Here is a fascinating NPR interview with Thomas Peterffy, the Hungarian who invented not one but two things crucial to financial markets today: one of the first computer programs to price options, and high-speed trading.


Today one of the richest in America, Thomas Peterffy recounts his youth in Communist Hungary where as a schoolboy he sold his classmates a sought-after Western good: chewing gum. Let’s disregard for a moment Peterffy’s recent political activities and rewind almost half a century.


Peterffy was a trader on Wall Street who came up with an option pricing program in the 1970s. The Hungarian-born computer programmer tells the story of how he figured out the non-random movement of options prices, programmed it, but could not possibly bring his computer on the trading floor at the time, so he printed tables from his computer with different option prices and brought the papers in a big binder into the trading pit. But the manager of the exchange did not allow the binder, either, so Peterffy ended up folding the papers and they were sticking out of his pockets in all directions. Similar practices were taking place at around this time in Chicago, as MacKenzie and Millo (2003) have documented. Trading by math was not popular, and his peers duly made fun of him: an immigrant guy with a “weird accent”, as Peterffy says. Sure enough, we know from Peter Levin, Melissa Fisher and many other sociologists’ and anthropologists’ research that trading face-to-face was  full of white machismo. But Peterffy’s persistence meant the start of automated trading and according to many, the development of NASDAQ as we know it.


The second unusual thing Peterffy did in the 1980s (!) was connect his computer directly to the stock exchange cables, directly receiving prices and executing algorithms at high speed. Peterffy describes in the NPR interview how he cut the wires coming from the exchange and plugged them straight into his computer, which then could execute the algorithms without input from a human. And so high-speed trading was born.


My intention here is not to glorify my fellow countryman, by any means, but to add two sociological notes:


1. On options pricing automation: although the story is similar, if not identical, to what is described by Donald MacKenzie and Yuval Millo (2003) in their paper on the creation of the Chicago Board Options Exchange, there seems to be a difference. The economists are missing from the picture. The Chicago economists who were involved in distributing the Black-Scholes formula to traders were a crucial part of the process by which trading on the CBOE became closer to the predictions of the theoretical option-pricing model. But in the case of Peterffy and the New York Stock Exchange, the engineering innovation did not seem to be built around the theoretical model. I am not sure he used Black-Scholes, even if he came up with his predictive models at the same time.


What does this seemingly pragmatic, inductive development of algorithm mean for the rise of automated trading? Moreover, how does this story relate to what happened in Chicago at the CBOE around this time, where economics turned out to be performative, where the Black-Scholes formula was what changed the market’s performance (MacKenzie and Millo)?


2. On high-frequency trading: picking up on conversations we had at the Open University (CRESC) – Leicester workshop last week, Peterffy was among the first who recognized something important about the stock exchanges. Physical information flow, ie the actual cable, is a useful way to think about presence “in” the market. While everyone was trading face-to-face, and learning about prices via the centralized and distributed stock ticker (another invention in and of itself), Peterffy’s re-cabling, if controversial, put his algorithms at an advantage to learn about prices and issue trades. This also became a fight about the small print in the contractual relationship between the Exchange and the trading party, but Peterffy’s inventions prevailed.


So much for a trailer to this automation thriller. We can read the full story of Peterffy in Automate This: How Algorithms Came to Rule Our World, a book by Christopher Steiner (2012), who argues that Peterffy’s 1960s programming introduced “The Algorithm That Changed Wall Street”. Now obviously, innovations like this are not one man’s single-handed achievement. But a part of the innovation story has been overlooked, and it has to do with familiarity and “fitting in”. Hence my favorite part of the interview, where Peterffy talks about the big binder he was shuffling into the trading pit (recounted with an unmistakable Hungarian accent):


“They asked ‘What is this?’ I said, these are my numbers which will help me trade, hopefully. They looked at me strange, they didn’t understand my accent. I did not feel very welcome.”


The fact that what became a crucial innovation on Wall Street came partly from an immigrant with a heavy accent, is a case in point for those chronicling the gender, racial and ethnic exclusions and inclusions that have taken place on Wall Street (for example, Melissa Fisher, Karen Ho, Michael Lewis).

