This article in the NYTimes discusses a site called KaChing [here].  It’s an online service that combines investment management information with social networking properties .  Users manipulate virtual portfolios as a means of gaining insights into and advice about how markets work. Today, the site will add a new feature that allows users to peg and copy expert trades.

Customers can set up brokerage accounts that automatically mirror the trades of a money manager, some of them professionals. [...] Each time the investors make a trade, KaChing will automatically make the same trades for the customer.

KaChing’s founders claim that allowing expert trades to become this transparent is a unique form of information sharing.

“The idea of an asset manager showing all his research, his holdings — it’s unheard-of,” said Mr. Carroll, now 27 and the vice president for business development at KaChing. “In the financial industry, the idea is that information is currency; they protect it with their lives.”

This raises questions about what exactly it means to share information:  Isn’t knowing a trade – that is, the moves an investor makes – different from knowing the reasons for the trade?  And if there are, indeed, different modalities of information sharing, what would be the differences in the types of networks/economic agents these would produce? I find myself searching my bookshelf for Gabriel Tarde’s The Laws of Imitation (Les lois de l’imitation)…

The subprime financial crisis has recently shown the limits of an abstract and disembodied view of financial markets and their so-called “efficiency”. A new interdisciplinary field of research ­ often known as “Social Studies of Finance” ­ has been purposefully tackling these limitations, and has developed with a view to “reembedding” financial practices into the social world. This collective dynamic of interdisciplinary research  (gathering areas such as sociology, economics, history, anthropology, political science, management studies, geography and so forth), is grounded on a stiff conviction: the need to study financial activities as forms of social life. Showing how financial reality is embedded in social networks, culture, technology, scientific knowledge and institutional contexts can renew our understanding of finance.

While in France research in the Social Studies of Finance has been particularly active and structured through the activities of an association holding a regular seminar (the Social Studies of Finance Association or “Association d’études sociales de la finance”), the label “Social Studies of Finance” has been evolving, on an international level, with a neat inclination towards topics such as the scientific and technical embeddedness of finance, especially through the “performativity program”. However along with this thriving program, numerous studies in social networks analysis have shed light on various aspects of the social embeddedness of finance. Similarly, contributions in cultural geography or in globalization studies have examined at length the social aspects of financialization. Anthropological perspectives on financial markets, meanwhile, seek to scrutinize the moral and cultural frameworks underlying financial transactions. New institutionalism, through its various versions, has emphasized the legal and political organization that underpins financial markets and thus thoroughly contributing to the “reembedding” of finance within the social world. Heterodox perspectives in economics have also entered into a fruitful dialogue with more sociologically-inclined approaches.

The purpose of this conference, a Parisian initiative of the Social Studies of Finance Association, is to provide a venue for a productive dialogue between different perspectives to be found in the Social Studies of Finance, to think about their respective contributions, their commonalities, their differences and the opportunities of hybridization they may bring to light, stressing their relevance in the current historical context.

Wanna know why we all started talking about high frequence trading? The answer is [here] in this article on the blog spot Zero Hedge,  in this week’s New York Magazine.

“There comes a time when a person must be willing sacrifice in order to defend what’s right.  And if I’m successful, this will be the proverbial first shot fired in an American Debtor’s Revolution…”

Ann Miche posted a video message last week to officially inform Ken Lay that she was staging a Debtor’s Revolt - she was refusing to continue paying off her BofA credit card.  While drawing attention to an unfair interest rate hike to 30% APR, she outlined her spotless 14 year history with the company and defended her ability to live within her budget despite being laid off.  For Miche, the interest rate hike by the bank was an unjustified attempt to recoup losses on the backs of the middle class.  “Had you left well enough alone, I would have continued to make my payments in good faith,” announced Miche, “but no, you had to bend me over for no other reason than papering over your mega-screw up.” The call to ‘civil disobedience’ against ‘evil thieving bastard’ was extended to other debtors.  The video went viral.  Soon Miche was appearing on Fox and Friends, and a representative from Bof A was calling to renegotiate her rate.

