DANIEL BEUNZA AND DAVID STARK
In Novum
Organum, one of the founding documents of modern science, Francis Bacon (1960
[1620]) outlined a new course of discovery. Writing in an age when the
exploration, conquest, and settlement of territory was enriching European
sovereigns, Bacon proposed an alternative strategy of exploration. In place of
the quest for property, for territory, Bacon urged a search for properties,
the properties of nature, arguing that this knowledge, produced at the
workbench of science, would prove a yet vaster and nearly inexhaustible source
of wealth.1
Three
centuries later, several recent innovations hold a similarly alluring promise
for Wall Street traders and modern economies. The creation of the NASDAQ in
1971 and of Bloomberg terminals in 1980 has given Wall Street an electronic
exchange three decades before the appearance of the commercial Internet. The
development of formulas for pricing derivatives such as the Black-Scholes in 1973
has given traders precision tools previously reserved for engineers. And the
dramatic growth in computing power since the introduction of the PC has given
traders the possibility to combine these equations with powerful computational
engines. The mix of formulas, data to plug into them, computers to calculate
them, and electronic networks to connect it all has been explosive, leading to
a decisive shift to ‘quantitative finance’ (Bernstein 1993; Dunbar 2000;
MacKenzie and Millo 2003). As a result, finance is today mathematical,
networked, computational, and knowledge-intensive.
Just as Bacon’s
experimentalists at the beginnings of modern science were in search of new
properties, so our quantitative traders have, in their quest for
Our
thanks to Pablo Boczkowski, Karin Knorr Cetina, Paul Duguid, Geoff Fougere,
Vincent Lepinay, Fabian Muniesa, Alex Preda, Benjamin Stark, and especially
Monique Girard for helpful comments and suggestions on a previous draft. We are
grateful to Oxford University Press for permission to reprint material from
‘Tools of the Trade’, Industrial and
Corporate Change 2004, 13(2).
profits,
gone beyond traditional properties of companies such as growth, solvency, or
profitability. Their pursuit has taken them to abstract financial qualities
such as volatility, convertibility, or liquidity, as different from
accounting-based measures as Bacon’s search was from the conquest of new
territory.
But, how
are the new properties to be found? Bacon’s radical proposal, at least in the
more standard reading, came with an equally novel strategy for its fulfillment,
a program of inductive, experimentalist science that contrasted sharply with
the method of logical deduction prevailing at the time. Is there a financial
counterpart to Bacon’s program of experimentation?
Our task
in this chapter is to analyze how a Wall Street trading room is organized for
this process of discovery. A trading room, as we shall see, is a kind of
laboratory in which traders are engaged in a process of search and experimentation.
At one level it would seem that their search is straightforward: they are
searching for value. And it would seem that the means for this search are
similarly obvious: use channels of high-speed connectivity to gather as much
timely information as possible and take advantage of sophisticated
mathematical formulae to process that information. At the very elite of the
profession, however, these means, in themselves, do not give advantage. You
must have them to be a player, but your competitors are likely to have them as
well. That is, the more that timely information is available simultaneously to
all market actors, the more advantage shifts from economies of information to
processes of interpretation. Moreover, what seems straightforward—value—is
exactly what is at issue.
The
challenge of search and experimentation must thus be re-specified: how do you
recognize an opportunity that your competitors have not already identified? At
the extreme, therefore, you are searching for something that is not yet named
and categorized. The problem confronting our traders, then, is a problem
fundamental to innovation in any setting: how do you search— when you do not
know what you are looking for but will recognize it when you find it?
To
explore this challenge, we conducted ethnographic field research in the Wall
Street trading room of a major international investment bank. Pseudonymous
International Securities is a global bank with headquarters outside the United
States. It has a large office in New York, located in the World Financial
Center in Lower Manhattan. With permission from the manager of the trading
room we had access to observe trading and interview traders. Our observations
extended to sixty half-day visits across more than two years. During that time,
we conducted detailed observations at three of the room’s ten trading desks,
sitting in the tight space between traders, following trades as they unfolded
and sharing lunches and jokes with the traders. We complemented this direct
observation with in-depth interviews. In the final year of our investigation,
we were more formally integrated into the trading room—provided with a place at
a desk, a computer, and a telephone. The time span of our research embraced the
periods before and after the September 11 attack on the World Trade Center (for
accounts of the trading room’s response and recovery, see Beunza and Stark
2003, 2004).
To
anticipate the major lines of our argument and provide a road map of the
sections of the chapter: in the following section we introduce the practices of
modern arbitrage—the trading strategy that best represents the distinctive
combination of connectivity, knowledge, and computing that are the defining
features of the quantitative revolution in finance. Arbitrageurs locate value by
making associations among securities. At the sophisticated level of trading at
International Securities there is a sharp premium on making novel, unexpected,
and innovative associations. In subsequent sections, we examine how such
associations are made at International Securities through heterarchical
organization, a form whose features we elaborate in more detail below.
