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Airplanes can't fly because it's too hot? Last spring , Dow Jones launched a new service called Lexicon, which sends real-time financial news to professional investors. This in itself is not surprising. The company behind The Wall Street Journal and Dow Jones Newswires made its name by publishing the kind of news that moves the stock market. They just want data—the hard, actionable information that those words represent. Lexicon packages the news in a way that its robo-clients can understand.
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It scans every Dow Jones story in real time, looking for textual clues that might indicate how investors should feel about a stock. It then sends that information in machine-readable form to its algorithmic subscribers, which can parse it further, using the resulting data to inform their own investing decisions. Lexicon has helped automate the process of reading the news, drawing insight from it, and using that information to buy or sell a stock.
That increasingly describes the entire financial system. Over the past decade, algorithmic trading has overtaken the industry.
From the single desk of a startup hedge fund to the gilded halls of Goldman Sachs , computer code is now responsible for most of the activity on Wall Street. By some estimates, computer-aided high-frequency trading now accounts for about 70 percent of total trade volume.
Algorithms have become so ingrained in our financial system that the markets could not operate without them. At the most basic level, computers help prospective buyers and sellers of stocks find one another—without the bother of screaming middlemen or their commissions.
High-frequency traders, sometimes called flash traders , buy and sell thousands of shares every second, executing deals so quickly, and on such a massive scale, that they can win or lose a fortune if the price of a stock fluctuates by even a few cents.
Other algorithms are slower but more sophisticated, analyzing earning statements, stock performance, and newsfeeds to find attractive investments that others may have missed. The result is a system that is more efficient, faster, and smarter than any human. It is also harder to understand, predict, and regulate. Algorithms, like most human traders, tend to follow a fairly simple set of rules. But they also respond instantly to ever-shifting market conditions, taking into account thousands or millions of data points every second.
At its best, this system represents an efficient and intelligent capital allocation machine, a market ruled by precision and mathematics rather than emotion and fallible judgment. But at its worst, it is an inscrutable and uncontrollable feedback loop.
Individually, these algorithms may be easy to control but when they interact they can create unexpected behaviors—a conversation that can overwhelm the system it was built to navigate. On May 6, , the Dow Jones Industrial Average inexplicably experienced a series of drops that came to be known as the flash crash , at one point shedding some points in five minutes.
Less than five months later, Progress Energy, a North Carolina utility, watched helplessly as its share price fell 90 percent. Also in late September, Apple shares dropped nearly 4 percent in just 30 seconds, before recovering a few minutes later. But most observers pin the blame on the legions of powerful, superfast trading algorithms—simple instructions that interact to create a market that is incomprehensible to the human mind and impossible to predict.
A good session player is hard to find, but ujam is always ready to rock. The Web app doubles as a studio band and a recording studio. It analyzes a melody and then produces sophisticated harmonies, bass lines, drum tracks, horn parts, and more. Once it recognizes them, the algorithm searches for chords to match the tune, using a mix of statistical techniques and hardwired musical rules. The rules-based module then uses its knowledge of Western musical tropes to narrow the chord options to a single selection.
The service is still in alpha, but it has attracted 2, testers who want to use the AI to explore their musical creativity—and they have the recordings to prove it. In this respect at least, ujam is like a human: It gets better with practice.
Ironically enough , the notion of using algorithms as trading tools was born as a way of empowering traders. Before the age of electronic trading, large institutional investors used their size and connections to wrangle better terms from the human middlemen that executed buy and sell orders. It took him nearly three years to build his stock-scoring program. He wanted his algorithmically derived system to look at stocks in a fundamentally different—and smarter—way than humans ever could.
They identified a number of variables—traditional measurements like earnings growth as well as more technical factors. He then tried to determine the proper weighting of each characteristic, using a publicly available program from UC Berkeley called the differential evolution optimizer.
Bradley started with random weightings—perhaps earnings growth would be given twice the weight of revenue growth, for example. Then the program looked at the best-performing stocks at a given point in time.
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It then picked 10 of those stocks at random and looked at historical data to see how well the weights predicted their actual performance. Next the computer would go back and do the same thing all over again—with a slightly different starting date or a different starting group of stocks.
