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900 Margaret Lock
Over the past two decades, custom-tailored technologies and theoretical models have
become ubiquitous features of financial markets. Contemporary markets mean screens
displaying an uninterrupted flow of prices in public places, financial products designed

with the help of complex mathematical models, software programs for the instant
display and analysis of financial data, and much more. Against the background of a
global expansion, this massive presence, together with the growing dependence of
financial transactions on both technology and formal modeling, raises the question
of the impact of science and technology on a fundamental institution of modern soci-
eties. The relevance of this question can be better understood if we take into account
the historical dimension of the processes through which science and technology have
penetrated financial transactions. Historians of economics and sociologists alike have
recently acknowledged that this impact should be measured in centuries rather than
decades (e.g., Sullivan & Weithers, 1991; Harrison, 1997; Jovanovic & Le Gall, 2001).
How do they contribute, then, to the preeminent position occupied by financial
institutions in developed societies? To what extent is finance shaped by science and
technology?
Since the mid-1990s, scholars from STS have become increasingly aware of these
questions. Working initially independently of each other, several scholars started
research projects on the role of science and technology in financial markets. The
output of these projects has materialized in books, journal articles, Ph.D. dissertations,
conferences, informal exchange networks, coordinated projects, as well as national
associations (e.g., the Association d’études sociales de la finance in France). Research
hosted at several universities in Western Europe and North America has grown at a
steady pace, attracting doctoral students, research funding, together with the interest
of academic publishers, and cross-fertilizing academic fields such as behavioral finance,
economic sociology, economic anthropology, international political economy, and
geography.
One question arising here is that of the background against which the interest of
STS scholars was directed toward finance. Several developments frame this moment,
independently of particular interests and motivations. (1) After the fall of the Iron
Curtain and toward the mid-1990s, the acceleration of global financial expansion
35 STS and Social Studies of Finance
Alex Preda

highlighted the central position occupied by technology and by formal models of
finance. (2) More or less celebratory media representations of the wave of financial
expansion contrasted with several severe crises toward the end of the 1990s, crises in
which formal models played an important role (e.g., the Long-Term Capital Manage-
ment crisis of 1998). These events triggered renewed discussions about the capacity of
financial markets to replace social policies and raised issues of trust, legitimacy, and
market constitution, directly involving both technology and financial theories. (3)
Since the mid-1980s, criticism of the central assumptions of neoclassical economics
had increased its pace in economic sociology as well as in the history of economics.
Insights and theoretical approaches developed in science and technology studies had
been fruitfully transferred to the history of economics, especially in the work of Philip
Mirowski (1989). Additional research in the history of financial economics (e.g.,
Mehrling, 2005; Bernstein, 1996) also highlighted the conceptual links between
physics (especially thermodynamics) and financial theory.
Against this background, a transfer of research topics, concepts, and approaches
from STS to the study of financial markets took place, to the effect that social studies
of finance (SSF) emerged as a new field of inquiry. Yet, SSF (which comprises different
emerging paradigms) cannot be seen as a mere extension or as an application of
science and technology studies to finance. First, there has been cross-fertilization with
other disciplinary fields, most notably perhaps with economic sociology. Second, SSF
did not simply take over already existing STS concepts but modified and enriched
them, developing its own research agenda. In the following, I discuss some of the most
important conceptual and topical links between STS and the social studies of finance,
thus exploring the SSF research agenda. In the first step of the argument, I show how
various SSF approaches conceptualize the relationship between knowledge and finan-
cial action, analogous to the STS conceptualization of the link between scientific
knowledge and practical action. In a second step, I examine how SSF approaches the
demarcation problem with regard to financial economics and to markets. I argue that
the social studies of finance take over, reformulate, and expand the demarcation
problem examined in science and technology studies. In the third step, I discuss the

concept of agency developed in SSF and show its similarities and differences with con-
cepts of agency present in science and technology studies as well as in economic
theory. The conclusion reviews the research agenda of the social studies of finance
and discusses potential cross-fertilization with the STS agenda.
FINANCIAL INFORMATION AND PRICE AS EPISTEMIC THEMES
Information has become a crucial concept of economic theory in the 1970s as a result
(and continuation) of efforts started during World War II in operations research (e.g.,
Klein, 2001: 131; Mirowski, 2002: 60), efforts aiming at optimizing action outcomes
based on random, incomplete data (e.g., tracking airplanes with guns and message
encryption). This required mathematical tools for transforming randomness into
determined patterns, tools that were combined with the notion (formulated by
902 Alex Preda
Friedrich von Hayek and the Austrian School of economics in the 1930s) that markets
can be seen as gigantic distributors of information, similar to a telephone switchboard
(Mirowski, 2002: 37). This fusion between a view of allocation processes as determined
by information on the one hand, and the formal processing of random signals in order
to identify determined patterns on the other, led to conceptualizing information as
additive signals, independently of the cognitive properties of the receiver. The effect
was to separate information from cognition; while the former was treated as a sort of
telephone signal, triggering a reaction from the receiver, cognition was deemed to be
irrelevant. Noise was equated with uncertainty (Knight, [1921]1985) and seen as a
blurring of determined (or meaningful) patterns, analogously to an encryption
machine that scrambles the message by inserting (apparently) random signals.
This concept of information as signals, which has proved influential in economic
sociology too (e.g., White, 2002: 100–101) is being contested by the game-theoretical
notion of information as choice of actions relative to signals under a fixed decision
rule (Mirowski, 2002: 380). This introduces the idea of rational expectations on the
part of economic actors (Sent, 1998: 22); expectations contain deterministic patterns
that filter the random signals. This second notion of information maintains the dis-
tinction to cognition, seen not as entirely irrelevant but as statistical inference.