The
cognitive challenge facing our arbitrage traders is the problem of recognition.
On one hand, they must be adept at pattern recognition (e.g. matching data to
models, etc). But if they only recognize patterns familiar within their
existing categories, they would not be innovative (Brown and Duguid 1998;
Clippinger 1999). Innovation requires another cognitive process that we can
think of as re-cognition (making unanticipated associations, re-conceptualizing
the situation, breaking out of lock-in).
The trading room is
equipped to meet this twin challenge of exploiting knowledge (pattern
recognition) while simultaneously exploring for new knowledge (practices of
re-cognition). Each desk (e.g. merger arbitrage, index arbitrage, etc.) is
organized around a distinctive evaluative principle and its corresponding
cognitive frames, metrics, ‘optics’, and other specialized instrumentation for
pattern recognition (Hutchins 1995). That is, the trading room is the site of
diverse, indeed rival, principles of valuation. And it is the interaction
across this heterogeneity that generates innovation. Rather than
bureaucratically hierarchical, the trading room is heterarchical (Stark 1999;
Girard and Stark 2002). In place of hierarchical, vertical ties, we find horizontal
ties of distributed cognition; in place of a single metric of valuation, we
find multiple metrics of value; and in place of designed and managed R&D,
we find innovations as combinatorics (Kogut and Zander 1992) that emerge from
the interaction across these coexisting principles and instruments. The trading
room distributes intelligence and organizes diversity.
Arbitrage
is defined in finance textbooks as ‘locking in a profit by simultaneously
entering into transactions in two or more markets’ (Hull 1996: 4). If, for
instance, the prices of gold in New York and London differ by more than the
transportation costs, an arbitrageur can realize an easy profit by buying in
the market where gold is cheap and selling it in the market where it is expensive.
But reducing arbitrage to an unproblematic operation that links the obvious
(gold in London, gold in New York), as textbook treatments do, is doubly
misleading, for modern arbitrage is neither obvious nor unproblematic. It
provides profit opportunities by associating the unexpected, and it entails
real exposure to substantial losses.
Arbitrage
is a distinctive form of entrepreneurial activity that exploits not only gaps
across markets but also the overlaps among multiple evaluative principles.
Arbitrageurs profit not by having developed a superior way of deriving value
but by exploiting opportunities exposed when different evaluative devices yield
discrepant pricings at myriad points throughout the economy.
As a
first step to understanding modern arbitrage, consider the two traditional
trading strategies, value and momentum investing, that arbitrage has come to
challenge.2 Value investing is the traditional ‘buy low, sell high’
approach in which investors look for opportunities by identifying companies
whose ‘intrinsic’ value differs from its current market value. Value investors
are essentialists: they believe that property has a true, intrinsic, essential
value independent from other investors’ assessments, and that they can attain
a superior grasp of that value through careful perusal of the information about
a company.
In
contrast to value investors, momentum traders (also called chartists) turn away
from scrutinizing companies toward monitoring the activities of other actors on
the market (Malkiel 1973). Like value investors, their goal is to find a profit
opportunity. However, momentum traders are not interested in discovering the
intrinsic value of a stock. Instead of focusing on features of the asset
itself, they turn their attention to whether other market actors are bidding
the value of a security up or down. Like the fashion-conscious or like
nightlife socialites scouting the trendiest clubs, they derive their strength
from obsessively asking, ‘where is everyone going?’ in hopes of anticipating
the hotspots and leaving just when things get crowded.
As with
value and momentum investors, arbitrageurs also need to find an opportunity, an
instance of disagreement with the market’s pricing of a security. They find it
by making associations. Instead of claiming a superior ability to process and
aggregate information about intrinsic assets (as value investors do) or better
information on what other investors are doing (as momentum traders do), the
arbitrage trader tests ideas about the correspondence between two securities.
Confronted by a stock with a market price, the arbitrageur seeks some other
security or bond, or synthetic security such as an index composed of a group of
stocks, etc.—that can be related to it, and prices one in terms of the other.
The two securities have to be similar enough so that their prices change in
related ways, but different enough so that other traders have not perceived the
correspondence before. As we shall see, the posited relationship can be highly
abstract. The tenuous or uncertain strength of the posited similarity or
co-variation reduces the number of traders that can play a trade, hence
increasing its potential profitability.
Arbitrage
hinges on the possibility of interpreting securities in multiple ways. Like a
striking literary metaphor, an arbitrage trade reaches out and associates the
value of a stock to some other, previously unidentified security.
By
associating one security to another, the trader highlights different properties
(qualities) of the property he is dealing with.
