Monthly Archives: January 2016

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The Rise of the Artificially Intelligent Hedge Fund | Wired

by CADE METZ | JAN 25, 2016

LAST WEEK, Ben Goertzel and his company, Aidyia, turned on a hedge fund that makes all stock trades using artificial intelligence—no human intervention required. “If we all die,” says Goertzel, a longtime AI guru and the company’s chief scientist, “it would keep trading.”

He means this literally. Goertzel and other humans built the system, of course, and they’ll continue to modify it as needed. But their creation identifies and executes trades entirely on its own, drawing on multiple forms of AI, including one inspired by genetic evolution and another based on probabilistic logic. Each day, after analyzing everything from market prices and volumes to macroeconomic data and corporate accounting documents, these AI engines make their own market predictions and then “vote” on the best course of action.

Though Aidyia is based in Hong Kong, this automated system trades in US equities, and on its first day, according to Goertzel, it generated a 2 percent return on an undisclosed pool of money. That’s not exactly impressive, or statistically relevant. But it represents a notable shift in the world of finance. Backed by $143 million in funding, San Francisco startup Sentient Technologies has been quietly trading with a similar system since last year. Data-centric hedge funds like Two Sigma and Renaissance Technologies have said they rely on AI. And according to reports, two others—Bridgewater Associates and Point72 Asset Management, run by big Wall Street names Ray Dalio and Steven A. Cohen—are moving in the same direction.

Automatic Improvement

Hedge funds have long relied on computers to help make trades. According to market research firm Preqin, some 1,360 hedge funds make a majority of their trades with help from computer models—roughly 9 percent of all funds—and they manage about $197 billion in total. But this typically involves data scientists—or “quants,” in Wall Street lingo—using machines to build large statistical models. These models are complex, but they’re also somewhat static. As the market changes, they may not work as well as they worked in the past. And according to Preqin’s research, the typical systematic fund doesn’t always perform as well as funds operated by human managers.

In recent years, however, funds have moved toward true machine learning, where artificially intelligent systems can analyze large amounts of data at speed and improve themselves through such analysis. The New York company Rebellion Research, founded by the grandson of baseball Hall of Famer Hank Greenberg, among others, relies upon a form of machine learning called Bayesian networks, using a handful of machines to predict market trends and pinpoint particular trades. Meanwhile, outfits such as Aidyia and Sentient are leaning on AI that runs across hundreds or even thousands of machines. This includes techniques such as evolutionary computation, which is inspired by genetics, and deep learning, a technology now used to recognize images, identify spoken words, and perform other tasks inside Internet companies like Google and Microsoft.

The hope is that such systems can automatically recognize changes in the market and adapt in ways that quant models can’t. “They’re trying to see things before they develop,” says Ben Carlson, the author of A Wealth of Common Sense: Why Simplicity Trumps Complexity in Any Investment Plan, who spent a decade with an endowment fund that invested in a wide range of money managers.

This kind of AI-driven fund management shouldn’t be confused with high-frequency trading. It isn’t looking to front-run trades or otherwise make money from speed of action. It’s looking for the best trades in the longer term—hours, days, weeks, even months into the future. And more to the point, machines—not humans—are choosing the strategy.

Evolving Intelligence

Though the company has not openly marketed its fund, Sentient CEO Antoine Blondeau says it has been making official trades since last year using money from private investors (after a longer period of test trades). According to a report from Bloomberg, the company has worked with the hedge fund business inside JP Morgan Chase in developing AI trading technology, but Blondeau declines to discuss its partnerships. He does say, however, that its fund operates entirely through artificial intelligence.

The whole idea is to do something no other human—and no other machine—is doing.
The system allows the company to adjust certain risk settings, says chief science officer Babak Hodjat, who was part of the team that built Siri before the digital assistant was acquired by Apple. But otherwise, it operates without human help. “It automatically authors a strategy, and it gives us commands,” Hodjat says. “It says: ‘Buy this much now, with this instrument, using this particular order type.’ It also tells us when to exit, reduce exposure, and that kind of stuff.”