According to Mirowski (2002: 389; 2006), there is a third concept of information as
symbolic computation, coming from artificial intelligence, which has proved less
influential than the other two. Relevant in this context is the fact that “information,”
as it is used in neoclassical economic theory, is seen analogously to phone signals.
Uncertainty (or noise) is understood as random signals, with no underlying mean-
ingful pattern, while cognition is taken either as irrelevant or as reducible to statisti-
cal inferences.
Financial markets can be seen thus as information processors, sending out price
signals (Paul, 1993: 1475) on the basis of which actors make their choices according
to (rational) decision rules. In this process, actors reciprocally anticipate their respec-
tive expectations and incorporate them into signals. In turn, these anticipations are
accompanied by dispersion and volatility, understood as a measure of ignorance and
uncertainty in the marketplace (e.g., Stigler, 1961: 214). Along with price observation
(Biais, 1993: 157), networks of relationships (e.g., Baker, 1984; Abolafia, 1996) and spe-
cialization (Stigler, 1961: 220) contribute to reducing noise.
Price signals are regarded as fully reflecting all the information available to market
actors (Stigler, 1961). This is also a key assumption of the efficient market hypothesis
(EMH). The presence of a large number of actors in the market, acting independently
of each other, handling all the relevant information they can get, is a fundamental
condition for market efficiency and liquidity (Fama, 1970, 1991; Jensen, 1978). These
participants “compete freely and equally for the stocks, causing, because of such com-
petition and the full information available to the participants, full reflection of the
worth of stocks in their prevailing prices” (Woelfel, 1994: 328).
EMH is related to the random walk hypothesis (RWH), which can be followed back
to Louis Bachelier’s treatment of stock price movements as a Brownian motion
STS and Social Studies of Finance 903
([1900]1964) and to Jules Regnault, a mid-nineteenth-century French broker
(Jovanovic & Le Gall, 2001). Prices are conceived of as similar to gas molecules, moving
independently of each other, with future movements being independent of past move-
ments. This tenet grounds models for computing the probability of future price move-

ments, such as the Black-Merton-Scholes formula (Mehrling, 2005; MacKenzie, 2006).
The EMH tenet was contested early by Benoit Mandelbrot, who noticed that price fluc-
tuations are inconsistent with a Gaussian distribution of securities prices (they gener-
ate “fat tails”) and that prices are scale-invariant (Mirowski, 2004: 235, 239: Mehrling,
2005: 97–98).
The assumption of market efficiency presupposes that at any given time economic
agents can distinguish between (meaningful) signals and noise, between the relevant
and the irrelevant, without recourse to issues of cognition. Several epistemological
problems arise here. (1) The distinction between prices and price data: prices as signals
cannot be separated from price data, which are not neutral with respect to produc-
tion and recording processes, as well as to their material support. Recording data
implies the use of technology; therefore, the question arises about how price record-
ing technologies shape price data and financial transactions with them (see the Social
and Cultural Boundaries of Financial Economics section). (2) The generation and
recording of data are not independent of formal and informal theoretical assumptions
about veridicality, consistency, homogeneity, reproducibility, comparability, and
memorization, assumptions that are incorporated into recording procedures and tech-
nologies and reflected in analysis and interpretation. How are these assumptions pro-
duced, and which social forces are involved in this process? (3) The use of price data
by financial actors implies observation, monitoring, and representation. These
processes, in their turn, require interpretation (provided by financial theories), skills,
and tacit knowledge.
Seen in this perspective, price data neither appear as given, natural, or determined
by the inherent rationality of financial actors, nor do they appear as analogous to
phone signals that trigger the recipients’ reactions. Rather, these data appear as prax-
eological structures (Lynch, 1993: 261), that is, as routine, accountable sequences of
social action. In this perspective, information, the key concept of financial econom-
ics (Shleifer, 2000: 1–3), is not treated as the natural starting point of investigation
but as a practical problem for financial actors. When using price data, academic econ-
omists share a set of epistemic assumptions with nonacademic financial actors:

