As noted by Callon (1998), the assumption that actors never calculate (as some sociological treatments make) is as unrealistic as the contrasting neoclassic position that market actors always do so. Instead, Callon argues for granting the possibility that actors might calculate, and asks how this might be accomplished. In response he offers an ‘anthropology of calculation’ – that is, a detailed attention to ‘the material reality of calculation, involving figures, writing mediums and inscriptions’ (Callon, 1998: 5). In other words, far from overlooking the social determinants of value, a proper sociological understanding of markets should expand the theoretical scope of ‘the social’ to encompass how collectively constructed calculative technology shapes the encounter between information and prices (Stark, 2000; Granovetter, 2004). From this vantage point, the neo-institutional work on analysts comes across as an under-calculative rendering of their activity. An over-calculative view of analysts While the imitation literature assumes that calculation is rarely feasible, the information processing perspective is hampered by the contrasting assumption that calculation is straightforward and unproblematic. As customary in the rational choice paradigm, Knightian uncertainty is assumed away with recourse to Savage’s (1954) theory of Bayesian decision-making. According to Savage’s
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model, rational decision-makers develop probability estimates by updating their subjective prior beliefs as incoming news arrive. Rational updating entails following the rules of Bayesian inference. Accordingly, two Bayesian decisionmakers facing the same news with different priors will update their estimates in the same direction, even if not necessarily by the same magnitude. Thus, for example, the arrival of good news about a company should make all rational decision-makers value the company more, not less, although by different degrees. As additional information arrives and updating continues, actors will converge in their estimates and their final position will be solely shaped by incoming information. While Bayesian convergence is a useful stylized portrayal of decision-making in numerous contexts, a detailed analysis of the cognitive mechanism it involves suggests that it can easily break down under Knightian uncertainty. If the range of future possible outcomes and probabilities is unknown (Knight, [1921] 1971), unforeseen contingencies prevent Bayesian updating. This blind spot of Bayesian models has been recognized even in contemporary economic literature, and is referred to as a ‘zero-probability event’ (Barberis, Shleifer and Vishny, 1998; Brandenburger, 2002). The related concept of ambiguity offers an additional reason. Savage’s model assumes that all rational decision-makers classify news in the same manner, whether as positive or negative. But in contexts of ambiguity, that is, of confusion over how a piece of news should be classified (March, 1987), different actors may update in different directions, barring convergence from taking place. In sum, in a world of Knightian uncertainty, economic calculation requires far more conditions than those considered in Bayesian models. Informationprocessing theories that build on Bayesian treatments do not address how market actors incorporate information into their estimates when this information is incomplete, ambiguous, divergent or contradictory. In particular, they do not attend to the social and cognitive mechanisms employed in representing, manifesting, and settling differences. For this reason, we refer to the processing approach as over-calculative. Bringing uncertainty back into analysis A more realistic theory of analysts would address how analysts combine mental models and social cues in their calculations to overcome the challenge of Knightian uncertainty. Empirically, this treatment would explain how the different estimates made by analysts arise, diffuse and evolve among them. Indeed, an emerging stream of literature, centred on the analysts-as-critics’ literature, has begun to address the significance of Knightian uncertainty for analysts. Analysts as critics A recent stream literature led by Ezra Zuckerman argues that analysts should be understood as critics (Zuckerman, 1999, 2004; Zuckerman and Rao, 2004). Building on the sociological work on critics developed by Hirsh and Podolny 20
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(Hirsh, 1972, 1975; Podolny, 1993, 1994; Benjamin and Podolny, 1999; Hsu and Podolny, 2005), Zuckerman argues that analysts function as specialists in conveying the worth of a stock when its value is uncertain. As critics, the activity that analysts perform is fundamentally based on classification: given the difficulty of simply plugging disputed or incomplete information into a valuation formula, analysts assess the value of a company by comparison with other companies in the same category. According to Zuckerman (1999), however, the comparative valuation undertaken by analysts takes place in a passive manner, leading to dysfunctional consequences for the companies being valued. Analysts strive to maintain legitimacy in the face of investors. This leads to a rigid insistence on fitting companies into existing slots (as opposed to creating new ones when required), which in turn makes investors screen out of their coverage those companies that do not belong to any pre-existing category, depressing their market value as a result. Consequently, analysts are said to create an ‘illegitimacy discount’ for hybrid organizations that perpetuates existing industry structure and stifles innovation. The critics approach to analysts sets the stage for several interesting and unanswered questions. For instance, how can the notion of categorical discount be reconciled with the observation that new analytical categories do emerge? Turning again to the years 1998–2000, we find a new industry category, ‘Internet and New Media’ (Institutional Investor, 1999: 107; Justin 2001: 179; Abramowitz et al., 2000: 136). Instead of being penalized with a discount, companies in this category – the so-called ‘dot-coms’ – actually traded at a rather generous valuation premium. One implication is that analysts may be drawing on a richer calculative tool-kit than calculation-by-category, giving them the possibility of valuing a company while arguing that it belongs to a new category. In addition, we observe that those Internet analysts who first granted higher valuations to Internet firms (as Blodget did), went on to enjoy very high rankings in Institutional Investor, suggesting that the creation of new categories plays an significant role in the value that investors accord to a security analyst. This suggests that legitimacy may not be the only pressure that analysts face, an assumption which begs the question of what parallel forces might also be in play. Pending questions To sum up, the literature on analysts is best seen in terms of three overriding streams: analysts as information processors, as imitators and as critics. Each perspective offers some insight into the roles played by analysts. At the same time, each raises further questions: How do analysts’ reports and industry knowledge help investors? How do new industry categories emerge? Why do some analysts become stars, while others remain unknown? These add up to a single central question: In real time, how do analysts value securities under conditions of extreme uncertainty? It is to this question that we turn our attention in the rest of this chapter.
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Methods
Our study examines the work of securities analysts with a grounded-theory, qualitative content analysis of selected analyst reports on Amazon.com during 1998 to 2000. We favour grounded theory for its potential to break out of the existing theoretical paradigm (Glaser and Strauss, 1967). Our aim, in other words, is not to verify hypotheses from the literature but is to develop new ones. In the following paragraphs we describe the different steps that we undertook to build theory, including our choice of theoretical sampling, constant comparison, theoretical saturation and the use of data slices. Research design and sample selection In operationalizing grounded theory, we chose a qualitative content analysis design. Qualitative content analysis is of particular value because virtually all previous treatments of analysts have focused on quantitative indicators such as price target accuracy or recommendation profitability, ignoring the actual text of the reports. To select our reports, we undertook a theoretical sampling procedure, choosing our reports on the basis of theoretical purpose rather than representativeness. Thus, for instance, to address the theoretical issue of how analysts confront Knightian uncertainty, we looked for a company whose future could not be easily extrapolated from the past. We centred on the emergence of the Internet during the so-called technological ‘bubble’ of 1998–2000, a technological discontinuity that induced Knightian uncertainty to the actors involved (Tushman and Anderson, 1986; Garud and Rappa, 1994; Christensen, 1997). Of the several candidate Internet companies to be analysed, the size and visibility of Amazon.com made it particularly appropriate. Our choice of focal analyst was equally guided by the principle of theoretical relevance. We focused on Henry Blodget, the analyst whose work was, according to Institutional Investor, most valuable to investors. Inference of hypotheses To develop our theoretical categories we followed the constant comparative method advocated by Glaser and Strauss (1967). We contrasted Blodget’s reports with those of rival analysts with maximally different messages along three different points in time. Our first comparison sought to understand the mechanisms of valuation used by analysts; for that purpose, we selected two analysts that valued Amazon very differently. On December 1998, securities analyst Henry Blodget famously valued Amazon at $400, while Jonathan Cohen valued it at $50. A comparison of the two reports suggested a new theoretical category, which we denote ‘calculative frame.’ Our second sampling decision sought to understand how frames mediated value. For that purpose, we chose the reports by Blodget and Barron’s journalist Abel Abelson. Both actors espoused drastically different frames, and 22
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Calculators, lemmings or frame-makers? The intermediary role of securities analysts

