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FOLDING: INTEGRATING ALGORITHMS ON THE FLOOR OF THE NYSE

Daniel Beunza London School of Economics

[email protected]

Yuval MilloUniversity of Leicester

[email protected]

February 26, 2013

Abstract

What are the consequences of automation on the social structure of a market? So far, existing

studies have focused on technologies designed to dilute social relations, overlooking instances

where the opposite effect might take place. Our ethnography of the NYSE addresses this gap

by exploring the automation of its trading floor at various points in time. First, we observed

the NYSE prior to automation in 2003, and outline the functions traditionally played by its

intermediaries. Second, we compared its two rounds of automation in 2006 and in 2008 using

a combination of ethnographic observation and interviews, and identify the mechanisms that

allowed the automated NYSE to effectively preserve its structure. We refer to this as folding.

Third, we use the 2008 round of automation as a natural experiment to test the separability of

the social and material aspects of the market. Finally, we use the Flash Crash of May 2010 to

examine the effects of automation on the ability of the NYSE to perform its traditional

exchange functions. Our proposed notion of folding contributes to economic sociology by

outlining how a market can retain the advantages provided by social structure while accessing

the benefits of automation.

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FOLDING: INTEGRATING ALGORITHMS ON THE FLOOR OF THE NYSE

The automation of financial markets poses new challenges to scholars of markets and

policy makers. Trading in American stock exchanges is now almost entirely dominated by

financial algorithms (Tabb, 2011). As with other automation processes, financial algorithms

have not only reduced labor costs but also transformed existing practices, altering the social

structure of the market by displacing traditional intermediaries such as floor brokers and

specialists. The risks posed by these changes became apparent with the Flash Crash of May 6,

2010, the fastest and second-largest percentage point decrease in history of the Dow Jones,

and widely attributed to the use of trading algorithms (CFTC/ SEC, 2011). The incidence of

the Flash Crash suggests that the consequences of automation have not been fully understood,

and calls for an analysis that focuses on its more profound effects. What are the consequences

of automation on the social structure of the market?

In grappling with financial automation, sociologists can benefit from an established

literature on market intermediaries but need to consider the effects of technology on these

intermediaries. In particular, sociologists have pointed to the embedded and institutionalized

nature of securities exchange (Baker 1984, Abolafia 1996). But by virtue of the time in which

this literature was developed, it presupposes the presence of a human at both ends of the

transaction – a presence that automation has consigned to the past. Alternatively, research on

the social studies of finance offers a clearer focus on technology (MacKenzie 2006;

MacKenzie and Millo 2003, Beunza and Stark 2004), but its emphasis on financial models is

different from the risks posed by automation. A related literature in science and technology

studies has explicitly considered the effects of new technology on social relations. For

instance, Callon (1998) has shown how that technology is introduced in markets to dilute the

effect of social ties, rendering the market closer to the atomized ideal espoused by economic

theory. But this is not the only possible outcome: for instance, Muniesa’s (2004) study of the

automation of the Bourse de Paris points to a very different effect, namely, the preservation of

the original social structure of the market. A more complete understanding of the effect of

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technology on social structure is thus needed to grasp the full consequences of market

automation.

Our study explores this by examining the introduction of algorithms in the New York

Stock Exchange (henceforth “the Exchange”). Founded in 1792, the Exchange is known for

its iconic trading floor and specialized form of market makers – the “specialist system.” The

NYSE’s trading floor remained vibrant through the 1990s even as rival exchanges in London

and Paris closed theirs. But in 2006 the Exchange confronted the regulatory mandate to

automate following the promulgation of Regulation National Market System by the Securities

and Exchange Commission. The Exchange decided on an original automation design that

would introduce algorithms while preserving its trading floor and intermediary structure of

specialists and floor brokers. Its first attempt to do so was largely unsuccessful, leading to a

steep drop in its market share (see Table 1). Its second attempt, introduced in 2008, led to a

stable market share and a robust performance during the Flash Crash of 2010, suggesting the

technical viability of integrating algorithms within an existing social structure.

---Table 1 here ---

Our study asks four questions about the automation of the NYSE. First, what were the

advantages of the original specialist system that the Exchange sought to preserve? We

examine this with ethnographic observations on the floor of the Exchange before automation

in 2003. We categorize the advantages of automation in three broad groups, coordination,

sensemaking and norm enforcement, and find that these are consistent with sociological

studies of intermediation (Simmel 1902 [1950], Burt 1992, Baker 1984, Obstfeld 2005) as

well as ethnographies of trading floors (Abolafia 1996, Zaloom 2001, Pitluck 2007).

We ask a second, related question. How did the Exchange automate its processes

while preserving its social structure? We identify the underlying mechanism by comparing

the two rounds of automation at the Exchange in 2006 and 2008 using a combination of

ethnography and oral history interviews. On the basis of these, we develop the notion of

folding. Drawing on a culinary metaphor, we define folding as a process of automation that

preserves the original social structure of a market or organization. In the realm of cuisine

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folding refers to the act of gently combining a delicate mixture into a thicker one without

impeding the ability of both to work as desired. For instance, when adding chocolate powder

to whipped cream the chocolate needs to be sprinkled on successive layers of cream rather

than stirred into it; this prevents the thicker chocolate from bursting the air bubbles of the

more delicate cream. In finance, we use folding to designate a form of automation that keeps

the social structure in place, thus preserving its original function.

Third, did was social structure preserved by retaining the original technology, or in a

new technological setting? This question speaks to an important academic debate on the

relationship between technology and society. In the past two decades sociologists of science

have challenged the traditional notion of social structure by putting forth the view that the

sphere of the social is inseparable from that of the material (Pickering 1993; see review by

Orlikowski and Scott 2008). Thus whereas sociologists rely on social structure to explain

phenomena in markets, science scholars contend that structure cannot be abstracted from its

material setting, and that it caused by such material setting.

Our study addresses this debate using the NYSE automation as a natural experiment

to test the separability of the social from the material. We find that the NYSE managed to fold

algorithms into the specialist system by creating a dual trading system that combined an

automated matching engine with the traditional specialist auction on the floor. These two

modes, however, did not coexist in parallel but were sharply separated in time through an

elaborate switching mechanism explored below. This has two implications. First, the

duplicative approach to folding chosen by the NYSE (a trading floor and a data center)

suggests that NYSE’s role structure could not be extended to a material setting different than

the original – a form of inseparability. Second, and in spite of this inseparability, the specialist

role existed in the minds of the actors at the NYSE with independence from any material

basis. Indeed, the central reason for the second round of automation was to preserve the

viability of specialists and floor brokers. We theorize these findings by building on Feldman

and Pentland’s (2003) notion of ostensive routines, and conclude that social structure,

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although inseparable from its original material setting, is an explanatory factor rather than a

consequence of social action.

Having explored the automation process at the NYSE, our study returns to the

problem of automation for the market at large. What are the consequences of the automation

for market properties such as liquidity and stability? To answer this question we exploit the

Flash Crash of 2010 as another natural experiment. The crash threatened all exchanges

equally, including the Nasdaq, Bats or DirectEdge (SEC/CFTC 2011). Yet the NYSE was the

only exchange that managed to avoid cancelling its trades after the crash; all the other

(automated) exchanges experienced hundreds of trade cancellations, undermining investor

confidence. We conclude that an intermediary social structure performs important functions

in the market that need to be taken into account in the ongoing design of market automation.

AUTOMATING THE MARKET INTERMEDIARY

Understanding market automation calls for a firm grasp of the ways in which market

intermediaries shape the exchange of securities. A voluminous literature in the economics of

market microstructure has explored these intermediaries using the industry term, “specialist

system,” to describe the NYSE and other exchanges. Saar (2010) defines a specialist market

as “a hybrid market structure that includes an auction component (e.g., a floor auction or a

limit order book) together with one or more designated market makers (‘specialists’) who

trade as dealers for their own account. The designated market makers have some

responsibility for the market.” Saar thus points to three aspects of intermediaries encoded in

the expression “specialist system”: a set of roles (specialist and broker), a practice (the call

auction), and a material setup, primarily the trading floor. Structure, practice and materiality

are thus bundled together in the academic conception of the NYSE intermediaries. Efforts to

understand the effects of automation of the NYSE need to take these three aspects into

account.

In doing so, we draw on two distinct intellectual traditions, economic sociology and

science and technology studies. Economic sociologists have focused on the structural

component of markets with the study of the intermediary. Appreciation for the intermediary

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goes back to the study of third party mediation formulated by Simmel (1902 [1950]).

Simmel’s third party profits from exploiting the disunion of the other two, as elaborated in

Burt’s (1992) concept of brokerage; or it can profit from moderating the forces that divide the

group, as formalized by Baker (1984), Khurana (2003), and Obstfeld (2005). This structuralist

approach is complemented by ethnographies of trading floors that explored the material and

embodied aspects of trading floors (Abolafia 1996, Zaloom 2001, Pitluck 2007). Trading,

these contend, is not only shaped by the structure of roles and social ties but also by the

material basis of trading such as the architecture of the building or the choice of open outcry

technology.

What do financial intermediaries do?

Our own reading of the aforementioned literatures offers an answer to the question of

what do intermediaries accomplish. Financial intermediaries within exchanges are particularly

relevant in markets characterized by opportunism and uncertainty (Baker 1984).

Intermediaries provide coordination (Khurana 2003, Abolafia 1996), sensemaking (Zaloom

2001, Pitluck 2007) and norm enforcement (Baker 1984, Abolafia 1996), as well as give rise

to their own form of opportunism (consistent with Burt 1992). We consider each of these

mechanisms below.

Coordination. One key contribution of intermediaries is to facilitate exchange by

coordinating the transacting parties. The theoretical mechanisms are particularly clear in

Khurana’s (2002) study of a different form of intermediary, executive recruitment firms.