assumptions about veridicality, consistency, homogeneity, reproducibility, and so on.
The scientific work of financial modeling or experimenting does not appear as embed-
ded in a type of understanding or rationality radically different from (and superior to)
the lay one. At the same time, since theoretical models are used in financial transac-
tions, they have not only a representational but also an instrumental quality. How do
they affect, then, the very assumptions they rely on? A first task on the research agenda
is, therefore, to investigate these price-related epistemic themes.
I begin with price observation: what does it mean to observe securities prices as
objective and given? Karin Knorr Cetina and Urs Bruegger have studied how dispersed
904 Alex Preda
traders observe prices in trading rooms with the help of computer screens. They argue
that price observation is above all a collective work (2002: 923–24) of reciprocal coor-
dination, which takes place over considerable geographical distances and does not
require spatial co-presence. What it requires is temporal co-presence: the observation
of the same price data at the same moment in time. Temporal co-presence, in its turn,
is achieved in a form of interaction which Knorr Cetina and Bruegger call face-to-
screen (2002: 940), in opposition to Erving Goffman’s face-to-face situation (1982):
personal interaction mediated and determined by the flow of prices on the computer
screen. Reciprocal coordination determines that price data can be accounted for as
objective and reproducible while being continuously generated in conversational
interactions. Whereas in the scientific laboratory spatial coordination (Gieryn, 2002:
128) plays an important role in the observation of scientific objects, in the trading
room it is temporal coordination that appears as crucial.
The laboratory appears as an “‘enhanced’ environment that ‘improves upon’ the
natural order as experienced in everyday life in relation to the social order” (Knorr
Cetina, 1995: 145). The trading room, by contrast, does not work as a system that
modifies and integrates an external (natural) order into the social order. Rather, the
trading room constitutes a reflexive system of data observation and projection (Knorr
Cetina 2005: 40) that brackets out the outside world: the price data it operates with
are generated in the system’s own conversational interactions. In the process of reci-

procal coordination, however, the data become objectified and treated as external with
respect to the system’s operations. A key role in this process is played by the computer
screen, on which financial actors project the outcomes of their interactions (i.e., the
price data). At the same time, similar to the scientific lab, trading rooms constitute
heterogeneous frameworks of distributed cognition (Beunza & Stark, 2004: 92), where
instruments and actors with different properties and skills, respectively, produce and
categorize the objects (i.e., financial products) of action.
This raises the question of the role played by price-recording and -displaying tech-
nologies with respect to epistemic themes such as veridicality and homogeneity.
Veridicality of price data implies that participants ascribe them a referential quality
while investing them with trust at the same time. Homogeneity implies that price data
are accessible in the same form to every participant (i.e., standardized), a requirement
derived from the condition of actors’ mutual coordination based on data observation.
The relationship between trust, standardization, and technology has been a central
STS issue during the last two decades (e.g., MacKenzie & Wajcman, 1985; MacKenzie,
2001a; Porter, 1995): technology disentangles data from the particular skills of indi-
vidual persons and invests it with abstract authority. Trust is displaced from personal
relationships and individual reputations and put on a mix of abstract competences
and iterable rules, incorporated in technology. With respect to price data, historical
studies of competing price-recording technologies show how their introduction to
financial markets in the late 1860s changed the veridicality of price data (Preda, 2003).
While one technology (the pantelegraph) attempted to confer veridicality on price
data by reproducing the signature of transaction partners, its competition (the stock
STS and Social Studies of Finance 905
ticker) disentangled price data from individuals and tied them to each other. Data thus
appeared as self-sufficient, abstract representations of a flow of transactions. Their
veridicality was grounded in the technology’s set of simple, iterable rules, which could
reproduce these data across various contexts.
Standardization of financial information involves calculative agencies (Callon, 1998:
6–12; 1999: 183)—that is, procedures and techniques through which the “economic”

is disentangled from the “social.” These procedures, provided by theoretical models,
are instruments through which a certain type of economic rationality is enacted. In
a study of standardized cotton prices in world markets, Koray Çalis¸kan (forthcoming)
investigates the social processes through which different stages of standardization are
attained. These stages, which Çalis¸kan, following Callon, calls “prosthetic prices,”
involve (1) the reciprocal fine-tuning of the traders’ pricing models and expectations,
(2) the projection of future prices based on commonly acknowledged calculations, and
(3) the narrative framing of pricing formulas.
A complementary aspect of standardization is how price data—made abstract and
taken out of the concrete contexts of their generation—are used by financial actors to
calculate and thus construct paths of collective action. A central dimension of finan-
cial calculation is that discursive sense-making procedures frame the data and make
it accountable—that is, practically intelligible—to financial actors. Several case studies
have examined the practices of accountants, who are confronted with the task of
meeting formal rationality criteria when dealing with financial information. These
studies show that accountants do not treat financial data as abstract, disembedded,
and universal but rather as depending on local procedures through which they are
made practically intelligible; these include negotiation, storytelling, and tinkering,
among others (e.g., Kalthoff, 2004: 168; 2005). Since the accountants’ criteria of formal
rationality depend on the generation of intelligible data, and the latter depend on
local sense-making procedures, it follows that in practice there can be no clear-cut dis-
tinction between formal, abstract rationality, on the one hand, and practical intelli-
gibility, on the other. Several authors have stressed the need for studies of
“ethnoaccountancy” (e.g., Heatherly et al., forthcoming; Vollmer, 2003), which should
focus on the practical methods through which financial data are generated and
invested with formal qualities. Examples are profit and costs as historical categories of
financial knowledge, local methods of accounting for financial data, and practical rules
for the classification of these data.
Observation, representation, and calculation of financial data are approached as
epistemic themes, in a manner that is both directly and indirectly influenced by