responded very differently to news of losses in May 1999. This disparity gave us the opportunity to see how analysts used their frames and led to a new construct, ‘framing controversies.’ Having established how analysts value companies and how their methods matter, we turned to the limits of calculative frames. The third episode in our comparison centers on a debate between Blodget and analyst Ravi Suria that took place in June 2000. Suria challenged Blodget’s perspective with a pessimistic report on Amazon. Blodget countered Suria’s attack with another report, but fellow analysts and investors abandoned Blodget’s frame. The episode gave us the opportunity to develop a new theoretical category that seeks to explain how calculative frames are abandoned. We refer to this category as ‘asynchronous confrontation.’ Data and sources Our primary sources of data were the analyst reports contained in the Investext database and the analyst rankings of Institutional Investor magazine. We obtained full-text Adobe PDF files of reports of these analysts from Investext, a database that stores the research reports written by analysts from investment banks and other financial research institutions. The rankings of securities analysts during the years 1998–2000 were obtained from Institutional Investor. Additional considerations Finally, we highlight two important characteristics of our grounded research design. First, the empirical validity of our findings is not based on the size of our sample; instead, our process of constant comparison aims at generating new hypotheses (Glaser and Strauss, 1967). The findings reported here, however, have yet to be explored in other settings. The second observation pertains to the involvement of our focal analyst, Blodget, in the analyst scandals of 2001. On November 2001, Blodget abandoned his job at Merrill Lynch as part of a judicial settlement with the Attorney General of New York, Eliot Spitzer. The settlement followed an investigation of Blodget’s internal communications with his colleagues at Merrill Lynch, revealing internal e-mails in which Blodget criticized some of the companies that he was officially recommending. The abrupt end of Blodget’s career as a security analyst might be interpreted as evidence that Blodget was not helping investors but simply deceiving them, rendering his reports an inadequate data source to learn about the mental models used by analysts. On this issue, we note that the episodes of conflict took place after the time period that we examine, and for companies different from Amazon (we elaborate this point in the next section below; see also Gasparino, 2005). More interesting though, is the question: How it is that analysts such as Blodget were able to convince investors and rise to the top of the rankings despite the potential for conflict of interest? Our chapter addresses this question by suggesting that what analysts do is create and provide a compelling frame that is persuasive.
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The financial controversy over Amazon.com
Barely one year after Amazon’s debut on Wall Street, a sharp controversy over its value erupted among Wall Street analysts. The company had opened up for business on the Web in 1995, and had placed itself under the eye of investors with its initial public offering in 1997. Within a year, the stock price had risen to unprecedented heights. As one of so many business cases put it, ‘never in the history of financial markets had a company reached such market capitalization . . . without a single dollar in profits’ (González, 2000: 11). Accordingly, a widespread debate ensued about the merits of the company, as well as about the Internet and electronic commerce in general. The controversy continued until the end of the year 2000, when the company’s mounting losses settled the case against the optimists. In this section we examine three episodes of this controversy and their lessons for the work of securities analysts. First episode: Blodget vs. Cohen On December 16th 1998, investors were faced with a blunt dispute over the value of Amazon. Blodget, an Internet analyst at Canadian bank CIBC Oppenheimer, raised his price target from $150 to $400 following the company’s stellar Thanksgiving sales. Such brusque change in the analyst’s recommendation was exceptional enough to be featured on the Wall Street Journal. On that same day, however, Jonathan Cohen of Merrill Lynch advanced a very different perspective: in a research note on the company issued only hours after Blodget’s report, Cohen rated Amazon a ‘sell’ and valued it at $50, arguing that it would never be able to reach the profits that Blodget predicted. The resulting controversy among investors was such that trading volume in Amazon stocks surpassed $100 million, more than ten times its average. The episode finally resolved itself in Blodget’s favour. The stock exceeded the $400 price target in three weeks, and Blodget entered Institutional Investor’s All-Star team. The uncertainty, tension and drama of December 1998 are hardly consistent with the literature on analysts. The information-processing approach presents analysts as aiming to have forecasting accuracy but the disparity between the two analysts seems too wide to be attributed to inaccuracy or measurement error. Similarly, the neo-institutional literature presents analysts as averse to deviating from the consensus and as unwilling to upset the companies they follow, but we find Blodget and Cohen clashing directly with each other, ignoring the consensus and, in the case of Cohen, bitterly criticizing Amazon. What, then, accounts for the sharp divergence among the analysts? Behind the numbers: formulae and estimates A broader survey of the work produced by Blodget and Cohen, including their September and October reports, offers additional clues about the origins of their