Like
Bacon’s experimentalists, arbitrage traders have moved from exploring for
territory (traditional notions of property) to exploring for the underlying properties of securities. In contrast
to value investors who distill the bundled attributes of a company to a single
number, arbitrageurs reject exposure to a whole company. But in contrast to
corporate raiders, who buy companies for the purpose of breaking them up to
sell as separate properties, the work of arbitrage traders is yet more
radically deconstructionist. The unbundling they attempt is to isolate, in the
first instance, categorical attributes. For example, they do not see Boeing Co.
as a monolithic asset or property, but as having several properties (traits,
qualities) such as being a technology stock, an aviation stock, a
consumer-travel stock, an American stock, a stock that is included in a given
index, and so on. Even more abstractionist, they attempt to isolate such
qualities as the volatility of a security, or its liquidity, its
convertibility, its indexability, and so on.
Thus,
whereas corporate raiders break up parts of a company, modern arbitrageurs
carve up abstract qualities of a security. In our field research, we find our
arbitrageurs actively shaping trades. Dealing with the multiple qualities of
securities as narrow specialists, they position themselves with respect to one
or two of these qualities, but never all. Their strategy is to use the tools of
financial engineering to shape a trade so that exposure is limited only to
those equivalency principles in which the trader has confidence. Derivatives
such as swaps, options, and other financial instruments play an important role
in the process of separating the desired qualities from the purchased security.
Traders use them to slice and dice their exposure, wielding them in effect like
a surgeon’s tools—scissors and scalpels to give the patient (the trader’s
exposure) the desired contours.
Paradoxically,
much of the associative work of arbitrage is therefore for the purpose of
‘disentangling’ (see Callon 1998 for a related usage)—selecting out of the trade
those qualities to which the arbitrageur is not committed. The strategy is just
as much not betting on what you do not know as betting on what you do know. In
merger arbitrage, for example, this strategy of highly specialized risk
exposure requires that traders associate the markets for stocks of the two
merging companies and dissociate from the stocks everything that does not
involve the merger. Consider a situation in which two firms have announced
their intention to merge. One of the firms, say the acquirer, is a biotech firm
and belongs to an index, such as the Dow Jones (DJ) biotech index. If a merger
arbitrage specialist wanted to shape a trade such that the ‘biotechness’ of the
acquirer would not be an aspect of his or her positioned exposure, the
arbitrageur would long the index. That is, to dissociate this quality from the
trader’s exposure, the arbitrageur associates the trade with a synthetic
security (‘the index’) that stands for the ‘biotechness’. Less categorical,
more complex qualities require more complex instruments.
Arbitrageurs,
do not narrow their exposure for lack of courage. Despite all the trimmings,
hedging, and cutting, this is not a trading strategy for the faint-hearted.
Arbitrage is about tailoring the trader’s exposure to the market, biting what
they can chew, betting on what they know best, and avoiding risking their money
on what they do not know. Traders expose themselves profusely—precisely because
their exposure is custom-tailored to the relevant deal. Their sharp focus and
specialized instruments gives them a clearer view of the deals they examine
than the rest of the market. Thus, the more the traders hedge, the more boldly
they can position themselves.
Arbitrageurs can reduce or
eliminate exposure along many dimensions but they cannot make a profit on a
trade unless they are exposed on at least one. In fact, they cut entanglements
along some dimensions precisely to focus exposure where they are most
confidently attached. As Callon (1998; Callon and Muniesa 2002; Callon,
Meandel, and Rabeharisoa 2002) argues, calculation and attachment are not
mutually exclusive. To be sure, the trader’s attachment is distanced and
disciplined; but, however emotionally detached, and however fleeting, to hold a
position is to hold a conviction.3 In the field of arbitrage, to be
opportunistic you must be principled, that is, you must commit to an evaluative
metric. And, as we shall see, to engage in complex, high-stakes trading, you
must also be able to collaborate with those who are attached to different
metrics.
How do
unexpected and tenuous associations become recognized as opportunities? How
could the traders at International Securities exploit the knowledge they had
(to recognize patterns that it had identified) while also exploring for new
opportunities (if you like, re-cognizing properties)?4 To do so, the
trading room adopted an organizational form that we characterize as heterarchy.
As the term suggests, heterarchies are characterized by minimal hierarchy and
by organizational heterogeneity. Heterarchies involve a distributed
intelligence (lateral accountability) and the organization of diversity
(coexisting evaluative principles).