science and technology studies. One of the contributions of SSF is to show that price
data—regarded as unproblematic both by financial economics and by economic soci-
ology—are constituted in a web of interactions involving both human actors and tech-
nological artifacts. While economic sociology has focused mainly on the study of
social-structural embeddedness of economic transactions, social studies of finance
show that information is the outcome of complex, multilayered interaction processes
and indistinguishable from cognition. At the same time, rationality criteria do not
906 Alex Preda
merely build a normative horizon for financial action but are actually generated and
used as practical tools in the actors’ transactions. This link between local practices and
theoretical horizons questions the relationship between financial theory—understood
both as prescription and as representation—and practical action. I turn now to this
aspect.
SOCIAL AND CULTURAL BOUNDARIES OF FINANCIAL ECONOMICS
As an established academic discipline, financial economics claims to build a theoret-
ical horizon for concrete actors and practices by enunciating the ideal conditions of
rationality under which efficient action becomes possible. As shown in the previous
section, a cornerstone of financial economics is the EMH, with the assumption that
all action-relevant information quickly becomes fully incorporated into securities
prices, and therefore actors can make transaction-relevant decisions based on data
about price variations. This incorporation mechanism is public; sufficiently large
numbers of actors have access to data about price variations so that no single person
or group can consistently control transactions. The probability of gaps between future
and actual prices can be computed according to a formal model and tested against
empirical data. In this account, the EMH, which has known several varieties, can be
seen as a deductive theoretical model of price behavior.
At this point, several questions arise: (1) about financial theory as the product of a
historical development and about the social and cultural factors playing a role here,
(2) about how the boundaries of this model were drawn, and (3) about the relation-
ship between the theoretical model and the empirical data against which it is tested.

The historiography of economics has presented modern financial theory as the result
of a straightforward development beginning with Louis Bachelier (and Jules Regnault
earlier) and continuing in the 1960s and the 1970s with the work of Eugene Fama and
Paul Samuelson, among others (e.g., Dimson & Moussavian, 1998: 93). Yet, a more
illuminating approach would be to follow the history of financial theory not as a string
of disembodied, asocial thoughts but as a series of social and cultural processes through
which its language, concepts, and objects of investigation take shape. Starting from
this premise, Alex Preda (2004a) has investigated the nineteenth-century prehistory
of financial theory and shown how a vernacular “science of financial investments”
reconfigured investor behavior as rational, grounded in attention and observation,
while linking the concept of price to those of news and information. This “science”
disentangled financial securities from gambling and prepared the field for a formal
treatment of price movements. At the same time, brokers like Jules Regnault applied
physical principles to the study of price variations (Jovanovic & Le Gall, 2001). Formal
models like Bachelier’s shifted from investor to price behavior, represented in an alge-
braic not a geometrical fashion. We are confronted here with the emergence of several
cultural boundaries (between rational and nonrational behavior, gambling and invest-
ing, human actors and prices) that lay the ground for the formal theory of efficient
markets.
STS and Social Studies of Finance 907
Although the prehistory of financial theory traced these cultural and conceptual
boundaries, the theory’s growth into a full-blown deductive, formal model took place
between the 1950s and the early 1970s. The more general intellectual background of
this process was a sustained program of economic research into information and opti-
mization algorithms, initiated during World War II at several U.S. research institutes.
Whereas neoclassical economics operated until then with a concept of utility modeled
on classical mechanics’ notion of energy, this research program had at its core the
concept of information, understood as patterns of signals similar to phone codes
(Mirowski, 2002: 7, 21). The growth of financial theory into the dominant academic
model, however, required further boundary work, concerning (1) theorists and prac-

titioners of formal pricing models and (2) financial theorists and the nonfinancial
economists in the academic world. The setting in which this second boundary was
traced was provided by U.S. business schools, which underwent a rapid “academi-
cization” in the 1960s, providing a home for financial economics, which otherwise
was sometimes marginalized in the more established economics departments.
As to the first boundary—although in the beginning practitioners were hostile to
pricing models and to the general assumptions of the EMH, some of them enrolled
this theoretical apparatus as a handy tool in their controversies and feuds with other
practitioners (Mehrling, 2005; MacKenzie, 2006). A central case studied by Donald
MacKenzie and Yuval Millo (2003) is that of the option pricing formula developed by
Fischer Black, Myron Scholes, and Robert C. Merton in the early 1970s. In the early
stages of its use, empirical data did not fit the predictions of the Black-Scholes-Merton
formula. Yet, traders on the Chicago Board Options Exchange (CBOE) used it because
of its cognitive simplicity, academic reputation, and free availability. The Black-
Scholes-Merton pricing formula offered traders a tool for coordinating their actions
and a guide to trading and hedging. The use of the Black-Scholes-Merton formula,
together with innovations in financial products, led to an increasing fit between
empirical data and theoretical predictions and thus ultimately to the academic and
practical success of this model.
The establishment of financial economics as a successful academic discipline and,
with it, of the EMH as a dominant theoretical model was the outcome of complex
social processes that traced the boundaries of finance as a domain of legitimate
theoretical conceptualization and empirical investigation. This was accompanied by
jurisdictional claims of practitioners, conflicts of interest among academic and
nonacademic groups, and a reconceptualization of market exchanges as optimization
algorithms. The boundaries between academic financial theory and practice, between
academic and other forms of expertise, appear as porous and shifting; vernacular con-
cepts of price as information have played a role in preparing the conceptual founda-
tions of financial theory, while the interests, practices, and institutions of
nonacademic groups have contributed in an essential fashion to the overall success of

