Also, yes, that’s right, I did both the yellow circle image and the tight-roping stick figure. And the tiling, I remember being so happy about the marble tiling.

Finally, there’s an end-of-year picnic in West Wilshire park. It’s on May 4, 1996. Please RSVP.

If you let me know, and I refer you, they give me more space (yay!), which would be nice. But I don’t even care if you are referred or not, just go try it.

Though as a total aside, the recent May incident where P&G dumped 1/3 of its value, then rebounded (from $60 to $40 back to $60) in 4 1/2 minutes seems more complicated than an accidental trade. In fact, the joint SEC-CFTC report (that’s a .pdf) is something of a doozy:

The quantitative evidence presented above suggest that a confluence of economic events, market forces, and trading system functionality led to a significant dislocation of liquidity in the June 2010 E-mini S&P 500 futures contract sometime between 2:30 p.m. and 3:00 p.m. on May 6, 2010.

Prior to that time, a number of economic events and market developments led to a broad-based market desire to lessen risk exposures. This translated into a downward movement in prices across financial markets in conjunction with significant trading volume. At or about 2:30 p.m., the electronic limit order book in the E-mini S&P 500 futures market exhibited a significant imbalance of sell orders and buy orders. In the backdrop of declining prices, this imbalance appears to have contributed to a sudden liquidity dislocation despite increased trading volume. At approximately 2:45 p.m., several sell orders executed deep into the limit order book, which coincided with a significant loss of depth, triggering the Stop Logic functionality. The Stop Logic functionality in the E-mini S&P 500 contract has been triggered a number of times in the past few years, including several times during the financial crisis in the fall of 2008, when market conditions may have resembled those seen on May 6, 2010. Activation of the Stop Logic functionality on May 6, 2010, initiated a five second pause in trading in the E-mini S&P 500 futures contract. After the five second pause, the limit order book became more balanced, which is its typical state, and the price of the E-mini S&P 500 futures contract recovered.

This is being called the Flash Crash of May 6, 2010.

Anyhoozle, the ATP Oil & Gas Corporation. The analyst for JP Morgan who covers the company, named Joseph Allman (in case you want to know whose work you can’t trust down the road), calculated that the company would need $500 million in additional capital, more than its entire market capitalization. You can see what happened, on June 13, 2010:

That’s end of trading day Monday. On Thursday, JP Morgan put out a second note, as Salmon quotes: “In our July 13 note, we stated that it appeared that ATPG would need $500MM of external capital. This model corrects that error and reduces that need to $50MM.”

Oops.

The lessons Salmon draws are things like, yep, people read sell-side research and act on it, and yep, stocks are hella volatile nowadays, and you betcha, listening to sell-side research without doing your own homework is hazardous to your wealth.

To me, what’s amazing here is how time horizons are highly shortened, and market reflexes are hair-trigger, and trading technologies are tightly coupled. Add people, mix, and there will inevitably be periodic blowups.

Which leads me to Pepsi, Scienceblogs, John Gruber, and Apple’s iPhone 4.

Scienceblogs is a community of science blogs (duh), hosted by what was once Seed Magazine (and what is now Seed Media Group). In early July, 2010, Scienceblogs began hosting a new blog, called Food Frontiers:

As part of this partnership, we’ll hear from a wide range of experts on how the company is developing products rooted in rigorous, science-based nutrition standards to offer consumers more wholesome and enjoyable foods and beverages. The focus will be on innovations in science, nutrition and health policy. In addition to learning more about the transformation of PepsiCo’s product portfolio, we’ll be seeing some of the innovative ways it is planning to reduce its use of energy, water and packaging

(this quote comes from an article by Curtis Brainard at Columbia Journalism Review, the original announcement having been taken down at Scienceblogs and replaced with an apologetic note). But yes, the Scienceblog in question was not just sponsored by PepsiCo, but actually would be a science-based nutrition blog written by PepsiCo. You can catch up at Bora Zivkovic’s (who as a result of this actually left Scienceblogs) massive linkdump.

As a result of this move, in fact, Scienceblogs seems to be falling apart. I would like to take this opportunity to look at the practice of corporate blogging, corporate-sponsored blogging, and well, corporate blog-whoring, all with a jaundiced eye. Seed Media Group’s CEO Adam Bly wrote a confidential letter to contributors (helpfully posted by the Guardian):

We have also hosted blogs on SB from research-based companies like Shell, Dow, Schering-Plough, GE, Invitrogen, L’Oreal (in Germany), and now PepsiCo. I want to address the logic and strategy behind this.

ScienceBlogs has consistently maintained editorial excellence. We syndicate content to the New York Times, National Geographic, and are indexed by Google News. So respected is our platform that the US State Department recently published a post on 3.14. We should all be very proud of what we have achieved in four short years. We have ensured editorial excellence not by editing your posts or telling you what to write – a first principle unique to SB that we will never change – but by learning over four years how to create an environment that encourages your best. We believe that one vital aspect of this SB environment is its intentional diversity. You are all expert at different things, care passionately about other things, and come from different backgrounds and countries. We think this is a good thing and we think it help makes SB tick for our readers. We also think that you cannot have a real conversation about science and its place and role in society unless you pursue and protect this diversity. It’s why we believe that all serious voices in science should have a seat at the table (and we’ve been consistent about what’s serious and what’s fringe or worse).

We think the conversation should include scientists from academia and government; we also think it should include scientists from industry. Because industry is increasingly the interface between science and society. It is our hope that the Xeroxes and Bell Labs of the future will have a real presence on SB – that they will learn from our readers and we will learn from them. That they will break stories on SB and engage our readers in the issues that concern them. The bloggers who blog on ‘corporate blogs’ on SB are necessarily credentialed scientists (we make sure of that), in some cases highly credentialed scientists who have published extensively in peer-reviewed journals. The fact that they work at a profit-making company does not automatically disqualify their science in our mind. And frankly, nor does it disqualify them in the eyes of the Nobel Prize Committee either.

