The Rise of Artificial Intelligence Investing
“Technologies continue to advance. They accelerate the progression of seemingly unrelated technologies, exploding what is possible at a pace that is difficult to fathom. This helps redefine what is possible today, and in our rapidly approaching tomorrow.”
Artificial intelligence is a branch of computer science that aims to create intelligent machines that teach themselves. Much of AI’s growth has occurred in the last decade. The upcoming decade, according to billionaire investor Mark Cuban, will be the greatest technological revolution in man’s history! This report will provide an understanding of AI, real-life examples, and AI’s effect on investing.
The Rise of Artificial Intelligence
It is springtime for Artificial Intelligence. More progress has been achieved in the past five years than in the past five decades. Rapid machine-learning improvements have allowed computers to surpass humans at certain feats of ingenuity, doing things that at one time would have been unfathomable. IBM calls the autonomous machine learning field ‘Cognitive Computing’. The ‘Cognitive Computing’ space is bursting with innovations; a result of billions of research and investment dollars spent by large companies such as Microsoft, Google and Facebook. IBM alone has spent $15 billion on Watson, its cognitive system, as well as on related data analytics technology.
Arthur Samuel’s checkers-playing program appeared in the 1950s. It took another 38 years for a computer to master checkers. In 1997, the IBM Deep Blue program defeated world chess champion Gary Kasparov. Around the time Deep Blue first started learning chess, Kasparov declared, “No computer will ever beat me.” That historic accomplishment took IBM 12 years.
Artificial Intelligence first started hitting the mainstream headlines in 2011 when IBM’s Watson beat two human contestants on TV’s Jeopardy. This was the landmark milestone of its time, especially if you consider one of the players was Ken Jennings (who holds the record for the consecutive wins (74) on the quiz show). Getting to that moment took five years. IBM’s Watson spent four of them learning the English language, and another year reading—and retaining—every single word of Wikipedia (plus a few thousand books!
No computer was ever supposed to master the game “Go”, but it did. Go was invented in China in 548 B.C. It is a game of ‘capture the intersection’ played on a 19×19 grid with each player deploying a combined cache of 300-plus black and white pebbles. Go is chess on steroids. In fact, the possible board permutations in Go vastly outnumber the board permutations of chess.
Designed by a team of researchers at DeepMind—an A.I. lab now owned by Google—AlphaGo was an A.I. system built with one specific objective: understanding how the game of Go was played and learning to play it very well.
AlphaGo’s minders never gave it the rules of the game! They fed it tens of millions of Go moves from expert players and the computer had to figure it all out. The concept of reinforcement learning was put to the test by way of millions of matches that the system played against versions of itself; neural network-versus-neural network. The results and key lessons were fed back to AlphaGo, which constantly learned and improved its game. The operative word is learned. AlphaGo not only knew how to play Go as a human would, but it moved past the human approach into a completely new way of playing.
The expectation-defying pace at which A.I. milestones are being reached, is only one reason why we believe we have crossed the Rubicon. Broader technological, societal and economic forces are coming together to create a historically unique backdrop for machine learning to have its day. We believe that there is a radical and irreversible change from Artificial Intelligence about to disrupt the investment industry. Autonomous Learning Investment Strategies (ALIS) is the next investment process paradigm, heralding what Wired Magazine recently called the ‘Third Wave’ of investing. ALIS is about to transform the investment process.
A.I., Machine Learning, and Deep Learning
Artificial intelligence is a branch of computer science that aims to create intelligent machines, and much of A.I.’s growth has occurred in the last decade. There is a fundamental difference between the broad category of A.I. and its subset categories, Machine Learning, and Deep Learning, which needs to be understood to avoid confusion. The easiest way to think of their relationship is to visualize them as concentric circles with A.I. as the entire realm, then machine learning, and finally deep learning which is driving today’s A.I. explosion. We graphed this for you.
