The Wall Street Journal (WSJ) recently published a headline article titled “Hedge Funds’ Pack Behaviors Magnifies Market Swings”. While it is not unusual to see the WSJ write on hedge funds and market swings, this article is unusual because it emphasizes the social ties linking investors. It reflects a sea change in the way that the public and the media view financial markets – and an opportunity for the social studies of finance (SSF) to reach a broader audience.

For the past decade, the quant metaphor has dominated public perceptions of financial markets. Institutional investors – particularly hedge funds – were seen as “quants” that used sophisticated computer models to analyze market trends. This idea went hand-in-hand with the view that markets were efficient – fueled by reliable, public data, proceed through sophisticated, rational algorithms, and powered by intelligent computer systems instead of mistake-prone humans.

Of course, the recent financial crisis has dislodged such beliefs. Instead of mathematical geniuses finding hidden patterns in public data, quants were revealed as Wizards of Oz – mere human beings capable of making mistakes. Their tools – computerized systems – went from being the enforcers of an efficient market to a worrying source of market instability. As stories about flash trading and inexplicable volatility popped up, the public even began to ask whether the quants were trying to defraud the public.

If institutional investors are mere humans instead of quantitative demigods, shouldn’t they also act like humans? And – shouldn’t their social natures affect the way they make investment decisions? The mainstream media is finally confronting such questions – which SSF has long raised. This particular WSJ article parallels a widely-circulated working paper by Jan Simon, Yuval Millo and their collaborators, as well as my own work under review at ASR.

The world is finally catching up with SSF. Will we finally be heard? It is our responsibility to reach out to the public and the media.

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 Freerisk.org.

The partners’ solution: a volunteer army of finance geeks. Their project, Freerisk.org, 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.

Bernie Madoff was sent to jail yesterday after he pleaded guilty to operating the world’s largest Ponzi scheme. His investment advisory firm had provided unusually and consistently high returns on clients’ investments, which turned out to be bogus, meaning that he wasn’t in fact investing the money, but instead paid out the money he got instantly to others who had trusted their money to him earlier.

Pension schemes are sustainable as long as there is more money flowing in than flowing out.* The technique is to take the money from the new contributors and give it to the old ones.

Madoff is in jail. European governments are on trial.

1.    Spot the difference.
2.    Is such a scheme necessarily wrong? Under what moral and calculative circumstances is it right?
3.    Compare with the current bank meltdown. Under what circumstances is a bank a Ponzi scheme? These schemes don’t invest the money into anything, instead they churn it right back out, but not to the same people who gave it. There is nothing on the asset side, only liabilities. Banks also take people’s money and give it to other people to use)? Banks that are going bankrupt today did invest, but their assets turned out to be worth next to nothing, so in the end they couldn’t satisfy their creditors. Is there a lesson here for financial intermediation and the length of financial circuits?

*Pay-as-you-go pension systems work by collecting social security contributions from active employees and use it to pay out pensions that are currently due.

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 http://papers.ssrn.com/sol3/papers.cfm?abstract_id=1285054).

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).

 

The British Bankers’ association’s London Interbank Offered Rate (LIBOR), the rate at which banks loan money to each other, is a good indication of how risky is the world is seen to leading banks. In the case of the US dollar rate, there sixteen banks on the panel that determines the LIBOR (see here for a great description of how LIBOR is determined

The LIBOR is the beating heart of the interbank system, and reacts instantly to new information. However, it also shows how risk perceptions, and following these, a potential recession, come about.

The LIBOR rates for the first 29 days of September show this vividly. The line marked O/N (you can disregard the S/N as the graph is for USD) is the overnight rate at which banks are ready to loan money to each other – the shortest period of loan. The jump on 16th of September to the 18th indicates the flight to look at the jittery. The longer periods follow suit (1 week, 2 week, etc), as can be seen, but more moderately. The jump is dramatic, of course, but more ominous is the longer-term change that the graph reveals. First, LIBOR rates have moved up from about 2.5% to almost 4%. This indicates the higher degree of risk assigned to loans. This on its own is important, but even more telling is the spread of rates across the different periods. While on 1st of September, the range between the lowest and the highest rate was 0.8%, (not taking into account the very volatile overnight rate), the range on 29th of September is only 0.09%! This shows that not only that banks see their environment as riskier than before, but they also distinguish less between more and less risky loans. In fact, they tend to see all loans, regardless of the period for which they were taken, as risky. Such, diminished distinction is a sure sign of flight to liquidity – institutional risk avoidance, but it is also a reflection, if it continues, of a slowdown in macroeconomic activity. If all loans are seen as high risk, less loans are going to be granted.