Let me address PepsiCo in particular. Of course we recognize – and of course so does PepsiCo! – that they’ve made a lot of money selling soft drinks and chips. But they also recognize that their future will be troublesome and time-limited without addressing the real and connected issues of obesity and under-nutrition in the world. PepsiCo employs thousands of scientists working on these problems and they are led by some very serious scientists – eg. their chief scientist worked at the Mayo Clinic and serves on the Board of Governors of the New York Academy of Sciences. (PepsiCo is the same place that makes Tropicana and Quaker Oatmeal.)

PepsiCo comes off like Nick Naylor in Thank you for Smoking: Though these lobbyists would like to see Cancer Boy die to prove their point, it is in Tobacco’s best interests that he LIVES, to continue using our product!

Seriously, read that whole memo. Basically the CEO says that 1) there are scientists at for-profit companies; 2) lots of places including the New Yorker and Atlantic give prime access to companies who pay for it; and 3) we need the money.

Now, there is a relatively interesting discussion here to be made about the ways that capitalism intersects with opinion, and the long, historic dependency of news media on paid advertisement. But I would also strongly suggest that for-profit actors are now (and perhaps have always) mobilized the form of more ‘objective’ outlets in order make more credible claims. For me, at its most base level, it comes down to this:

People are more inclined to believe you if they see you as an impartial observer than if they see you as a partial advocate. If you are on the corporate (or political) payroll, you can not be trusted and your opinion is suspect. Impartiality doesn’t even quite get at this, though. Maybe transparent. But not quite, since transparent can be easily dismissed as obviously unable to form a helpful opinion. Fair-minded. That’s more what I am talking about.

This is the fact on which 99% of the complaints and justifications are based. PepsiCo can not be trusted to speak on science issues because they can not be believed to be fair-minded.

And here is where John Gruber comes in. Recently, Apple has had some problems with its recently released iPhone 4. Apparently, its antenna is constructed in such a way that holding it in a particular way attenuates the signal. Consumer Reports called this a design defect in one place and an antenna problem in another. The NYT article calls it a ‘design flaw’.

Apple held a press conference to push back against these criticisms, where Steve Jobs personally spoke and answered questions about the phone’s antenna, its performance against other phones, and Apple’s remedy for the problem (giving away phone cases to physically prevent users from bridging the antenna gap and triggering the loss of signal).

Gruber runs a (wonderful) blog called Daring Fireball, where he both curates and commentates on the internet, with a strong preference for Apple products and a clearly insider-y take on all things Apple. He is not on the payroll, which he jokingly comments about. But still, he seems to have been one of a members of the press who got to tour Apple’s Antenna Testing Lab after the press conference (I don’t know him, and I don’t know this for certain. But that first ‘jokingly’ link notes at the end that some members of the press got to go, the second notes that ‘this is the lab a few of us got to tour in person’).

And here’s the thing. During the press conference, Jobs was at pains to show that other phones had a comparable flaw, and that this was a ‘weak spot’. Early on, Gruber uses quotes around “weak spot”, noting that it is Apple’s parlance, or that it is “Jobs’s term for the infamous lower-left gap in the antenna frame”.

But then a funny thing happens. After a slew of posts about other manufacturers’ phones (and running snide commentary about their lack of attention, as well as how his checks from Apple should keep rolling in), Gruber comes to some conclusions, an Antennagate Bottom Line. Here, he begins:

What is not in dispute: the iPhone 4 antenna has a weak spot in the lower-left corner of the frame, marked by the black line in the frame. When covered by your hand, this antenna suffers from attenuation. This is much like other smartphones.

After running through the evidence and opinion, including Apple’s $100 million dollar remediation effort for something Gruber himself considers a fairly minor trade-off for a great phone, he concludes:

Anyway, bottom line on the iPhone 4 antenna: it has a weak spot but there’s no evidence that it’s a significant, let alone catastrophic, problem in practice. It’s telling that the criticism surrounding this issue has shifted, quickly, from speculation about a technical defect in the iPhone 4 hardware to criticism over the tone of Apple’s response to it.

What certainly could be described as a design flaw, a design defect, or an antenna problem is magically transformed into a weak spot. A weak spot implies a small flaw in an otherwise ‘working’ device; a design flaw implies a ‘broken’ device. And the truth of it is, I do not know which of these is the case here (or both, depending on you, your network, your other options, your disposition towards Apple, etc). But I do know that Apple is deeply, deeply invested in it being the former. “Weak spot”, turns into weak spot, turns into insignificant problem.

It is equally apparent to me that by co-opting Gruber and other journalists, bloggers, and opinion makers, whose credibility precisely rests on their reputation as unbiased observers, Apple is doing just what PepsiCo was doing when it signed on to create a Scienceblog. And of course Gruber knows this. The whole sarcastic schtick about his payola checks suggests that he knows that if he really was on the Apple payroll, his opinions would simply. matter. less.

I’m not really sure what to do with this problem. But in an era of increasing information-by-the-many, it’s one that is likely to get much worse before it gets much better.

If you hate your current service or are thinking about what it might entail, let me know, and I’m happy to proselytize on their behalf.

This issue is not new as such, as speed of electronic trading (in the form of co-location, or having your servers physically located nearer to an exchange to eliminate tiny latency times in execution) has been an issue for years. See the industry magazine here (from March of this year) and here (from 2007) [both are pdfs].