Artificial Intelligence – Any technique that enables computers to mimic human intelligence using logic. At Equitas we do not consider this true “artificial” intelligence as the computers are programmed with algorithms that tell it what to do. This is more the automation of human intelligence and should really be in another category. Robo-Advisors is a term used in this category which is misleading and does not involve robots or artificial intelligence at all. Rather, robo-advisors are algorithms built to automate the calibration of a financial portfolio to the goals and risk tolerance of the user. The term has a chic appeal that has made it popular which tells us that people intuitively have a desire for “real” artificial intelligence.
Machine Learning – A subset of A.I. that includes statistical techniques that enable machines to improve at tasks with experience. This is the first real step into artificial intelligence in our opinion. Machine Learning at its most basic is the practice of using algorithms to parse data, learn from it, and then make a determination or prediction about something in the world. So rather than hand-coding software routines with a specific set of instructions to accomplish a particular task, the machine is “trained” using large amounts of data and algorithms that give it the ability to learn how to perform the task.
Deep Learning – A subset of Machine Learning composed of algorithms that permit software to train itself to perform tasks. IBM Watson is an example of this. Deep Learning has enabled many practical applications of Machine Learning and by extension the overall field of AI. Deep Learning breaks down tasks in ways that makes all kinds of machine assists seem possible, even likely. Driverless cars, better preventive healthcare, even better movie recommendations, are all here today or on the horizon because of Deep Learning.
The amount of data available is staggering. At the core is the explosion of digitally stored data. Over 80% of this data is raw and unstructured data such as satellite images and Twitter. Since there is more data available, fast comprehension of that data is important. Let us stop for a moment to try to comprehend this deluge.
Digital information is measured in bytes. One bit (short for binary digit) is the smallest unit of data in a computer. Eight bits are equal to one byte. Prefixes, denoting mathematical powers, allow us to keep track of all these bytes. (Remember the good old gigabyte?) The typical hard drive of a single PC in 1995 would have been one gigabyte. Then terabyte came along. One terabyte is one trillion bytes. It takes 1024 terabytes to make up one petabyte. The 2014 IDC Digital Universe Study found that 90% of all petabytes are believed to have been created since 2012.
In 2006, there were only around 100 exabytes worth of data on the internet. Today, that number is about 10,000 exabytes. We are now counting data in zettabytes, which equals one thousand exabytes. Just to show scale, one zettabyte is more than four million times the size of the entire US Library of Congress!
What human can keep up with all that data unassisted? Artificial intelligence computers can comprehend data faster than humans can and with exponentially less cost. A million dollars of computing power in 1980 costs less than 4 cents today.
Human Behavioral Sciences
Webster Definition of Behavioral Science:
a branch of science such as psychology, sociology, or anthropology that deals primarily with human action and often seeks to generalize about human behavior in society.
Understanding human behavior is crucial for investors, according to Alliance Capital Management CEO Lewis Sanders, who talked about behavioral finance and its role in pricing anomalies and forecasting bias during a presentation at Wharton. The human emotions of fear, greed, elation, regret, inertia, etc. may have more to do with investment behavior than fundamentals. This is especially true in a stock market that some sources show is now over 40% invested in passive index funds and ETFs. Passive investments care nothing about fundamentals and only move with the emotional whims of the investors. Society has been observing human behavior for 5,000 years. Unlocking the key to behavioral finance could be the “Holy Grail” of investing. We think AI has the best chance of succeeding and gaining the competitive advantage.
In the investment management business, it is now the time of the Robo-Advisors. The term “robo-advisor” was essentially unheard-of just five years ago, but it is now commonplace in the financial landscape. The term is misleading and does not involve robots or artificial intelligence at all. Rather, robo-advisors are algorithms built to calibrate a financial portfolio to the goals and risk tolerance of the user. Users enter their goals, age, income, and current financial assets. The advisor (which would more accurately be referred to as just an “allocator”) then spreads investments across asset classes and financial instruments in order to reach the user’s goals. Robo-advisors have gained significant traction with millennial consumers who do not need a physical advisor to feel comfortable investing, and who are less able to validate the fees paid to human advisors. It reveals a desire for real AI computer service.