Mid-twentieth
century, there was general consensus about the ideal attributes of the modern
organization: it had a clear chain of command, with strategy and decisions
made by the organizational leadership; instructions were disseminated and
information gathered up and down the hierarchical ladder of authority; design
preceded execution; the latter was carried out with the time-management
precision of a Taylorist organizational machine. By the end of the century, the
main precepts of the ideal organizational model would be fundamentally
rewritten. The primacy of relations of hierarchical dependence within the firm
and the relations of market independence between firms became secondary to
relations of interdependence among networks of firms and among units within the
firm (Kogut and Zander 1992; Powell 1996; Grabher and Stark 1997).
To cope
with radical uncertainties, instead of concentrating its resources for
strategic planning among a narrow set of senior executives or delegating that
function to a specialized department, heterarchical firms embark on a radical
decentralization in which virtually every unit becomes engaged in innovation.
That is, in place of specialized search routines in which some departments are
dedicated to exploration while others are confined to exploiting existing knowledge,
the functions of exploration are generalized throughout the organization. In
place of vertical chains of command, intelligence is distributed—laterally.
With its flattened hierarchy, the absence of separate offices for the room’s
few managers, its open architectural plan, and its collegial culture, the trading
room at International Securities shows collaborative features of such distributed
intelligence.
Heterarchies,
however, are not simply non-bureaucratic. Heterarchies interweave a
multiplicity of organizing principles. The new organizational forms are
heterarchical not only because they have flattened hierarchy, but also because
they are the sites of competing and coexisting value systems. They maintain and
support an active rivalry of multiple evaluative principles. A robust, lateral
collaboration flattens hierarchy without flattening diversity. The coexistence
of more than one evaluative principle produces a creative friction (Brown and
Duguid 1998) and fosters cross-fertilization. It promotes organizational
reflexivity, the ability to redefine and recombine resources. Heterarchies are
not simply tolerant of diversity among isolated and noncommunicating factions;
the organization of diversity is not a replicative redundancy but a generative
redundancy. It is the friction at the interacting overlap that generates
productive recombinations. The challenge is to create a sufficiently common
culture to facilitate communication among the heterogeneous components without
suppressing the distinctive identities of each. Heterarchies create wealth by
inviting more than one way of evaluating worth.
This aspect of heterarchy
builds on Knight’s (1921) distinction between risk, where the distribution of
outcomes can be expressed in probabilistic terms, and uncertainty, where
outcomes are incalculable. Whereas in neoclassical economics all cases are
reduced to risk, Knight argued that a world of generalized probabilistic
knowledge of the future leaves no place for profit (as a particular residual
revenue that is not contractualizable because it is not susceptible to measure
ex ante) and hence no place for the entrepreneur. Properly speaking, the
entrepreneur is not rewarded for risk-taking but, instead, is rewarded for an
ability to exploit uncertainty. The French school of the ‘economics of
conventions’ (Boltanski and Thevenot 1991, 1999; Thevenot 2001) demonstrates
that institutions are social technologies for transforming uncertainty into
calculable problems; but they leave unexamined the incidence of uncertainty
about which institution (‘ordering of worth’) is operative in a given
situation. In this light, Knight’s conception of entrepreneurship can be
re-expressed: entrepreneurship is the ability to keep multiple evaluative principles
in play and to exploit the resulting ambiguity (Stark 2000). Restated, entrepreneurship
in this view is not brokerage across a gap but facilitating productive friction
at the overlap of coexisting principles.
Distributing Intelligence and Organizing Diversity in the
Trading Room
A Desk with a View of the
Markets
The
trading room at International Securities offers a sharp contrast to the
conventional environment of corporate America. Unlike a standard corporate
office with cubicles and a layout meant to emphasize differences in hierarchical
status, the trading room is an open-plan arrangement where information roams
freely. Instead of having its senior managers scattered at window offices along
the exterior of the building, the bank puts managers in the same desks as their
teams, accessible to them with just a movement of the head or hand.
Underscoring the importance of sociability, the bank has limited the number of
people in the room to 150 employees and has a low monitor policy so people can
see each other. Computer programmers and other critical, technical support
staff are not separated but have desks right in the trading room.
Whereas
the traders of the 1980s, acutely described by Tom Wolfe (1987) as Masters of
the Universe, were characterized by their riches, bravado, and little regard
for small investors, the quantitative traders at International Securities have
MBA degrees in finance, Ph.D.s in physics and statistics, and are more
appropriately thought of as engineers. None of them wears suspenders.
The
basic organizational unit of the trading room is a ‘desk’, and it is here that
the organization of diversity in the trading room begins by demarcating
specialized functions. The term ‘desk’ not only denotes the actual piece of
furniture where traders sit, but also the actual team of traders—as in ‘Tim
from the equity loan desk’. Such identification of the animate with the
inanimate is due to the fact that a team is never scattered across different
desks. In this localization, the different traders in the room are divided
into teams according to the financial instrument they use to create
equivalencies in arbitrage: the merger arbitrage team trades stocks in
companies in the process of consolidating, the options arbitrage team trades
in ‘puts’ and ‘calls’,5 the derivatives that lend the desk its name,
and so on. The extreme proximity of the workstations enables traders to talk to
each other without lifting their eyes from the screen or interrupting their
work. The desk is an intensely social place where traders work, take lunch,
make jokes, and exchange insults in a never-ending undercurrent of camaraderie
that resurfaces as soon as the market gives a respite.