Still with the on-going Goldman Sachs story: yesterday, during one of the hearings of the American Senate Governmental Affairs subcommittee we had one of these rare chances where worldviews collide ‘on air’. In yesterday’s hearing, Senator Carl Levin was questioning former Goldman Sachs Mortgages Department head Daniel Sparks about matters related to selling of structured mortgage-based financial products known as Timberwolf, during 2007. The full transcript is not available (you can see the video here), but a few lines can give us a gist of the dialogue that took place. When Levin asks Sparks why Goldman Sachs hid from the customers their opinion of the value of Timberwolf (a product that an internal GS memo described as a ‘shitty deal’), Sparks answers that ‘there are prices in the market that people want to invest in things’. On another occasion exchange, when asked what volume of the Timberwolf contract was sold, Sparks answered: ‘I don’t know, but the price would have reflected levels that they [buyers] would have wanted to invest at that time’.

This reveals the incompatibility in its naked form. While Levin focused on the discrepancy between the opinions among Goldman Sachs’ employees about the value of the product and between the prices paid for these financial contracts, Sparks placed ‘the market’ as the final arbiter about matters of value. That is, according to this order of worth it does not matter what one thinks or knows about the value of assets, it only matters what price is agreed on in the market. Both Levin and Sparks agree that not all information was available to all market actors. However, while this is a matter for moral concern according to Levin’s order of worth, it is merely a temporary inefficiency according to Sparks’ view.

Moreover, the fact that this dialogue took place in a highly-visible political arena, a televised Congressional hearing, entrenches the ‘ideal type’ roles that Levin and Sparks play. Sparks, no doubt at the advice of his lawyers, played the role of the reflexive Homo economicus, claiming, in effect, that markets are the only device of distributional justice to which he should refer. Levin, in contrast, played the role of the tribune of the people, calling for inter-personal norms and practices of decency. These two ideal type worldviews, as Boltanski and Thevenot show, cannot be reconciled. What we call ‘the economy’, then, is oftentimes the chronology of the struggle between these orders of worth

I have just received from COST US, a Google group dedicated to corporate sustainability, links to articles about technologies that may reshape how investors and consumers politically engage with companies.

The first one, from the corporate blog of Hitachi, discusses the happy marriage between the Global Reporting Initiative and XBRL language. The GRI is a non-profit that advocates a system for environmental and social reporting, and XBRL is a new format for electronic reporting. This natural union could be one of those happy combinations of content and platform, like mp3s and the ipod.

It’s clear that by providing preparers and users of data with the means to integrate financial and so-called nonfinancial data (i.e., that which discloses a company’s environmental and social performance), XBRL offers exciting possibilities. The potential for XBRL to provide the users of corporate sustainability performance data with the leverage to push and pull information that meets their requirements is certainly there. That was the thinking behind the first version of an XBRL taxonomy for GRI’s sustainability reporting guidelines, released in 2006.

The second one, a Wired magazine article, introduces the efforts of tech-savy programmers to appropriate XBRL for their own activism. See

The partners’ solution: a volunteer army of finance geeks. Their project,, provides a platform for investors, academics, and armchair analysts to rate companies by crowdsourcing. The site amasses data from SEC filings (in XBRL format) to which anyone may add unstructured info (like footnotes) often buried in financial documents. Users can then run those numbers through standard algorithms, such as the Altman Z-Score analysis and the Piotroski method, and publish the results on the site. But here’s the really geeky part: The project’s open API lets users design their own risk-crunching models. The founders hope that these new tools will not only assess the health of a company but also identify the market conditions that could mean trouble for it (like the housing crisis that doomed AIG).

These are exciting developments for sociologists of finance. As Callon has argued, it is the tools that market actors use to calculate that end up shaping prices. There are politics in markets, but they are buried under the device. Following the controversy as it develops during the construction of the tools is the key way to unearth, understand and participate in it. This is of course, a favorite topic of this blog, of several books and of an upcoming workshop, “Politics of Markets.”

One open question, as Gilbert admits, is whether the “open source” approach and tool building will take up.

So, how many companies are tagging their sustainability disclosures in this way? The answer is: surprisingly few. Why is this? Perhaps companies are unaware of the ease with which it can be done. As previous contributors to this blog have noted, XBRL is not that hard an idea to get your head round, and implementing the technology involves very little in terms of investments in time or cash.

An alternative model is Bloomberg’s efforts at introducing environmental, governance and social metrics on their terminals (a worthy topic for another post).

In an op-ed on today’s New York Times, Sandy Lewis and William Cohan give voice to an argument that has been roaming through in specialized financial websites and blogs. Organized financial exchanges such as the New York Stock Exchange, the authors argue, are a fundamental part of any economic recovery.