formal pricing models.
Although a central tenet of EMH is that securities prices move in a random fashion
and cannot be predicted, technical analysis (or chartism) maintains that prices move
908 Alex Preda
according to predictable patterns. In spite of this inconsistency with (and of attacks
from) academic theory, chartism has been successful with financial practitioners for a
century. How can a vernacular form of expertise coexist with an established academic
theory asserting the opposite? How can it maintain success with practitioners over
long periods of time? The investigation of these issues, pertaining to studies of demar-
cation and expertise (Evans, 2005; Collins & Evans, 2003), recently has been started
(e.g., Preda, 2004b). At the same time, the impact of financial theory on markets,
together with the prominent role played by technology, raises the issue of agency:
how are the structures of financial action changed by formal pricing models, by price-
recording and data-processing technologies? How is the organization of markets
affected by them?
IMPACT OF THEORETICAL MODELS AND TECHNOLOGY:
AGENCY IN FINANCIAL MARKETS
The “technologization” of stock exchanges started in the late 1860s with the stock
ticker, followed by cinema screens in the 1920s, teletypewriters in the 1930s, and com-
puters in the early 1960s. In the 1950s, the New York Stock Exchange (NYSE) drafted
plans for computer recording of trading data, and in 1962 it formulated the aim of
developing a “complete data processing system” that “will mechanize virtually all
present manual operations in the Exchange’s stock ticker and quotation services”
(NYSE, 1963: 48–49). In 1963, a special study of the Securities Exchange Commission
(SEC) recommended to the U.S. Congress the automation of financial markets.
In foreign exchange markets, Reuters introduced the first monitor screen and key-
board in 1967 and the Monitor Dealing Service (a system of computerized transac-
tions) in 1970. In the early 1980s, the PC won over proprietary systems in brokerage
offices, a process that facilitated the automation of major financial exchanges such as
Euronext (formed in 2000 by the merger of the Paris, Brussels, and Amsterdam stock

exchanges). The coexistence of automated and nonautomated financial exchanges has
highlighted technology-induced differences in price and volatility patterns (Franke &
Hess, 2000: 472), raising the question of the role of technology in the constitution of
securities prices. In the late 1990s, the first electronic exchange networks (ECNs) were
approved by the SEC as platforms for financial transactions. In 2006, ECNs like
Archipelago merged with the NYSE.
Neoclassic economic theory, for its part, has conceived agents as isolated individu-
als, endowed with calculative capacities, desires, and preferences, which remain unaf-
fected by their relationship with other human beings or with artifacts (Davis, 2003:
167). Combined with the prevailing notion of information as signal, this has led to
conceiving economic agents as atomistic calculators who process external signals and
take decisions (Mirowski, 2002: 389). Nevertheless, studies of market microstructure
question these agential assumptions (e.g., O’Hara, 1995: 5, 11).
One of the lasting theoretical and empirical contributions of social studies of science
has been to stress the irreducibility of agency to human intentionality or will and to
STS and Social Studies of Finance 909
show that scientific theories and technological artifacts shape future paths of collec-
tive action. At least two concepts mark the STS contribution: (1) theoretical (or disci-
plinary) agency, concerning the ways in which conceptual artifacts (like scientific
models or mathematical formalisms) change cultural and social structures (e.g.,
Pickering, 1995: 145), and (2) sociotechnical agency, concerned with the role of mate-
rial arrangements and of technological artifacts (e.g., Bijker, 1995: 192, 262; Bijker et
al., 1987). The STS conceptualization of agency differs from technological determin-
ism in that technology (1) is not seen as preconfiguring paths of action, (2) implies
not only constraints but also social resistance, and (3) is not seen as distinct from but
as a form of social action. Consequently, the computerization of financial exchanges
is not seen as inevitable but as the result of specific social interests, conflicts, and group
mobilization.
Studies of theoretical and sociotechnical agency have investigated (1) how the pro-
duction of formal models and technologies shape future paths of action (the producer