The most interesting part of Miche’s argument is as follows:  “You can send all the collections agencies after me that you want. You can call me 50 times a day if you want. I don’t own any real estate. I don’t own any assets. I don’t even have a permanent job right now. And even if I did, you ‘d have to get a court order to garnish my wages.  And considering how many people are defaulting on your credit card accounts right now, the civil courts are going to be backed up for years.  YEARS! You can ruin my credit. But the banks are loaning money anyway. So the way I figure it, Mr. Lay, I’ve got nothing to lose. So stick that in your bailout plan and smoke it.”

Miche’s statement draws attention to the delicate, relational nature of credit.  Credit is a social contract that must be configured and re-configured to keep both parties actively involved.  It is a loss of elasticity in this relationship, in favor of rigid statistical forecast and prediction, that belies the current credit crisis.  The youtube address which invokes the return phone call by the BofA representative re-infuses Miche’s relationship with BofA with the responsive flexibility that is essential to conserving the credit contract.

The sacrifice involved in revolting, which Miche notes to her fellow debtors, is a good indication of the rigid nail that has been holding the credit contract together in recent years: “You will have to search your own sole to know if you are willing to take a stand and be willing to sacrifice your credit score to stop this outright financial rape.” As she points out, once consumers no longer feel compelled to hammer that nail, the current contract will begin to fall apart.

Rob Wosnitzer conceived of the argument in this post and wrote it together with Martha Poon. It responds to the discussion of how real time information could contribute to MBS evaluation at the end of the previous post.

A.    MBSs have always been structured with an awareness of ‘real time’ shifts in the status of the individual loans/mortgages. Any loan pool – whether these are actual pools of loans or the derivative CMO/CDOs – is built around a set of assumptions about its likely prepayment rate, or even re-payment rate. A 30 year mortgage is scheduled to have a 30 year life.  However, homeowners can disrupt this schedule by paying more in principal at opportunistic moments, as well as by deploying refinancing strategies in various interest rate environments.

B.     Back in the day, the person whose job it was to interpret  ‘real-time’ information on mortgages was called a ‘tape-cracker’. This person would await the periodic release of a ‘data tape’ from the agencies – Fannie & Freddie – with reports on prepayment rates on pools of mortgages.  Tape crackers operated with a time-critical posture – the quicker the agency information was gleaned and input to re-price the MBS universe, the greater the advantage conferred to the trading desk which moved to reposition their book. (Rob: I always liked the name of that job: tape cracker — it was like possessing special, secret skills, like a safe-cracker or code-breaker spy.)

C.     The oscillating rate of prepayments has the potential to wreak havoc on portfolios.  For example, take a Portfolio Manager (PM) who purchases a CMO with an average life of 14 years. Let’s make the overall interest rate 8% (which translates into mortgage rates of roughly 8.75% or 9.00%). Now let’s say interest rates decrease and mortgage rates fall, by say, 1% to 7.75%.  In the new conditions, homeowners are more likely to refinance, paying off their mortgages early.  The PM, therefore, would get stuck with a pool of money that needed to be reinvested — but in a much lower interest rate environment overall.  So the only way the PM could ‘match’ his original 8% was to take on more risk.

D.    One of the consequences of this intrinsic instability in CMO’s was to create IO’s and PO’s – ‘interest only and ‘principal only’ streams – in which the mortgages would literally be stripped in half.  Here’s how it would work: if a PM thought interest rates would be stable or rise over time they would purchase the IO portion. Conversely, if the PM felt that interest rates were expected to drop, which would incite a wave of refinancing, they would buy the PO portion at a very deep discount to par (say, 80 cents on the dollar). If this second bet was correct the profit margin could be very handsome since prepayments pay 100 cents on the dollar.  (Rob: Over time, traders devised ways to create synthetic securities from these segregated streams. They combined IO’s with high-interest paying pools of mortgages and PO’s with low-interest mortgages.  Another strategy was to engineer a security from Treasury strips and IO’s from a CMO.)