I have three things to add:

1) Liquidity is bullshit. This has always been the case, but the use of volume and ‘continuity of price’ as a proxy for liquidity is increasingly ignorant. There is a historical trajectory here – the shift from locals making money off of institutions (who provided liquidity) to institutions making money off of locals (who provide liquidity), but the cat-and-mouse nature of high frequency trading means that a terrific amount of traffic in quotes with little value added for price discovery. The metaphors of financial markets being ‘deep’, ‘liquid’, whatever, are inadequate to the task of capturing what is the value of a working market. I don’t think there are replacements yet.

2) Markets are sausage, and exchanges, regulators, and large financial services firms are the sausage-makers. This is definitional, and important. The HFT article peeks at the varied ways that exchanges provide incentives for some players over others. To wit, a large all-or-nothing order at the CME/CBOT doesn’t have to go into the normal queue of orders. Ok, so the people trading 1000 lots can just screw first-in-first-out. The NYSE privileges high-frequency-traders by incentiving them to ‘make markets’ (liquidity providers, right!). Regulators privilege bulls over bears by making it harder to sell short than to buy long.

All of this is true, and yet whatever ugly, improbable, unfair (or too fair) set of practices, rules, and activities that comes out of this process is, by definition, a market. A market is whatever exchanges, the SEC/CFTC/Fed, and financial firms say it is. It is a Durkheimian ‘social fact.’ That is, it is factual but not ‘real’ in the sense of pre-social.

3) Insiders win. All of the technological and financial innovation that might theoretically make markets more accessible, more democratized, more ‘fair’, simply do not. In absence of a real idea of what a market should be, and an acknowledgment that whatever the current sausage-making apparatus spits out defines what a market is, we in fact have an edifice that benefits insiders by design.

That electronic trading consolidates power rather than democratizes it is not a new thing to point out, but I wish there was a bit more attention paid to this dynamic.

Ha, I step out for an evening and realize I’m being called out by name, even praised, after I was so unkind to the orgtheory. Brayden you’re trying to make me reconsider…

A few things. First, I’m lumping together into ‘aggregation’ and ‘wisdom of crowds’ a number of different types of activities. Recommendation engines and the Hollywood Stock Exchange (where I’m currently ranked 47943th, with a lifetime ROI of +1,164.95%) are very different, and rather than go with the it’s complicated routine, I lumped a bunch together. I’d go a step further than Lena and say it’s a categorical error to suggest either expert opinion or crowd-sourced outcomes are generated with same logics. I’d like to hear more about the relative value of expert-vs-the crowd across these differences.

Second, design-wise, I might be wrong about exploitation v. exploration. I had in mind that if you put a movie on HSX and try to value it, you will be way off on movies that don’t already conform to existing kinds of movies. Or, if you base your decisions on what kinds of things people like/want, you end up with Playstation 3 and Xbox 360, and miss the Wii – because people didn’t ‘want’ it before it existed. Ugh, I’m getting muddy.

But I might be wrong – I mean, Paul DePodesta (of Moneyball, soon a movie with Brad Pitt and ironically undervalued at $31.75 on HSX) explicitly noted that he used data and no assumptions about existing scouting experts to come up with a new way to assess player value. Clearly exploration through data mining.

(and a propos Mike’s comment above, why 538 and Netflix does well, it seems as though it is something about the difference between regression analysis and Singular Value Decomposition/factor analysis; but that requires more explanation)

But my problems are theoretical, and here I enlist Hahn and Tetlock’s definitive Information Markets: A New Way of Making Decisions. Which for a theoretical basis starts with (on p2) “Why do information markets work as well as they do?” And then references….The Wisdom of Crowds. And then moves on to design. And Surowiecki’s theoretical answer?

“At heart, the answer rests on a mathematical truism. If you ask a large enough group of diverse, independent people to make a prediction or estimate a probability, and then average those estimates, the errors each of them makes in coming up with an answer will cancel themselves out. Each person’s guess, you might say, has two components: information and error. Subtract the error, and you’re left with information…With most things the average is mediocrity. With decision making, it’s often excellence. You could say it’s as if we’ve been programmed to be collectively smart” (p10-11).

Not really obvious at all. There is no answer why a market metaphor would result in something better than experts. But we have the technology to do it, and it seems experimentally to work, and in web 2.0 we can get users to do this all for free! And so screw it – off with the design team and on with the user testing.

Okay. Imagine sitting back in the day with none other than the Buddha himself. The two of you are discussing life, love, and various other zen things. Then, all of a sudden a man approaches you both offering some fresh Indian rice. When the rice is finished cooking, you realize, “Oh my lord. I forgot my soy-sauce back in the future!” The Buddha tells you to calm down and he smiles and reaches into his woven napsack and pulls out his own personal bottle. You graciously accept his gift, and soon enough your stomach is full of a delicious medly of flavors and such. You lean back, completely content. A wide smile stretches across your face.

That’s what this album is like.

79 out of 95 listeners found this review helpful.

The point is fractal, incidentally. In this ‘old’ system, for policy advice you would call on sociological experts (naturally, though maybe other lesser social scientific experts if you’re interested in worse advice). In organizations, you would look for marketing advice from your marketing division, operations from operations, finance from finance. Obviously the more general the point I make the more fault you can find with it. And you would be right. But bear with me for a moment.