side) and (2) how the use of theories and technological artifacts affect collective action
and transform communities (the user side). It has been argued that user groups act as
market intermediaries, thereby playing a special role with respect to social diffusion
and agency (Pinch, 2003). With respect to the field of finance, it becomes relevant to
examine how theoretical models and technologies are produced and adopted in finan-
cial markets and how their use affects financial transactions and changes the markets’
organizational patterns.
Financial models (like the Black-Scholes-Merton formula) do not merely formulate
a set of rules that, when applied, will ensure that these transactions meet efficiency
and rationality criteria. If we take these models as normative, we risk a determinist
position, according to which financial agents simply follow theoretical prescriptions.
If we accept the representational character of formal models, we take financial trans-
actions as an isolated asocial domain of investigation and assume a naturalist stance
(MacKenzie, 2001b).
To avoid these conceptual difficulties while preserving a notion of theoretical
agency, Michel Callon (1998) has suggested the concept of performativity. According
to Callon, economic theory shapes the way in which transactions are conducted and
markets are organized; it has a performative character. A program of research on per-
formativity should involve an investigation of the social forces, groups, interests, and
mechanisms through which successful theoretical intervention is performed. An
example in this respect (Callon, 1998) is the reshaping of an agricultural produce
market by economic consultants, a reshaping that enacts a normative model of
rationality. This enactment, however, is not automatic but involves conflicts between
interest groups, persuasion, and the mobilization of organizational structures and
artifacts.
Theoretical agency (performativity) consists of two opposite yet closely intertwined
processes. The first is the demarcation of the boundaries between the economic and
the social, or disentanglement (Callon, 1999: 186)—a process through which ethical
and social aspects are redefined as outside the sphere of transactions. The second
910 Alex Preda

process is the social entanglement between producer and user groups, through which
they reciprocally tune their interests and enroll heterogeneous resources to realize
these interests. In the language of actor-network theory, performativity then implies
the creation of a heterogeneous network that defines its interests and mobilizes ade-
quate resources while tracing conceptual and cultural boundaries in such a manner
that the outcome of this process (e.g., empirical data, results) appears to reinforce
the resources (e.g., confirm the abstract model). However, since the outcome of
boundary-marking (data) is neither independent of the resources used (model) nor
interest-neutral, it follows that model and data circularly reinforce each other, in a
way similar to the bond existing between theory producers and users.
Theoretical agency (or performativity) combines then the normative aspect of eco-
nomic theories with the reflexive character of economic knowledge: normative models
of economic processes are developed by academic researchers and at the same time
monitored by market actors, who adopt and adapt these models to their own inter-
ests, practices, and situations.
Empirical studies such as MacKenzie and Millo’s (2003) historical analysis of the
Black-Scholes-Merton option pricing formula have highlighted how traders on the
Chicago Board Options Exchange (the user community) imposed Black-Scholes prices
on those who believed them to be too low. In using the formula as a tool in hedging
and trading, traders started pushing down options prices; in doing so, they generated
prices that fitted those predicted by the theoretical model. This, in turn, acted as an
empirical confirmation of the theoretical model. The use of the formula by options
traders, together with the introduction of new financial products, narrowed the gap
between theoretical predictions and actual prices. At the same time, the use of the
option pricing formula changed the organization of derivative markets, their legal
definition, and the structure of financial products. On these grounds, MacKenzie
(2004; 2006: 17) distinguishes generic performativity, Barnesian performativity, and
counter-performativity. While generic performativity designates the use of the model as
a tool by practitioners, Barnesian performativity means that users will generate such
data as to confirm the model’s predictions, without the data being directly derived

from the model. Counter-performativity, by contrast, designates the situation where
the use of a theoretical model engenders counter-productive imitation: price data
generated by imitative trades no longer match the model’s predictions (e.g., “fat tail”
distributions).
Other studies, such as Philip Mirowski and Edward Nik-Khah’s (2007) investigation
of wavelength auctions, have argued that the boundary between the economic and
the social is never perfect, since group interests and structures play a dominant role.
Moreover, this boundary is marked by conflicts of interests between competing user
and producer groups, who form alliances. Mirowski and Nik-Khah show how in the
auctions of mobile phone wavelengths competing alliances between phone compa-
nies (user groups) and experimental economists (theory producers) were formed, with
objectives and agendas that fused theoretical and political aspects. They also argue
that the complexity and heterogeneity of financial expertise (ranging from academics
STS and Social Studies of Finance 911
to securities analysts, accountants, and merger lawyers), together with the prominent
role of group interests, require a more nuanced approach to theoretical agency than
that provided by the concept of performativity. The overall argument resonates with
the requirement for a more intense analysis of various, even contradictory forms of
financial expertise needed for a better understanding of how boundaries are produced
and maintained in finance (see also Miller, 2002).
The second aspect of agency is related to the massive reliance of global financial
markets on technological systems for data processing and transactions. Enmeshed with
this aspect are issues such as (1) the social forces that advance the technologization
of financial transactions, both in a historical and in a contemporary perspective; (2)
the assumptions that underlie the design of trading programs; (3) the effects of tech-
nology on the organization of financial exchanges and the perception of financial
data; and (4) the ties between technology and forms of financial expertise like securi-
ties analysis.
With respect to the first issue, recent historical studies have shown that, when the
first price-recording technology (the stock ticker) was introduced on the NYSE, user