Each desk has developed its own way of
looking at the market, based on the principle of equivalence that it uses to
calculate value and the financial
instrument
that enacts its particular style of arbitrage trade. Merger arbitrage traders,
for example, keen on finding out the degree of commitment of two merging
companies, look for a progressive approximation in the stock prices of two
companies. They probe commitment to a merger by plotting the ‘spread’
(difference in price) between acquiring and target companies over time. As with
marriages between persons, mergers between companies are scattered with regular
rituals of engagement intended to persuade others of the seriousness of their
intent. As time passes, arbitrage traders look for a pattern of gradual decay
in the spread as corporate bride and groom come together—that is, a descending
diagonal curve on their Bloomberg screens, not unlike the trajectory of a
landing airplane.
Convertible
bond arbitrageurs, by contrast, do not obsess about whether the spread between
two merging companies is widening or narrowing. Instead, they specialize in
information about stocks that would typically interest bondholders, such as
their liquidity and likelihood of default. At yet another desk, index
arbitrageurs, in their attempt to exploit minuscule and rapidly vanishing
misalignments between S&P 500 futures and the underlying securities, specialize
in technology to trade in high volume and at a high speed. Thus, within each
team there is a marked consistency between its arbitrage strategy, its visual
displays, its mathematical formulae, and its trading tools.
Such
joint focus on visual and economic patterns forges each desk into a distinctive
community of practice, with its own evaluative principle, tacit knowledge,
social ties, and shared forms of meaning (Lave and Wenger 1990). This includes
a common sense of purpose, a real need to know what each other knows, a highly
specialized language, and idiosyncratic ways of signaling to each other. It
even translates into friendly rivalry toward other desks. A customer sales
trader, for example, took us aside to denounce statistical arbitrage as ‘like
playing video games. If you figure out what the other guy’s program is, you can
destroy him. That’s why we don’t do program trades’, he explained, referring to
his own desk. Conversely, one of the statistical arbitrage traders, told us,
in veiled dismissal of manual trading, that the more he looks at his data (as
opposed to letting his robot trade) the more biased he becomes.
Homogeneity within a desk
facilitates speed and sophistication to navigate crowded and fast-moving
capital markets. But the complex trades that are characteristic of our trading
room, however, seldom involve a single desk/team in isolation from others. It
is to these collaborations that we turn.
The
desk, in our view, is a unit organized around a dominant evaluative principle
and its arrayed financial instruments (devices for measuring, testing, probing,
cutting). This principle is its coin; if you like, its specie. But the trading
room is composed of multiple species. It is an ecology of evaluative
principles.
Complex
trades take advantage of the interaction among these species. To be able to
commit to what counts, to be true to your principle of evaluation, each desk
must take into account the principles and tools of other desks. Recall that
shaping a trade involves disassociating some qualities in order to give
salience to the ones to which your desk is attached. To identify the relevant
categories along which exposure will be limited, shaping a trade therefore
involves active association among desks. Co-location, the proximity of desks, facilitates
the connections needed to do the cutting.
Whereas
in most textbook examples of arbitrage the equivalence-creating property is
easy to isolate, in practice, it is difficult to fully disassociate. Because of
these difficulties, even after deliberate slicing and dicing, traders can still
end up dangerously exposed along dimensions of the company that differ from the
principles of the desired focused exposure. We found that traders take into
account unintended exposure in their calculations in the same way as they
achieve association—through co-location. Physical proximity in the room allows
traders to survey the financial instruments around them and assess which
additional variables they should take into account in their calculations.
For
example, the stock loan desk can help the merger arbitrageurs on matters of
liquidity. Merger arbitrage traders lend and borrow stock as if they could
reverse the operation at any moment of time. However, if the company is small
and not often traded, its stock may be difficult to borrow, and traders may
find themselves unable to hedge. In this case, according to Max, senior trader
at the merger arbitrage desk, ‘the stock loan desk helps us by telling us how
difficult it is to borrow a certain stock’. Similarly, index arbitrageurs can
help merger arbitrageurs trade companies with several classes of shares. Listed
companies often have two types of shares, so-called ‘A-’ and ‘K-class’ stock.
The two carry different voting rights, but only one of the two types allows traders
to hedge their exposure. The existence of these two types facilitates the work
of merger arbitrageurs, who can execute trades with the more liquid of the two
classes and then transform the stock into the class necessary for the hedge.