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disparity. Between September and December 1998, Blodget and Cohen wrote a total of five documents each. These reveal the nuts and bolts of their calculative technique. Our examination of these early reports revealed that the key to their difference lies in the analysts’ choice of estimates. Cohen’s revenue estimate for the year ending in December 2000 was $0.8 billion (Cohen and Pankopf, 1998a:10), whereas Blodget’s was three times higher, totalling $2.5 billion (Blodget and Erdmann, 1998: 10). Similarly, Cohen’s operating margin estimate stood at a conservative 10 per cent (Cohen and Pankopf, 1998a: 6), versus a more generous margin estimate by Blodget of 12 per cent (Blodget and Erdmann, 1998: 13). Compounded in their respective formulae, these differences produced the valuation gap of $400 versus $50. Examining the justifications that both analysts give to their respective estimates, we observe a striking regularity: both analysts draw from categories, analogies and key metrics. Below, we examine how these three elements allowed the two analysts to estimate Amazon’s future operating margin and revenue. 1. Margin estimate. Blodget estimated an aggressive 2003 operating margin of twelve per cent. In explaining his figure, Blodget first rejected the use of Amazon’s current profits to predict the future operating margin of the company. Blodget argued that as a young start-up company, Amazon was still in its initial money-losing phase. The proper proxy for Amazon’s long-term margin was instead the margin of a similar company. Blodget went on to consider four possible, similar companies, from book retailers to Internet portals. He wrote:
Most investors appear to come to one of four conclusions regarding the future profitability of Amazon.com’s business model: (1) It will never make any money; (2) It will have a 1%–2% net margin, like other retailers; (3) It will have an 8% net margin, like ‘direct’ manufacturer Dell, or (4) It will have a 15% net margin, like a Dell-Yahoo hybrid (Blodget and Erdmann, 1998: 13).

