Superstar prices would ruin artists’ integrity; superstar dealers “trample the morals of the market, treating an artwork overtly as a commodity, with status as well as investment overtones” (151). These dealers potentially further destabilize the art market, resulting in negative effects on everyone else.

That artists themselves would bear the brunt of the suffering as a result of lower prices was a feature, not a bug. True, these lower, more slowly rising prices for an artist’s work benefit collectors much more than artists. Prudent pricing would make artists more responsible. Velthuis also argues that “for most artists pricing prudently means that they cannot make a living form their work” (155). But for dealers, this moment of purification allowed them to re-establish control over art prices more broadly, and to do so within a moral framework.

There are parallels to the so-called Pain Caucus, so named because in the face of massive unemployment, these policy-makers and federal reserve chairpeople think that short-term deficits are the crises on which we should focus. There is a moral element here as well: that after the profligate borrowing and spending of the early ‘aughts, workers need to suffer some. Why it is workers who need to bear the brunt of this suffering, I leave to your own political imagination.

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.

Prices in financial markets are not like prices in other kinds of markets. When you buy gas, you see prices posted for the various grades of gasoline. These prices are for buying gas; theoretically speaking, this is: a) a posted, fixed price – as opposed to a barter or negotiated price); and b) this is a market-taking rather than a market-making price – the price is for something that you can only buy, but not sell. You can only take what is offered, not make your own market.

By contrast, financial prices (and I’m going to use stock prices and financial prices interchangeably, though they are not necessarily interchangeable) are negotiated prices for a continuous auction. There is no single price for a futures contract, a stock issue, or a derivative. Instead prices consist of four main elements: bids, offers, bid quantities, and offer quantities. Buyers provide a price and quantity at which they would be willing to purchase a stock (a bid), while sellers provide a price and quantity at which they would be willing to sell (an offer). When these bids and offers “match” – if a buyer is willing to meet a sellers offer, or a seller is willing to meet a buyers bid – a trade will take place, until there are no longer any bids or offers at that price. If the bids and offers are not matched, there will be no trade, and the current “price” is understood to be the currently-best bids and offers.

For example, if there is a bid to buy 100 shares of Intel stock (INTC) at a price of twenty, and an offer to sell 50 shares at a price of 21, the current “price” is “twenty bid at twenty-one offer,” or 20-21. If the potential buyer and seller decide to stay at their respective prices, no trade will occur; the market will simply stay 20-21 for as long as they hold out. If, however, the buyer decides to raise her bid to 21, a trade will take place: she will buy the 50 shares that the seller was willing to offer, and have 50 shares left over still to buy. Those 50 shares left over would become the currently-best bid. The price would move to “21 bid.” If the next best offer were 22, the price would be 21-22. If the next best offer were 23, it would become 21-23. This process continues throughout the trading session, with the market constantly fluctuating depending on the bids and offers available to trade.

In this fashion, the last reported price is a useful piece of information, but much less than complete. More complete is to know the current best bid, best offer, and the quantities associated with them.

Even more complete would be to know the entire list of bids and offers behind the currently-best ones.

Why is that? Well, you could imagine that you think you’re looking at a market like the snaily one here:

If this were the case, perhaps you might say that there is mildly more shares on the buy side than the sell side, or upward pressure on the price. If you were a seller, deciding whether or not to take the best bid available (in the hopes that the share prices were falling, or simply trying to sell your existing shares at the best possible price), you might think you’re making a decision based on a relatively small bid. My willingness to buy or sell at a particular price is contingent not just on my own absolute feelings about the stock price, but also on how much I think I can get for it – which is a question about not just my intentions, but others’ intentions as well.

Because, what if you knew that the market looked like this snaily-cat one?

If this were the case, where there is relatively strong resistance at 19 (lots of people wanting to buy, or a single big buyer at 19), your decision-making process might be different. And knowing the intentions of buyers and sellers not yet in the marketplace makes a huge difference.

Each trade involves a trade-off, between being first and getting the price you want on the one hand, and signaling your intentions to the rest of the world on the other. Since most financial markets work with some version of a ‘First in, First out’ algorithm (or FIFO, meaning that at the same price, earlier orders get filled before later orders), making your intentions visible early gets you a good spot in the FIFO sequence, ensuring a better price.

But, any time you signal that you want to buy or sell something, other people can react to your desires, potentially changing their own in the process. This might get you a worse price, if you ‘tell’ other people you are a buyer, they might raise their offers as sellers. And vice versa. If you know that someone is itching to buy 10,000 shares of stock you own, you may decide to sell at a higher price. And so, better/faster price vs. signaling your intentions and getting a worse price.