According to Hodjat, the system grabs unused computer power from “millions” of computer processors inside data centers, Internet cafes, and computer gaming centers operated by various companies in Asia and elsewhere. Its software engine, meanwhile, is based on evolutionary computation—the same genetics-inspired technique that plays into Aidyia’s system.

In the simplest terms, this means it creates a large and random collection of digital stock traders and tests their performance on historical stock data. After picking the best performers, it then uses their “genes” to create a new set of superior traders. And the process repeats. Eventually, the system homes in on a digital trader that can successfully operate on its own. “Over thousands of generations, trillions and trillions of ‘beings’ compete and thrive or die,” Blondeau says, “and eventually, you get a population of smart traders you can actually deploy.”

Deep Investing

Though evolutionary computation drives the system today, Hodjat also sees promise in deep learning algorithms—algorithms that have already proven enormously adept at identify images, recognizing spoken words, and even understanding the natural way we humans speak. Just as deep learning can pinpoint particular features that show up in a photo of a cat, he explains, it could identify particular features of a stock that can make you some money.

Goertzel—who also oversees the OpenCog Foundation, an effort to build an open source framework for general artificial intelligence—disagrees. This is partly because deep learning algorithms have become a commodity. “If everyone is using something, it’s predictions will be priced into the market,” he says. “You have to be doing something weird.” He also points out that, although deep learning is suited to analyzing data defined by a very particular set of patterns, such as photos and words, these kinds of patterns don’t necessarily show up in the financial markets. And if they do, they aren’t that useful—again, because anyone can find them.

For Hodjat, however, the task is to improve on today’s deep learning. And this may involve combining the technology with evolutionary computation. As he explains it, you could use evolutionary computation to build better deep learning algorithms. This is called neuroevolution. “You can evolve the weights that operate on the deep learner,” Hodjat says. “But you can also evolve the architecture of the deep learner itself.” Microsoft and other outfits are already building deep learning systems through a kind of natural selection, though they may not be using evolutionary computation per se.

Pricing in AI

Whatever methods are used, some question whether AI can really succeed on Wall Street. Even if one fund achieves success with AI, the risk is that others will duplicate the system and thus undermine its success. If a large portion of the market behaves in the same way, it changes the market. “I’m a bit skeptical that AI can truly figure this out,” Carlson says. “If someone finds a trick that works, not only will other funds latch on to it but other investors will pour money into. It’s really hard to envision a situation where it doesn’t just get arbitraged away.”

Goertzel sees this risk. That’s why Aidyia is using not just evolutionary computation but a wide range of technologies. And if others imitate the company’s methods, it will embrace other types of machine learning. The whole idea is to do something no other human—and no other machine—is doing. “Finance is a domain where you benefit not just from being smart,” Goertzel says, “but from being smart in a different way from others.”

Source: Wired


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Artificial intelligence: Can Watson save IBM? | FT.com

by Richard Waters | January 5, 2016

Hopes pinned on developing system into a powerful and profitable set of services

The history of artificial intelligence has been marked by seemingly revolutionary moments — breakthroughs that promised to bring what had until then been regarded as human-like capabilities to machines.

The AI highlights reel includes the “expert systems” of the 1980s and Deep Blue, IBM’s world champion-defeating chess computer of the 1990s, as well as more recent feats like the Google system that taught itself what cats look like by watching YouTube videos.

But turning these clever party tricks into practical systems has never been easy. Most were developed to showcase a new computing technique by tackling only a very narrow set of problems, says Oren Etzioni, head of the AI lab set up by Microsoft co-founder Paul Allen. Putting them to work on a broader set of issues presents a much deeper set of challenges.