These headhunters, he argues, are particularly useful in contexts of opportunism and

uncertainty: as he puts it, in contexts of “few buyers and sellers, high risk to both parties, and

institutionalized gaps between them” (Khurana 2003: 241). At its most basic, the coordinative

function of headhunters entails matching, that is, mobilizing the intermediary’s contacts to

expand the array of potential trading partners. Coordination extends to buffering the

uncertainties entailed in the transaction. It also includes pacing the rhythm at which the

parties interact by dictating a schedule and offering their resources help the transaction take

place. While not explicitly labeling the process as coordination, ethnographies of trading

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floors have identified how brokers and market makers accomplish these functions. Abolafia

(1996), for instance, underscored the role of NYSE specialists in matching buyers and sellers,

and in buffering imbalances in demand and supply.

Sensemaking. Ethnographies of exchanges have also outlined how trading floors

produce social cues that allow the transacting parties to engage in sensemaking, that is, give

meaning to incoming orders and prices amidst market uncertainty. For instance, Zaloom

(2001, 2006) has documented how these cues arise non-purposefully in the very act of

trading: “because of the physical and emotional information conveyed with numbers,” she

argues, “not all bids and offers are equal” (Zaloom 2001: 263). An order conveyed with a

fearful voice, for instance, elicits a different response than one with confidence. A more

purposeful form of sensemaking helps intermediaries overcome the problem of adverse

selection identified in the economics literature. As miscrostructure economists have

established, a customer agreeing to trade at a given price may be trading because he or she

knows something that the other side does not, posing a risk for the latter (Glosten and

Milgrom 1985). This so-called “lemons” problem discourages transactions, drying up

liquidity. But as the ethnographic work of Pitluck (2008) shows, intermediaries can address

this problem by crafting their discourse appropriately. For instance, they can disclose the

identity of the seller in a limited fashion by using an abstract category rather than the full

name. This semi-identification can give meaning to the seller’s actions, eliminating the

buyer’s suspicions of adverse selection while protecting his or her identity. As Pitluck (2008:

3) concludes, “a market’s anonymity is a social byproduct of market participants’ strategic

interaction as they exchange, conceal, or reveal identity information.” We refer to the

combined task of information exchange and social cues display as partial disclosure.

Norm enforcement. The sociological literature discusses yet another intermediary

role of the exchanges, namely, enforcing norms and limiting opportunism. Baker (1984)

established that market makers in an exchange enforce norms such as not selling while prices

are falling. They do so by “freezing out” opportunistic colleagues from trading (Baker 1984:

782). Norm enforcement has also been a traditional role of the specialist, as shown in

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Abolafia’s (1996) ethnographic study of the NYSE. Over the years, he found, the Exchange

instituted formal controls over its specialists, including “affirmative” and “negative”

obligations, an embryonic computerized auction, and a bureaucracy that awarded new listings

to compliant specialists. These formal means of control were supplemented by an informal

culture of “rule veneration” (Abolafia 1996) whereby the Exchange’s rulebook was

repeatedly cited and known to everyone on the floor.

Opportunism. Sociologists have also explored the problems that intermediaries in

trading floors can themselves create. These go back to the intermediary’s tendency, discussed

by Burt (1992), to exploit his or her structural advantage. In the context of trading floors,

these ideas are echoed in Abolafia’s (1996: 105) analysis of the NYSE prior to the reforms of

the 1960s: “in the 1920s,” he explains, “specialists aided and abetted ‘bear raids’ and

manipulated their stocks” (see also Brooks 1969, Sobel 1975). The paradox is thus clear:

while trading floors aim at limiting opportunism among transacting parties, they can also

generate their own form of opportunism. Indeed, Abolafia (1996) posits a feedback model in

which intermediary opportunism ebbs and flows: extreme opportunism prompts regulators

and exchange bureaucracies to impose restrain, but the resulting reduction in opportunism

leads to laxer controls. At the time of his study, 1990 to 1992, Abolafia was impressed by the

restraint at the NYSE, which contrasted sharply with his observations on the culture of the

investment banks and commodities exchanges. But according to his model, such restrain can

be the breeding ground for opportunism later on.

In sum, sociological studies highlight three key functions of intermediaries in

exchanges, as well as a danger of opportunism. Yet these studies are only the first step, for

they all refer to a human standing between other humans. In outlining the effects of

automation on the intermediary, studies should examine its effects on coordination,

sensemaking, norm enforcement and opportunism. A greater analytical focus on technology is

thus required, and more specifically a focus on the algorithmic technology that has automated

financial exchanges.

How does automation reshape the market intermediary?

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By automation we refer to the introduction of technology that increases labor

productivity, as in the replacement of workers by machines. In finance, the native expression

for automation is the “introduction of algorithms.”1 In theorizing the effect of these

algorithms we draw on the broader literature on science and technology studies. Of particular

relevance is research in actor-network theory, which has granted agency to the objects (Latour

1991, Callon 1986). Specifically, Callon’s (1998) argued that technology is often introduced

in markets with the express objective of reducing the effect of social relations on value. In the

example he gives, a change in the architecture of the strawberry auction house in the French

town of Sologne reduced the influence that social ties between farmers and traders had on the

value of strawberries (Callon 1998, Garcia-Parpet 2007). By separating or “disentangling” the

strawberries from the social relations between buyer and seller, the French auction house

redefined the value of the fruit around intrinsic properties such as weight, size, ripeness, etc.,

bringing the market closer to the economic ideal of the atomized decision-maker (see also

MacKenzie and Millo, 2003).

The sociological literature has also pointed to the possibility that automation might be

a complement rather than a substitute of the existing structure. Muniesa’s (2004) study of the

Bourse de Paris notes that the committee charged with automation initially entertained a

design that preserved the trading floor, allowing automated and manual trading to

complement one another. By keeping the trading terminals on the exchange floor rather than

at the banks’ offices, the French planners hoped to have “something like criee groups

[crowds] with computers” (Muniesa 2004: 16). But the committee eventually changed course

and eliminated the trading floor altogether. Indeed, the historical site of Paris Bourse at the

Palais Brongniart is nowadays devoid of brokers or market makers, and used primarily to host

1 Financial algorithms are akin to the algos used in social media: relevance algorithms used by Google, user recommendations in Amazon’s site, or customized newsfeeds shown by Facebook. In all these, the algorithm mediates the inputs of users to reproduce some of the advantages of the social context. In a similar vein, financial algorithms – whether in order matching or order execution– replace tasks traditionally performed by humans such as making sales calls or working an order. By automating a task previously performed by intermediaries, algorithms can render these intermediaries redundant and thus reshape social structure.

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corporate events. Although the complementarity between automation and social structure was

not the focus of Muniesa, we drew inspiration in his initial use of the term, as in “Folding a

Market Into a Machine,”2 for our own definition.

Our interest in such complementarity goes beyond the theoretical. An automation

path that preserves social structure could address a key problem that legal scholars have

highlighted when discussing automation, namely, the loss of the norm-enforcement

mechanisms that make markets and organizations viable (David, 2010). According to Lessig

(2000), automation entails a fundamental change in the governance of social activity. Legal

and social norms are replaced by computer code, and power is shifted to new groups with

different interests (see Barley [1986] for a related argument at the level of the organization).

Left unattended, Lessig adds, this replacement of rules by code is at risk of being hijacked by

technologists. The latter may find ways to profit from opportunistic activities that were

previously barred by social norms, yielding technologies that run ahead of the system’s ability

to manage them. Lessig insists that automation must be designed in a way that produces what

he calls “electronic communities” rather than “hyper-connected networks” dominated by

opportunism. The complementarity that Muniesa hints at, if theoretically developed, may thus

offer a framework to address Lessig’s concerns.

The separability between the social and the material

The distinct automation path of the NYSE speaks to a related academic debate over

the sociological conceptualization of technology. Starting with the work of Simmel and

Weber, sociology has been premised on a notion of social structure that is independent of the

material setting – an “enduring and relatively stable patterns of relationship between different

entities or groups” (Levi Martin 2012: 4). Science scholars have challenged this view of

2 The differences can be explained by the multiple meanings of the term folding. According to the American Heritage Dictionary of English Language (4th ed, 2004), it is defined as “to make compact by doubling or bending over parts,” as in a folding a sheet of paper or the laundry. But folding is also defined as “to blend (a light ingredient) into a heavy mixture with a series of gentle turns,” as in folding beaten egg into batter. While Muniesa built on the first of these meanings (compacting social relations in a material artifact), we developed our notion on the basis of the second meaning (blending new technology in an existing pattern of social relations).

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structure by contending that there is a co-constitution between the social and the material

(Pickering 1993). Within science studies, actor network theorists have argued that social life

is made durable by material associations, and that as a result the social is a consequence rather

than an explanation for action (Latour 1986, 2005). Whether in the form of co-constitution or

in the stronger actor-network formulation of the social as outcome, these scholars posit an

inseparability between the social and the material, challenging traditional sociological

formulations of social structure (Orlikowski and Scott 2008).

More recently, Feldman and Pentland (2003) have contributed to this debate by

detailing a different form of separability between the social and the material. The authors

distinguish between the ostensive (abstract, ideal) and the performative (enacted, concrete)

aspect of organizational routines. Against Latour (1986), the authors argue that routines entail

an ostensive aspect that is abstract, distributed, and manifested in overlapping narratives used

by organizational actors. This view implies a different understanding of the role of routines in

organizational change. Such change, the authors argue, can take place when performative

routines differ from their ostensive ideal and the difference is retained. The work of Pentland

and Feldman (2003) has implications for the aforementioned debate over the separation of the

social and the material. Because the ostensive aspect of routines emphasized is akin to social

structure, their study can be read to imply that social structure can exist regardless of its

material basis.

Our study provides an setting to advance this debate. If social structure and the

material setting are indeed separable, organizations should be able to preserve their original

structure when introducing a new technology. If, on the other hand, the two were inseparable

we would not expect to see the original structure surviving automation. Our study speaks to

this in connection with the automation of the NYSE.