How much of the total buy and sell orders are shown varies dramatically by market. Some exchanges only reveal the size at the best bid and offer, others show their book 10 or 20 prices deep. Here’s the trading screen from the Swiss Exchange:
Here you can see bids, offers, and quantities well above and below the current market price. Other places don’t show any bids and offers.

And it’s between transparency and non-transparency that the arguments about dark pools of liquidity. Dark pools are alternative trading systems that report trades but not bids, offers, and quantities. They are trying to solve the problem of large orders moving the markets too much in their execution. But they are doing it badly.

Burned Art and Murakami from Peter Levin and Jennifer Lena on Vimeo.

Our tech is still low, but hopefully getting better (it looks better when it’s on Vimeo and not embedded). And we are again deeply hoping for some comments, reactions, and thoughts. And our first video is still around as well, if you are interested in seeing where it began.

If you have thoughts, we’d love to hear them, but we hope you’ll be at least a little kind – this is one of those situations where your self-identity as brutally honest should not trump your self-identity as gracious.

And the links to the videos we reference: Fashion File: Making an Hermes Bag, and Contemporary Art Preview

Art and Markets 1: Selling Crafts and Art from Peter Levin on Vimeo.

It is also central to the making of commodities, as I’ve argued before. But if you are deciding how to make real-world investments, it is worth understanding the criteria by which the commodities you are interested in are judged. This is not a direct ‘buy company x’, ‘sell company y’ kind of argument, just a way to help understand where experts are coming from. It is also where experts are most likely to be wrong in their misapplication of measures to the values they are measuring. That’s the Moneyball argument, that the ways that players were being commensurated were at best inaccurate.

In any case, I thought I’d post a table of what I have in mind, and see if it leads anyplace interesting. This is what blogs are for, right?

In these cases, the idea is that a simple quantified measure is not sufficient; you need to know enough content to understand the criteria used to make the transformative assessments of qualities through quantities. And though VaR has beautiful problems, it is in fact the ways that assessments across different kinds of financial instruments are made; likewise centrality, price per square foot, etc.

Incidentally, this conception I think bridges some of the more highfalutin discussions of performativity and social studies of finance to the more mundane world of organizations and work. But that’s not my point right here and now.

Where to begin? Well, begin at the beginning. Banks make home loans. But they would prefer (or have been convinced that they prefer) not taking the risk of holding on to those loans. It ties up capital, and individual loans run the risk of non-repayment. Banks tend to be fee-happy and risk-averse, so if they can take fees without incurring risk, they would like to do so.

Mortgages are odd ducks, finance-wise, because the US government decided long ago that there is social value in supporting home ownership. As such, interest paid on a primary home mortgage is tax deductible. At least one analysis notes that this is the third highest tax expenditure for the federal government from 2005-2009. Let me repeat this, though it is an aside here – the tax deduction for interest on mortgage payments is the third highest tax expenditure, behind exclusions for employer contributions to health care and employer contributions to retirement plans.

The way a (fixed) mortgage works is that early payments are mostly interest, with a sliver of principal; while later payments are mostly principal, with a sliver of interest. In addition, homeowners have the option of pre-paying their principal, by making an extra payment per month, for example – this reduces future interest payments, and reduces the overall cost of a loan.

What a pain for a bank! Uncertainty over how much the total loan amount will be, uncertainty over the timing of the loan. And sensitivity to changes in interest rates, since when rates go down, homeowners can take out a new loan at a lower rate, pay off the entirety of their existing loan, and ‘take equity’ out of their homes.

So, how can a bank off-load this risk, and instead of sitting on the uncertainty of existing loans, take some fees and reduce their exposure? Securitization! Taking a bunch of loans, packaging them into a single income stream, and then re-selling that income stream off to investors is one way for commercial banks to take fees from mortgages but also to get them ‘off the books’ by selling them immediately to investment banks. But how to do this financial alchemy?

By grouping a bunch of loans, it is possible to create a common income stream of monthly payments. This stream can then be packaged and sold (this is, in principle, how municipal bonds are turned into investment vehicles as well). An investment bank can transform these individual loans into streams of payments. But housing is special! Fannie Mae, a quasi-governmental institution that insures many home loans, does this work for mortgages. Here’s how it works:

As Fannie Mae describes it:

The mortgage-backed security process begins with a mortgage loan. The loan is made by a financial institution or other lender to a borrower to finance or refinance the purchase of a home or other property. These loans are made to borrowers under varying terms (e.g., 15-year, 30-year, fixed-rate, adjustable-rate, etc.); during the life of the loan, the balance is generally amortized, or reduced, until it is paid off. The borrower usually repays the loan in monthly installments that typically include both principal and interest.

