As these first-order investments began to slow down—at least compared with the rate at which the economy needed to grow—everyone began looking for a way to goose them up. That’s where derivatives came in. Instead of buying shares of stock or whole bonds or mortgage notes, derivatives let investors bet directly on the changing value of those instruments over time. Instead of buying actual stocks and bonds, investors buy the right to buy or sell these instruments at some point in the future.
Most of us understand how derivatives let investors make bigger bets with less money up front. Investors can purchase the options on a stock for a tiny fraction of the cost of the stock itself. They only pay for the actual stock once they exercise the option. Hopefully, the shares are worth a lot more than the strike price on the option, in which case the investor has made money.
But on a subtler level, the investor hasn’t merely leveraged his money; he has leveraged time. The option is a financial spring-loading mechanism, packing the future’s price fluctuations into today’s transactions. The investor is no longer investing in a company or even its debt, but rather in the changing value of that debt. In other words, instead of riding up the ascending curve of value, derivatives open the door to betting on the rate of change. Value over time, over time.
In the effort to compress time ever further, this process can be repeated almost ad infinitum. Traders can bet on the future price of derivatives—derivatives of derivatives—or the future price of those, or even just the volatility of price swings. At each step along the way, the thing being invested in gets more abstract, more leveraged, and more time compressed. In the real world, value can’t be created fast enough to keep up with the rate of expansion required by interest-driven currency, so it instead gets compressed into financial instruments that pack future value into present transactions.
Regular people end up compressing time into money the same way. Back when I still believed I could afford to purchase a three-bedroom apartment in New York City, the real estate agent showed me residences far out of my price range. She explained to me that I could qualify for an ARM, or an adjustable-rate mortgage, with a special introductory interest-only rate for the first five years. This way, I would have a monthly payment I could afford, even though I wouldn’t be working toward reducing the principal. After the first five years, the loan would adjust to a more normal rate—one much greater than I could afford. She told me that wouldn’t be a problem, since at that point I could simply refinance with another five-year ARM.
The idea was that that apartment would be worth more by the time I needed to get a new mortgage, so I would be able to refinance at a higher assessed value. As the market kept going up, the meager portion of the apartment I owned would be going up in value as well, giving me the collateral I would need to refinance. Each time, the cash amount I need to finance remains the same, but the percent of the apartment that debt represents goes down. So, in theory, the more the price of the apartment goes up, the more I own, and the more easily I can refinance.
Of course, that didn’t happen. Luckily for me I didn’t buy the apartment (or, rather, I didn’t buy the mortgage for that apartment). But the hundreds of thousands of Americans who did accept similar bargains ended up in big trouble. Instead of increasing, the value of the homes sunk below the amount that was owed on them. As of this writing, 31 percent of all residential properties with mortgages are under water, or what industry analysts call negative equity.26 Owing more on a thirty-year mortgage than one’s house is currently worth is just another way of saying present shock.
A few traders did see the writing on the wall and understood that the housing market had become too dependent on these temporally compressed lending instruments. Famously, even though they were selling packaged loans to investors and pension funds, Goldman Sachs determined that the financing craze was unsustainable and began betting against the mortgages through even more derivative derivatives called credit default swaps. When it came time for the company on the other side of those default swaps, AIG, to pay up, only the US government could print enough money to bail them out.27
By today’s standards, however, Goldman’s successively derivative bets seem almost quaint. Their investing decisions were still based in what they saw as the likely future of an unsustainable system. Their contribution to the tragedy from which they hoped to benefit notwithstanding, they were making a prediction and making a bet based on their analysis of where the future was heading. Temporally compressed though it may be, it is still based on making conclusions. Value is created over time. It is a product of the cause-and-effect, temporal universe—however much it may be abstracted.
A majority of equity trading today is designed to circumvent that universe of time-generated value altogether. Computer-driven or algorithmic trading, as it is now called, has its origins in the arms race. Mathematicians spent decades trying to figure out a way to evade radar. They finally developed stealth technology, which really just works by using electric fields to make a big thing—like a plane—appear to be many little things. Then, in 1999, an F-117 using stealth was shot down over Serbia. It seems some Hungarian mathematicians had figured out that instead of looking for objects in the sky, the antiaircraft detection systems needed to look only for the electrical fields.28
Those same mathematicians and their successors are now being employed by Wall Street firms to hide from and predict one another’s movements. When a bank wants to move a big quantity of shares, for example, it doesn’t want everyone to know what it is doing. If news of a big buy leaked out before the big buy could be completed, the price may go up. To hide their motions, they employ the same technique as stealth planes: they use algorithms to break their giant trade into thousands of little ones, and do so in such a way that they look random. Their sizes and timing are scattered.