The ‘new’ system rests on a Wisdom of Crowds knowledge. That is, if you take a bunch of people and ask them their opinions, you can get a better fix on uncertain knowledge than you can with a small number of experts. Now, Surowiecki himself is not this simple: at minimum one must overcome problems of cognition, coordination, and cooperation. But this said, proponents of this kind of system point to rather stark indicators of success. Google’s PageRank (though I find the idea that they use 500 million variables and 2 billion terms absurd); Yelp; the Iowa Electronic Markets; Metacritic. And in the more general point, we see a substitution of ‘market’/crowdsourcing/datamining as a substitution for design, marketing, strategy. Here I mean the A/B testing ad absurdium as a substitute for design. Data-mining as a substitute for marketing. Quantitative finance as a substitute for market forecasting.

This whole edifice actually rests on a kind of efficient markets hypothesis, or more specifically a Friedrich Hayek-type consolidation of ‘adverse’ knowledge (meaning, in this context, private knowledge) via a market mechanism. While Hayek wanted to argue that market-based societies are better than centrally-planned societies, his work has become the intellectual touchstone of all things information market. And really that’s what it comes down to. Crowd-sourcing: a replacement of expertise with market.

However, there are some things to think about here that make this ‘new’ system quite problematic. And I ain’t sayin’ so just because I’m an expert (after all, the policy people really don’t come talking to sociologists, despite my preferences). There are one specific and one theoretical.

The first specific is that some people are just crazy, and aside from creating a tail-end of a distribution curve, it’s not at all clear what these folks contribute to the crowd. Old but still hilarious is Andy Baio’s Amazon Knee-jerk Contrarian Game. Personally, I like the ratings game at Yelp, an often-loved but massively crowd-sourced guide. Take, for instance, the Museum of Modern Art in NYC (i.e., the, or one of the, best modern art museum in the US and the world):

Why 1 star? Its just a horrible place to visit never ever again, screw this contemporary art thing, the exhibits they had going on were……… yeah no way to describe the sheer disappointment in the place. The place is designed to shock and awe you, all it did was bore me.
and
Most of the exhibits at MoMA are just random objects or B.S. paintings–hardly classifiable as art.
I could just go down to my garage or get a toddler to paint on a canvas to receive the MoMA experience. No crowds or superinflated entrance fees there, either.
and
I was so jazzed to go there. Many people I know raved about it.
All I came away with from this place was one word: Overrated.
Quality Modern Art is subjective. In my mind, for the hype this place gets is unwarranted. So sad…

So how do these reviews contribute to overall ratings systems? More broadly, what if the feedback/view/idea/opinion from your customers is just wrong? In 2.0 way of thinking about things, this is like saying that a market price is incorrect – it is axiomatically impossible, barring something wrong with the system (an information problem being the first culprit). And there is no ‘expert’ to say otherwise.

More theoretically, it has never really be adequately explained why a ‘market-like’ information crowd-sourcing should work. I understand why markets might produce a price that incorporates most public and private information about a commodity. But the widespread substitution of expertise with data mining and crowd-sourcing is a market metaphor more than a market. Why should a metaphor work? This is at the heart of someone like Daniel Davies’ criticism. And I get that sometimes aggregation does work. But there’s no good reason why.

My own feeling is that, using March’s metaphor of ‘exploitation’ and ‘exploration’ (where the first is the plumbing of existing knowledge/arenas, and the second is the seeking out of new opportunities), aggregation mechanisms are better at exploitation than exploration. They do better with existing standards of knowledge, of tastes, of commodities, than they do with something that is new. You know, Blue Ocean and such. I think there are better solutions for a 2.0 world that combine expertise and aggregation (for instance, Five Thirty-Eight‘s work on the 2008 elections that combined data mongering with theoretically-driven and field-visit-driven analysis). But this post is already too long.

One way is to just decide, you do this part, I’ll do that part, and we’ll split the proceeds. This is very local ownership. It is tied directly to you and your friend’s relationship, the specifics of the business venture, the particulars of the agreement you forge. For the current purposes, I want to call this concrete finance.

An alternative way to divvy up your company is to create shares of one sort or another.

How to divvy up the profits from you & your friend's cookie business.

What you’ve done formally is to create an instrument to stand in as financial value. It’s a kind of abstraction. The ownership no longer depends so specifically on the relationship between you and your friend. The rights to ownership, and maybe future profits and losses, are now formally invested in the shares, not just in your relationship. When you agree to partition the company into shares, you are turning the rights to future management and earnings into these more abstracted instruments.

What makes them ‘abstract’? Well, they become abstract in two distinct but related ways. First, the shares themselves are worth something. So ownership of the shares are ‘worth’ whatever profits come out of your business at the end of the year, plus they are worth whatever someone will pay to get those profits. If your business is great, with bright prospects, someone might well be willing to pay a premium for your shares. Or expect a discount if your business’ prospects are lousy. This means that there are actually two values to shares, a representational value and a market value: the representational value is how much value the shares stand in for (i.e., your share of the profits), and the market value is how much someone would buy or sell those shares for.

The second way they become abstract is that the value the shares can be thought about in ways that are not specific to the business that you started. For you, the shares are a shorthand for the blood, sweat, tears, and rewards you get for taking chances on and working in your business. For someone else, the shares can be represented as cash returns over time. And these cash returns over time are compare-able to other cash returns over time from other kinds of things. For instance, owning a totally different kind of business, buying and selling rare coins, or lending money to arms dealers all have ‘cash returns over time’ that can be compared favorably or unfavorably to your shares. In this way, what was once only understandable as a local, concrete financial arrangement between you and your best friend gets brought into the wider universe of other financial stuff. This is what I want to call abstract finance.

What's a pension fund manager doing with shares of a cookie business? Making profit.

UAL's bumpy ride. Is this human error or market error?