E.     In the world of MBS, defaults are anticipated.  What ‘subprime’ means is that the security is structured to anticipate that a greater percentage of the pool may default.  This is why CMO deals are over-collateralized. The ‘residual’ holder of a CMO structure puts up pure equity in exchange for a percentage of the ‘residual’ mortgages which remain.  These residuals are rated below investment grade.  This means that if the pool pays off as expected – default rates remain under a certain threshold – the residual owner is compensated at an above-average spread and vice-versa.

F.      The debacle from sub-prime escalated because the residual holders were the banks themselves, or the loan originators (i.e. Countrywide).  As increasing defaults accrued, the protection provided by over-collateralization was exhausted.  So the problem was, indeed, sparked by declining credit quality, but what created systemic shock was the highly concentrated ownership of these residual tranches. Tett’s observation in ‘Fool’s Gold’ is that the problem of BISTRO structures was finding an outside party to assume the ‘residual’ risk. Enter AIG. As Tett showed, the issue became pronounced when an increasing number of banks started keeping the ‘residuals’ on their balance sheet (i.e. in SIV’s), and a few dominant players, such as AIG, stepped in to insure the associated risk.

G.    Given that defaults are already being anticipated in MBS’s – hence over-collateralization – how would real time information significantly alter the valuation process?  It was a known fact that (sub-prime) pools deteriorate.  The subprime problem wasn’t really related to time sensitive information.  It was the unusually sensitive structure of the loans (risk layering through exotic features in addition to weakly positioned consumers made subprime loans vulnerable to external shocks); and it was also a liquidity issue — buyers became unwilling to commit capital to these products.

H.    Consumer credit information in the form of FICO scores are only used at the time of MBS structuring.  As Martha’s work suggests, FICO scores helped MBS become liquid by enabling the credit quality of the borrower to be represented in a capturable and comparable fashion.  That FICOs change as credit behavior changes over time, might add real time element to MBS valuation. But it would also make the variability of FICO visible.  FICO becomes particularly volatile in economic conditions where unemployment and credit line retractions [see previous post] can dramatically affect consumer performance.  Visible variation would potentially erode FICO’s robustness as a secondary market facilitator.

I.       The assumption that real-time information would create some degree of stability anchors itself in the very idea of a self-regulating, self-policing market.  Given the history of CMO’s, it would be inevitable that traders and financial modelers would devise strategies to isolate those portions of the pools experiencing distortions, and create new vehicles to even out risk, just like IO’s and PO’s.  Rather than ‘regulate’ themselves, market participants would simply modify production to capitalize on the the newly traceable volatility implied by enhanced real-time information.

Here is a widespread claim: The problem with quant models is that they forgot a few variables.  This position on the causes of crisis plays into two dominant arguments: The first is that the methodology of modeling is fundamentally sound; what was wrong was the models themselves which need tinkering. The second, also discussed in this article in today’s NYTimes, is that social network analysis is part of the solution because it can track the contagious spread of ideas about markets that can lead to panic that exacerbates market malfunction.

My question is: what is the methodological basis of social network analysis?  It is perhaps ironic that some of the biggest disciplinary critics of the economists are now working on an algorithmic platform that allows them to be folded into the logic of economics and quantitative finance. The Times article cites an NSF grant awarded to at team at Cornell to work on collective behavior. The team is populated by computer scientists, economists and finance experts… with narry a sociologist in sight.

This article by Bruno Latour explaining how new quantitative techniques that allow individuals to be tracked, rather than treated in aggregates, are fundamentally different than traditional statistical tools, may be useful starting point for addressing these issues. Latour’s insight is that the history of quantification is not just a story about doing better statistics by adding factors to combat incompleteness; rather the progress of models has involved the rise of novel type of quantification methods that have changed the nature of the world that these techniques presume to model.