Because mortgage loans may take years to pay off, lenders must find ways to replenish their funds in order to make more mortgage loans. To do this, lenders sell groups of mortgages with similar characteristics into the secondary mortgage market to issuers or guarantors of mortgage-backed securities, including Fannie Mae.

It is possible to create an income stream from one person’s mortgage, but n=1 is a bit of a problem. Even if the person seems like a good risk, what if they flake? What if they sell their home tomorrow and repay the loan immediately? Refinance? Go broke? There are too many variables with any given individual to securitize their loan. But a gaggle of owners, that’s something else. If you could pool homeowners (or mortgages, rather) into clusters with similar characteristics, then you have some degree of certainty. It is a process not unlike stratified sampling – 3br homes, similar demographics, with a dog, and we can guess that as a whole this group becomes somewhat more predictable.

Now, this stream of payments can then be turned into something with bond-like qualities.

Normally, Fannie Mae will issue a security in exchange for the underlying pool of mortgages. This is the ‘collateral’ part of the collateralized mortgage obligation. Fannie Mae will swap the underlying loans in exchange for a bundled payment obligation based on those loans – and then, Fannie Mae will distribute disparate payments into my new corporate entity. Let’s call it Peter’s Pool of Payments. PPP is the securitized version of the underlying pool of mortgages. My stream of mortgage payments has now been packaged.

I can now take my stream of payments and disburse them how I like:

For instance, I can split this security into 1000 equal shares, and sell the shares. Or I can separate payments into ‘classes’, with the first class getting paid fully before the second class, before the third, etc. Or I can split the interest and principal payments. These different kinds of mortgage-backed securities go by such names as: Grantor Trusts, REMICs, and SMBs, all designating the appropriate ways they have been classed or ‘tiered’ into payments.

This is all based on how well and to whom you could sell these shares. If you have investors who are sensitive to interest rate risk, you can have one class pay out interest from the pool of mortgages, while another class pays out principal. In theory, this means that the interest payments are more front-loaded, and more sensitive to interest rate changes. Another way to go is to tier (or tranche) the shares by default risk, so that class one gets fully paid before class 2, before class 3.

Ok, that’s it. I want to tell a story about how Merrill Lynch used CMOs – and in particular a vehicle that split a pool of mortgages into Interest-only and Principal-only financial instruments – to facilitate totally screwing some folks over. Who knows, maybe those folks are you!

(As a side-note, which has received much ink, and deservedly so, much of the sub-prime lending problems are coming because these CMOs are being used for more complicated derivative transactions** at the same time that: (a) the pool of mortgages has grown dramatically; and (b) the payment of underlying mortgages has become dependent on continually rising housing prices combined with continually falling interest rates. Since (b) has not continued, and (a) is institutionalized in our financial system, the system breaks down. This probably needs its own post. Maybe with different graphics.)

**For instance, how much should one of these CMOs, which is a 15-year pseudo-bond, be worth today? And then, how much should a future on this pseudo-bond be worth? That is, how much is the future obligation on this 15-year pseudo-bond be worth today? Derivatives on these instruments gets tricksy very quickly. At the end of 2007, Citibank had something like $50 billion in CDO exposure, UBS had $20-something billion, Merrill Lynch disclosed $27 billion.

Happy January Effect.

There are two main ways, though we should frankly see both as conventions. The first is cost-plus, as she rightly points out. The ‘plus’ is something to fuss about, and how much to pay artists does just regress the problem back into costs. In other words, deciding an appropriate ‘cost’ for artists reproduces the problem. Artists may say, well, “pay me cost-plus, where cost is expenses to get out there, lodging, time-for-my-talent.” And then we say deciding an appropriate ‘cost’ for time-for-my-talent reproduces the problem. And so on.

Which leads to the second method, something like ‘what the market will bear.’ In practice, this means what people are willing to pay, combined with what you’re willing to accept. But – the world doesn’t come with supply-and-demand curves attached. Most markets are not totally variable-priced markets – markets where every price is individually negotiated. Capital markets are exception more than rule, and even when organizations do try to achieve this ideal, we think they kind of suck (I’ve long thought it would be fascinating to walk down the aisle of an airplane in flight surveying passengers about how much they paid, then making your results public. And by fascinating I mean riot-inducing.)

