No humans required, the fully autonomous AI running a Wall Street hedge fund

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WHY THIS MATTERS IN BRIEF

  • Companies that use artificial intelligence to augment their human workers is nothing new, but companies that are run by artificial intelligence, without any human input, are


 

I’ve been documenting the emergence of fully autonomous organisations – organisations that are able to run and scale themselves without the need for any human operators, or intervention – sometimes also known as Distributed Autonomous Organisations (DAO), for years now and I’m now starting to see the first production DAO’s appear and enter the market.

Aidyia, a new fully autonomous hedge fund is a case in point, turned on late last year it’s a hedge fund that makes all of its stock trades using artificial intelligence (AI) – with no human intervention required – and as you’ll see Bridgewater Associates, the world’s largest hedge fund, Goldman Sachs and maybe JPMorgan Chase might not necessarily be that far behind. In fact the pace and rate of change in the industry is now becoming so rapid that it even has its own dedicated event – the Battle of the Quants, the next installment of which is in Frankfurt later this month.

 

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“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, who could be fully autonomous arguably by 2025, and Point72 Asset Management, run by big Wall Street names Ray Dalio and Steven Cohen – are moving in the same direction, and then there’s Goldman Sachs who are also on their own self-announced automation drive.

 

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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 AI systems can analyse 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.

 

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“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.

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, and after a longer period of test trades. According to a report from Bloomberg, the company has worked with the hedge fund business inside JPMorgan Chase, who recently rolled out AI to automate their legal work,  in developing AI trading technology, but Blondeau declines to discuss its partnerships. He does say, however, that its fund operates entirely through AI.

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.”

 

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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.”

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 AI, 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.”

 

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He also points out that, although deep learning is suited to analysing 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.

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.”

 

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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: Aidyia, Bloomberg, JPMorgan Chase

About author

Matthew Griffin

Matthew Griffin, Futurist and Founder of the 311 Institute is described as “The Adviser behind the Advisers.” Among other things Matthew keeps busy helping the world’s largest smartphone manufacturers ideate the next five generations of smartphones, and what comes beyond, the world’s largest chip makers envision the next twenty years of intelligent machines, and is helping Europe’s largest energy companies re-invent energy generation, transmission and retail.

Recognised in 2013, 2015 and 2016 as one of Europe’s foremost futurists, innovation and strategy experts Matthew is an award winning author, entrepreneur and international speaker who has been featured on the BBC, Discovery and other outlets. Working hand in hand with accelerators, investors, governments, multi-nationals and regulators around the world Matthew helps them envision the future and helps them transform their industries, products and go to market strategies, and shows them how the combination of new, democratised, powerful emerging technologies are helping accelerate cultural, industrial and societal change.

Matthew’s clients include Accenture, Bain & Co, Bank of America, Blackrock, Booz Allen Hamilton, Boston Consulting Group, Dell EMC, Dentons, Deutsche Bank, Deloitte, Deutsche Bank, Du Pont, E&Y, Fidelity, Goldman Sachs, HPE, Huawei, JP Morgan Chase, KPMG, Lloyds Banking Group, McKinsey & Co, PWC, Qualcomm, Rolls Royce, SAP, Samsung, Schroeder’s, Sequoia Capital, Sopra Steria, UBS, the UK’s HM Treasury, the USAF and many others.

Comments
  • Nat Stones#1

    7th March 2017

    Very, very interesting indeed. Particularly interested in the likes of Numerai. I suppose we are yet to see if the inherent presuppositions of the ‘incentive model’ behind crypto-currencies (which to an extent – query this however – inform the AIHF movement as a whole) is founded on solid thinking. But that’s true of anything and doesn’t invalidate the idea if it isn’t perfect (much like a self-driving car having an accident doesn’t disprove the viability of self-driving cars, as the case is over-analysed because of outlier status, and we forget the ‘set’ of cars that fail with drivers at the wheel). Thanks – as ever.

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