In order to identify this stealthy algorithmic movement, competing banks hire other mathematicians to write other algorithms that monitor trading and look for clues of these bigger trades and trends. The algorithms actually shoot out little trades, much like radar, in order to measure the response of the market and then infer if there are any big movements going on. The original algorithms are, in turn, on the lookout for these little probes and attempt to run additional countermoves and fakes. This algorithmic dance—what is known as black box trading—accounts for over 70 percent of Wall Street trading activity today.
In high-frequency, algorithmic trading, speed is everything. Algorithms need to know what is happening and make their moves before their enemy algorithms can react and adjust. No matter how well they write their programs, and no matter how powerful the computers they use, the most important factor in bringing algorithms up to speed is a better physical location on the network. The physical distance of a brokerage house’s computers to the computers executing the trades makes a difference in how fast the algorithm can read and respond to market activity.
As former game designer Kevin Slavin has pointed out in his talks and articles,29 while we may think of the Internet as a distributed and nonlocal phenomenon, you can be closer or farther from it depending on how much cable there is between you and its biggest nodes. In New York, this mother node is fittingly located at the old Western Union Building on 60 Hudson Street. All the main Internet trunks come up through this building, known as a colocation center, or carrier hotel. Its fiber optic lines all come together in a single, 15,000-square-foot “meet me” room on the ninth floor, powered by a 10,000-amp DC power plant.
If a firm owns space anywhere in that building, its computers are sitting right on the node, so its algorithms are operating with a latency of effectively zero. Algorithms running on computers all the way down on Wall Street, on the other hand, are almost a mile away. That adds up to about a two-millisecond delay each way, which means everything. A fast computer sitting in the carrier hotel can see the bids of other algorithms and then act on them before they have even gone through. The purpose of being in close isn’t simply to front-run the t
rade but to have the ability to fake out and misdirect the other side. Algorithms don’t care what anything is really worth as an investment, remember. They care only about the trade in the present.
In a virtual world of black box trading where timing is everything, getting closer to the “meet me” room on the ninth floor of 60 Hudson Street is worth a lot of money. Firms are competing so hard to position their computers advantageously that the real estate market in the neighborhood has been spiking—quite unpredictably, and only explained by the needs of these algorithms for quicker access. Architects are busy replacing the floors of buildings with steel in order to accommodate rooms filled with heavy servers. Both the real estate market and the physical design of Lower Manhattan are being optimized for algorithms competing to compress money into milliseconds. It is becoming a giant microchip or, better, a digital stopwatch.
When the only value left is time, the world becomes a clock.
LIVING IN RAM
When an economy—or any system, for that matter—becomes so tightly compressed and abstracted that only a computer program can navigate it, we’re all in for some surprises. To be sure, algorithms are great at increasing our efficiency and even making the world a more convenient place—even if we don’t know quite how they work. Thanks to algorithms, many elevators today don’t have panels with buttons. Riders use consoles in the lobby to select their floors, and an algorithm directs them to the appropriate elevator, minimizing everyone’s trip time. Algorithms determine the songs playing on Clear Channel stations, the ideal partners on dating websites, the best driving routes, and even the plot twists for Hollywood screenplays—all by compressing the data of experience along with the permutations of possibility.
But the results aren’t always smooth and predictable. A stock market driven by algorithms is all fine and well until the market inexplicably loses 1,000 points in a minute thanks to what is now called a flash crash. The algorithms all feeding back to and off one another get caught in a loop, and all of a sudden Accenture is trading at $100,000 a share or Proctor & Gamble goes down to a penny.30 Ironically, and in a perfect expression of present shock, the leading high-frequency trading exchange had a high-profile flash crash on the same day it was attempting to conduct its own initial public offering—and on the same day I was finishing this section of the book. The company, BATS Global Markets, runs a stock exchange called Better Alternative Trading System, which was built specifically to accommodate high-frequency trading and handles over 11 percent of US equities transactions. Their IPO was highly anticipated and represented another step in technology’s colonization of the stock exchange.
BATS issued 6 million shares for about $17 each, and then something went terribly wrong: their system suddenly began executing trades of BATS stock at three and four cents per share.31 Then shares of Apple trading on the BATS exchange suddenly dropped 10 percent, at which point the company halted trading of both ticker symbols. Embarrassed, and incapable of figuring out quite what happened, BATS took the extremely unusual step of canceling its own IPO and giving everyone back their money.
What will be the equivalent of a flash crash in other highly compressed arenas where algorithms rule? What does a flash crash in online dating or Facebook friendships look like? What about in criminal enforcement and deterrence, particularly when no one knows how the algorithms have chosen to accomplish their tasks? Algorithmic present shock is instantaneous. Its results impact us before it is even noticed.