Few technologies have attracted the sort of claims that IBM has made for Watson, the computer system on which it has pinned its hopes for carrying AI into the general business world. Named after Thomas Watson Sr, the chief executive who built the modern IBM, the system first saw the light of day five years ago, when it beat two human champions on an American question-and-answer TV game show, Jeopardy!

But turning Watson into a practical tool in business has not been straightforward. After setting out to use it to solve hard problems beyond the scope of other computers, IBM in 2014 adapted its approach.

Rather than just selling Watson as a single system, its capabilities were broken down into different components: each of these can now be rented to solve a particular business problem, a set of 40 different products such as language-recognition services that amount to a less ambitious but more pragmatic application of an expanding set of technologies.

Though it does not disclose the performance of Watson separately, IBM says the idea has caught fire. John Kelly, an IBM senior vice-president and head of research, says the system has become “the biggest, most important thing I’ve seen in my career” and is IBM’s fastest growing new business in terms of revenues.

But critics say that what IBM now sells under the Watson name has little to do with the original Jeopardy!-playing computer, and that the brand is being used to create a halo effect for a set of technologies that are not as revolutionary as claimed

“Their approach is bound to backfire,” says Mr Etzioni. “A more responsible approach is to be upfront about what a system can and can’t do, rather than surround it with a cloud of hype.”

Nothing that IBM has done in the past five years shows it has succeeded in using the core technology behind the original Watson demonstration to crack real-world problems, he says.

Watson’s case

The debate over Watson’s capabilities is more than just an academic exercise. With much of IBM’s traditional IT business shrinking as customers move to newer cloud technologies, Watson has come to play an outsized role in the company’s efforts to prove that it is still relevant in the modern business world. That has made it key to the survival of Ginni Rometty, the chief executive who, four years after taking over, is struggling to turn round the company.

Watson’s renown is still closely tied to its success on Jeopardy! “It’s something everybody thought was ridiculously impossible,” says Kris Hammond, a computer science professor at Northwestern University. “What it’s doing is counter to what we think of as machines. It’s doing something that’s remarkably human.”

By divining the meaning of cryptically worded questions and finding answers in its general knowledge database, Watson showed an ability to understand natural language, one of the hardest problems for a computer to crack. The demonstration seemed to point to a time when computers would “understand” complex information and converse with people about it, replicating and eventually surpassing most forms of human expertise.

The biggest challenge for IBM has been to apply this ability to complex bodies of information beyond the narrow confines of the game show and come up with meaningful answers. For some customers, this has turned out to be much harder than expected.

The University of Texas’s MD Anderson Cancer Center began trying to train the system three years ago to discern patients’ symptoms so that doctors could make better diagnoses and plan treatments.

“It’s not where I thought it would go. We’re nowhere near the end,” says Lynda Chin, head of innovation at the University of Texas’ medical system. “This is very, very difficult.” Turning a word game-playing computer into an expert on oncology overnight is as unlikely as it sounds, she says.

Part of the problem lies in digesting real-world information: reading and understanding reams of doctors’ notes that are hard for a computer to ingest and organise. But there is also a deeper epistemological problem. “On Jeopardy! there’s a right answer to the question,” says Ms Chin but, in the medical world, there are often just well-informed opinions.

Mr Kelly denies IBM underestimated how hard challenges like this would be and says a number of medical organisations are on the brink of bringing similar diagnostic systems online.

Applying the technology

IBM’s initial plan was to apply Watson to extremely hard problems, announcing in early press releases “moonshot” projects to “end cancer” and accelerate the development of Africa. Some of the promises evaporated almost as soon as the ink on the press releases had dried. For instance, a far-reaching partnership with Citibank to explore using Watson across a wide range of the bank’s activities, quickly came to nothing.

Since adapting in 2014, IBM now sells some services under the Watson brand. Available through APIs, or programming “hooks” that make them available as individual computing components, they include sentiment analysis — trawling information like a collection of tweets to assess mood — and personality tracking, which measures a person’s online output using 52 different characteristics to come up with a verdict.