So, the interesting part is in how the theory of ‘what the market will bear’ meets the practice of ‘how much do we charge for this concert/album/art?’ And all of JL’s subsequent discussion is indeed the practical implementation of this second strategy.

Of course, this doesn’t answer her question, which is, what’s the appropriate comparable: 1) compare by artist?; 2) compare based on format?; or 3) compare based on venue? My real answer to that specific question is, I have no idea. It certainly appears that the concert organizers are pushing for ‘based on venue’, while some people recognize ‘based on artist’. I would say that the ‘based on format’ is harder, since the format is the unique part.

To get more general, let me hypothesize that comparables are more likely to be based on things that are, well, comparable. A CD of a totally unique band with a totally unique sound would be more likely to be priced based on ‘other CDs’. Outsider artists doing unique photography would be compared with other outsider artists (rather than other photographs). This is certainly an empirical question, but there’s my stake in the ground.

There is still a paper or book or something to be written about baselines, but this is not it. My nephew (who loves Mr. Krabs from Spongebob) will tell you that you need money to buy one of his toy cars from him, but the amount of money you need is incomprehensible. It’s all ‘coin money’ to him, regardless of whether it’s 3 dimes, two quarters, or 20 pennies – he has no baselines and so no means by which to compare.

Clumping quantitative derivatives traders together is getting to be a little too gross a distinction, but there are, it seems, a small number of strategies being pursued by these kinds of traders. Two of the most important are: 1) pairs trading and 2) long-short equity trading. Pairs trading is where an historical relationship between two securities are found, tested, and analyzed; and then when the pairs get out of this relationship for no good reason, trades are executed expecting that this relationship reasserts itself. This differs in spirit, but not entirely in practice, from arbitrage trading, which is trading on the differences between two kinds of products which are ‘supposed’ to be identical (a company’s stock and its bonds, for example, or a company’s stock in two trading environments).

Long/short equity trading is when a market-neutral position is taken, whereby long positions are made up of ‘losers’ (under-performing stocks), while short positions are made up of ‘winners’ (over-performing stocks). This strategy is based on the idea that outliers will eventually revert to the mean – effectively betting on consistent market overreaction. Given the behavioral finance work on how people tend to, you know, overreact to news, this makes sense. And because your position is effectively market-neutral – long positions offset short positions – it is possible (for broker/dealers) to highly leverage these positions.

These are two main strategies, and for the most part, they work. So, what happened in August? Here’s my reading of the two articles: First CDOs – the bundled up derivatives from housing loans that are spoken about when we talk about the ‘sub-prime lending crisis’, went into the sink. Second, an outlying series of days in the week of August 6 caused a dramatic drop in the long-short strategy.

The effects of these two events (possibly, though it’s not clear if they were linked) was the following: a bunch of funds began selling equities to liquidate positions in order to meet margin calls for their CDO investments, which pushed pairs trading into abnormal positions. This caused more hardship, and all sorts of things happened – funds sold blue-chips to raise margin cash; and bought back short positions causing price relationships to swing further out of historical norms. Khandani and Lo speculate that one or more of the long-short funds liquidated its position, and that it turned out that many other funds were also engaged in these kinds of trades, so that when one or two got out, everyone else got hammered.

What makes this interesting? A couple of things. First, as markets are brought into tension with one another via pairs trading, they effectively create a new product, with new features and sometimes not-well-understood properties. A Collateralized Debt Obligation (CDO) can package together such disparate cash flows as sub-prime loans and airplane leases; by virtue of the CDO itself, these otherwise distinct streams are linked together. Second, as traders and funds are brought into tension with one another via sharing similar trading strategies, it creates collective action events that simply do not resemble atomized traders. It’s not surprising that the expression of these problems is always liquidity – IMHO that’s a fancy finance way of saying that people act in concert, when they are ‘supposed’ to act according to their own preferences.

Some other interesting things from the Blow-up:
- it’s estimated that 38% of all equities are traded automatically, and that this percentage may rise to over 50% in the next three years;
- computers for some high-frequency traders execute hundreds of thousands of trades every day.

There is a giant blind spot here, rooted in financial theory, enabled by even cautious policy-makers and economists, and executed by very smart people. Sometimes at home when we watch commercials, my partner and I play a game called ‘good for them, or good for us’ – to see if a product or policy or feature is good for the company or good for consumers. I would guess that the number of people who would argue that derivatives trading today is ‘good for us’ is very small.