Of these, Blodget opted for Dell Computers and its ‘direct’ sales model. Both companies sold directly to customers, and both had the same gross margin. Thus, Blodget concluded, ‘a mature Amazon.com will be able to generate Delllike profitability.’ (Blodget and Erdmann, 1998: 13). Cohen’s margin estimate, on the other hand, was a more modest ten per cent margin. He justified this lower figure by categorizing Amazon as a bookstore and adding that bookstores are characterized by low operating margins. He noted:
Bookselling is an inherently competitive and low-margin business. Because the intellectual property value contained in published works typically represents only a small portion of the price to end-users, we do not expect that moving that business to an online environment will meaningfully change those characteristics (Cohen and Pankopf, 1998a: 1).

In short, Cohen emphasized Amazon’s book selling core, ignoring the company’s potential to leverage its e-commerce capabilities into other products.

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To this categorization-based argument, Cohen added an analogy: Amazon, Cohen argued, was like Barnes & Noble. Indeed, Cohen went as far as to argue that Amazon was even inferior to the latter in several ways, because, as he noted:
Amazon’s current market capitalization of $4.0 billion is roughly equivalent to more than twice the capitalization of Barnes & Noble, a highly profitable company with more than 1,000 retail outlets and a vastly larger revenue base (Cohen and Pankopf, 1998a:3).

2. Revenue estimate. Blodget estimated Amazon’s revenue for the year 2000 at a very aggressive $2.5 billion, whereas Cohen estimated far more conservative revenues of less than $1 billion. Blodget justified his estimate by proposing that Amazon belonged to an entirely new industry category, ‘the Internet Company.’ He argued:
We see [Amazon] as an electronic customer-services company in the business of helping its customers figure out what they want to buy (. . .) and then delivering it to them at a good price with minimum hassle (Blodget and Erdmann, 1998: 1). We see no reason, therefore, why Amazon will stop with books, music, and videos. Over the next few years, we wouldn’t be surprised were it to add software, toys, credit cards, auctions, foods or whatever product offering makes sense (Blodget and Erdmann, 1998: 20).

Thus, we see that Blodget estimated without concern for the number of books or CDs sold that the figure implied. His categorization was crucial in allowing him to develop his estimate. Cohen also relied on categories to justify his choice of margin estimate. He categorized Amazon as a bookseller, which implied a more limited revenue growth, since book retailing as a whole ‘is an inherently competitive and lowmargin business’ (Cohen and Pankopf, 1998a: 1). Calculative frames The regularities described so far suggest that the combined use of categories, analogies and metrics in analysts’ reports makes up a whole with an entity on its own. To underscore this point and to highlight its theoretical relevance, we denote by calculative frame the internally consistent network of associations, including (among others) categories, metrics and analogies, that yield the necessary estimates which go into the valuation of a company. (See Figure 1 for a representation of these frames). Calculative frames are not just an abstract entity, but also have a tangible presence that takes the obvious form of text, tables and numbers in the reports of analysts, as well as of Excel spreadsheet files. We found anecdotal evidence of this materiality in an interview with a portfolio manager at a large Wall Street mutual fund. Having said that he ‘rarely’ used the price targets and recommendations produced by securities analysts, the portfolio manager added: ‘what I do is, I call up the analyst and say “hey, can you send me your model?” Then he sends me the spreadsheet and I can find out exactly which are the assump26
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Calculators, lemmings or frame-makers? The intermediary role of securities analysts

Blodget

Category

Cohen

Category

Internet company

Book retailer

Analogy

Key metric

Analogy

Key metric

Dell

Revenue

Barnes & Noble

Profits

Figure 1: Blodget and Cohen’s calculative frames for Amazon.com Blodget’s calculative frame included a choice of category, an analogy and a key metric with which to value Amazon.com. Cohen’s frame included the same elements, but with different contents. Source: Blodget and Erdmann (1998) and Cohen and Pankopf (1998).