At the back of their minds, most customers still have some ambitious “moonshot” project they hope that the full power of Watson will one day be able to solve, says Mr Kelly; but they are motivated in the short term by making improvements to their business, which he says can still be significant.

This more pragmatic formula, which puts off solving the really big problems to another day, is starting to pay dividends for IBM. Companies like Australian energy group Woodside are using Watson’s language capabilities as a form of advanced search engine to trawl their internal “knowledge bases”. After feeding more than 20,000 documents from 30 years of projects into the system, the company’s engineers can now use it to draw on past expertise, like calculating the maximum pressure that can be used in a particular pipeline.

To critics in the AI world, the new, componentised Watson has little to do with the original breakthrough and waters down the technology. “It feels like they’re putting a lot of things under the Watson brand name — but it isn’t Watson,” says Mr Hammond.

Mr Etzioni goes further, claiming that IBM has done nothing to show that its original Jeopardy!-playing breakthrough can yield results in the real world. “We have no evidence that IBM is able to take that narrow success and replicate it in broader settings,” he says. Of the box of tricks that is now sold under the Watson name, he adds: “I’m not aware of a single, super-exciting app.”

To IBM, though, such complaints are beside the point. “Everything we brand Watson analytics is very high-end AI,” says Mr Kelly, involving “machine learning and high-speed unstructured data”. Five years after Jeopardy! the system has evolved far beyond its original set of tricks, adding capabilities such as image recognition to expand greatly the range of real-world information it can consume and process.

Adopting the system

This argument may not matter much if the Watson brand lives up to its promise. It could be self-fulfilling if a number of early customers adopt the technology and put in the work to train the system to work in their industries, something that would progressively extend its capabilities.

“Once it’s working, you want to be leading the adoption,” says Shaun Gregory, head of technology and strategy at Woodside. “You’re ahead in knowledge and learning. Machines never forget,” he says.

Another challenge for early users of Watson has been knowing how much trust to put in the answers the system produces. Its probabilistic approach makes it very human-like, says Ms Chin at MD Anderson. Having been trained by experts, it tends to make the kind of judgments that a human would, with the biases that implies.

In the business world, a brilliant machine that throws out an answer to a problem but cannot explain itself will be of little use, says Mr Hammond. “If you walk into a CEO’s office and say we need to shut down three factories and sack people, the first thing the CEO will say is: ‘Why?’” He adds: “Just producing a result isn’t enough.”

IBM’s attempts to make the system more transparent, for instance by using a visualisation tool called WatsonPaths to give a sense of how it reached a conclusion, have not gone far enough, he adds.

Mr Kelly says a full audit trail of Watson’s decision-making is embedded in the system, even if it takes a sophisticated user to understand it. “We can go back and figure out what data points Watson connected” to reach its answer, he says.

He also contrasts IBM with other technology companies like Google and Facebook, which are using AI to enhance their own services or make their advertising systems more effective. IBM is alone in trying to make the technology more transparent to the business world, he argues: “We’re probably the only ones to open up the black box.”

Even after the frustrations of wrestling with Watson, customers like MD Anderson still believe it is better to be in at the beginning of a new technology.

“I am still convinced that the capability can be developed to what we thought,” says Ms Chin. Using the technology to put the reasoning capabilities of the world’s oncology experts into the hands of other doctors could be far-reaching: “The way Amazon did for retail and shopping, it will change what care delivery looks like.”

Ms Chin adds that Watson will not be the only reasoning engine that is deployed in the transformation of healthcare information. Other technologies will be needed to complement it, she says.

Five years after Watson’s game show gimmick, IBM has finally succeeded in stirring up hopes of an AI revolution in business. Now, it just has to live up to the promises.