NASDAQ’s response was to tell investors, tough cookies:

Once trading resumed 90 minutes later, UAL shares rebounded, but they still closed off 11% for the day at $10.92. Nasdaq, a unit of Nasdaq OMX Group Inc., said that it had reviewed transactions involving UAL shares during a 13-minute period before the halt and that all trades will stand. A trade for 100 shares at a penny apiece occurred on another exchange after the trading halt on Nasdaq and was later cancelled.

The reason for NASDAQ’s response is worth noting, even if it is not noted. Exchanges have a deep stake in defending the view that there are market errors, and then there are human errors. And a market error – an error that implicates the platform and mode of trading itself in the systemic mucking up of financial transactions – is deeply problematic for exchanges. Attributing the error to human errors – that buyers/sellers correctly placed orders which were correctly matched – allows exchanges to maintain their self-image as market infrastructure.

I’m sure Google and Income Securities Advisors Inc. will have more to say about the incident.

In markets 2.0, I refer to the data that is generated as part of normal market transactions. The 2.0 references web 2.0, where the ‘social data’ generated by web interactions and transactions has acquired a kind of life of its own. I make no super-special claims that markets now are wholly different from markets in the past. But some of their properties have become increasingly visible and important.

Let’s take the basic case. In a financial market, if Gordon Gecko and I make a trade, a couple things happen. First, money goes in one direction, and a commodity or security goes in the other direction.

Basic model of a market transaction

I’m playing a bit fast and loose here, but you get the idea. Let’s call this markets 1.0. Exchanges like the occur all the time in lots of different places. And if you wanted to generate outwards, I interact with all kinds of people in ‘markets 1.0′ mode – iTunes when I listen to a song, colleagues when I ask about a job, my bank when I enter into a transaction. I have in mind a particular case (capital markets) of what I think is a more general phenomenon.

When our transaction is ‘seen’ by others, in the form of posting it publicly or trading in a public forum, an additional piece of data gets generated by our transaction. It is this generated piece of data that I want to offer some more sustained analysis.

The signal has itself become a commodity

This puff of data – the reporting of a transaction between traders in a stock exchange or Board of Trade – was itself contentious back in the day. When, at the turn of the 20th century, the Chicago Board of Trade was trying to distinguish speculation from gambling (no mean feat even then) one tool in its arsenal was to prohibit the distribution of its quotations to so-called bucket shops. If Gordon Gecko and I were to wager on the daily closing prices of the NYSE by betting each other $50 that it would be higher or lower than its opening, that would be gambling. If we bought or sold daily futures contracts based on the NYSE’s daily closing prices, that would be speculating. But without access to the NYSE prices, it would be impossible for us to bet on its opening or closing prices. This is what the CBOT was trying to do. It was arguing that it could restrict Western Union from distributing its quotes to some organizations, based on what those organizations were doing with the quotes. And the Supreme Court, in 1905, confirmed their right to do so.

Contracts under which the Board of Trade furnishes telegraph companies with its quotations, which it could refrain from communicating at all on condition that they will only be distributed to persons in contractual relations with, and approved by, the Board, and not to what are known as bucket shops, are not void and against public policy as being in restraint of trade either at common law or under the Anti-Trust Act of July 2, 1890.

- Board of Trade v. Christie Grain & Stock Co., 198 U.S. 236 (1905)

This kind of restriction would be surprising today. Especially since there are places like InTrade where the point of the thing is to wager on the future prices of the NYSE. Now, imagine if the NYSE just decided to no longer distribute the time & sales data for stocks traded on their exchange. We know that, like private equity arrangements that happen all the time, transactions would continue. But that piece of extruded market information – that signal – has become central to our conception of what the stock market is and how it works. It has become an indicator of our society’s health, even of popular sentiment towards US foreign and domestic politics.

More specifically, this information-bit has become central to the work of other traders in figuring out how to act within the market itself. How do we know this? At Eurex, the dominant European futures exchange, quote traffic has increased pretty dramatically in just the last few years.

Requests for quotes per day on Eurex futures exchange

And it is not just an increase in trading, but an increase in the desire for more and faster information about the trading in which other people are engaging. As Voyles notes in the article, in 2000, Eurex had 1.2 quotes per contract. In 2004, it was 2.16 quotes per contract. In 2005, 7.88 quotes per contract. And in 2006, it had climbed to 17.09 quotes per contract. People are taking the quotes and using them in real-time for analysis of their potential trades, to inhabit and back-test models, and to seek patterns in the noise that is the market.

This is my point: that the quotes themselves have come to have a value both within the context of trading, but also outside the immediate decision to engage in a market transaction.

In a now-classic 1981 article, Harrison White suggested that the appropriate way to think about production markets was less about supply-meeting-demand and more that production markets are “tangible cliques of producers observing each other. Pressure from the buyer side creates a mirror in which producers see themselves, not consumers” (White 1981: 543-4). I think we might be seeing something similar in financial markets, with quotes, time & sales, trends, and the like acting the part of the mirror.

And derivative to this, once the data becomes identifiable not just as a time & sales transaction, but as a transaction tied to someone they know already? Well, the importance of the data deepens and its effects on trading increases. In one of my favorite examples, Georges Harras and Didier Sornette created simulations to model traders acting independently, with some amount of shared information, and with a large amount of shared information. It turns out that the largest price swings in response to both news and market events comes when (simulated) agents enact a high level of copying among traders.

Swings are highest when insiders share information about their activities

Information about the market and generated by the market become vital as you trade on price changes rather than on anything particular you want or need from the transaction itself. (As an aside, I think this is intimately related to the shift from trading issues to trading generalized, objectified risk, but alas there is only so much to put in a post).

Some of the broader consequences of markets 2.0 are pretty remarkable.