Will quant models survive?  Yes.  But the simple narrative that financiers will continue to tinker with their toys might not be enough to explain their resilience…

The 2009-2010 program of the monthly seminar of the “Association d’études sociales de la finance” is now available online. The seminar is to be held at the MSH in Paris. All welcome (seminar organized by Pierre de Larminat).

(For an earlier plug about this seminar, see this blog’s first post.)

There is no doubt that Obama’s address to Congress on heath care last night was a superb piece of political craftsmanship.  The purpose of the speech was to take the issue back from the boarders of fringe discussions.  By casting health care as a moral-national imperative, he made a hard-to-dodge (unless you’re an unpatriotic jerk) argument that there should be bipartisan agreement on the definitive need for a heath coverage plan of some kind, even if the details are not fully consensual. The address was designed to re-secure political will.

The US heath care debate is largely a question of finance.  Two points of the proposed plan struck me as potentially interesting sites of empirical research.

1. An insurance exchange to replace the idea of a federally run heath care program. “Now, if you’re one of the tens of millions of Americans who don’t currently have health insurance”, Obama said, “the second part of this plan will finally offer you quality, affordable choices…We will do this by creating a new insurance exchange – a marketplace where individuals and small businesses will be able to shop for health insurance at competitive prices. Insurance companies will have an incentive to participate in this exchange because it lets them compete for millions of new customers. As one big group, these customers will have greater leverage to bargain with the insurance companies for better prices and quality coverage.”

A non-profit insurance agent run by the state has been trounced on all sides.  It’s been found offensive on the grounds that the government can’t be trusted to run anything efficiently; AND on the grounds that private insurers could never compete against a government run agency.  Now that this idea seems to have been displaced in favor of a more palatable ‘marketplace’, my question is: What kind of market device will have to be built to get such an exchange going?  Who is going to build it?  And what will be the role of government in designing the dimensions of this exchange?

2. The cost of good care. Although recycling savings from improved efficiency within the existing system to pay for extended coverage to include more people sounds appealing, there has been very little said in the debate to examine how costs are built into medical practice.  Over treatment to protect against tort law is only one part of this.  Incentives for compensation are another.  As the eminent physician, Dr. Relman commented in the NYTimes today, “The main drivers of medical inflation are fee-for-service payment of physicians, and all the other incentives in the current medical care system for increasing providers’ income. We will not control costs without major changes in the way medical care is organized, delivered and paid for.”

As medicine and finance intervene, there is an excellent opportunity opening up to recapture some of our science studies roots.  Forget superfluous costs for a moment. What are the costs within the practice of ‘good’ medicine? How is a cost calculated when it is confronted with scientific efficacy?  It seems that there are things to be learned by looking at how costs are ballooning within the very heart of what it means to interact with and intervene upon the body from a sound medical perspective.

In a (too) simple example, medicine is an industry that pushes for disposable, single-use consumption. Where tools were once sterilized and reused, latex gloves, paper sheets, the plastic speculum etc… medical practice calls for more and more disposable things and technologies. (This reminds me of the difference between a French café where the counter attendant washes out the cup and spoon by hand with a cloth rag at a sink, and a Starbucks, where they give you a full sized cup for a single expresso, a disposable stick, and a paper girdle to keep you from burning your hands, all of which are promptly disposed in a trash can.)  How is this happening from a cost perspective, and why?

Health care. It’s the next frontier of financial research…

Vanity Fair produces some SSF insights. The magazine compiled a list of the 100 people/things to blame for the economic crisis. The list is sorted alphabetically and in B you can find, among other things ‘Bloomberg terminal’. This is how Vanity Fair explains why Bloomies, as they are known affectionately in the City (thanks, Jan!) should take some blame for the mess:

11. Bloomberg Terminals.

Because they led Wall Street to believe it was invincible. If you wanted to play the futures market on the commodity price of bulk clams in Manila, the Bloomberg machines could do it. If you wanted to leverage egg prices in Bulgaria against the long bond in Brazil, and pay for it in yen, they could do that, too. Hell, if you wanted to eventually self-finance a run for mayor of New York City, underwritten by the idea that information is currency, and the fact that these terminals had become the mandatory desktop toy for every budding financial master of the universe, you couldn’t invent a better A.T.M. They were all-knowing, and all-powerful. Still, for all their financial wizardry, there was also the law of unintended consequences: If you give enough monkeys enough typewriters, you may get Shakespeare. But if you give enough electronic monkeys enough high-tech typewriters, sooner or later you’ll definitely get a financial meltdown.