and producer groups (stock brokers and telegraph companies, respectively) formed
alliances to promote their monopoly and control price data. This technology displaced
bodily recording techniques, standardized data, and disentangled authority and
credibility from individual actors (Preda, 2006). In her investigation of the Chicago
Board of Trade, Caitlin Zaloom (2003) has confronted the question of the CBOT’s
bitter resistance to automation, in contrast to the latter’s enthusiastic adoption by the
Paris Bourse in the late 1980s, as studied by Fabian Muniesa (2000, unpublished).
Zaloom’s argument is that trading technologies are multilayered and embedded in
local settings, being represented not only by software programs but also by the body
techniques and spatial arrangements traders use to communicate and gather relevant
information. Lack of trading automation does not mean the absence of any technique;
traders rely on a set of distributed, heterogeneous techniques for solving informational
problems. The body techniques employed by traders have developed into change-
resistant routines, intertwined with networks of personal relationships and with a
social hierarchy on the trading floor. This constellation of specific routines, spatial
arrangements, and social relationships is perceived by participants as proprietary and
as inaccessible to outsiders. Automation is resisted by traders and perceived as a
menace to their privileges, to the existing networks of relationships, and, above all
perhaps, to established ways of gathering and processing information. Instead of being
perceived as reducing informational uncertainties, automated trading is seen as
increasing social uncertainties. Resisting it does not mean that the CBOT traders resist
any kind of technology. On the contrary: they mobilize the existing techniques as a
unique resource in fighting off attempts to change the ways in which they gather and
process information.
In contrast to the CBOT’s resistance to computerized trading, Fabian Muniesa shows
how automation was successfully introduced to the Paris Bourse. In the 1980s, the
912 Alex Preda
problem of the Paris Bourse was to attract customers by offering distinct features that
other stock exchanges did not possess. This competitive pressure had been heightened
by the relatively marginal position of the Bourse with respect to other major exchanges

(London and New York), by the deregulation of the London Stock Exchange in 1986,
and by the latter’s subsequent technological upgrading. The management of the Paris
Bourse adopted (and adapted) a system of computerized trading (CATS, or computer-
assisted trading system) introduced on the Toronto Stock Exchange in 1975 with
limited success. Yet, in modifying the CATS system (which became CAC, or Cotation
assistée en continu), the Paris Bourse was confronted with the problem of the assump-
tions (among others, about equilibrium and fairness) that should underlie the trading
algorithms. These assumptions determine the design of the trading algorithm software
and, consequently, the processes through which securities prices are formed (or what
market participants call “price discovery”).
From the beginning, it becomes clear that pricing is not a natural process involv-
ing the identification (or discovery) of an already existing “ideal” or “objective” price.
Rather, pricing appears as a complex social process of negotiation involving interest
groups, software, economic theories, and computer networks, among others. The
absence of human intermediaries (i.e., brokers) does not imply the absence of any
negotiation process but rather displacement and distribution among heterogeneous
actors. While working in the tradition of the actor-network theory characteristic of
the Paris school of STS, Muniesa highlights both producer- and user-related aspects of
sociotechnical agency. On the producer side, he shows that the successful introduc-
tion of automated trading on the Paris Bourse was brought about by an alliance of
managers, brokers, and software engineers who reciprocally tuned their positions and
adapted existing technologies to local constellations of interests. An outcome of this
reciprocal tuning was the presentation of trading software as embodying “a vision of
the market” (Muniesa 2000: 303)—that is, a “perfect” theory of market equilibrium
that was not imported as a given into this alliance but produced by it. The agential
character of formal equilibrium models has less to do with their normative character
than with their role as a resource in such a heterogeneous alliance.
On the user side, the trading software enables participating actors to compute prices,
which they afterward project as “true,” “real,” and “discovered.” This mode of com-
puting differs from previous ones (which used statistical means) and implies stan-

dardization, without being reducible to it. As performed by the software, the
calculation of prices is standardized and displayed to actors from a central source.
Traders appear as anonymous participants in transactions, known only to a central,
data-providing authority (the computer). Yet, exactly because participation in trading
is anonymous and routed via a technological authority, actors need to reciprocally
coordinate their expectations by inferring personal or categorical identities from the
computerized display of price data. Coordination of expectations, in turn, allows
traders to project future courses of actors and to construct the market as a collective
movement of human and nonhuman agents, a movement that grounds evaluations
STS and Social Studies of Finance 913
of market fairness and justice. Personal agency and technical agency combine to
configure the market as an entity sui generis, with a life of its own.
FINANCIAL MODELS, TECHNOLOGY, AND RISK
The starting point of my argument (presented in the Financial Information and Price
as Epistemic Themes section) has been the centrality of the concept of information
in financial economics. Acknowledging this position means investigating the ep-
istemic premises of this concept, its cultural trajectory in the history of economics, as
well as its links with technology. A significant link is that between information and
risk: a standard argument of financial economics (taken over by economic sociology
as well) is that economic actors gather and distribute information to process uncer-
tainties into risks (e.g., Stinchcombe, 1990: 5), thereby enabling economic decisions.
Yet, if information cannot be separated from (tacit and explicit) forms of knowledge
and expertise, depending on heterogeneous constellations of human actors and arti-
facts, it follows that the said forms of knowledge, together with group relations and
concrete technologies, will have an impact on how financial risks are produced and
managed. Since financial risk constitutes a major problem in a global world (as repeat-
edly illustrated by the crises of the late 1980s and 1990s), investigation of this area
offers a potential for practical contributions as well.
On a first, micro-interaction level, financial risk appears as a discursive device that,
combined with body technique and with price-recording technologies, is employed