But such transformation can be prohibitively expensive if one of the two
classes is illiquid. To find out, merger arbitrageurs turn to the index
arbitrage team, which exploits price differences between the two types.
In other
cases, one of the parties may have a convert provision (i.e. its bonds can be
converted into stocks if there is a merger) to protect the bondholder, leaving
merger arbitrageurs with questions about how this might affect the deal. In
this case, it is the convertible bond arbitrage desk that helps merger
arbitrage traders clarify the ways in which a convertibility provision should
be taken into account. ‘The market in converts is not organized’, says Max, in
the sense that there is no single screen representation of the prices of
convertible bonds. For this reason: ‘We don’t know how the prices are
fluctuating, but it would be useful to know it because the price movements in
converts impacts mergers. Being near the converts desk gives us useful
information’.
In any
case, according to Max, ‘even when you don’t learn anything, you learn there’s
nothing major to worry about’. This is invaluable because, as he says, ‘what
matters is having a degree of confidence’. By putting in close proximity teams
that trade in the different financial instruments involved in a deal, the bank
is thereby able to associate different markets into a single trade. As a senior
trader observed: ‘While the routine work is done within teams, most of the
value we add comes from the exchange of information between teams. This is necessary
in events that are unique and non-routine, transactions that cross markets, and
when information is time-sensitive’.
Thus, whereas a given desk
is organized around a relatively homogeneous principle of evaluation, a given
trade is not. Because it involves hedging exposure across different properties
along different principles of evaluation, any given trade can involve
heterogeneous principles and heterogeneous actors across desks. If a desk
involves simple teamwork, a (complex) trade involves collaboration. This
collaboration can be as formalized as a meeting (extraordinarily rare at
International Securities) that brings together actors from the different desks.
Or it might be as primitive as an un-directed expletive from the stock loan
desk which, overheard, is read as a signal by the merger arbitrage desk that
there might be problems with a given deal.
To see
opportunities, traders use the mathematics and the machines of market
instruments. We can think of traders as putting on the financial equivalent of
infrared goggles that provide them with the trader’s equivalent of
night-vision. The traders’ reliance on such specialized instruments, however,
entails a serious risk. In bringing some information into sharp attention, the
software and the graphic representations on their screens also obscure. In
order to be devices that magnify and focus, they are also blinders. According
to a trader, ‘Bloomberg shows the prices of normal stocks; but sometimes,
normal stocks morph into new ones’, such as in situations of mergers or bond
conversions. If a stock in Stan’s magnifying glass—say, an airline that he
finds representative of the airline sector—were to go through a merger or bond
conversion, it would no longer stand for the sector.
An even
more serious risk for the traders is that distributing calculation across their
instruments amounts to inscribing their sensors with their own beliefs. As we
have seen, in order to recognize opportunities, the trader needs special tools
that allow him to see what others cannot. But the fact that the tool has been
shaped by his theories means that his sharpened perceptions can sometimes be
highly magnified misperceptions, perhaps disastrously so. For an academic
economist who presents his models as accurate representations of the world, a
faulty model might prove an embarrassment at a conference or seminar. For the
trader, however, a faulty model can lead to massive losses. There is, however,
no option not to model: no tools, no trade. What the layout of the trading
room—with its interactions of different kinds of traders and its juxtaposition
of different principles of trading—accomplishes is the continual, almost
minute-by-minute, reminder that the trader should never confuse representation
for reality.
Instead
of reducing the importance of social interaction in the room, the highly
specialized instruments actually provide a rationale for it. ‘We all have
different kinds of information’, Stan says, referring to other traders, ‘so I
sometimes check with them’. How often? ‘All the time’.
Just as Francis Bacon
advocated a program of inductive, experimentalist science in contrast to
logical deduction, so our arbitrage traders, in contrast to the deductive
stance of neoclassical economists, are actively experimenting to uncover
properties of the economy. But whereas Bacon’s New Instrument was part of a
program for ‘The Interpretation of Nature’,6 the new instruments of
quantitative finance—connectivity, equations, and computing— visualize, cut,
probe, and dissect ephemeral properties in the project of interpreting markets.
In the practice of their trading room laboratories, our arbitrage traders are
acutely aware that the reality ‘out there’ is a social construct consisting of
other traders and other interconnected instruments continuously reshaping, in
feverish innovation, the properties of that recursive world. In this
coproduction, in which the products of their interventions become a part of the
phenomenon they are monitoring, such reflexivity is an invaluable component of
their tools of the trade.