For each weighting, the test would be run thousands of times to get a thorough sense of how those stocks performed. Then the weighting would be changed and the whole process would run all over again. Once this process was complete, Bradley collected the 10 best-performing weightings and ran them once again through the differential evolution optimizer.
The optimizer then mated those weightings—combining them to create or so offspring weightings. Those weightings were tested, and the 10 best were mated again to produce another third-generation offspring. The program also introduced occasional mutations and randomness, on the off chance that one of them might produce an accidental genius.
These academics brought to trading desks sophisticated knowledge of AI methods from computer science and statistics. And they started applying those methods to every aspect of the financial industry. These algorithms break up and optimize those orders to conceal them from the rest of the market. This, confusingly enough, is known as algorithmic trading.
Still others are used to crack those codes, to discover the massive orders that other quants are trying to conceal. This is called predatory trading. The result is a universe of competing lines of code, each of them trying to outsmart and one-up the other.
And the job of the algorithmic trader is to make that submarine as stealth as possible. Rather than focus on the behavior of individual stocks, for instance, many prop-trading algorithms look at the market as a vast weather system, with trends and movements that can be predicted and capitalized upon.
These patterns may not be visible to humans, but computers, with their ability to analyze massive amounts of data at lightning speed, can sense them. The partners at Voleon Capital Management, a three-year-old firm in Berkeley, California, take this approach. Voleon engages in statistical arbitrage, which involves sifting through enormous pools of data for patterns that can predict subtle movements across a whole class of related stocks.
Situated on the third floor of a run-down office building, Voleon could be any other Bay Area web startup. Geeks pad around the office in jeans and T-shirts, moving amid half-open boxes and scribbled whiteboards. The other cofounder, CEO Michael Kharitonov, is a computer scientist from Berkeley and Stanford who formerly ran a networking startup.
To hear them describe it, their trading strategy bears more resemblance to those data-analysis projects than to classical investing.
They require you to look at hundreds of thousands of things simultaneously and to be trading a little bit of each stock. To the human eye, an x-ray is a murky, lo-res puzzle. But to a machine, an x-ray—or a CT or an MRI scan—is a dense data field that can be assessed down to the pixel. No wonder AI techniques have been applied so aggressively in the field of medical imaging.
But the machines can. It aggregates hi-res image data from multiple sources—x-rays, MRIs, ultrasounds, CT scans—and then groups together biological structures that share hard-to-detect similarities. For instance, the algorithm could examine several images of the same breast to gauge tissue density; it then color-codes tissues with similar densities so a mere human can see the pattern, too.
At the heart of the technology is an algorithm called Hierarchical Segmentation Software , which was originally developed by NASA for analyzing digital images from satellites.
This way, hidden features or diffuse structures within a region of tissue can be identified. In other words, puzzle solved. In late September , the Commodity Futures Trading Commission and the Securities and Exchange Commission released a page report on the May 6 flash crash. Both trades were subsequently canceled. The activity briefly paralyzed the entire financial system.
The report offered some belated clarity about an event that for months had resisted easy interpretation. In the wake of the flash crash, Mary Schapiro, chair of the Securities and Exchange Commission , publicly mused that humans may need to wrest some control back from the machines.
In the months after the flash crash, the SEC announced a variety of measures to prevent anything like it from occurring again. The agency is considering requiring trading algorithms to include a governor, which limits the size and speed at which trades can be executed.
But these are not ways of controlling the algorithms—they are ways of slowing them down or stopping them for a few minutes. Today a single stock can receive 10, bids per second; that deluge of data overwhelms any attempt to create a simple cause-and-effect narrative. For individual investors, trading with algorithms has been a boon: Today, they can buy and sell stocks much faster, cheaper, and easier than ever before.
But from a systemic perspective, the stock market risks spinning out of control. Even if each individual algorithm makes perfect sense, collectively they obey an emergent logic—artificial intelligence, but not artificial human intelligence. It is, simply, alien, operating at the natural scale of silicon, not neurons and synapses.
We may be able to slow it down, but we can never contain, control, or comprehend it. Felix Salmon felix felix salmon. Jon Stokes jon arstechnica. But ads help us keep the lights on. Either way, you are supporting our journalism. All of us at WIRED appreciate your support!
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