in managing the “trading self” (Zaloom, 2004: 379). While more general economic
discourse ascribes a negative connotation to risk, the practice of financial actors is to
approach it as something that is not entirely manageable through calculations and
formulas but requires narrative framings and classifications (see also Mars, unpub-
lished; Kalthoff, 2005).
On a different, organizational level, financial risk is made sense of with the help of
technologies like software programs and formal models, which saw a rapid, worldwide
expansion in the 1990s. Tracing the sources of this expansion, Michael Power (2004)
argues that technologies such as enterprise risk management (ERM) originated in a
cultural shift that put emphasis on shareholder value and on increased performances
of company stock prices in the market. ERM was implemented in banks all over the
world to control financial exposure and to prevent overengagement in financial trades.
Yet, since such technologies are based on algorithms that automatically overrule
human actors’ decisions, a reciprocal tuning of traders and software is no longer pos-
sible. The introduction of standardized risk measurement technologies, managed from
outside the trading floor, blocks out the local skills and personal knowledge of human
actors, which play an important role in avoiding financial loss. Risk-measurement
technologies are not instruments that measure an external given reality (“risk”) but
tools of financial action (Holzer & Millo, 2004: 16). These models change the very
phenomena they are supposed to represent; consequently, their use does not auto-
matically diminish financial risks and volatility (see also MacKenzie, 2005: 78). While
914 Alex Preda
traders use models to calculate option prices and exposures, they also observe and
imitate each other, to the effect that “superportfolios” emerge. In situations of finan-
cial instability, the use of the same pricing formulas in the same way, with the same
trades, can have destructive effects.
CONCLUSION
I have argued that a distinctive feature of social studies of finance is the investigation
of scientific models, technology, and forms of expert knowledge in financial institu-
tions. Is SSF then to be regarded as a subfield of STS? Are financial institutions complex

enough to support an emerging discipline over longer periods of time? What would
the SSF research program look like?
Undoubtedly, the majority of SSF studies has been done by academics trained in the
sociology of science and technology, or who had an established reputation in STS.
Many of them continue to conduct parallel research projects in both fields. The major
themes of investigation—such as observation, representation, boundary marking,
agency, and risk, to name but a few—had already been successfully investigated with
respect to science and technology. Yet, in spite of the clear affinities and influences,
SSF does not appear as a mere subdomain of STS. There are several reasons: the first
is that SSF combines epistemic topics with the study of problems relevant in areas like
economic sociology and behavioral finance, bringing a genuine contribution to the
study of financial institutions. One of these problems is the pricing mechanism: while
financial economics has noticed the impact of technology, it has been the role of
SSF studies to show how price data, theoretical assumptions, trading software, and
computer networks influence the constitution of securities prices. Another genuine
contribution is related to the analysis of information as the cornerstone of financial
markets. While financial economics and economic sociology have understood infor-
mation as signal processing and treated it as a black box, SSF has highlighted the social
and institutional origins of this concept as well as the epistemic and cultural assump-
tions on which financial information is constituted.
A second reason for the growing disciplinary autonomy of SSF is that it has made
conceptual contributions, acknowledged as such, in disciplines such as sociology,
behavioral finance, and the history of economics, an example being the concept of
performativity, which can be seen as an extension and modification of the notion of
agency developed in the sociology of science and technology. Another example is the
concept of markets as a reflexive system, built on an analogy with the concept of lab-
oratory. This indicates growing disciplinary autonomy, without affecting the ties
between STS and SSF. Owing to the close personal and intellectual ties between these
fields, I expect them to stay in a lively dialogue.
A further question with respect to the possibility of disciplinary autonomy is

whether the field of inquiry is deep enough to support continuous SSF research in the
long run. I can confidently venture the following: the research done since the mid-
1990s is a mere scratch on the surface of the field. There is a wealth of uninvestigated
STS and Social Studies of Finance 915
or under-investigated topics, both historical and contemporary. A short list would
include the social and epistemic history of competing price-recording technologies,
the development of trading software and the interface between the software industry
and financial markets, trading robots, the social history of financial information as a
commodity, the emergence of epistemic intermediaries like financial analysts, the
growing role of financial expertise, the relationship between formal financial models
and vernacular economics, the relationship between academic theories and nonacad-
emic ones, and vernacular forms of financial knowledge and theories. The field shows
enough depth and relevance to support research in the long run.
While there is neither a formal research program, comparable, for instance, with the
strong program in the sociology of scientific knowledge (but see Preda, 2001), nor a
single school (comparable to the Edinburgh, Paris, or Bath/Cardiff schools in STS), this
can be seen rather as an advantage, since it allows the inclusion of various research
interests and approaches. Nevertheless, the possibility cannot be excluded that formal
research programs will emerge and that we will witness more internal differentiation
after the initial growth period. Already several distinct approaches are configuring: one
centered on the concept of performativity and influenced by (but not limited to) the
actor-network theory perspective and another one grounded in the tradition of labo-
ratory studies and centered on field work in the trading room. I expect that further
empirical studies and theoretical contribution will deepen the differentiation process.
In any case, the prominence of financial institutions in our world, together with the
growing role of financial theories, expertise, and technologies, make this one of the
most exciting developments to have emerged from STS.
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