tions that go in.’ One sign that the circulation of Excel files is prevalent on Wall Street is that the first page of analyst reports always cite the direct phone number and email address of the analyst, one for each analyst if the report is co-authored. Securities analysts as frame-makers The notion of calculative frame suggests a new perspective on the intermediary function performed by analysts. The rise of Blodget from obscurity to celebrity following his December call indicates that providing new frames is an important part of analysts’ work. Doing so helps investors by equipping them with the tools that are needed to measure company value. Accordingly, we denote by frame-making the activity of creating, providing, and promoting calculative frames such as we see being done in the work of Blodget and Cohen. Second episode: Blodget vs. Barron’s The notion of frame-making put forward above gives rise to an additional question: How do these frames shape the way analysts use information? A subsequent episode in the controversy over Amazon speaks to this issue. In April 1999, Amazon announced larger end-year losses than it initially anticipated. One month later, a highly critical article in Barron’s written by journalist Abel Abelson interpreted Amazon’s statement as proof that the company was severely over-valued. Shortly afterwards, Blodget challenged Abelson’s arguments in a special research report, titled ‘Amazon.Bomb? Negative Barron’s Article.’ Again, we see two disparate reactions to the same piece of information, namely, Amazon’s expected 1999 performance. In this section we explore this disparity
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to understand how frames mediate the incorporation of new information into existing valuations. Consider first Abelson’s article. The journalist criticized Amazon’s strategy on several grounds: margins in book retailing, he claimed, were low. Amazon’s model of a virtual bookstore did not help, for the company was spending too much in acquiring customers. Amazon’s expansion into CDs ‘only (. . .) proved so far (. . .) that it can lose money selling books and lose still more money selling CDs’ (Abelson, 1999: 5). Compared with Barnes & Noble, Abelson concluded, Amazon was overvalued. The journalist proposed a total value for Amazon between $10 and $25, a paltry one seventh of the company’s market price at the time. Blodget’s reply to Abelson used the information mobilized by the journalist, but interpreted it differently. Blodget began his report by acknowledging Abelson’s criticisms, but went on to address each of the points raised by the journalist and concluded that most of them were not reasons for concern and in some cases, were in fact reasons to buy the stock. Consider, for example, Blodget’s treatment of Amazon’s lack of profitability. As noted above, Abelson had emphasized Amazon’s losses. In reply to this, Blodget wrote,
As any smart investor understands, there is a big difference between ‘losing’ money and ‘investing’ money. Amazon.com is investing money, not losing it, so near-term profitability is not a good measure of future worth. Put another way, if Amazon.com were to cut back on its investments in order to post a near-term profit, we believe it would be worth considerably less in three to five years than it will be if its current investments pay off. (Blodget and Anning, 1999: 6).

In presented Amazon’s losses as investments, Blodget performed a judo-like manoeuvre that reinterpreted his opponent’s information in a way that not only altered but actually reversed its implications (Figure 2).

Blodget

Category

Abelson

Category

Internet company

Internet book retailer

Analogy

Key metric

Analogy

Key metric

Dell

Revenue

Barnes & Noble

Profits

Figure 2: Blodget and Abelson’s calculative frames for Amazon.com Both frames included a choice of category, an analogy and a key metric with which to value Amazon.com, but with different contents. Blodget (1999) and Abelson (1999). 28
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Interpretation vs. Bayesian updating The disparity among the two analysts’ assessments challenges the Bayesian model that inspires the information processing literature. According to Bayes’ model, all rational analysts should update their probability assessments in the same direction; instead, we observe that Blodget and Abelson did so in opposite ways. Furthermore, these differences can be explained by the frames used by the analysts. Abelson categorized Amazon as a traditional retailer, saw it as being analogous to Barnes & Noble, and chose to focus on its lack of current profitability. As a result, greater losses were for him an additional sign that Amazon’s business model was not working. Blodget, on the other hand, did not see 1999 losses as a relevant measure of future value. This prompted him to focus instead on how the company’s investments could increase its future revenue. The theoretical lesson here is that in contexts of ambiguity, when different and inconsistent bodies of meaning are available to explain the same set of news, analysts accord meaning to it with recourse to their existing frame. The implication is that calculative frames mediate how analysts accord meaning to information. Framing controversies An additional lesson from the episode is that debate about and discrepancies between frames persist over time. Comparing Blodget’s December frame with the one in May, we see that news of greater losses did not make Blodget modify his December 1998 frame; instead, it prompted him to redefine the news as positive. We conclude that analysts tend to persist in their positions due to perseverance in their frames, and refer to these continued disparities as framing controversies: sustained differences in valuation that arise from a disparity in calculative frames. Third episode: Blodget versus Suria The two previous episodes present the work of security analysts as ongoing controversies over how to calculate value. But seeing analysis in terms of divergence rather than consensus leads to an important question: if a frame can coexist with its opposite for a sustained period of time, does that mean it can survive forever? In other words, are framing controversies ever closed? And if so, how? To address these questions, we set out to examine the mechanisms of frameadoption and frame-abandonment used by analysts and investors. We centre on a third and final episode which presents a striking change in the fortunes of Henry Blodget. On June 3rd 2000, analyst Ravi Suria of Lehman Brothers wrote a scathing report on Amazon. Suria, a convertible bond analyst, proposed a broad revision of prevailing thinking about the company: Amazon, he argued, was a traditional retailer. When measuring the company as such, its performance appeared rather mediocre. Furthermore, Suria argued, the company could well
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run out of money within a year. The analyst rated Amazon a ‘sell,’ prompting intense trading activity during that day as well as several articles in the financial press (eg, The Economist, 2000: 65). One month later, on June 29th, Blodget countered Suria’s attack with an optimistic report on Amazon. The report emphasized the company’s similarities with America Online, an Internet company that overcame difficulties and produced outstanding returns to investors. This time, however, Blodget’s arguments failed to persuade investors. The price of Amazon began a long decline. The analyst gradually fell out of favour with portfolio managers in the Institutional Investor rankings, and Suria’s contrarian success turned him into a star analyst (Vickers, 2000: 25). Blodget’s reversal invites the question of what led investors and fellow analysts to believe him in December 1998 and change course two years later. The reports themselves do not answer this, for both Suria’s attack and Blodget’s defence of Amazon relied on a solid, three-pronged calculative frame based on categorizations, analogies and key metrics. Thus, for instance, Suria categorized Amazon as having the operational and cash flow characteristics of a retailer, while Blodget insisted that its low stock price was due to poor sentiment surrounding the e-commerce sector. In terms of analogies, Suria related the company to online and traditional firms such as Best Buy, Musicland, Barnes & Noble, Borders and Books A Million; whereas Blodget focussed his report on an analogy to AOL. And in terms of metrics, Suria focussed on retailer variables such as working capital and cash flow, whereas Blodget insisted on more ethereal, Internet-like, variables such as sales growth, stock market undervaluation or the quality of its management. Both analysts, in short provided a tight, internally consistent explanation. Why did investors choose one over the other? (See Figure 3 for a graphical representation). In search of an explanation, we enlarged our lens to include the economic and social context surrounding the analysts at the time.