RESEARCH METHODS

Research Site

Our ethnographic design departs in various ways from the canonical single-site,

single-period approach to fieldwork. In addition to our primary period of observation during

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2008-10, we relied on observations in 2003, on oral history interviews, on interviews within

the field of securities trading during 2008-10, on follow-up interviews in 2011-12, and on the

incidence of the Flash Crash. We discuss these in greater detail below.

Data sources

Fieldwork at the NYSE 2008-10. Our primary data entails fieldwork at the NYSE

during 2008-2010. By then, the regulatory mandate that forced the Exchange to automate had

already been promulgated, and the Exchange was in the midst of profound change. The

turmoil became clear in our difficulties at gaining access. Our initial point of contact in the

Research division of the Exchange was made redundant after our second interview. We

persevered, and after several attempts were invited by an official to visit the floor. It was

during that visit that we serendipitously met the chairman, Duncan Niederauer, and he agreed

to a case study of the transformation of the Exchange. As a result, over the period 2008 to

2010 we made 31 visits to the NYSE, interviewed 19 officials, including its chairman, the top

management team and several floor governors. We conducted detailed observation of the

floor booths of two brokers, VDL and Rosenblatt Securities, and of the post of Bank of

America, later sold to Getco Llc. We interviewed the designated market makers and floor

brokers responsible for these booths, as well as the clerks that worked with them. In addition,

we also observed two regular market openings, one market closing, and one special situation

during the record-volume stock-rebalancing auction of Citibank Group.

Observations in 2003. In making sense of the observations noted above, we benefited

from previous fieldwork at the NYSE. We first visited the Exchange in June 2003. At the

time we interviewed a specialist, a floor broker, a research official and a compliance officer,

witnessed a market opening ceremony, and conducted observations on the trading floor and at

the Luncheon Club. This provided a precious window of observation into a world that would

subsequently disappear and gave us grounds to compare the Exchange before and after

automation.

Follow-up after 2010. Our data also extends beyond 2010. We conducted five

follow-up interviews in person at the NYSE during 2011, as well as five telephone interviews.

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We also conducted nine follow-up interviews in person during 2012. These allowed us to

gauge the response of the Exchange to the Flash Crash and lent historical continuity to the

changes that the Exchange had put in place during 2008, including the sale of the Bank of

America’s floor operations to the algorithmic trading company Getco Llc. These changes also

led us away from the Exchange for interviews with ex-specialists that had been laid off in the

four years since our study began.

Interviews outside the NYSE. We complemented our ethnographic data with

interviews of industry participants and academics in financial exchanges. This encompassed

officials at competing exchanges, including the President and Chief Executive Officer of the

International Stock Exchange, the Chairman and three officials at the American Stock

Exchange, as well as three high-ranking officials at the Nasdaq. We also interviewed a high-

ranking official at Goldman, who has both a sales trading desk and a dark pool. Our data also

went beyond practitioners to include regulators, consultants and academics. Of these we

interviewed two officials at the Securities and Exchange Commission, including the Chief

Economists of the SEC during the key period when Reg-NMS had been enacted, Lawrence

Harris. We also interviewed industry consultants and academic specialists on market

microstructure, including Stephen Wunsch, Wayne Wagner, and William Harts. In addition,

we interviewed influential market microstructure academics such as with Lawrence Glosten

and Charles Jones, and attended three academic seminars in market microstructure.

Analysis

To build theory from the case, we make use of various analytical strategies.

Following Agar (1986), we identified breakdowns in our initial conception of the

phenomenon and reconceptualized our thinking around them. We also used grounded theory

as defined by Glaser and Strauss (1967) by conducting two within-case comparisons. We first

compare the two rounds of automation at the NYSE: the so-called Hybrid of 2006-08 with the

New Generation Model implemented in 2008-10. Because the first automation round was

relatively unsuccessful and the second one was relatively more successful, the comparison

allows us to identify the mechanism that underlies the simultaneous automation and

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preservation of the original social structure. Second, we compare the practices at the NYSE

before automation in 2003 with those after the second automation in 2008-10. Finally, the

Flash Crash of 2010 provided us with a form of natural experiment. The crash affected all the

equities exchanges and trading venues in the US, but their performance varied dramatically.

The differences in impact provide us with grounds to explore whether the NYSE’s automation

design gave superior capabilities than its counterparts. The use of an extended ethnography

also responds to the call by Feldman and Pentland (2008: 312) for studies that are both

ethnographic and longitudinal in order to uncover the relationship between espoused

(“ostensive”) structures and actual routines.

We complemented our organizational ethnography with interviews and observations

in the exchanges industry at large. We observed trading and interviewed the management of

rival exchanges, attended technology fairs, conferences, and interviewed regulators as well as

technologists. This contextualization was important given the external nature of the impetus

that forced the Exchange to automate: because the decision to automate was taken by

regulators outside the NYSE, it was critical to leave the Exchange to capture both sides of the

debate that led to enforced automation (see Fligstein [2008] for a general elaboration of this

argument).

While our main method for data collection is ethnographic, the methodological scope

of our investigation is also historical sociology. As such, we regard the data we collected –

about the day-to-day actions of the actors at the NYSE – as being contingent upon wider,

transformative events (Paige, 1999). Our choice of this method is motivated by three main

reasons. First, the transformative nature of events that financial markets faced in the last

decade are similar, although at a narrower scale, in many respects, to the grand events of

discontinuity and change that were the backdrop for the social theories of Marx and Weber

(Skocpol, 1984; Riley, 2006). Second, the automation of financial markets and its

implications is clearly a “big” question – an issue that both academics and the wider public

are interested in and therefore call for a historical scope investigation (Mahoney and

Rueschemeyer 2003). Third, the multiplicity of arenas and scales where automation of

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financial markets took place and the complex potential causalities that accompany such

events fit the comparative-historical approach in historical sociology (Mahoney, 2004).

AUTOMATING THE NYSE

2003: The NYSE Before Automation

It did not take us long to witness the social nature of the NYSE. We first entered its

iconic trading floor at 9:25 am in the morning of May 23, 2003. We were there to observe the

opening of the market, guided by an Exchange official called Murray Teitlebaum. Looking up

from the floor we saw the chairman of the NYSE, Richard Grasso, standing on a podium and

accompanied by a mixed troupe that included high-ranking military officers and Miss

America (O. Jennifer Rose) dressed in full Beauty Queen costume –including bathing suit,

crown and band. As time approached 9:30 am, the crowd of brokers on the floor began

clapping, the bell clanged and a raft of camera flashes immortalized the moment. After the

bell, a loudspeaker invited everyone to join as Miss America sang the national anthem to

commemorate Memorial Day. Such elaborate beginning of the trading day --with the

exception of the anthem-- is performed daily at the NYSE. The ceremony was revamped by

Grasso in 1995 in response to a technology-themed advertisement campaign by the Nasdaq.

By inviting celebrities and giving television stations access to the floor, Grasso created an

event that could be broadcast: a live performance, in response to Nasdaq’s electronics. The

strategy proved successful, and by 2003 the Exchange counted 55 international television

networks broadcasting live from its floor through the day. Social interaction is thus not a

means to conduct transactions at the NYSE, but a central feature of its brand and identity,

celebrated and leveraged by management.

The NYSE, we quickly learnt, rested on a social division of labor between specialists

and floor brokers – the so-called specialist system. Specialists acted as both principals and

agents on designated stocks; brokers represented client’s orders. As principals, specialists

were expected to “make a market” on designated stocks, that is, act as counterpart to buyers

and sellers; as agents, specialists were expected to hold call auctions. Brokers represented

clients’ orders. Specialists stood still at the trading post; brokers took clients’ orders from

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vertical telephone booths in the periphery of the floor and placed those by walking to the

specialists post in the center of the floor. Specialists wore sober suits; clerks and brokers

dressed in colorful jackets. Seen from above, then, the Exchange looked like many other

markets: a few individuals standing still, surrounded by others walking between them. Like a

market on a public square, and unlike the trading rooms of investment banks, the Exchange’s

floor grouped together both sides of the transaction on the same space, allowing for face-to-

face interaction across the sensitive buyer-seller divide.

What functions did this peculiar setup allow? We addressed this question with our

ethnographic observation. We started by walking to the post of Robert Hardy, a specialist on

several French companies that stood at the post of Fleet Financial. Hardy gave us the first

clues into the nature of the specialists’ job, confirming the presence of the three mechanisms

discussed above: coordination, sensemaking and norm enforcement.

Coordination. Hardy coordinated buyers and sellers by conducting call auctions at

his posts at designated times, with a clerk behind him typing the prices he dictated on a

computer terminal known as the Display Book. To do so, Hardy established the price at which

demand and supply equilibrated, an activity that the Exchange denoted “price discovery.”

These call auctions had a special feature: they batched all the orders before setting a price,

“pouring” orders “like water on a swimming pool,” as someone explained us. This reassured

brokers that their orders would be processed at the same time, preventing the proverbial rush

to the fire exit. An ex-specialist gave us the following example:

Let’s say you are a seller for 200,000; you are a seller for 200,000; you are a seller for 200,000; I’m a specialist, I come in and say ‘Calm down, all right, just everybody calm down, what do you have to do? [moving his head left] What do you have to do? [moving right] What do you have to do?’ Ok the market right now is $20 bid for 100 shares and a million shares offered at $21’ you show it on the screen. ‘Ok what do you want to do? You want to sell 100 shares now at the dollar? Ok now you sold 100 shares, now the market is $19 for 100 shares, do you want to sell another 100 shares?’ ‘ok let’s calm down, let’s see if we can find some buyers, let’s see what happens at various prices, let’s talk this thing out, let’s do business’.

The quotation suggests that price discovery is peculiar form of economic intervention,

smoothing prices by managing people. Such pacing of the rhythm resulted in “slowing the

transacting, preserving the flow,” as another specialist explained. The role of time, we

16


realized, was particularly important in trading because liquidity is a temporal variable.