The text is meant to be light and funny, after all, this is Vanity Fair, not Journal of Finance, but there are a few important nuggets of truth and even, some implicit calls for further research. First, can it be that the availability of Bloomberg terminals, providing immediate access to wealth of information promoted over-trading? A classic paper by Barber and Odean tends to indicate so. Barber and Odean, however, do now explore the techno-social infrastructure that enables overtrading. Alex Preda’s study about the language of day traders may be a step in this direction.

Second, Bloomberg terminals do much more than simply provide the single trader the ability to compose and execute complex, exotic trades. The terminal subscription includes instant messaging service that is used intensively by traders. Opinions, trading ideas and even ‘model-verified’ ideas flow quickly and effortlessly among Bloomberg users. What is the role of this market device in establishing ‘consensus trades’? How do such machines increase opinion clustering among market actors?

There is one thing I get from Martha’s post. FICO scores are a key intellectual salvo in the “Berkeley controversy” between performativists and institutionalists. Do tools matter? Can devices be political? Martha’s account suggests a resounding yes.

Here’s why. Calculative tools, and specifically the FICO scores, are the key missing link between the interests of bankers and the interests of CDO investors in the credit crisis. In the Fligsteinian account of the crisis, Wall Street bankers induce the credit debacle by switching from selling prime mortgages to selling subprime mortgages… to preserve their profit margins. They certainly pursued their interests. But what the account seems to overlook is that any market transaction requires the agreement of two parties. Granted that bankers were offering crap. The question is – why were subprime investors buying it?

The institutional answer to this is isomorphism. It was junior portfolio managers who probably ended up buying up those subprime securities form greedy bankers. Why? Because their misguided bosses, aware that their rivals were racking up millions by going subprime, told them to do so… or else look for another job. Subprime investors are then the social fools that advanced Wall Street’s interests at the expense of their own.

I find this answer unsatisfactory. As with any diffusion story, it does not explain how the bandwagon got going: once every lemming is falling off the cliff, one can see herding at work. But why did heading for the cliff become a hot trend in the first place? My second misgiving to the “fools” explanation is that it assumes that subprime investors were not actively calculating the risk of their move. Presumably, those greedy bosses did not want to lose their jobs either. So how did they gauge the risks of their bet?

Martha’s work provides the answer. They did so with an instrument they had ready at hand – the FICO scores. And the FICO score was ready for them because it had already been developed to gauge something else entirely different: the risks of legitimate, investment grade prime mortgages.

The argument is so strong that it needs to be made clear: we would not have had a subprime crisis without FICO scores. Only the existence of a calculative tool can explain why one party – Wall Street – was able to advance their interests at the expense of another on a voluntary, free market exchange basis. As Martha writes, a case of “innovation mediated politics.” (For those who are not yet subscribed to the journal, here’s a link to the draft of the paper)

In advancing the empirical basis of this explanation, there are several points that can be pursued.

First, even if we grant that FICO scores allowed subprime investors to engage in a risk-return calculation, it is not a given that the answer would be “buy subprime.” Why was it? Here, Tett and MacKenzie’s account is critical: the problem was the incorrect correlation assumptions in the CDO matrices. So I would like to know more about the relationship between theirs and Martha’s account.

Second, if Martha’s mechanism is indeed at work, we should be able to find other historical parallels. That is, situations in which the forbidden trashy low end of an asset is brought into acceptability by the application of risk management tools used for the legitimate safer half. Are there examples of this? I’d like to suggest the development of junk bonds by Michael Milken in the late 1980s.