Why isn’t the Obama administration working night and day to give the public a vastly increased amount of detailed information about what happens in financial markets? Ever since traders started disappearing from the floor of the New York Stock Exchange in the last decade of the 20th century, there has been less and less transparency about the price and volume of trades. The New York Stock Exchange really exists in name only, as computers execute a very large percentage of all trades, far away from any exchange.

As a result, there is little flow of information, and small investors are paying the price. The beneficiaries, no surprise, are the remains of the old Wall Street broker-dealers — now bank-holding companies like Goldman Sachs and Morgan Stanley — that can see in advance what their clients are interested in buying, and might trade the same stocks for their own accounts. Incredibly, despite the events of last fall, nearly every one of Wall Street’s proprietary trading desks can still take huge risks and then, if they get into trouble, head to the Federal Reserve for short-term rescue financing.

As it turns out, the Securities and Exchange Commission has been thinking long and hard about these issues. The paradox, however, is that we got to this situation thanks to a regulatory reform, “Reg-NMS,” promoted from the SEC itself. By requiring NYSE specialists to respond within a second to the orders of brokers, live trading on the floor of the NYSE was fundamentally challenged. Ironically, the objective at the time was to promote transparency.

The $75 billion dollar Homeowner Affordability and Stability Plan announced by Obama today promises to relieve between 4-5 million responsible households who are facing foreclosure.  Included in the plan are provisions to work with mortgages that are not yet in default.  For some well framed examples of the types of calculations for refinancing the Administration expects the plan to enable see here.  It is noteworthy that the plan does not dispute the price of the house nor does it compensate owners who have paid down value that has now been lost; it simply permits the readjustment of loan amount, where ratio rules might have impeded modifications, to the current value of the property. In other words, it provides access to refinancing.

A key word in the plan is responsible which is repeated again and again in the Administration’s Fact Sheet.  There is a strong populist emphasis on demonstrating that unscrupulous speculators will not be rescued or rewarded.  We won’t know how eligable homeowners will be defined until guidelines are announced.  This is expected to occur on Wed. March 4th. In watch points, look for the use of FICO scores, in the the determination of individual responsibility…

What Obama has called the most sweeping economic package in US history (see video), was signed into law just a few hours ago.  The Act will support a reduction in taxes, investment in green technology, and ostensibly save some 3 million jobs in what have been billed as critical areas – broadband, roads, levies, mass transit, high-speed rail, education, health care modernization, smart meters in homes, increased research funding…  All of these are areas of investment designed to create productivity, and save working families and government institutions money.

What seems to be missing though is a plan for financial recovery.  Ah yes, finance is being handled by Geithner in a separate Bank Bailout (see video here). It may be true, as Obama said in the inaugural address, that economic strength is derived from ‘the makers and doers of things’. What is perhaps puzzling is that financial agents are not seen as productive – as active doers and makers of things and value – and not just as supportive of economic activity through the flow of credit.

Geithner has suggested that “the financial system is working against economic recovery”.  What if the productivity of the financial system is at the heart of economic recover?  Unfortunately, the systems of production and finance continue to remain separate objects of governance in the eyes of the state…

For the plan’s transparency, visit You can read the full 1073 page bill here. And light of Daniel’s last post on tombstones (toys that mark deals on Wall Street) check out the ‘save the date’ magnet add on the bottom of this ABC news blog.

The credit crisis has imposed on Americans a crash course on the risks of financial models. If derivatives, as Warren Buffet famously put it, are “financial weapons of mass destruction,” models are now seen as the nuclear physics that gave rise to the bomb — powerful, incomprehensible and potentially lethal. Given their dangers, what should Wall Street do with its models?

At one extreme, skeptics have attacked models for their unrealism, lack of transparency, and limited accountability. Models, they charge, are black boxes that even expert users fail to understand. Models become dangerously inaccurate when the world changes. And whenever a bad model fails, it is all too easy for traders to conjure up the “perfect storm” excuse. Wall Street, the skeptics conclude, needs to curtail its addiction to models.

At the other extreme, academics in finance and Wall Street practitioners dismiss the backlash as barking up the wrong tree. Models certainly produce the wrong results when fed the wrong assumptions. But the real culprit in this case is not the model, but the over optimistic trader in his greedy quest for the bonus. Paraphrasing the National Rifle Association (“guns don’t kill people, people kill people”), defenders of models place the blame with bad incentives: “models don’t kill banks,” we hear them saying; “bankers kill banks.” To the proponents of modeling, then, the crisis underscores the need for yet more calculations. That is, for bigger and better models.