Liquidity refers to the availability of counterparts during a given interval of time. Batching

orders as the specialists did extended the length of time used for matching, increasing the

likelihood of finding a counterparty. In this sense, price discovery is similar to that of the

headhunters analyzed by Khurana (2003): by managing the speed of the interaction between

buyer and seller, both headhunters and specialists ensured that the rhythm of activity was not

destructive to the successful completion of the transaction. Indeed, in their efforts at pacing

trading, the specialists went as far as to routinely freeze the computerized Display Book in

order to prevent it from sending orders during the call auction. Freezing became later on the

basis for a lawsuit by SEC discussed below.

Another form of coordination was the specialists’ role as market makers, dealing in

stock for their own proprietary account in order to limit volatility. Specialists, Hardy

explained, were “like shock absorbers,” compensating the pressures of supply and demand to

make prices more stable. This contributed to fulfill one of their affirmative obligations,

namely, keeping a “fair and orderly market.” To do this, Hardy relied on the information he

had about the upcoming orders in the book. The resulting temptation to sell ahead of a client’s

order, “front-running” the customer, was partly managed by a negative obligation: the

specialist was not allowed trade for his own account at a price at which an unexecuted agency

order he or she was holding could be executed. In practice, then, the specialists took a

position in the reverse direction of the latest price movement: if the specialist received many

sell orders, he would want to buy (Rutigliano interview). The specialists also relied on price

and order book signals: as we saw, Hardy used price charts with trend lines as a retail investor

would. Such was the Exchange’s preference for public information that the use of personal

mobile phones was not allowed on the floor. In sum, the specialists’ market-making activity

performed a coordinative function and relied primarily on trading flow information to do so.

Sensemaking. Our observations also underscored the importance of sensemaking. We

observed this at Hardy’s post when a short broker named Salvatore walked to his post.

“Salvatore and I,” he said, touching Salvatore’s shoulder in appreciation, “worked together

17


fifteen years ago.” But Salvatore’s visit post was not a courtesy call: “still here snooping?”

asked another broker when Salvatore arrived at the post. Salvatore, it turned out, had a large

buy order; instead of simply handing it over to Robert he told him about it. “I think it’s a little

heavy,” Robert replied, suggesting that there were many other buy orders at that time, and

that Salvatore might want to come back later. This partial disclosure of book orders enabled

Salvatore to better time his order. The practice, known as “giving a view,” allowed specialists

to provide information to elicit additional orders without compromising the positions of the

existing ones. We observed this yet again as we followed Dan, another floor broker, from one

specialist post to another. When Dan approached a specialist with a question, the answer was

“stock’s hanging in there, lots of machine buying, Morgan’s a seller, Merrill has an interest.”

In subsequent interviews with industry consultants we learnt just how elaborate and

important the conversations between buyers and sellers on the floor were. Conversations were

crucial for matching large blocks of shares, where the problem of adverse selection is most

acute. Adverse selection limits the ability of specialists and floor brokers to disclose the size

of their position for fear of influencing the other side. But as we learnt from microstructure

consultant Wayne Wagner, some disclosure is inevitable: given the adversarial nature of

transacting, one side cannot interest the other without saying something about the size and

nature of the block (Wagner interview). As Wagner eloquently wrote in a trade journal, this

complicates matters, as even a minimal disclosure exposes the actor to opportunism: “it is

impossible to draw a black and white distinction between seeking liquidity and violating

confidentiality (…) The market maker cannot accelerate liquidity arrival without revealing

trading interest” (Wagner, 2004: 5). Matching large orders thus calls for a high level of trust

among the negotiating partners. It was this trust that allowed NYSE specialists to tap into the

latent demand and supply for stocks held by institutional investors (Wunsch interview; see

also Wunsch 2011). In short, sensemaking at the NYSE took place through partial disclosure

and was particularly important for large blocks of shares.

Norm enforcement. We learnt about the Exchange’s norm enforcement mechanisms

from Robert Hardy over an elaborate breakfast at the NYSE’s Luncheon Club. Conflicts were

18


managed on the floor through a combined mechanism of formal rules and informal norms.

This mechanism included the figure of the floor governor. Governors worked for the

Exchange, and controlled a bureaucratic system of listings allocation that rewarded the

specialists who followed the norms. Hardy provided us various examples of instances where

he controlled his self-interest. Referring to one particular instance, he said:

The governor said to me, ‘Bobby, that was a good trade.’ I lost money on twelve consecutive orders, and then made money on the last one. But all got the same price.

The implication from this quotation is that, as Abolafia (1996) argued, specialists were

characterized by restraint as much as by eagerness to profit. The Exchange had developed

internal mechanisms that rewarded specialists to leave money on the table for the sake of the

good functioning of the system.

Formal organization was not the only means of norm enforcement. As we followed

Dan, the floor broker mentioned above, from one post to the next, we also noticed how he

addressed, backslapped and saluted with nicknames everyone he met on his way. Everyone on

the floor was Johnny, Jimmy or Bobby; there were no Johns, James or Roberts. Indeed, over

the course of our many interactions --whether at the elevator, on the floor, or elsewhere-- we

observed that actors had developed an ability to make a quick joke that acknowledged the

presence of the other without being formulaic. Interactions were humorous, fast, witty and

casual. This was consistent with those of Baker (1984), who emphasized the importance of

network cohesiveness to forestall opportunism and bring about an orderly market.

In sum, our observations at the NYSE in 2003 showed that the Exchanged appeared

to fulfill the three functions of intermediaries discussed by sociologists. But our fieldwork

also revealed the limitations of the trading floor.

Physical limits of the floor. Chief among the limitations of the floor was its almost

complete disregard of new information technology. Indeed, as the Exchange expanded from

one to five rooms over the decades, walking to each post became increasingly difficult for

brokers. We understood this as we overheard an elevator conversation between two brokers.

Both used a special cloakroom to change their shoes before entering the floor and avoid back

19


pain. One of them had even gone past rubber soles, and began “experimenting” with his son’s

skateboarding shoes. Similarly, as trading volume rose the clerks who worked for the

specialists found typing the orders on their Display Books increasingly difficult, with some

doing so at the unlikely pace of seven keystrokes per second (Pastina interview). On so-called

Marlboro Friday of 1993 (named after the sharp fall in Philip Morris’ price) the clerk at the

Philip Morris post famously typed on his Display Book at such furious speed that the machine

ended up breaking down from overheating. In short, by 2003 the Exchange seemed to be

bursting at the seams, prompting us to ask ourselves (and write on our notes) whether we

might not be seeing a world about to disappear.

Opportunism. Although our limited observations in 2003 did not hint at any form of

broker opportunism, soon after our initial fieldwork in 2003 the Exchange confronted various

lawsuits against its Chairman and specialists. In July 2003, news that the board of the NYSE

had granted Richard Grasso a combined retirement and compensation package of $190 m.

prompted widespread media outcry. Grasso’s resignation did not put an end to the threat to

the institution: in May 2004, the SEC and New York State’s attorney general Eliot Spitzer

submitted a civil lawsuit accusing Grasso and other board members of manipulating the

NYSE board, and in October 2006 the New York Supreme Court ordered Grasso to repay the

NYSE part of the compensation package. However, this was reversed in 2008 following an

appeal.

Short after the Grasso lawsuit, in 2003 the SEC also sued a number of the specialists.

They were accused of neglecting their obligations by engaging in inter-positioning

(unnecessarily placing of an order at a price between current bids and offers), of front running

(trading ahead of a client’s order), as well as of freezing the Display Book as mentioned

above. Following an internal investigation, on October 2003 the NYSE fined five of the seven

specialist firms $150 million for habitual abuse of their market roles. The specialists firms

also agreed to pay $240 million to settle with the Exchange but the SEC persisted with the

case separately. In 2006, however, a judge reversed the conviction of leading specialist David

Finnerty in the SEC court case (Colesanti, 2008).

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The lawsuits prompted a top management change at the NYSE that paved the way for

automation. Grasso had been a vocal defender of the trading floor and a detractor of

automation; following Grasso’s departure, the Exchange’s board appointed John Reed, the ex-

CEO of Citigroup, as interim chairman. Reed was a noted promoter of technology in finance,

known for introducing the ATM in the 1970s. He introduced a new governance structure at

the Exchange and led a search that culminated in the appointment of another technology

enthusiast, John Thain, as Chief Executive (Gasparino 2006). In barely more than year, the

NYSE lost its chief executive, changed its governance procedures, and appointed a new chief

executive. Yet these changes were soon overshadowed by an all-encompassing regulatory

reform introduced by the SEC.

2004-08: Algorithms vs. intermediaries

Starting in 2005, a sequence of changes in regulation and technology combined to

create an algorithm-based approach to securities exchange that challenged the dominance of

the specialists at the NYSE. These changes go back the late 1960s: starting then, and over the

course of the following four decades, the SEC mandated automation to put in place a form of

managed competition among exchanges, known as the National Market System. The NMS, as

it is generally known, connects the various exchanges via order routers, and routes every

order to the exchange with the best price. The NYSE, long considered a “slow” manual

exchange relative to automated competitors, was initially excluded from the SEC’s

requirement to connect to the National Market. But by the early 2000s the SEC, with

Lawrence Harris as chief economist, opposed the NYSE’s exception on the grounds that it

gave specialists an unfair advantage in the form of a “look-back option” (Harris interview). In

December 2005 the SEC promulgated Regulation National Market System (Reg-NMS),

requiring disclosure and immediate tradability of prices in all the exchanges of the National

System, including the NYSE. This provision, known as Rule 611 and informally referred to as

the “trade-through rule,” forced the Exchange to respond to an order within a second. Because

the humans on the floor could take up to 30 seconds, the NYSE found itself in urgent need to

accommodate automated trading.