Key assets: Systems depend on diverse data flows

There is a secret weapon in the race between leading tech companies to create the most effective forms of artificial intelligence: access to large amounts of data. For IBM, as it tries to make Watson a new standard for AI in the business world, this could turn out to be an under-appreciated advantage, according to some experts.

At the heart of intelligent machines are algorithms that search through large volumes of data to identify patterns and make surmises. Machine learning — the basic technique behind many of the recent advances in AI — relies on using large amounts of data to train systems in this way.
“A lot of what is now emerging with AI technologies has to do with data,” says Kris Hammond, a computer science professor at Northwestern University.

One of the biggest reasons for recent advances in AI has been the availability of large amounts of data online with which to train systems. Google has perfected its search systems by harnessing the abundant data it collects about the online behaviour of its users. While IBM cannot match the massive trove of information Google has at its disposal, what it lacks in volume it hopes to make up for in industry-specific detail.

“Google has one kind of data — consumer sentiment data. We have a vast amount of [more diverse] data,” says John Kelly, head of research at IBM.

The more industry-specific data it is fed, the smarter it will become at solving business problems. As customers pour their own corporate information into Watson in order to train it, IBM stands to be a beneficiary.

Watson’s “expanding corpus of information in many domains” could turn out to be one of IBM’s main assets in the AI race, says Mr Hammond.

Last year, IBM turned to acquisitions to boost its reserves of data. These included the $1bn it spent to buy Merge Healthcare, a company that handles large amounts of medical images. It has been folded into Watson Health, the first industry-specific business unit to be spun out of the Watson division.

It also spent $2bn to buy the digital assets of the Weather Company, with the aim of feeding its weather data into forecasting systems geared to understanding weather-related business risks, among other functions.

“Between our customers and what we’ve acquired, we’re amassing quite a data set,” says Mr Kelly.

Spinning this basic raw material into computing gold still requires serious technical skills. But if it can persuade customers to contribute their own data to the task of making Watson smarter, it could deliver the sort of head start that will make it hard for rivals to catch up.

Source: Financial Times


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The New Quant Hedge Fund Master

by Nathan Vardi | JAN 4, 2016

This story appears in the January 18, 2016 issue of Forbes.

On a recent rainy October evening, Peter Muller, 52, sits at a piano on the stage of Manhattan’s City Winery, playing with the band from his third album, Two Truths and a Lie. In between songs about love, heartbreak and relationships, like his Bruce Hornsby-reminiscent “Kindred Soul,” Muller describes the long, strange trip he has taken in and out of high finance.

The tieless suits in attendance, from places like Goldman Sachs and Blackstone, paid as much as $1,000 a ticket to raise nearly $55,000 for the Robin Hood Foundation. And while Muller tells them of his early discovery of music, the existential crisis of his 30s, buddies he left behind in California and his family, there is a sense that many in the room just want to be in the orbit of the hottest hedge fund manager on Wall Street today. “I know you all had your choice of hedge fund manager CD-release parties,” quips Muller. “Thank you for choosing ours.”

Pete Muller is the latest, greatest member of a growing band of hedge funds that use complex math and computer-automated algorithmic models to buy and sell stocks, futures and currencies based on statistical correlations and aberrations that can be found in the market. During 2015, when many hedge fund managers–from mighty activists like Bill Ackman to noted short-sellers like David Einhorn–lost money, Muller spun the market’s volatility into gold. The largest fund of his three-year-old PDT Partners firm, which oversees $4.5 billion, was up 21.5% net of fees in the first 11 months of 2015.

“We knew that Pete has a magic touch,” says J. Tomilson Hill, the billionaire who runs Blackstone’s $70 billion hedge fund investment unit. “I happen to be a big fan of Cézanne, and Pete is in his own way as gifted as Cézanne was.” Paul Tudor Jones, the billionaire hedge fund manager, adds: “He is up there with the best and brightest–bar none.”