Fragmentation of Liquidity via Dark Pools
We’re seeing something remarkable in the world of finance, an attempt in the name of ‘not affecting the market’ to minimize the market signal thrown off by trades by institutional investors. Because so many people are on the lookout for big traders making big trades, these big traders have begun disguising their activities and hiding their tracks. This is not new – when I worked on a trading floor, there was a shotgun-pumping hand signal for ‘reload’, meaning that someone is buying 5000 contracts 50 at a time. Big ‘paper’ (institutional investors) often were ‘lurking in the tall grass’, meaning that they were buying or selling little bits but everyone knew they had lots more trading to do.

But the phenomenon of dark pools is more significant because in the equities markets, institutional investors are opting out of the main trading markets in favor of private trading systems. These systems are literally parasitic on the prices of the larger trading exchanges – trades in these ‘dark pools’ are not made public, actors are not identified. This produces a ripe opportunity for fraud, of course, but it also is a way of saying that it is in at least some peoples’ interests to minimize the market signal their trading throws off.

And the pools of dark liquidity are large, and growing. Currently, about 10% of all equity trades are made through dark pools. Think about it this way: the market signal from trading is both increasingly relied upon to make trades and at the very same time, it is being masked by an increasing number of traders.

Monetizing (and transforming) the signal
A second consequence of markets 2.0 is the transformation of this signal from something ‘epiphenomenal’ to something both more central and having properties and value in its own right. Information markets fall broadly into this category, including my favorite, the Hollywood Stock Exchange. You pretend to buy or sell ‘movie stocks’ and the subsequent signals generated by these fake transactions are then used by the HSX as a product to sell:

HSX syndicates the data collected from the Exchange as market research to entertainment, consumer product and financial institutions and as original content to radio, television and print media. Founded in 1996, HSX is now a subsidiary of Cantor Fitzgerald, L.P. HSX is headquartered in Century City, California.

In and outside the financial arena, this emphasis on the formerly unmined bits of market slag – which refers to the byproducts produced when smelting ore into metal – have been transformed into marketing and market gold. Take a look at last.fm, or mint.

It’s time to get on top of this stuff, economic sociologists. Time past, in fact.

This is a further discussion of the Markets-are-culture/Markets-have-culture dichotomy, with some implications. The MAC-MHC dichotomy is really a shorthand for the troubling phenomenon whereby economic sociologists and fellow travelers either treat the market as the dependent variable (mkts-are-culture) or as an intrinsically economic phenomenon with sociological independent variables (mkts-have-culture). The former are most often associated with the social studies of finance/performativity crowd, but also those looking at, say, the constitution of new commodities, or market agency. The latter is most easily conceptualized as the embeddedness/network crowd – that markets are influenced by their embeddedness in political/cultural institutions.

Where the MAC goes wrong is that it treats markets like a windup toy – the sociological ‘action’ occurs in the creation of the market, but once it is wound up, it tools along as a straight-up economic market. Yes, there is constant maintenance, but the ‘work’ is largely antecedent to the market action. Where the MHC goes wrong is in its implicit assumption that, absent sociological forces, markets work like straight-up economic markets. That is, markets are only sociological to the extent that there are measurable effects of networks, culture, or political institutions on their operation. Otherwise, markets are what markets are.

My issue is two-fold: that this dichotomy is largely an either/or proposition, and that it tends to break along the market phenomena being studied. Unsettled markets, and cultural markets, tend to be analyzed with the MAC approach, while settled markets and more economic-ish markets tend to be analyzed with the MHC approach. It is no accident that MacKenzie and Millo look at the origins of the CBOE, or that Wayne Baker looks at the CBOE a decade later.

Which brings us to the current housing/subprime/credit crisis.
The current market crisis is: 1) a market linkage crisis, made possible via 2) new forms of commensuration (in the form of quantitative finance models and information technologies), resulting in 3) new forms of risk that have not as yet become calculable.

Commensuration and Risk

The derivatives market for mortgage products is typical in its general outline and highly particular with regards the quirks of housing. I’ve gone over some of this before, but the upshot is that the transformation that’s interesting is the securitization of mortgages into collatoralized debt obligations. The alchemy that allows investment banks to transform individual mortgage loans into pooled income streams, into tractable debt instruments, into salable investment units, is the magic here. The commensuration required to achieve this alchemy is impressive, though not particularly unusual (if you can make pollution into a commodity, you can make mortgage payments – at least mortgage payments are already in the form of money streams).

Commensuration is the process of taking two or more qualitative differences, and making them comparable via some third metric. So individual, qualitative distinctions in university applicants can be made comparable via test scores. Cost-benefit analysis is another good example, whereby distinctive potential public works projects can be evaluated according to a ratio and placed hierarchically according to some quantitative metric. This is not new as such – Wendy Espeland turned me on to it as my advisor, and it’s been an old concept made new again through Theodore Porter and others interested in quantification. But what it alerts us to in this case is that risk/return has itself become this kind of commensurative metric.

And further, it has itself become a sort of commodity to buy and sell. To be sure, no exchange sells risk as risk. But financial instruments can be (and are being) reduced to specific kinds of metrics – volatility, return, and historical distribution of gains/losses. This now-bundle of quantities is what I mean when I say that risk is now a commodity. Qualitative distinctions across different financial products (gold, currencies, hogs, equities, mortgages) are made commensurable via a quantitative assessment of their risk.

Market Linkages

Ian Domowitz, in 1993, made the argument that the increasing shift towards electronic trading in futures trading (which had hit equities already, but had not yet really had much impact in the commodity futures world) would provide the basis for merger and consolidation in the industry. The argument was that trading platforms would provide a kind of de facto standardization that would increase the possibilities that otherwise incommensurate systems could merge.