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Reg-NMS had profound intellectual roots. According to Muniesa (2007), the

computer-inspired view of financial markets that lies at the root of automation was first

sketched in the utopian vision of an economist, Fischer Black (1971). The renown economist,

who also pioneered the computerization of libraries and hospitals as a consultant for Arthur

D. Little (Mehrling 2005), was a strong advocate of automating the NYSE (Harts interview).

At the heart of Black’s proposal lied a vision of trading as processing information, and of the

Exchange as a self-organized book of orders -- namely, a database. The critical role of price

discovery performed by NYSE specialists, Black argued, could be left to investors posting

their own bids and asks through limit orders. As for the specialists’ obligation to “keep fair

and orderly markets,” Black interpreted it as a mandate to dampen price volatility. This form

of intervention, he argued, was inconsistent with an efficient market and random-walk prices.

In an automated exchange, Black (1971: 87) concluded, “there will be little need for dealers,

market makers, or block positioners who maintain quotes for their own accounts.” In the

following four decades, academic economists specialized in exchanges coalesced into a

subfield known as “market microstructure,” and debated the relative benefits of automation

and by the mid-2000s support for automation was widespread (e.g. Hendershott et al 2011).

Beyond the work of academics, Reg-NMS would not have been possible without the

efforts of private entrepreneurs. Between 1971 and 2004 these contributed to fulfil Black’s

vision of a disintermediated and computerized exchange in various ways. The first was

Instinet, a private company that offered computer links between institutions in the early

1970s, “with no delays or intervening specialists” (Behrens interview). In 1996, and taking

advantage of the then-new Internet technology, online trading entrepreneurs founded Island

Inc. The company formed a so-called electronic communication network (ECN) that matched

and executed internally the orders sent by the clients (Katz interview). At the heart of an

ECN was a matching algorithm with explicit rules for prioritizing orders: unlike dealer-

dominated exchanges like Nasdaq, ECNs delegated the pricing of stocks to an algorithm. In

effect, this did away with the market maker’s prerogative to set prices: as one of our

interviewees put it, in an ECN “every customer is a dealer.” In 1998, the SEC put the ECNs

22


on an equal footing with Nadaq market makers with Regulation Alternative Trading Systems,

giving them access to the inter-exchange order-routing system the SEC had built in the 1970s.

Following this move, and unable to compete, a nearly-bankrupt Nasdaq acquired three ECNs

in 2003 and replaced its market makers with algorithms. As a Nasdaq executive explained,

“the ECNs won” (Concannon interview).

The private efforts at automation were intensified during the 2000s. Wall Street banks

and brokers, long opposed to the Exchange’s dominance, worked alongside new automated

exchanges like Bats Exchange and Direct Edge. Run as consortia for the benefit of the banks,

these exchanges offered low prices, fast speeds and quickly took market share away from the

NYSE (Wolkoff interview, Williams interview). These new exchanges instituted a system of

payment for order flow: by offering rebates to customers who entered limit orders but were

not immediately executed, they compensated for ”adding liquidity” to the system (Harris

interview). The first venue to do so was Island, the Chicago-based ECN (Mackenzie and

Pardo-Guerra 2013).

Partly in response, buy-side funds such as Fidelity followed suit by developing

technology for automated order execution. To do this, technology firms like Pragma and Deep

Value developed algorithms that broke up large orders into small parts, replicating the

specialists’ ability to minimize market impact of larger orders by “working an order.” The

best known among these was the so-called VWAP, or “Volume-Weighted Average Price”

algorithms. These helped ECNs by providing a mechanism to handle large orders, rendering

the matching capability of the NYSE trading less unique. By the end of the 2000s, the VWAP

had become the metric by which sales traders were being evaluated, thus institutionalizing the

use of algorithms -- even for the purpose of evaluating humans.

Beyond execution, so-called “high frequency trading” firms introduced algos in their

trading strategy, unleashing large volumes of transactions and coupling them with frequent

cancellation of orders. Their activity in electronic market-making predominantly involves

“providing liquidity,” that is, posting limit orders that others execute against. This was

conducted by large hedge funds such as Chicago-based Citadel, as well as by specialist firms

23


like Chicago-based GETCO (Global Electronic Trading Co.), Kansas City-based Tradebot,

and Amsterdam-based Optiver.

The automated alternative to the NYSE was further developed with the rise of so-

called “dark pools.” Brokers-dealer firms such as Goldman or Credit Suisse started to offer a

specialized order-matching service in which prices were only displayed after a trade had been

executed. In this manner, these dark pools avoid publicizing bids and asks, allowing fund

managers to trade large blocks of shares without creating price movements against them or

having orders sliced by execution algorithms.

By 2006, the combination of the SEC’s regulation, automated exchanges, algorithmic

execution, high-frequency trading and dark pools made it possible for investors to trade

stocks without intermediaries at the NYSE. A combination of economists, entrepreneurs and

regulators had offered an alternative model: the exchange as a database. And in an important

sense, their efforts appear successful: as microstructure economists like Hasbrouck and Saar

(2010) have argued, spreads in the most-often traded indexes have narrowed following the

introduction of Reg-NMS (see also Hendershott et al., 2011).

In sociological terms, the automation enforced by the SEC can be read as an attempt

to disentangle equities trading from the social relations between brokers and specialists. As

someone told us at the NYSE, the SEC believed that the NYSE is “nothing more than an old

boy’s network, a bunch of guys who take care of each other.” The SEC, with Lawrence Harris

as its chief economist, were not unlike the chairman of the strawberry auction house described

by Callon (1998): a reformer who found the relationship between market participants much to

close for the proper functioning of the market.

Indeed, there was a clear sense of technological optimism among industry

participants in 2008. Automation would not only improve market structure, but also eliminate

opportunism. For instance, according to Gregory Maynard, Systems and Product Strategy

Director at electronic pioneer International Securities Exchange, automation would reduce

behavior among traders to the explicitly permitted, eliminating ambiguity. According to him,

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On the floor there are all these rules that you’re not allowed to disobey. In the computer there are all these rules that you’re allowed to follow and … nothing else. The rules tell you what you can do in the computer, they tell you what you can’t do on the floor (Maynard interview; our emphasis).

In other words, to Maynard automation was a form of better norm enforcement. This view is

exactly the reverse as Lessig’s (2000): whereas Lessig warned about the risk of opportunism

posed by automation, Maynard expected dubious manual practices to disappear in the process

of translating norms into code. His argument for automation was not only about efficiency but

a moral one.

By the end of 2005, the discussion about automation had concluded, and Reg-NMS

was in place. In 2006 the head of the NYSE, John Thain, began a long march towards

automation. He started with demutualization in 2006, enriching the Exchange’s existing

members by buying their seats, and shifting control of the Exchange’s board in the process.

Thain continued by diversifying the Exchange’s business away from floor trading, acquiring

the large ECN Archipelago and rebranding it as part of the NYSE, thus achieving an

appearance of higher market share (Concannon interview). In addition, Thain moved away

from trading low-margin US equities by merging with Euronext, a European exchange

conglomerate active both in equities and higher-margin derivatives. The combined effect of

these moves was to reduce the power of the specialists, as well as the relative importance of

the NYSE floor to ten percent of the Exchange’s overall revenue. Having seized control and

diversified the business away from the specialists, Thain proceeded to lead the first round of

automation, culminating in a new trading model tellingly known as HybridMarket.

2008-10 The automation of the NYSE

We returned to the floor of the NYSE in February 2008. Our goal was to understand

how the Exchange had addressed the challenge posed by automation. We started with a

guided visit to the floor with Murray Teitlebaum, the same official that we met in 2003. This

time however the floor looked remarkably emptier: “things have been very, very difficult,”

Murray explained. He added that the Hybrid, NYSE’s response to Reg-NMS, had failed to

live up to its promise. The NYSE had lost the bulk of its market share, dropping from a peak

25


share of 83 percent in 2003 to 27 percent in 2009, and specialist firms had laid off clerks and

specialists to the point that three of the five trading rooms of the NYSE had closed.

---- Table 1 here ----

In effect, Hybrid was attempting a difficulty compromise between automated and

manual trading. The Exchange had disabled the artificial limit that constrained its previous

automated system (called “Direct +”) to trades smaller than 1,000 stocks. It also preserved the

specialists at the post, giving customers a choice between automatic and manual auctions. But

it soon became clear that this was not working as intended. A basic problem was speed:

NYSE’s servers and routers were designed for reliability rather than timeliness (Leibowitz

interview), and processing an order took 360 milliseconds while the competitors were in the

ten-millisecond range (Pastina interview) and in some cases even less (MacKenzie and Pardo-

Guerra 2013).

A more serious problem was the incompatibility between algorithms and specialists:

while algos matched orders in a continuous auction, specialists engaged in a discrete call

auction that made it impossible for them to interact with a continuous electronic order flow

(Wunsch interview). As an ex-specialist explained, “the bid that you think you are matching

has already been hit and it’s offered there [somewhere else]” (O’Donnell interview). As

others put it, “the order walks away from you.” As a result, the participation rates of the

specialists, which measure the degree to which they engaged as principal, fell from 20 percent

to between 1 and 2 percent. One specialist firm even handed back its own license rather than

attempting to sell it, arguing that it was worth nothing. Similarly, floor broker participation

rates (their percentage of volume traded, relative to the total) fell from 10 percent to 3-4

percent. At some point, an Exchange official recounts, “it became clear that while the

operation was a success, the patient was really dying” (Pastina interview).

The extent of the dissatisfaction at the Exchange became clear when we met with

Robert Hardy, the specialist we had followed in 2003. We found him at the post of a different

firm, LaBranche & Co, tall and impeccably dressed as usual. Yet this time he appeared

concerned. “I don’t see the market coming back until financials are doing better,” he told us

26


about stock prices, resorting to the sensemaking routine he knew well. As he spoke we

noticed a peculiar artifact resting on the top of one of his monitors: a small statue of a bull

with a folded cylinder of paper protruding from behind it. The bull was labeled “Hybrid” and

the cylinder read “ECNs.” When we asked about it, the clerk in Hardy’s post gave us an

embarrassed look. “No, it’s ok,” Hardy said to him. And, to us: “that’s what we think is

happening with the market right now. We’re getting screwed by the ECNs.” One of the

specialists went on to describe the problems that other NYSE officials had already

highlighted: a proliferation of ECNs and dark pools, had led to a system that was unstable and

unfair. “Stocks are now very volatile, very thin margins. Before, it used to be everyone on an

equal footing. Now, the people who have the bigger computer and the more money are

winning. It’s a poor system.”