Just as
Latour (1987) defined a laboratory as ‘a place that gathers one or several
instruments together’, trading rooms can be understood as places that gather
diverse market instruments together. Seen in this light, the move from
traditional to modern finance can be considered as an enlargement in the number
of instruments in the room, from one to several. The best scientific laboratories
maximize cross-fertilization across disciplines and instruments. For example,
the Radar Lab at MIT in the 1940s made breakthroughs by bringing together the
competing principles of physicists and engineers (Galison 1997; Galison and
Thompson 1999). Similarly, the best trading rooms bring together heterogeneous value
frameworks for creative recombination.
How do
the creativity, vitality, and serendipity stemming from close proximity in the
trading room yield new interpretations? By interpretation we refer to processes
of categorization, as when traders answer the question, ‘what is this a case
of’? but also to processes of re-categorization such as making a case for. Both
work by association—of people to people, but also of people to things, things
to things, things to ideas, etc.
We saw
such processes of recognition at work in the following case of an announced
merger between two financial firms. The trade was created by the ‘special
situations desk’, its name denoting its stated aim of cutting through the
existing categories of financial instruments and derivatives. Through close
contact with the merger arbitrage desk and the equity loan desk, the special
situations desk was able to construct a new arbitrage trade, an ‘election
trade’, that recombined in an innovative way two previously existing
strategies, merger arbitrage and equity loan.
The
facts of the merger were as follows: on January 25, 2001, Investors Group
announced its intention to acquire MacKenzie Financial. The announcement
immediately set off a rush of trades from merger arbitrage desks in trading
rooms all over Wall Street. Following established practice, the acquiring
company, Investors Group, offered the stockholders of the target company to
buy their shares. It offered them a choice of cash or stock in Investors Group
as means of payment. The offer favored the cash option. Despite this, Josh,
head of the special situations desk, and his traders reasoned that a few
investors would never be able to take the cash. For example, board members and
upper management of the target company are paid stocks in order to have an
incentive to maximize profit. As a consequence, ‘it would look wrong if they
sold them’ John said. In other words, their reasoning included ‘symbolic’
value, as opposed to a purely financial profit-maximizing calculus.
The
presence of symbolic investors created, in effect, two different payoffs—cash
and stock. The symbolic investors only had access to the smaller payoff. As
with any other situation of markets with diverging local valuations, this could
open up an opportunity for arbitrage. But how to connect the two payoffs?
In developing an idea for
arbitraging between the two options on election day, the special situations
desk benefited crucially from social interaction across the desks. The special
situations traders sit in between the stock loan and merger arbitrage desks.
Their closeness to the stock loan desk, which specialized in lending and
borrowing stocks to other banks, suggested to the special situations traders
the possibility of lending and borrowing stocks on election day. They also
benefited from being near the merger arbitrage desk, as it helped them
understand how to construct an equivalency between cash and stock. According to
Josh, head of the special situations desk:
[The idea was generated by]
looking at the existing business out there and looking at it in a new way. Are
there different ways of looking at merger arb?... We imagined ourselves sitting
in the stock loan desk, and then in the merger arbitrage desk. We asked, is
there a way to arbitrage the two choices, to put one choice in terms of
another?
The
traders found one. Symbolic investors did not want to be seen exchanging their
stock for cash, but nothing prevented another actor such as International
Securities from doing so directly. What if the special situation traders were
to borrow the shares of the symbolic investors at the market price, exchange
them for cash on election day (i.e. get the more favorable terms option), buy
back stock with that cash and return it to symbolic investors? That way, the
latter would be able to bridge the divide that separated them from the cash
option.
Once the
special situation traders constructed the bridge that separated the two choices
in the election trade, they still faced a problem. The possibilities for a new
equivalency imagined by Josh and his traders were still tenuous and untried.
But it was this very uncertainty—and the fact that no one had acted upon them
before—that made them potentially so profitable. The uncertainty resided in the
small print of the offer made by the acquiring company, Investors Group: how
many total investors would elect cash over stock on election day?
The
answer to that question would determine the profitability of the trade: the
loan and buy-back strategy developed by the special situations traders would
not work if few investors chose cash over stocks. IG, the acquiring company,
intended to devote a limited amount of cash to the election offer. If most
investors elected cash, IG would prorate its available cash (i.e. distribute it
equally) and complete the payment to stockholders with shares, even to those
stockholders who elected the ‘cash’ option. This was the preferred scenario
for the special situation traders, for then they would receive some shares back
and be able to use them to return the shares they had previously borrowed from
the ‘symbolic’ investors. But if, in an alternative scenario, most investors
elected stock, the special situations desk would find itself with losses. In
that scenario, IG would not run out of cash on election day, investors who
elected cash such as the special situations traders would obtain cash (not
stocks), and the traders would find themselves without stock in IG to return to
the original investors who lent it to them. Josh and his traders would then be forced
to buy the stock of IG on the market at a prohibitively high price.