Blodget
Internet company

Suria
Retailer

AOL

Revenue

B & N, Books a Million

Cash flow

Figure 3: Blodget and Suria’s calculative frames for Amazon.com Source: Suria and Oh (2000), Blodget (2000a). 30
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Perhaps the most crucial background event in June 1999 was the continued lack of profitability of Amazon.com. Back in 1998, Blodget had asked investors to wait for three years before demanding profits. He recommended the stock for ‘strong-stomached, long-term investors,’ and wrote that he ‘expected the company’s accumulated deficit to balloon to more than $300 million before it finally starts reporting profits in the spring of 2001’ (Blodget and Erdmann, 1998: 25). Two years later, however, that profitability seemed more distant than ever. By October 1999 Amazon announced that it would pursue diversification rather than higher profits. Blodget reacted to this by claiming to be ‘simply exhausted by the endless postponement of financial gratification’ (Veverka, 1999: 64). On July 26th 2000, Amazon even failed to reach the revenue figure that Blodget predicted, making the analyst appear hopelessly out of touch. On the following day, six analysts simultaneously downgraded their recommendations for the company’s stock. Determinants of frame adoption and abandonment The central issue examined in this episode relates to the dynamics of frame abandonment. How do market actors abandon a frame that they previously espoused? The events presented so far suggest that frames are abandoned through a confluence of forces (Mohr, 1982) that include elements of information processing and the social context. Analysts and investors reacted to information about Amazon, but the manner in which they did so – the timing, the process and the reasons – was defined by their social context. We observe that news investors and analysts sold Amazon and lowered their ratings immediately after Suria’s report of June 22nd and Amazon’s earnings announcement of July 27th. The question is, why? After all, they did not lower their ratings when Barron’s Ableson had attacked Amazon’s performance and Blodget’s framing just over a year ago. So, what had changed? The episode underscores the central importance of the social context in shaping the reactions by the actors to data that is generated and whether they consider such data as meaningful information or not. Specifically, we want to draw attention to the notion of time that is built into analyst’s frames as a critical element that determines how data that is generated in real time will be treated by market actors. As with other initiatives where entrepreneurs build milestones into projections (Garud and Karnoe, 2001), Blodget too, in his initial 1998 report, had built time into his frame by suggesting that investors would have to wait till 2001 to observe positive performance on the part of Amazon. Consequently, a key difference between the Ableson and Suria episodes was the proximity of June 2000 to the January 2001 milestone for profitability that Blodget had established. In contrast with May 1999, when Blodget simply reframed news of losses as positive, in June 2000 Blodget was unable to persuade market actors to come around to his view. Another way in which the social context shaped interpretation was through the general discrediting of the ‘Internet’ industry. The calculative frames
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espoused by Amazon analysts and investors appeared to be nested in a broader ‘New Economy’ frame that became suspect, dragging Blodget’s frame along with it. The social movement’s literature defines two frames as nested when one is the conceptual subset of the other (Turner and Turner, 1986). Nested frames, in other words, are arranged within one another ‘like Chinese boxes’ (Lindquist, 2001: 13). Changes in the credibility of the larger frame impact those frames nested within it. In the case of Amazon, Blodget’s frame relied heavily for his generous revenue growth estimates on the categorization of the company as an Internet firm (Blodget and Erdmann, 1998: 10). When the dot-com crash undermined the credibility of the New Economy frame in April 2000, Blodget’s frame became vulnerable. In this manner, the decision of investors and analysts to abandon Blodget’s frame appears to have been based in part of the decisions made by other market actors, namely, disenchanted investors in New Economy stocks. Once Blodget’s frame became vulnerable, an opportunity opened up for Suria. The bond analyst marshalled several pieces of evidence – Amazon’s losses, its large size or low inventory turnover – into a compelling narrative that displaced Blodget’s. From this, we observe that investors and analysts were led by Suria, adopting the categories, analogies and metrics proposed by the analyst. Indeed, we see a massive and sudden abandonment of Blodget’s frame on July 27th. This simultaneous move was in fact prompted by Blodget’s and his public (and failed) forecast of Amazon’s sales two days before. By forecasting sales of $579 million, Blodget defined as disappointing any sales figure below it. This presence of prominent actors acting as a coordinating force underscores once again the importance of the social context in frame abandonment. In sum, the episode suggests an abandonment process that combines the information available to the actors and the broader social context. This stands in contrast to the existing literature. Whereas the information-processing approach views information as the sole determinant of abandonment and the imitation approach barely considers changes in frames, the lesson that emerges from this third episode is that frames are abandoned on the basis of concrete information (lack of profits, low sales growth, etc.), interpreted on the basis of the social context. Such context includes the actions taken by market actors with regards to other companies in the form of nested frames; the initiatives of emerging frame-breakers, and the milestones created by the frame-makers themselves.