Amidst its limitations, Hybrid had one promising feature. It could shift between

automated and manual trading. This feature, known as Liquidity Replenishment Point (LRP),

shifted the auction from automatic to manual when prices moved beyond a certain volatility

threshold. This feature was aimed to dampen volatility under crises by conducting a call

auction at the specialist post. Hybrid thus restored the specialists’ ability to pace the rhythm

of trading. It also recreated the crowd of floor brokers at the post, allowing them to engage in

sensemaking.

A second attempt at automation: the New Generation Model. A change in the

management of the Exchange in December 2007 created an opening for reforming Hybrid.

Following the departure of John Thain for Merrill Lynch, the Exchange appointed his second

in command, Duncan Niederauer, as chief executive officer. Niederauer started by investing

$500 million on automation, building a state-of-the-art data center in Mahwah, New Jersey,

that would allow high frequency traders to co-locate their servers. But Niederauer also had an

appreciation for the NYSE’s heritage, and made clear his interest in maintaining the floor

while continuing to invest in technology – a strategy described as “all things to all people.”

To reform Hybrid, Niederauer assembled a team of executives that were both familiar

with the NYSE and had enjoyed successful careers outside it. This included ex-specialists

27


such as Michael Rutigliano, ex-customers like Joseph Mecane, and ex-competitors such as

Larry Leibowitz. As Niederauer explained, “the management team that’s here observed [the

NYSE] as an outsider.” Niederauer created two additional jobs, Specialist Liaison and Floor

Broker Liaison, to ensure that the needs of those two collectives were taken into account in

the redesign of the system. Niederauer thus brought in a governance framework for the design

of automation that gave voice to the users, rather than just catering to technologists.

The management team at the Exchange went on to debate how to redesign Hybrid.

There were two key issues in the discussions. First, how to preserve the interaction between

brokers and specialists? Some wanted to maintain the obligation for brokers to conduct trades

by walking to the specialists post, while others did not (Willis interview). A second issue

under discussion was how to regain block trading. Some participants argued for an order type

that would be invisible to participants (O’Donnell interview).

Our key observation from the interviews that recounted these discussions is the

attention that the participants devoted to debate the specialist role. “What does it mean to be a

specialist?” asked Leibowitz philosophically, in conversation with us. “Being a floor broker

or a specialist,” Rutigliano told us, “was a ballet … I get goose bumps thinking about it.” It

was, a floor governor and ex-specialist summed up, “what I did best” (Barry interview).

Indeed, the director of floor operations of a leading floor brokerage firm, Gordon Charlop,

went as far as enrolling into a doctoral program in management and writing a dissertation

about the specialist. He argued that at the NYSE, “the move away from the distinctive floor

trading system to an electronically mediated one shows signs of isomorphic forces at work”

(Charlop, 2009:ii). In short, the management team at the Exchange were not just part of social

structure, but also reflexive observers of it.

The new system that emerged from these debates was labeled New Generation

Model, and launched in November 2008. The new system introduced a clear separation in

time between automatic and manual trading. It also introduced a number of measures aimed

increasing specialist activity during the automated trading. We consider these below.

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The new specialists. The Exchange started by relaxing some restrictions that

specialists confronted, allowing them to use algorithms to interact with the algorithmic order

book. To do so, the Exchange removed the agency function of the specialists: they would no

longer represent customers’ orders, and would only act as principals. In turn, specialists gave

up on their advance look at the order book. These changes pushed the specialist away from

price setting to a more peripheral but tenable role as participant. The Exchange also altered

the economics of the specialists, introducing parity and subsidies. Parity was aimed at

incentivizing specialist participation, and subsidies would ensure the specialist system

survived. Given the fundamental nature of these changes, their name was changed to

“designated market maker” (DMM).

The Exchange also gave the new specialists new tools to interact with the algorithmic

order book. For this it relied on interface design expert Brad Paley. Paley increased the

usability of the algorithms by building a graphic user interface that allowed for the use of

different algos simultaneously. This interface allowed the specialist to alter the size of the

disparity that triggered buy and sell orders. The narrower the disparity, the more “aggressive”

the strategy, like “fishermen who fish with different net sizes” (Paley interview). Paley also

included two extra grey boxes on the specialists’ screen showing the most recent large trades

and the largest holders of the stock, giving the new specialists a form of social cue. In this

way, Paley’s work sought to algorithmically reproduce the opportunities for sensemaking that

specialists had in a manual trading floor. This “steering wheel for algos” would, Paley hoped,

restore the specialists’ ability to compete by increasing the usability of the algos.

But the transition to algorithmic trading at the specialist post was a long and complex

process. We grasped the dimension of the challenge in June of 2009 during a visit to the

specialist post of Bank of America. There, we met and observed the work of the designated

market maker for Goldman Sachs, Peter Giachi. Giachi stood at his post, but instead of

talking to the floor brokers who walked up to him as we saw Robert Hardy do --not a single

broker did during the hour we visited the post-- he focused on six screens in front of him. His

trading strategy reproduced the mean-reversion approach he used in a manual environment,

29


but now the algorithm did the information processing. “Before,” he explained, he would see

“sell sell, and suddenly sell, buy, sell, sell, buy, and go ‘this is it, this is it.’” Now, his

algorithm replicated the approach: when the price of a stock moved more than three dollars

away from the VWAP, it bought. Yet Giachi did not seem willing to delegate his trading to

the algorithm completely. “I’ve got seventeen algos,” he explained. “They’re carrying the

noise back and forth. They have no mind of their own. But what they allow me to do is wait

till I can commit capital.” Giachi, we concluded, was using technology as an aid to his manual

skills. The integration of algorithms and specialists had restored the latter’s ability to interact

with the order book, but it had yet to yield trading strategies that leveraged the distinct

possibilities of the algos.

Two years after our visit to Giachi, Bank of America sold the post where Giachi

worked. The buyer was Getco Llc. The move came as a surprise, given Getco’s standing as a

leading algorithmic market maker: why would the company expand into the trading floor?

The firm, a Getco executive explained to us, was not planning on making changes to the

specialist’s market-making, but rather to focus on a new area -- client services. The firm,

[Is hoping to do] A much better job of servicing the clients of the post, the forty companies that each DMM is a specialist for. These companies need a lot of communicating with the DMM because their investor relations officer often wants to know what’s going on with the stock. More than one half of the time is service. We have three full time people upstairs doing it.

Indeed, he explained, one of the key advantages of the NYSE over Nasdaq in attracting

listings was the existence of a live person on the floor. Getco was thus building on the

sensemaking function of the specialists, but shifting the focus from internal communication

(with floor brokers) to external communication, with listed companies. Another reason for

Getco’s move appeared to be gaining legitimacy from the presence on the floor: as a Getco

spokesman told The Financial Times, “it’s important to demonstrate that we’re in this for the

long haul and align ourselves with an institution that’s been around for a couple hundred

years” (Demos 2012). This second reason is particularly interesting, as it can be seen as an

attempt by algorithmic traders to enter a voluntary regime of norm enforcement by locating

30


themselves on the floor of the NYSE, pointing to a possible role for the NYSE as an

institution that privately enforces norms for high frequency traders.

In addition to new tools, the New Generation Model introduced other changes to the

specialists’ job. These integrated norm enforcement within algorithmic trading. The

specialists’ quoting obligations were retained, although reduced; and the Exchange created a

new version of the new specialist, called Supplemental Liquidity Provider, that did not have

to be on the floor and had similar (though lighter) obligations as the designated market

makers on the floor. The decision to have market makers outside the floor was a logical

implication of automation: in a world of algorithms, actors no longer need to be on the same

space to trade. By allowing traders outside the floor, and especially by subjecting them to

obligations, the Exchange leveraged algorithms for the purpose of norm enforcement.

The NYSE also made an unsuccessful effort to recreate block trading. It developed a

new type of order that would not be visible in the book, the Non-Displayed Reserve Order

(O’Donnell interview), and developed block trading platforms known as MatchPoint, as well

as a feature known as BlocTalk. But these efforts, which can be seen as attempts at

reproducing partial disclosure, were of no avail as trading in large blocks of shares had shifted

to dark pools. Indeed, by 2012 the combined market share of dark pools and internalized

order matches within broker dealers added up to an unprecedented one third of total volume

in US equities trading (See MacKenzie and Pardo-Guerra 2013: 45).

The new floor brokers. As with the specialists, the Exchange re-equipped floor

brokers. Instead of buying and selling from the specialist as they used to, brokers were to do

so from the algorithmic book directly. To that end, the NYSE allowed brokers to transform

their rudimentary handheld terminal (a tablet-like portable computer) to support execution

algorithms. In this way, brokers went from using the handheld for trade annotation to using it

for trade execution, putting them on a similar footing as off-floor brokers.

We observed these changes in action at the broker’s booth of VDM Institutional

Brokerage in August 2009. There, we saw Benedict Willis use his NYSE-designed handheld

(known as eBroker) with a stylus. “By standing here, he said, “I am technically in every one

31


of the crowds.” Instead of walking frantically from one specialist post to another, Willis just

tapped on the screen. These new handhelds thus reproduced some of the sensemaking

possibilities of a trading crowd at the post with a messaging application. In one of his screens,

Benedict had a list of tags with the badges of the other floor brokers who were buyers or

sellers in a given stock. He explained,

You can just tap a button, you can look at a stock and find out who the players were in it and then you can actually tap the line where the broker’s badge is and you’ll get like a messaging window.

Benedict demonstrated this by opening up a screen, writing a question mark and sending it to

a colleague. After a few seconds, the colleague replied with another handwritten note, saying

“just stray. sorry,” meaning that he did not have any specific information, and apologized for

not being able to provide insight.