The
profitability of the trade, then, hinged on a simple question: would most
investors elect cash over stock? Uncertainty about what investors would do on
election day posed a problem for the traders. Answering the question, ‘what
will others do?’ entailed a highly complex search problem, as stock ownership
is typically fragmented over diverse actors in various locations applying
different logics. Given the impossibility of monitoring all the actors in the
market, what could the special situation traders do?
As a
first step, Josh used his Bloomberg terminal to list the names of the twenty
major shareholders in the target company, MacKenzie Financial. Then he
discussed the list with his team to determine their likely action. As he
recalls: ‘What we did is, we [would] meet together and try to determine what
they’re going to do. Are they rational, in the sense that they maximize the
money they get?’
For some
shareholders, the answer was straightforward: they were large and well-known
companies with predictable strategies. For example, Josh would note: ‘See...the
major owner is Fidelity, with 13%. They will take cash, since they have a
fiduciary obligation to maximize the returns to their shareholders’.
But this
approach ran into difficulties in trying to anticipate the moves of the more
sophisticated companies. The strategies of the hedge funds engaged in merger
arbitrage were particularly complex. Would they take cash or stock? Leaning over,
without even leaving his seat or standing up, Josh posed the question to the
local merger arbitrage traders: ‘ “Cash or stock?” I shouted the question to
the merger arbitrage team here who were working [a different angle] on the same
deal right across from me. “Cash! We’re taking cash”, they answered’.
From their answer, the
special situations traders concluded that hedge funds across the market would
tend to elect cash. They turned out to be right. The election trade illustrates
the ways in which co-location helps traders innovate and take advantage of the
existence of multiple rationalities among market actors. The election trade can
be seen as a re-combination of the strategies developed by the desks around
special situations. Proximity to the stock loan desk allowed them to see an
election trade as a stock loan operation, and proximity to risk arbitrage
allowed them to read institutional shareholders as profit maximizers, likely to
take cash over stock.
At mid-century,
organizational analysts at Columbia University led by Robert Merton and Paul
Lazarsfeld launched two ambitious research programs. On one track, Merton and
his graduate students examined the origins and functioning of bureaucracy; on
a second, parallel track Merton and Lazarsfeld established the Bureau of Radio
Research to examine the dynamics of mass communication. Whereas our Columbia
predecessors charted the structure of bureaucratic organizations in the era of
mass communication, the research challenge we face today is to chart the
emergence of collaborative organizational forms in an era of new information
technologies.
Trading
rooms provide an opportunity to explore the terms of that research challenge
(Knorr Cetina and Bruegger 2002). Electronically connected to markets of
global reach, the traders at International Securities reach out to colleagues
only a few paces away to calibrate the tools of their trade. The trading room
is an ecology of knowledge in which heterarchical collaboration is the means to
solve the puzzle of value.
If trading rooms offer an
opportunity for the sociology of finance to make contributions to
organizational theory, the problem of value that is at the core of finance
means that the sociology of finance can make a fundamental contribution to
economic sociology as well. In its contemporary form, economic sociology
arguably began when Talcott Parsons made a pact with economics. You, the
economists, study value; we sociologists study values. You study the economy;
we study the social relations in which economies are embedded. But the
sociology of finance can ally with others who did not sign that pact (White
1981, 2001; Boltanski and Thevenot 1991; Stark 2000; Thevenot 2001; Callon and
Muniesa 2002; Girard and Stark 2002). In doing so, we should put problems of
valuation and calculation at the core of our research agenda. Just as
post-Mertonian studies of science moved from studying the institutions in which
scientists were embedded to analyze the actual practices of scientists in the
laboratory, so a post-Parsonsian economic sociology must move from studying the
institutions in which economic activity is embedded to analyze the actual
calculative practices of actors at work.
1. We owe this insightful
reading of Bacon’s writings, including Novum Organum and his (often unsolicited) ‘advices’ to
his sovereigns, Elizabeth I and James I, to Monique Girard.
2. See especially Smith
(2001), who refers to these strategies as fundamentalist and chartist.
3. Zaloom (2002, 2003) correctly
emphasizes that, to speculate, a trader must be disciplined. In addition to
this psychological, almost bodily, disciplining, however, we shall see that the
arbitrage trader’s ability to take a risky position depends as well on yet
another discipline—grounding in a body of knowledge.
4. We are re-interpreting
March’s (1991) exploitation/exploration problem of organizational learning
through the lens of the problem of recognition. On a separate but related
challenge in a new media startup, see Girard and Stark (2002).
5. A put is a financial option
that gives its holder the right to sell. A call gives the right to buy.
6. Novum Organum translates as ‘New
Instrument’. Bacon contrasts the deductive method of ‘Anticipation of the Mind’
to his own method of ‘Interpretation of Nature’ (Bacon 1960 [1620]: 37).
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