Indeed, Muller’s fund is so coveted that even Wall Street’s power elite are willing to effectively grovel to get in on PDT’s action. Many hedge funds stipulate that limited partners remain “locked up,” or prevented from redeeming funds, for a predetermined period, usually one year. PDT is the opposite. Its biggest investor, Blackstone, actually agreed to be locked up for no less than seven years–in return for Muller’s assurance that he would not kick it out of his biggest fund for the same period of time. Others requested the same lockup restrictions–and were refused. Even more astonishing is Muller’s 3% of assets under management fee, and performance fees that rise to 50% of profits for benchmark-beating performance, compared with the already maligned industry standard of 2-and-20.

“Our goal is to be the best quantitative investment firm on the planet, but not in terms of number of assets, in terms of quality of the products,” says Muller in his first interview since opening his new firm. “To take money out of the market with as little risk as possible and build a place people who are smart are drawn to.” Muller’s niche formula has also let him take plenty of money out of the market personally: Forbes estimates that in the last three years alone he’s made $200 million before taxes, including gains on his own capital.

It’s mid-November 2015, the U.S. stock market has given back all of its gains, and hedge fund managers around the globe are wringing their hands in anticipation of sending out another batch of disappointing investor letters. Muller is sitting in his Manhattan office. He is wearing a gray sweatshirt and jeans and has a Zen-like calm. His research chief has just left his office after telling Muller about a promising finding that could lead to the improvement of one of PDT’s main models. “When people buy or sell in a desperate or hurried fashion, it tends to be helpful to us,” says Muller, who is otherwise tight-lipped about what has gone right this year.

There are two screens in Muller’s office: a flat-panel display on his desk showing the movement of his hedge fund’s positions and a much larger screen on the wall that displays a real-time high-definition stream of the surfing beach at the foot of his house just north of Santa Barbara, Calif. When he’s at PDT’s headquarters in New York City and the waves are big, Muller sometimes yearns to be hanging ten. Luckily this doesn’t happen too often, because Muller spends two-thirds of his time at his California home, where responding to “surf’s up” is a regular ritual.

Muller may not appear to be a workaholic like many other Wall Street titans, but he is obsessive about his algorithms and problem solving, and he can get lost in deep thoughts for hours, days. His fear of burnout is real–he already dropped out of Wall Street once in 1999–and diversions like music and surfing are almost a necessity.

Muller grew up in suburban Wayne, N.J. His father was an electrical engineer and his mother a psychiatrist. He was good at numbers and loved music. At Princeton he studied math and played in a jazz band. After graduating, he headed to northern California to play music for rhythmic gymnasts and, figuring he had to pay the bills, eventually went to work for BARRA, a pioneering research firm that catered to quantitative financial firms. In 1992 he joined Morgan Stanley in New York as a proprietary trader to see if he could use math and computers to trade himself. Some of his investment banking colleagues were skeptical about the new math guy in the office. He called his group Process Driven Trading, or PDT. “I wanted to win and prove myself,” Muller says.

Nobody outside the bank knew it, but for a long time Muller was Morgan Stanley’s supersecret weapon, making big contributions to its earnings each year, hidden in the firm’s income statement under “principal transactions.”

Muller was able to carve out his own quiet area at Morgan Stanley’s Manhattan headquarters, where his team of math nerds could dress casually away from the bank’s testosterone-fueled, high-octane trading hordes. Muller became intensely focused on figuring out patterns that could help him beat the market. It was thrilling and exhausting. He thought and talked about it all the time–couldn’t even sit through a Broadway show without stressing over it. “He is really smart, but a lot of smart people get lost in theory,” says Kim Elsesser, a computer programmer and mathematician from MIT, and Muller’s first key hire. “He also has very high expectations of himself and other people.”

As Muller gained success and autonomy at Morgan Stanley his behavior became somewhat erratic. He detached from the office at a second home in Westport, Conn. in part because the pressures of work were overwhelming. His mind became so overloaded with mathematical formulas that he could no longer play music. Crossword puzzles became an escapist obsession; he even created them for the New York Times. By 1999 Muller started to feel like he could no longer find happiness on Wall Street.