Domowitz actually understates what has happened since then. There have indeed been widespread mergers. There have indeed been widespread mergers. A number of European exchanges merged to form EuroNext, which merged with the NYSE to form NYSE Euronext. Deutsche Terminbörse (DTB) combined with the Swiss exchange (SOFFEX) to form EurExchange (Eurex) in 1998. The Chicago Mercantile Exchange and the Board of Trade merged in 2007.

More interestingly, though, is the commensurative effects electronic trading has had on global financial markets. This occurred through the combination of back-end electronic trading and clearing, along with the growth of organizations interested in trading across exchanges and not just within them. Exchanges in this model are not standardized per se, but they are made inter-operable via their electronic back-end systems. It is possible to ‘see’ prices across a wider range of markets now than ever before. It is the difference between having three open telephone lines and a 15-second-delayed to three exchanges trading three different products, and having all three markets on the same screen (and available to be manipulated by mathematical modeling) in real-time.

Combine this new organizational comparability with the commensurative activity I outlined above (a financial technology – some version of Harry Markowitz-type portfolio management) and we have something altogether new: rather than trading individual markets in currencies, real estate, government bonds, or corn futures, trading firms began seeing these things as variations of risk and were able to trade these things as risk.

It is as if the previous market world was a series of silos, each trading their own particular markets, using methods specific to the commodity and moving with the rhythms and cadences of their own particular worries. This new world is a post-silo one – not because shifts in oil are having direct consequences on the price of gold (though they may). But because both of these commodities are translatable into objectified risk.

New forms of Systemic Risk

Historically, the ‘solution’ to the problem of taking on too much risk is to diversify it. That is, to spread risk across a number of uncorrelated markets, so that if something goes wrong in one place, it will be offset by gains in another place. Add to this, in the new post-silo’d environment, the idea that risks are going to be a function of returns and volatilities. Insulation in the financial world, then, should be a matter of reducing vol, while diversifying investments.

But in this new linked environment, the opposite is happening as an effect. Consider, for instance, Amir Khandani and Andrew Lo’s analysis of the August 2007 blow-up among quantitatively-oriented hedge funds (it’s a pdf link). A sudden need on the part of some firms to reduce their positions resulted in a series of days where lots of firms got slammed. As an increasing number of firms had been making similar investments (or rather, using similar investment strategies) in equities hedging, the returns to those investments had declined. As a consequence, to generate good returns, more leverage was required. When a few firms began to unwind positions, a much larger community of connected traders got slammed.

And why did some firms have to unwind positions? Khandani and Lo suggest the following:

the events of August 2007 caught even the most experienced quantitative managers by surprise. But August 2007 is far more significant because it provides the first piece of evidence that problems in one corner of the financial system—possibly the sub-prime mortgage and related credit markets—can spill over so directly to a completely unrelated corner: long/short equity strategies. This is the kind of “shortcut” described in the theory of mathematical networks that generates the “small-world phenomenon” of Watts (1999) in
which a small random shock in one part of the network can rapidly propagate throughout
the entire network.

Brian Hayes, in a provocative talk at the NY Academy of Sciences, made a similar assessment: that we are seeing an increasing number of firms employing multi-dimensional investment strategies – that is, instead of focusing on equities or futures, firms are doing both.

In this new environment, this multi-dimensional strategy has the effect of linking rather than isolating financial crises. The end result is more systemic kinds of market crises – not crises within any particular market, but crises of markets. These are systemic crises, cutting across individual markets. The expression of these crises, as they are now and will likely continue to be for the foreseeable future, things like liquidity events. Liquidity events are generalized market failures caused by a lack of participation, unknown pricing, or an unwillingness to provide a ‘well-ordered market’. In liquidity events, markets don’t fall, they disappear. They fail.

The first:

In 1818 the directors of the London Missionary Society sent a mechanical clock to grace the church at its first station among the Tswana in South Africa. No ordinary clock – its hours were struck by strutting British soldiers carved of wood – it became the measure of a historical process in the making. Clearly meant to proclaim the value of time in Christian, civilized communities, the contraption had an altogether unexpected impact. For the Africans insisted that the “carved ones” were emissaries of a distant king who, with missionary connivance, would place them in a “house of bondage.” A disconsolate evangelist had eventually to “take down the fairy-looking strangers, and cut a piece off their painted bodies, to convince the affrighted natives that the objects of their alarm were only bits of coloured wood” (Moffat 1842: 339). The churchman knew, however, that the timepiece had made visible a fundamental truth. The Tswana had not been reassured by his gesture; indeed, they seem to have concluded that “the motives of the missionary were anything but disinterested.” And they were correct, of course. In the face of the clock they had caught their first glimpse of a future time, a time when their colonized world would march to quite different rhythms.

- Jean and John Comaroff, Of Revelation and Revolution, Volume 1, p. xi

And the second:

The first step toward implementing the system was a pilot study, involving one specialist and two or three stocks, to determine whether a specialist could physically monitor an automatically executing trading system while simultaneously performing other activities. Because of the narrow physical dimensions of specialists’ posts, Loss enlisted the Exchange’s carpenters to mount a wooden platform on a pedestal-pipe two feet above the pilot specialist’s post, where the ATS computer screen would stand and display its prices for all to see. A brochure explaining the new system was distributed to member brokers. The Friday before the pilot study was to begin, Loss left his office for what he described as his first relaxing weekend in months. The following Monday morning, all was in readiness for the start of the pilot program. Except…

As soon as Loss walked into his twenty-third-floor office he received a phone call from the Exchange’s floor manager. “You’ve gotta come down and see this,” said the manager.