Does Wall Street need more models or less models? We see this as a false choice. The debate, in our view, needs to shift from the models themselves to the organization of modeling. We have identified a set of organizational procedures, which we call “reflexive modeling,” that lead to superior financial models.

Consider, first, what a financial model ultimately is. Whether as an equation, an algorithm or a fancy Excel spreadsheet, a financial model is no more than a perspective, a point of view about the value of a security. Models are powerful: they reveal profit opportunities that are invisible to mom-and-pop investors. But there’s a catch: they do not always work. Because stock prices are the outcome of human decisions, financial models do not actually work like the iron law of Newtonian gravity.

Models, then, pose a paradox. They hold the key to extraordinary profits, but can inflict destructive losses on a bank. Because a model entails a complex perspective on issues that are typically fuzzy and ambiguous, they can lock traders into a mistaken view of the world, leading to billionaire losses. Can banks reap the benefits of models while avoiding their accompanying dangers?

Our research suggests how. We conducted a sociological study of a derivatives trading room at a large bank on Wall Street. The bank, which remained anonymous in our study, reaped extraordinary profits from its models, but emerged unscathed from the credit crisis. For three years, we were the proverbial fly on the wall, observing Wall Street traders with the same ethnographic techniques that anthropologists used to understand tribesmen in the South Pacific (The study can be downloaded at

The key to outstanding trades, we found, lies outside the models. Instead, it is a matter of culture, organizational design and leadership.

The bank that we observed introduced reflexivity in every aspect of its organization. From junior traders to their supervisors, everyone at the bank was ready to question their own assumptions, listen for dissonant cues, and respect diverse opinions.

How? As many have already suggested, individuals certainly matter. The bank hired people with a healthy dose of humility and an appreciation for the limits of their smarts. This often meant older traders rather than younger hotshots.

But the key to the bank’s reflexiveness did not just lie in individuals. By reflexiveness we don’t mean super-intelligent traders engaged in some heroic mental feat – splitting and twisting their minds back on themselves like some intellectual variant of a contortionist. Reflexivity is a property of organizations.

The architecture of the bank, for instance, was crucial. The open-plan trading room grouped different trading strategies in the same shared space. Each desk focused on a single model, developing a specialized expertise in certain aspect of the stocks.

To see why this was useful, think of a stock as a round pie. Investors on Main Street often eat the pie whole, with predictably dire consequences. The professionals that we saw, by contrast, sliced stocks into different properties. Each desk was in charge of a different property, and the different desks then shared their insights with each other. This could happen in a one-minute chat between senior traders across desks, or in an overheard conversation from the desk nearby. This communication allowed traders to understand those aspects of the stock that lay outside their own models — the unexpected “black swans” that can derail a trade.

Sharing, of course, is easier said than done. The bank made it possible with a culture that prized collaboration. For instance, it used objective bonuses rather than subjective ones to ensure that envy did not poison teamwork. It moved teams around the room to build the automatic trust that physical proximity engenders. It promoted from within, avoiding sharp layoffs during downturns.

Most importantly, the leadership of the trading room had the courage to punish uncooperative behavior. Bill, the manger of the room, made it abundantly clear that he would not tolerate the view, prominent among some, that if you’re great at Excel, “it’s OK to be an asshole.” And he conveyed the message with decisive clarity by firing anti-social traders on the spot — including some top producers.

In other words, the culture at the bank was nothing like the consecration of greed that outsiders attribute to Wall Street. We refer to it as “organized dissonance.”

The bank went so far as to use its own models to be reflective upon modeling. The traders translated stock prices into the model estimates developed by their competitors. This information often planted healthy doubts on the traders’ own estimates, sending them back to the drawing board when necessary. Interestingly, this form of “reverse engineering” was accomplished by using the traders’ own models in reverse, much as one can flip a telescope to make something close-up look like it is far away.

Our study suggests that a lack of reflexivity –that is, the lack of doubt on the part of banks– may be behind the current credit crisis. We are reminded of infantry officers who instructed their drummers to disrupt cadence while crossing bridges. The disruption prevents the uniformity of marching feet from producing resonance that might bring down the bridge. As we see it, the troubles of contemporary banks may well be a consequence of resonant structures that banished doubt, thereby engendering disaster.

This blog post was coauthored with David Stark. David Stark is chair of the Department of Sociology at Columbia and is the author of The Sense of Dissonance (Princeton University Press, 2009).