“I was out of balance personally,” Muller says. He went on sabbatical, rediscovering his love of music partly by busking in New York subway stations and sojourning in far-off places like Bhutan. After returning in 2000 he spent the next several years essentially as an advisor to the fund he created, PDT. Muller today likens it to a kind of executive chairman position that left him time to do other things, such as practice yoga and produce two music albums with titles like Just One Lifetime. He also met his wife, Jillian.

The soul-searching lasted about seven years, and Muller says it sent his trading operation into a period of stagnation. Muller then rolled up his sleeves and came back full-time to PDT in 2006. Unfortunately his return just about coincided with the quant meltdown of 2007, when the precipitate drop in subprime mortgage securities triggered deep losses for many firms. Under pressure from Morgan Stanley, Muller was forced to liquidate part of his portfolio. “Morgan Stanley Star Is Among Those Battered; No Time for Music Now,” the Wall Street Journal ‘s front page blared.

As with many on Wall Street, the financial crisis changed the game for Muller. He had produced the kind of returns that would have made him a billionaire had he been an independent hedge fund manager. But working for Morgan Stanley always appealed because he didn’t have to worry about raising cash, appeasing clients or back-office details. It was plug and play. He couldn’t invest his own money in PDT, but he was well-paid, receiving a cut of his unit’s profits, and could singularly focus on solving market puzzles.

There was also the tricky issue of the intellectual property Muller developed but Morgan Stanley owned. But 2008 exposed the danger of being dependent on one client, namely Morgan Stanley. It also gave birth to the Volcker Rule, a piece of legislation designed to make it impossible for a proprietary trader like Muller to work at a bank like Morgan Stanley. Over the next few years Muller engaged in on-again, off-again negotiations with the Wall Street firm about their operating arrangement.

“We preferred to stay together, but as the Volcker Rule emerged it became clear that would not be permitted,” says Jim Rosenthal, Morgan Stanley’s chief operating officer, who led the last round of negotiations with Muller. “Sadly, this was a business that was a steady source of revenue and profitability and did not pose significant risks to the firm.”

In the end Muller would manage Morgan Stanley money until the end of 2012 and control the intellectual property Morgan Stanley was no longer permitted to use. Under the terms of the deal Morgan Stanley would get a cut of the fee revenue of the new, independent PDT for an undisclosed period of time.

On New Year’s eve 2012 Muller transferred all of his group’s investment positions from Morgan Stanley to PDT Partners. It wasn’t only the positions and intellectual property that came with him–so did every single member of his 80-person staff. Invigorated, Muller went to work, increasing his new business and nearly doubling his employees.

“It feels great to have your own place,” says Muller from his office on the top floor of a midtown Manhattan building formerly occupied by Random House. “I never felt like I had to have my name on the door, but I didn’t own it before, and in hindsight I didn’t recognize the psychological impact of that.”

In order to make his mathletes more comfortable Muller has had special glass walls constructed that are slightly curved to deflect sound and maintain the quiet workplace needed for concentration. Outside those quiet areas there are Ping-Pong and foosball tables near the kitchen and meeting rooms with whiteboards covered with mathematical formulas. Employees never wear suits; they run book clubs and organize poker nights that Muller sometimes attends. (He has made a final table at the World Series of Poker.)

Not much is known about Muller’s black box models. He traded using two different strategies at Morgan Stanley that have morphed into the two hedge funds he now runs. The PDT Partners Fund is a statistical arbitrage fund built on models that have never had a down year. The $3 billion fund was up 21.5% in the first 11 months of 2015, and given its high fees, its gross returns were running at about 40%. Since inception in 2013 PDT Partners Fund has produced annualized net returns of 18.5%. Another fund, $1.5 billion Mosaic, has a longer time horizon and had produced returns of 10.5% net of fees through November of last year and 8.5% annualized in three years. PDT also has a Fusion Fund, which allocates cash between PDT Partners and Mosaic.