At least on the stock market, participation in this pressure cooker is optional. Many investors are no longer willing to take the chance and have decided not to share their marketplace with algorithms that can outcompress them or, worse, spin the whole system out of control. Stock traders are leaving Wall Street in droves and opting for the dark pools of Geneva where they can exchange shares anonymously. While dark pools may have first served simply to conceal institutional-trading activity from the public, now they are being used by investors who want to conceal their trading activity from algorithms—or simply distance themselves from the effects of algorithmic spring-loading.32
They have resorted to one of the two main strategies for contending with the perils of overwinding in the short forever, which is simply to avoid spring-loaded situations altogether. Traders lose the advantages of superconcentrated, high-frequency trades, but regain access to the accretive value of their investments over time. They lose the chance to cash in on the volatility of the marketplace irrespective of what things may be worth, but get to apply at least a bit of real-world knowledge and logic to their investment decisions. Plus, they are insulated from the chaotic feedback loops of algorithms gone wild.
It’s not a matter of abandoning the present in order to make smart investment decisions and more secure trades. If anything, nonalgorithmic trading represents a return to the genuine present of value. What is this piece of paper actually worth right now in terms of assets, productivity, and, yes, potential? Rather than packing in nine derivative layers’ worth of time over time over time into a single inscrutably abstract ticker symbol, the investment represents some comprehensibly present-tense value proposition.
There are many other healthy examples of businesses, communities, and individuals that have opted out of spring-loading in order to enjoy a more evenly paced world. In Europe, where the leveraging of the long-distance, debt-generating euro currency has proved catastrophic for local industry and commerce, people are turning to alternative currencies that insulate them from the macroeconomic storm around them.
In the Greek town of Volos, for example, citizens are experimenting with a favor bank. Mislabeled a barter economy by most journalists, this effort at a cashless economy is actually an exchange network.33 Everyone has an account online that keeps track of how many Local Alternative Units, or tems, they have earned or spent. People offer goods and services to one another, agree to terms, and make the exchange. Then their accounts are credited and debited accordingly. No one is allowed to accumulate more than 1,200 tems, because the object of the system is not to get individuals wealthy by storing currency over time, but to make the community prosperous by encouraging trade, production, and services.
The man who devised the website for one of these networks says that the euro crisis had rendered the Greeks “frozen, in a state of fear. It’s like they’ve been hit over the head with a brick; they’re dizzy. And they’re cautious; they’re still thinking: ‘I need euros, how am I going to pay my bills?’”34 This is the vertigo of the short forever, present shock caused by absolute dependence on a time-bound medium of exchange. It was so overleveraged that when it broke, it exploded like an overwound spring. The scales of interaction were an almost quantum mismatch: global investment banks and national treasuries do not have the same priorities as tiny villages of people attempting to provide one another the foods and basic services they need to sustain themselves. The macroeconomics of Greek debt—as well as that of the Spanish, Italian, and Portuguese—accumulated and compressed over decades, but unraveled in an instant, overwhelming the real-time economy of people.
The scale and nature of their new form of exchange encourages real-world, real-time interaction as well. Members of the various local networks meet on regular market days to purchase goods or negotiate simple service agreements for the coming week. This is a presentist economy, at least in comparison with the storage-based economics of the euro and traditional banking. Nothing is spring-loaded or leveraged, which makes it harder for these markets to endure changing seasonal conditions, support multiyear contracts, or provide opportunities for passive investment. But this style of transaction still does offer some long-term benefits to the communities who use it. Human relationships are strengthened, local businesses enjoy advantages over larger foreign corporations, and investment of time and energy is spent on meeting the needs of the community itself.
Greek villagers in the shadow of a failing euro aren’t the only ones abandoning time-compressed investment strategies. The town
of Volos represents just one of hundreds of similar efforts throughout Europe, Asia, and Africa, where centrally administrated, interest-bearing currencies no longer support the real-time transactions of people—or ask so much for the privilege as to be impractical for the job. These currencies are not limited to struggling, indebted nations but are emerging in the lending economies such as Germany as well, where macrofiscal trends inhibit local transactions.35
Meanwhile, on the other side of the economy—and the world—commodity traders have been present shocked by the real-time nature of today’s agriculture and marketplace. Commodity traders generally buy and sell futures on particular goods. Their function (other than making money for themselves) is to help farmers and buyers lock in prices for their goods in advance. A clothing manufacturer, fearing rising cotton prices, may want to purchase its crop in advance. The commodity trader is hired to purchase futures on cotton, specifying the date and price. Meanwhile, a cotton grower—who suspects that prices on his commodity may actually go down over the next year—may want to get cash at today’s rates. He calls his commodity trader, who makes a deal across the commodities pit with the trader working for the clothing maker.
The traders themselves understand the subtleties of the time compression that they are performing for their clients. Their business is about storing value, wagering on the future, and then using contracts to leverage present expectations against future realities. They may make a lot of money without creating any tangible value, but they do help create liquidity in markets that need it and force a bit of planning or austerity when something is going to be in short supply.
Present Shock: When Everything Happens Now Page 19