Nevertheless, the handheld and a brokers’ crowd are not complete substitutes. While

a crowd creates unintended communication trajectories (Hutchins 1995), the handheld

requires brokers to purposefully communicate with each other. Indeed, the lack of crowds at

the specialist post was a cause for concern for Benedict Willis. The management team at the

NYSE concurred: as one official explained, “at the pods it’s a busy beehive, but cross

pollination is not happening,” suggesting that brokers and specialists were not engaging in

sensemaking during routine trading (Pastina interview).

In a related decision, the Exchange relaxed the internal rules about what brokers

could trade on the floor. Traditionally floor brokers were barred from trading with others

outside the floor; by relaxing this restriction the NYSE allowed brokers to buy or sell from

other exchanges. We saw the effects of the new policy at the brokerage booth of Rosenblatt

Securities. During our visit, we were struck by the large number of screens -- covering the

entire wall of the booth. We soon found out why: Rosenblatt traded in the different exchanges

from the floor of the NYSE, and different trading venues required different screens. “If I am

only at one center [exchange] I am missing all this volume, I’ve got a problem,” explained

Gordon Charlop, Director of floor operations. The brokers, we eventually understood, were

re-aggregating the liquidity that was fragmented across exchanges because of Reg-NMS.

32


Market open and close. The New Generation Model also preserved the market open and

close auctions as they were in 2003. These auctions, with prices shouted at the post, offered

partial disclosure of the interests of the actors in the crowd, allowing the specialists to open

the stock at the price they thought was going to reach during the first minutes of trading

rather than at that where the last buy and sell orders crossed. According to an ex-specialist,

this prevented manipulation and led to opening prices that were “more representative” of

supply and demand (O’Donnell interview). Specialists traditionally benefited from these

auctions because they could see who bid at various prices, and could use the information in

matching orders. Preserving the manual open and close auction also gave the new specialists a

critical source of revenue, around 70 percent of their total income, according to Rutigliano. It

also facilitated sensemaking during regular trading hours by preserving a routine of daily

interaction between brokers and specialists, much as soldiers develop preparedness during

peacetime by constantly reproducing battle situations.

New use of space. The NYSE complemented the new tools discussed so far by

building new booths for brokers, replacing their vertical phone stations with horizontal desks

and with stools that allowed them to sit rather than stand. The new booths, known as “market

pods,” also gave brokers room to use four screens rather than one. The goal, according to Lou

Pastina, was to achieve better integration between automation and social interaction on the

floor. “They can trade on their screens most of the day,” he explained, “and then for the open,

close and when something happens, they can simply take their handhelds and go to the post

where the action is happening.” A more cynical view, voiced by a floor participant, was that

the Exchange was putting in the booths as a response to the dwindling numbers on the floor:

the new booths “give the appearance that the place if full.” The Exchange also redesigned the

specialist posts by eliminating obsolete plasma screens previously used for manual auctions,

resulting in a less cluttered post. To these, the Exchange added a Starbucks coffee bar,

highlighting the less hectic nature of work in an automated environment.

Folding

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As we have detailed so far, the introduction of the New Generation Model in 2008

marked a shift from the NYSE’s first attempt to automate, Hybrid. More importantly, the

New Generation Model managed to preserve the role of the specialists and floor brokers.

According to NYSE officials, the new specialists increased their participation from three

percent in 2007 to thirteen percent in 2009, and brokers tripled their participation from two to

six percent during the same time (Rutigliano interview). Given the subsidies handed out to

specialists, it is unclear on the basis of our data whether the New Generation Model is

profitable or sustainable for the Exchange, but we can safely conclude that it kept the

specialists and brokers alive.

How was folding accomplished? We infer the mechanisms behind folding from the

differences and similarities between the Hybrid and the New Generation Model. These two

were similar in three key respects: both had a trading floor, both could shift from algorithmic

to manual trading during crises, and both preserved manual trading during the market open

and close. This suggests that a dual trading mode (manual and algorithmic) was a key part of

the folding that we observed. At the heart of the NYSE’s efforts at folding thus lie two

different and expensive material structures: a trading floor and a data center. And standing

between these two are the Liquidity Replenishment Points, an organizational provision that

shifts trading mode between manual and algorithmic.

In addition to similarities, there were also differences between the two rounds of

automation at the NYSE, and these hint at additional theoretical features of successful

folding. First, the New Generation Model eliminated manual trading from normal trading

hours, suggesting that a strict separation in time is necessary for algorithms to coexist with

humans. Second, the state-of-the-art data center that the Exchange built in Mahwah (the size

of three football stadiums) brought its algorithmic order matching engine up to speed with

rival exchanges. Both of these aspects, separation and the greater speed, pertain to the pace of

trading. For folding to be viable, the speed of the algorithmic mode needs to be in line with

that of other exchanges, and manual and automatic trading must be separated so that their

speeds are not out of line. The other differences between the Hybrid and the New Generation

34


Model point to the need for new tools and a more integrative platform. The NYSE equipped

brokers and specialists to engage in algorithmic trading during normal trading hours,

suggesting that retooling –building a dedicated material basis-- is required for intermediaries

to be active in algorithmic mode. Finally, the Exchange changed its rules to develop the floor

into a more inclusive platform, loosening restrictions, and introducing a new form of off-floor

specialist .

Sociomaterial duplication. Our comparison between the Hybrid and New Generation

Model suggests that the core mechanism behind folding is a reorganization of agency around

two distinct modes, manual and automatic. These are separated in time but not in place:

during crises and the open and the close, the Exchange functions exactly as it did

traditionally, based on the specialist auction at the post; during normal trading hours the

Exchange functions somewhat like other automated exchanges, matching orders

algorithmically. The key difference is that brokers and specialists participate in this market

from the trading floor but, then again, not as they do during manual trading but in a different

role. The manual and the automatic thus do not coexist in time, in line with their different

rhythms. The result is not just material duplication, but what we call --following the

terminology of Orlikowski and Scott 2008-- sociomaterial duplication. By this we mean the

use of seemingly redundant technologies for the purpose of sustaining multiple social

structures. This mechanism is in some sense similar to the dual-engine system used by some

oil tankers: one low-consumption engine for sailing in the open sea; and another one, less

efficient but more versatile, for approaching the harbor and docking.

A second component of this reorganization of agency is the creation of synergies

between the two modes, manual and algorithmic. These centered on providing the

intermediaries on the floor the means to continue working in algorithmic mode. This required

a change in practices: as Leibowitz put it, during the day they “become high frequency

traders,” that is, engage with the algorithmic matching engine rather than among themselves.

To make this possible, the Exchange reduced the specialists’ role from price-setters to

35


premium users, and gave them parity and subsidies. We see these changes as components of

sociomaterial duplication, because they allow the actors to operate in a dual mode.

Separability of the material and the social

The efforts at folding at the NYSE speak to the aforementioned debate over the

separability of the social and the material . Because the NYSE sought to preserve the

specialist system in a different technology, its attempt can be read as a natural experiment that

tests whether social structure exists with independence of the material setting. Specifically,

this test can be seen as follows: were the Exchange able to extend the roles of specialist and

broker to the algorithmic mode, one could conclude that a given social structure can exist in

different material settings, and thus that the social is separate from the material. If on the

other hand the Exchange was unable to extend its structure to a different material setting, one

could conclude that the social and the material cannot be separated.

The NYSE’s natural experiment offers two lessons. First, because the NYSE’s

attempt at folding was based on duplicating its modes of operation, the Exchange cannot be

said to have extended the specialist structure in an automated setting. As noted, the specialists

and brokers were internally described as operating as high-frequency traders, and do not

interact in call auctions at the post during normal opening hours (“cross pollination is not

happening”). We interpret these as a fundamentally different role for the intermediaries. This

suggests that the specialist role was inseparable from manual auctions on the floor, and hints

at the possibility that the social and the material are indeed inseparable.

The natural experiment provides a second lesson. Even if the social and material

appeared to be inseparable, the social structure appeared to have a separate existence from its

material basis. Indeed, one of our more puzzling observations was the extent to which the

management team at the Exchange was focused on the survival of “the specialist”. That these

executives would pay such attention to an abstract role suggests that social structure existed in

what Feldman and Pentland (2003) call the “ostensive” realm, that is, apart from any specific

material setting. Such ostensive (as opposed to material) existence was critically important

because it guided the efforts of the Exchange in designing its automation. Thus in contrast to

36


Latour (2005), who sees the social only as an outcome of material associations and not as an

explanatory cause for economic phenomena, our observations at the Exchange suggest that

the social, as in the abstract role called “the specialist,” was an explanatory cause for social

action, providing a roadmap for the NYSE in its automation. We conclude that social

structure is thus not simply an emerging consequence of action, but an orienting guide for it.

Such ability of social structure to shape action does not play out in a “latent” form or through

the “hidden social forces” criticized by Latour (1986), but through the ability of

organizational actors to be reflexive about their own social world. It is not that social structure

shapes society because some academic sociologist says so, but because of the actors

themselves mobilized structure, and specifically roles such as “the specialist” it to achieve

their own objective, which in this case was the preservation of the intermediary functions

performed by the NYSE. We expand on this last point below.

Effects of folding

Our analysis so far has emphasized the process of automation, but not yet considered

its outcome. Did folding prove effective for the Exchange? This question shifts the focus of

from the roles to the functions played by the intermediaries at the Exchange: were they able to

continue providing the coordination, sensemaking and norm enforcement they provided in

2003? Our analysis suggests the NYSE retained some (but not all) of the social properties of

the original trading floor. In manual trading, the New Generation Model preserved the

sensemaking advantages of partial disclosure during the open and the close, and during crises.

The New Generation Model also retained norm enforcement in the form of the positive

obligations of the new specialists and the secondary liquidity providers. Finally, it sacrificed

the coordinating functions of price discovery and the matching of blocks. In automated

trading, the Exchange preserved norm enforcement, coordinated buyers and sellers through

the matching algorithm as algorithmic exchanges do, and offered no form sensemaking. In

short, the NYSE preserved its original functions in manual mode, and lost a significant part of

them in automated mode (see Table 2).