Returns like that are beginning to rival the long-reigning king of quants, Renaissance Technologies, known for market-defying consistency and for producing a net worth of $14 billion for its professorial founder, James Simons. While Muller is not yet even a billionaire, some say he is the new Simons.

Like Renaissance PDT is a Ph.D. farm, with 35 researchers who spend most of their days developing trading algorithms. They are organized into five teams by the asset class and time horizons they work on. Some work on futures contracts with longer holding horizons while others toil with statistical arbitrage strategies that trade stocks over a medium time horizon of several weeks. Most efforts to come up with new models tend to start with two-week-long deep dives but can grow into research projects that last a year. PDT has one open problem today that its people have worked on for four years.

And while most Wall Street research analysts expect their best ideas to find their way into firm portfolios within weeks or months, PDT takes an academic approach to portfolio change. Researchers know that their models may not affect returns for two years or more. In fact, PDT is still using models today with concepts that were initially developed 15 years ago, but models do decay over time and need to evolve with the market. “We are more intent in building a group of Ferraris than a bunch of Toyotas,” says Tushar Shah, research chief at PDT.

Finding the right minds for Muller’s model-making is almost as hard as decoding statistical arbitrages hidden in markets. Big data and sheer computing power have become a driving force in PDT’s business model. Like other quants, PDT routinely competes with tech firms for leading programmers and mathematicians. It is now hiring more computer engineers than mathematician-researchers. Experts in machine learning are in high demand, so poaching talent from the likes of Google and Microsoft has become popular of late.

It’s not always the eye-popping first-year salaries of several hundreds of thousands of dollars that hook new Ph.D. recruits. PDT researcher John Sun, 30, was finishing up an MIT Ph.D. in electrical engineering and computer science when he got an e-mail from Eunice Baek, Muller’s longtime partner who manages recruiting. Sun would frequently get these sorts of e-mails and ignore them, but this one pulled him in. It said people at PDT like Lord of the Rings, science fiction and board games such as Settlers of Catan.

Like most PDT job candidates, Sun was flown to New York for a 36-hour interview on the second Saturday of November at PDT headquarters, where Muller and his partners tried to determine if Sun had the smarts and was someone with whom they could spend a lot of time. The guts of the recruiting weekend include a modeling interview and then collaborative algorithmic-based problem-solving games in which candidates are separated into small teams that Muller watches closely. PDT has a 3.5% turnover rate, and while the hours are not grueling, the work is demanding–trying to solve stock market puzzles often ends in failure.

“I want their shower time because in the shower they are thinking about things that get them to solve the problems,” says Muller.

While Muller is supersecretive about the details of his models even to limited partners, who seem to invest on faith–he is adamant that PDT does not engage in ultra-high-frequency trading. Ferreting out small market inefficiencies is core to PDT’s strategy, and what is also clear is that, for Muller, the more trading going on in markets the better. “There is a limitation on how much volume we can trade naturally built into our systems,” Muller says, adding he will almost certainly return some capital to his investors at the end of 2015, as he did in 2014. Trust in Muller’s machines is paramount, and he rarely intervenes manually, even when jarring upheavals temporarily defy his model’s predictions.

Spending seven months of the year with a surfboard at the ready or composing in front of a keyboard, instead of obsessively staring at a CNBC ticker, probably gives Muller’s PDT an advantage. Instantaneous information and constant volatility are the new reality of global markets. Whether it is index investing or robo-advisors, the discipline and brainpower of machines are winning on Wall Street. The rise of the quants is just beginning. “It will get harder, but we are prepared, and as information becomes more widely available and computing power increases, the strength of our models will improve,” Muller says. “Quantitative investing is the best way to manage money, period.”

Source: Forbes