Discussion
Our comparison of the different reports in the previous section led to three core theoretical concepts that address the meaning of analysis under uncertainty: calculative frames, frame-making and framing controversies. These build up to a comprehensive theory of analysis under uncertainty, detailing the elements of a calculative frame: how it appears, how it functions and when it is abandoned. In 32
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Calculators, lemmings or frame-makers? The intermediary role of securities analysts

the following paragraphs we revisit the three Amazon episodes. In doing so, we consider how our theory relates to alternative accounts of securities analysis. How do analysts confront Knightian uncertainty? The first incident in the controversy over Amazon provides suggestive information on the nature of analysis under uncertainty. Our examination of the reports by Blodget and Cohen suggests that analysts overcome Knightian uncertainty by developing calculative frames, that is, analogies, categorizations and choices of metrics that allow them to value companies even their future is unknown. We denote this activity by frame-making. The notion of frame-making challenges the lemming view of securities analysts that characterizes neo-institutional sociology and behavioural finance. The concept explains how and why analysts deviate from the consensus of their peers. As noted above, the analyst profession is structured around the rankings assembled by Institutional Investor. As with any other ranking, visibility breeds recognition and vice-versa, leading to a potentially closed loop in which newcomers would be excluded from the top. One way in which unknown analysts can break into high-ranking positions is by offering investors a dramatic profit opportunity. In an information-rich context such as Wall Street, the quickest route is by providing an original interpretation, namely, a new calculative frame. The case of Blodget illustrates this mechanism: Blodget rose from obscurity to top Internet analyst in 2000 precisely because of the original frame he developed in December 1998. In short, the notion of frame-making emphasizes the strong incentives that exist for analysts to produce original work. Our perspective contributes to economic sociology by building on its central claim that markets are social. Markets are social, we contend, because calculation is social. The concept of calculative frame highlights the cognitive and material infrastructure that underlies economic calculations of value. Securities analysts make possible the world of calculation hypothesized by orthodox economics. In this manner, our perspective contrasts with the orthodox view that calculation is innate, abstract and individual. Instead, we view calculation as framed, material and social, in other words, as performed. Frame-making is thus consistent with the emerging performativity literature in economic sociology and its central claim that economic theories and artefacts format the economy by shaping the way in which value is calculated (Callon, 1998; Beunza and Stark, 2004; MacKenzie and Millo, 2003; Preda, 2006; Callon and Muniesa, 2005; MacKenzie, 2006). Our view also contrasts with the literatures in behavioural finance and neoinstitutional sociology. These portray decision-making as purely imitative and non-calculative. In contrast, the concept of frame-makers reconciles the concept of imitation with that of calculation. Market actors adopt the calculative frames employed by successful peers, but this does not mean that they abandon calculation altogether. Again, our view in this manner is consistent with Callon (1998) and the performativity literature.
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Finally, the notion of frame-making qualifies and extends the analysts-ascritics’ literature by identifying the devices that critics mobilize to perform assessments. The notion of frame-making builds on the basic claim of the critics’ literature, namely, that Knightian uncertainty is the central problem that analysts and other intermediaries confront. We contend, however, that critique is not a purely mental activity but a material one as well. Analysts are active builders of frames, rather than passive classifiers of stocks into categories. In doing so, frame-making adds again to the recent literature in the social studies of finance and its attention to the ‘metrological’ instruments used within the capital markets (Callon, 1998; Beunza and Stark, 2004). In addition, our perspective departs from the critics approach and its claim that analysts exert a conservative influence on the market. We document the tendency of successful analysts to disrupt, rather than perpetuate, existing industry categorical schemes. How do calculative frames operate? The second episode in the Amazon controversy provides a compelling illustration of calculative frames at work. Frames persist in the face of disconfirming evidence, and sustained differences in frames lead to prolonged divergences in valuation. We refer to these divergences as framing controversies. The notion of framing controversies departs from the neoclassic view of Bayesian information processing. According to the latter, there is a single correct way to assemble existing information on company value. Over time, disagreements about valuation will disappear, because rational analysts eventually converge. By contrast, the notion of frame-making takes issue with this idea, arguing that divergence is persistent within the capital markets. Framing controversies also depart from the behavioural tenet that failed predictions bring irrationality to the capital markets. The behavioural finance literature presents analyst inaccuracy as ‘biases’ that is, as mistakes that hamper the efficient functioning of the market. By contrast, the notion of framing controversies suggests that divergent predictions in fact contribute to market efficiency. Frames guide investors in interpreting incoming information, stabilizing the meaning of news across the investor community and over time. In addition, controversies underscore legitimate differences in perspectives, allowing investors to better locate their own position in ongoing debates (eg, what Brenner [1991: 24] referred to as ‘an articulate case from both the bull and the bear’). Furthermore, the coherence of the categories, analogies and key metrics chosen by the analysts suggests that their positions are individually rational, at least in the sense provided by Savage (1954), that is, internally consistent. The notion of framing controversy builds on the concept of scientific controversies developed in the social studies of science and technology literature. The latter are defined as ‘alternative accounts of the natural world’ whose persistence in the face of experimental data suggests that they ‘are not directly given by nature, but . . . as products of social processes and negotiations’ (Martin and 34
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Calculators, lemmings or frame-makers? The intermediary role of securities analysts