“What am I going to see?” Loss remembers asking.

“Just come down.”

Minutes later, Loss saw for himself: In a pile of sawdust on the floor near the designated specialist’s post lay his Automated Trading System equipment. Someone had used an ax or a saw to cut a semicircle through the wooden platform and disconnect it from the post.

The jagged teeth marks on the wood, Loss recalled years later, “Looked like Jaws had just been through.”

- Marshall Blume, Jeremy Siegel and Dan Rottenberg, Revolution on Wall Street, pp. 195-196

From CoreFiling’s insight blog: “It won’t be very long before it is those documents – the bar-coded financial disclosures – that will be the primary materials consumed by financial market systems to help analysts and investors make decisions about the best way to invest. This is vastly more sophisticated than today’s processes that rely on slow and inaccurate re-keying of a subset of the financial information published by companies.”

This is commensuration more than just standardization, since the tags are designed to be specific to a particular business enough so that everyone is not required to give the same information, yet the tags are standardized enough that everyone is required to give information that can be made comparable. The pitch for companies (other than, because otherwise we’ll fine you and take away your business license) is that XBRL will make their financial reporting less costly, less prone to error, and ultimately more efficient.

Personally, I think this is a flat out misrepresentation of what’s going on here. XBRL helps one group of people orders of magnitude more than anyone else: investors. And the trade-off between increased government efficiency and business streamlining of compliance data on the one hand, and increased ability for data-gatherers for banks, hedge funds, and the investor class is totally totally off the charts. What this will end up doing is: 1) creating a standard way for companies to report financials; 2) creating some increased efficiency for government entities to keep tabs on the finances of these organizations; and 3) create a massive additional datastream for financial services and investment firms to work with. If you think it is a challenge for public firms to resist making short-term decisions based on financial analysts’ quarterly reports of earnings now, wait until this information is directly readable by quant trading models.

This would be an amazing dissertation topic. I would track: a) the creation of the standard; b) the adoption of the standard around the world; c) how XBRL is being incorporated into financial modeling; d) the before-and-after effects of XBRL on market prices for firms; and e) qualitatively, what gets excised from XBRL, or rather, what remains incommensurable about firms, governments, etc.

UBMatrix
XBRL’s main site
US SEC’s ‘Interactive Data Viewers’
Microsoft uses XBRL
US GAAP XBRL Taxonomy (GAAP is the accounting standard in the US)
CoreFiling

What matters in life is the equation probability × consequence. This point might appear to be simple, but its consequences are not.

Suppose that you are deriving probabilities of future occurrences from the data, assuming that the past is representative of the future. An event can be an earthquake, a market crash, a spurt in inflation, hurricane damage in an area, a flood, crops destroyed by a disease, people affected in an epidemic, destruction caused by terrorism, etc. Note the following: the severity of the event, will be in almost all cases inversely proportional to its frequency: the ten-year flood will be more frequent than the 100 year flood – and the 100 year flood will be more devastating.

Now comes word that some number (actually up to 5 now) of undersea cables have been cut, knocking a wide area of the Middle East off the internet, particularly the route between Europe and Egypt, and from there to the rest of the Middle East.

But where is the 100 year flood? What appears to have happened is a connected series of accidents and snafus, including possibly the weather, an anchor dragging along the sea floor, or who knows what. Mysterious. What I would contend, drawing from org theory, is that what is more dangerous than a 100 year flood is a sequence of preventable, unforeseen errors. That is, it is the disruption of the routine more than a freakish activity that is most likely to create accidents and crises. The routine fire in a particularly bad location, a minor earthquake in an unexpected place, a sequence of coupled organizational routines that lead one-to-another into disaster. It’s not that you shouldn’t be looking for the next giant storm that’s inevitably coming down the pike, but more problematic are the breaks in the caulk around the tub that floods the electrical box, that shorts the grid. Or a failure in the bathrooms at the airport.

Read your Saul Alinsky, and get in the game.

All else aside, I think we are approaching a new era of privacy issues, related to data mining and what counts as anonymity, what counts as data, who owns it, and who profits from it. The sociometrics model only works when we have things like smart-badges, ambient microphones, and unobtrusive surveillance. Alex Pentland, one of the MIT researchers (rightly) sees quite benign benefits from these technologies, including making non-face-to-face interactions more effective and efficient.

Transcripted from Alex Pentland’s website (.wmv movie link):

We can really measure exactly when you nod your head, and exactly the inflection of your voice, and exactly where you look, and all those things that you sort of know are important, it’s the social language. You can have a microphone that aims at different people and has a little bit of processing in there. We don’t listen to anything that violates privacy, we’re just looking at features of language. And that’s not particularly worrying to people because the words are never recorded, the meaning is never recorded. It’s really just social signals. ‘You were being pretty pushy there’. Or ‘You weren’t really being very forward there’. And we can combine that with measurements of performance to ask, how is it that your social presence affects your performance? Your working with other groups? And if we can do that, the evidence from the literature is that we can improve the working of groups, the functioning of organizations, by a lot. Not just 1 or 2 percent, but 20 or 30 or 40 percent. And of course, those are the things that drive profitability, that drive performance.

And it is indeed true that they are not recording words, or meaning, just social signals. But in an era where phone companies are data mining all voice and data activity, and financial firms are looking at aggregated transactions in the search for suspicious transactions (.pdf link), I would suggest that ‘just social signals’ are no longer a free resource. At minimum, we need a new language to capture what we used to, but no longer mean by anonymous.

And I’m not against what Pentland et al are up to, not by a long shot. I just think there are some issues that come into play that we’ve only begun to think about practically, and that we’ve not at all come to terms with theoretically or in the underlying social scientific research.