---- Insert Table 2 ----

37


In commercial terms, folding appears to have had a somewhat positive effect on the

performance of the NYSE: while its share of the US equities market since the New

Generation Model was introduced in 2008 did not return to the NYSE peak of 82 percent in

2003, market share remained constant at around 25 percent. Automation may thus have

contributed to stop the market share decline of the NYSE, but not to reverse it. Similarly,

automation did not contribute to improve the market capitalization of the parent company of

the NYSE, the NYSE Euronext Group, which fell by half in the aftermath of Reg-NMS and

never recovered. But it is difficult to draw conclusions from the evolution of the share price.

The failure to boost stock valuation on the part of the New Generation Hybrid can also be due

to the vastly smaller importance of equities trading in the Group’s overall profits, and is

consistent with recent news that the management of the Exchange agreed to an acquisition by

the Intercontinental Exchange (ICE) (Tabb, 2012).

--- Table 2 here ---

The Flash Crash. The commercial impact of the redesigned NYSE is not the only

way to measure its success, and perhaps not the most relevant one. The redesign was put to

critical test during the Flash Crash of May 6, 2010. The official report on the crash blamed a

Kansas-based fund that set the parameters of its Sell Algorithm too aggressively (SEC-CFTC

2011). The rapid selloff of 75,000 E-Mini contracts prompted what organizational theorists

would describe as a breakdown in sensemaking. Indeed, as high frequency trading funds

absorbed part of the selling volume (with a net long position of 3,300 contracts), their volume

of transactions went up to as much as 140,000 E-Mini contracts. This is usual for high

frequency funds as they routinely issue numerous order cancellations in the process of

trading. Such high volume of transactions had an unexpected effect on the Sell Algorithm. As

the official report explains, “the Sell Algorithm … responded to the increased volume by

increasing the rate at which it was feeding the orders into the market, even though orders that

it already sent to the market were arguably not yet fully absorbed” (SEC/CFTC 2011: 3). In

other words, the mistaken response of the Sell Algorithm to the trades of the other algorithmic

traders flooded the market, prompting a sharp decrease in price. At the root of the problem

38


was the use of a decision rule that proved fatal on the part of the Sell Algorithmic: it used

trading volume as a proxy for liquidity, whereas in fact, as the official report argues, “in times

of significant volatility, high trading volume is not necessarily a reliable indicator of market

liquidity” (SEC/CFTC 2011:3). The fatal interaction between decision rules in various

algorithmic participants can be seen as an algorithmic breakdown in sensemaking.

The Flash Crash offers an opportunity to understand the effect of automation on the

American equities market. As noted above, a primary goal of the automation was

disintermediation. This has been largely achieved, with more than three quarters of traded

volume now handled by algorithmic order matching at Nasdaq, Bats or dark pools and

internalizers. But because one of the stock exchanges, the NYSE, retained its intermediary

structure, the impact of the Flash Crash on the various exchanges can shed light on the merits

of an intermediary structure. Simply put: which exchanges performed better during the Flash

Crash?

In that regard, the performance of the NYSE during the crisis appears to be vastly

superior to that of algorithmic exchanges. We focus on one key figure: order cancellations.

Such was the dislocation of prices during the Flash Crash that the SEC decided to cancel all

trades beyond a 20 percent band of the prevailing price twenty minutes before the crash. This

led to massive cancellations in all exchanges, except for the NYSE. As stated by Jane

Kissane, legal counsel of the NYSE, in a letter to the SEC (Kissane 2010: 4):

In the aftermath of May 6, other exchanges … engaged in a much criticized process of cancelling approximately 15,000 trades as ‘clearly erroneous.’ In contrast, not a single NYSE trade (excluding NYSE Arca, its electronic version) was required to be cancelled.

This lack of cancellations was not due to disengagement: the NYSE’s market share between

2:30 pm and 3 pm was 26 percent, as compared to 21 percent on prior days (Kissane 2010: 5).

As a result of the NYSE’s shift to manual model, the counsel adds, prices on the NYSE were

far less volatile than prices on electronic exchanges. One interesting case is Arca, the

electronic version of the NYSE. This experienced similar trade cancelations as other

algorithmic exchanges, which is further confirmation of the superiority of the shift to manual

39


trading. It is not that the NYSE did better because of its location or brand (Arca had all these),

but because of its reliance on the intermediaries on the floor.

We elaborate on this point. In explaining the superior performance of the NYSE,

officials point to the Exchange’s ability to switch from automated to manual auctions with the

Liquidity Replenishment Points, which were highly active during the crash. While on a

normal trading day there around are 50 LRPs activated, on the day of the Flash Crash there

were more than 70,000 (Pellechia interview). In a much-discussed article in Tabb Forum,

microstructure specialist Stephen Wunsch (2010) concurs:

The partially manual LRPs allowed the Big Board to apply some measure of old-fashioned reasonability tests to price formation. As a consequence, no NYSE trades printed at zero or anywhere close to it. Unlike all the other stock exchanges, the NYSE did not have to break any trades.

As Wunsch notes, the reason for the NYSE’s superior performance was its ability to sustain

sensemaking. Humans on the floor could draw on social cues and their prior experience to

establish that the sudden drop in the Dow Jones was purely due to factors internal to the

market, with no economic news that could justify it. As soon as the crash hit stocks like

Accenture and Procter and Gamble, floor brokers were running to the post of the designated

market maker and conferring among themselves. “What the market makers had to remember

was,” an Exchange official explains, “what’s happening everywhere else is not real” (Pastina

interview; our emphasis).

A related reason for the greater stability of the NYSE was norm enforcement. As

Wunsch (2010) emphasizes, once the problems started, high-frequency traders withdrew

liquidity from the system:

Their high frequency market makers, sensing trouble, disappeared. With little else in their books, the market orders pushed prices to where the stub quotes were, producing ridiculous trade prices. With no floor governors or other manual processes to spot the difference between real trades and market structure failure, the electronic NMS printed them all.

This argument was echoed by NYSE officials. For instance, the low price in one of the more

erroneously traded stocks, Procter and Gamble, was $39 in other exchanges but $56 at the

NYSE. The reason for the difference, according to an Exchange official, was that unlike

40


market makers at database exchanges, NYSE specialists had a positive obligation to commit

capital (Mecane interview). Back in 2008, he explained,

One of the flaws of electronic markets is that in general people don’t have obligations with respect to the market so they come and go as they please. So if they get nervous about a situation, a macroeconomic event, a political event, they go away.

In other words, the absence of obligations can create bouts of illiquidity. Indeed, in the wake

of the crash, the SEC sought to emulate the advantages of the NYSE’s Liquidity Replenishing

Points by mandating individual-stock circuit breakers across exchanges in the US (Metha

2011). Unfortunately, those do not emulate the judgment provided by the specialists, and

were often unnecessarily triggered (Wunsch 2011).

In sum, the NYSE performed far better than the algorithmic exchanges during the

Flash Crash. It increased its overall participation, its designated market makers honored their

obligations, its prices were less volatile, and it did not cancel any trades. Such superior

performance points to the weakness of the purely automated model, and to the limits of the

information-processing conception of securities trading held by Black (1971) and others.

Instead, it points to the strengths of the market intermediary and the sociological notion that

markets beset by opportunism and uncertainty need intermediaries. The specialists and floor

brokers at the NYSE provided coordination, sensemaking and norm enforcement that helped

market actors confront radical uncertainty and limit the individual incentive to pull out under

crisis.

DISCUSSION AND CONCLUSION

Our study contributes to economic sociology by engaging with the scholarly debate

on the effects of automation in markets. Existing studies have portrayed intermediation as a

automation as a dilution of social relations. Our study of the NYSE proposes instead the

notion of folding, that is, a design of automation that preserves the original social structure of

a market or organization. Folding, our study suggests, can be accomplished by incorporating

new technologies while preserving the original ones and having a mechanism that shift

between the two. The duplicative approach followed by the NYSE also points to the

difficulties of separating a given structure of social relations from the original material basis

41


that sustained it. At the same time, the prominence of social structure in the discussions on

automation at the Exchange suggests that structure shapes action, rather than being a

consequence of it.

Our study also offers public policy implications. The redesign of the NYSE was put

to test during the Flash Crash of May 6, 2010. The solidity of the Exchange’s performance

under crisis suggests that the SEC’s vision of markets as information processing devices may

lack mechanisms to deal with uncertainty and opportunism. By contrast, our study points to

the value of intermediary structures and offers a guideline for the introduction of algorithms

in markets that preserve this structures. Future attempts at automating a securities market, as

in the ongoing reform of the American equities market or in Europe’s MiFID, should take

into account the functions performed by the original intermediaries.

42


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Table 1. Market Share of the NYSE. Note: Data is for market share for all us equities turnover, Tape A. Source: Thompson Reuters

Year Market share2000 80.00%2001 80.61%2002 78.14%2003 77.92%2004 76.07%2005 76.03%2006 71.00%2007 44.27%2008 29.41%2009 27.16%2010 27.78%2011 26.86%2012 23.80%

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Table 2: Functions of the NYSE’s New Generation Model.

Automated trading (9.30 am to 4.00 pm)

Manual trading (market open and close, and LRP activation)

Coordination

Pacing No Yes

Buffering Yes Yes

Matching No Yes

Sensemaking No Yes

Norm enforcement Yes Yes

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APPENDIX A. Glossary of financial terms.

Parity: the ability of a market maker such as the NYSE specialist to participate in an order at the same price (on par) as the customer.

Look-back option: a financial option that allows investors to "look back" at the underlying prices occurring over the life of the option and then exercise based on the underlying asset's optimal value.

Stub quote: An offer to buy or sell a stock at a price so far away from the prevailing market that it is not intended to be executed, such as an order to buy at a penny or an offer to sell at $100,000.

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