Richards, 1995:510; see also Bloor, 1976; Nelkin, 1979; Latour, 1987). Framing controversies extend the notion of scientific controversies to the capital markets, contributing to the emerging literature in the social studies of finance and its focus on the content of economic transactions, as opposed to social networks that underlie them (Lépinay and Rousseau, 2000; MacKenzie and Millo, 2003; Knorr Cetina and Bruegger, 2003; Beunza and Stark, 2004; Preda, 2005; Callon and Muniesa, 2005; MacKenzie, 2006). How are frames abandoned? Our analysis of the first two Amazon episodes established that valuation is a joint outcome of the information and the frame previously espoused by the actor. How, then, do actors decide to adopt or abandon a frame? The controversy over Amazon of June 2000 suggests why a certain frame might be abandoned. The focal event this time was the critical report that bond analyst Ravi Suria wrote on Amazon on June 2000. Despite Blodget’s best efforts, analysts and investors abandoned Blodget’s frame and adopted Suria’s instead. This change resulted from a confluence of events: the dot-com crash, vulnerabilities that emerged in Blodget’s frame, and Suria’s report. All these combined to shape a new interpretation of Amazon’s performance. The episode suggests that changes in frames arise from the combined effect of information and the social context. Specifically, it suggests that market actors respond to information, but interpret this information within a social context of controversy. This controversy plays out as rivalry in frames, nested frames and self-imposed milestones. Such view contrasts with the information-processing approach, which contends that news of economic events are the sole determinant of change in opinion. It also departs from the imitation approach, which contends that market actors are fundamentally responsive to each other rather than to incoming news. In our view, then, economic actors are attuned to both ongoing events and the social context in which these occur.

Conclusion
Our objective in this chapter was to clarify the economic function performed by security analysts in contexts of Knightian uncertainty. The core finding emerging from our study is that securities analysts function as frame-makers. In the presence of Knightian uncertainty, analysts make up for their partial knowledge of the world by becoming active builders of interpretive devices that bracket, give meaning and make it possible to develop quantitative point estimates of value. We refer to these interpretive devices as calculative frames. These frames include categorizations, analogies and the selection of key dimensions of merit. In contexts of extreme uncertainty, we propose that the primary activity undertaken by securities analysts is not to forecast prices or to give investment advice, but to develop calculative frames.
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Note
1 Beunza’s work has been supported by grant SEJ2005-08391 from the Spanish Dirección General de Investigación Científica y Técnica and Garud’s by summer research support grants from the Stern School of Business, New York University. The authors wish to thank Adam Brandenburger, Michel Callon, Emilio Castilla, Gary Dushnistsky, James Evans, Isabel Fernandez-Mateo Fabrizio Ferraro, Mauro Guillen, Witold Henisz, Robin Hogarth, Donald MacKenzie, Fabian Muniesa, Sarah Kaplan, Peter Karnoe, Javier Lezaun, Yuval Millo, Kamal Munir, Mikolaj Piskorski, Peter Roberts, Naomi Rothman, Graham Sewell, David Stark, Mary O’Sullivan, Mark Zbaracki, Ezra Zuckerman and other participants at the 2003 EGOS ‘Markets-in-the-making’ colloquium, 2003 Barcelona Gestió seminar, 2004 Performativity Workshop in Paris, EOI Seminar at Wharton and Strategy Research Forum in Athens for their inputs on earlier drafts of this paper.

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