Artificial intelligent hedge funds are on the rise as well. The application of AI in the hedge fund industry is still at an early stage. Some hedge fund managers are utilizing AI as a partial input into their trading process (retaining their discretionary control over investing and risk management) while others, ‘pure AI hedge funds’, have outsourced both the trading and risk management aspect to the machine with minimal input from the fund manager.
The biggest success of a computer driven manager is Renaissance Technologies LLC an East Setauket, New York based investment management firm founded in 1982 by James Simons, an award-winning mathematician and former Cold War code breaker. Renaissance specializes in systematic trading using only quantitative models derived from mathematical and statistical analyses. Renaissance is one of the first highly successful hedge funds using quantitative trading—known as “quant hedge funds”—that rely on powerful computers and sophisticated mathematics to guide investment strategies.
Note that these are scientists and mathematicians, NOT portfolio managers. Change in business tends to come from the outside. The Medallion Fund uses an improved and expanded form of Leonard Baum’s mathematical models, improved by algebraist James Ax, to explore correlations from which they could profit. Simons and Ax named it Medallion in honor of the math awards that they had won.
Because of the success of Renaissance in general and Medallion in particular, Simons has been described as the best money manager on earth. By October 2015, Renaissance had roughly $65 billion worth of assets under management, most of which belong to employees of the firm. Renaissance’s flagship Medallion fund is completely computer model driven with no input from humans. Renaissance’s manager Jim Simons states, “We (employees) never override our models.” From 1994 through mid-2014, the fund averaged a 35% net annual return!
 Rubin, Richard (16 June 2015). “How an Exclusive Hedge Fund Turbocharged Its Retirement Plan”. Bloomberg. Retrieved 1 November 2015.
A.I. Hedge Fund Database
As can be seen in the figure above, the Eurekahedge AI/Machine Learning Hedge Fund Index currently consisting of about 30 funds, has outperformed traditional hedge fund managers such as quants, CTA’s, trend-followers, and the average global hedge fund index since 2010. Leon Cooperman, an old-school veteran, summed up the current state of the industry succinctly as “under assault”. An old school style of research may include playing golf with company management in order to “perceive” some valuable insight into the company. A few months later, Cooperman was charged by the S.E.C. with insider trading. He has refuted the charges. While some managers who have relied on human judgment may adopt ‘big data’ scraping methods going forward, transitioning into an industry that is evolving at warp speed is unlikely to be easy. Could research insights gained through artificial intelligence be deemed insider information?
Don’t Worry, Humans are Not Obsolete!
All is not lost for human beings! Apparently, humans and machines work well together. The chart below shows the results. By one estimate, as of 2011, there were some 206 all-time highest-rated chess performances (victories requiring the fewest moves) in tournaments that included humans, computers, and so-called cyborgs (combinations of man and machine – named from Star Trek of course).
One study of those performances showed 80% of the games were turned in by the cyborgs. Now consider the possibility of a cyborg-type scenario with humans and machines working together within the context of running an equity long/short hedge fund. Humans making grand strategic decisions—take digitized health-care records as a boom sector worth following for example—aided by machine-learning algorithms that equal the intellectual firepower of 10,000 analysts. Such a scenario sets the stage for endless investment possibilities not to mention a potentially epic organizational culture clash between MBAs and scientists.
The Third Wave
I have been in the industry since the 1980s when bank trust departments were the investment norm. The industry innovations came from outside the traditional asset management industry. This was predicted by Clayton Christiansen, the Harvard Business School Professor and architect of disruptive innovation. The first wave of discretionary hedge fund managers came from proprietary trading desks, floor traders, and event driven risk-arb firms; not Fidelity, Vanguard, or a bank trust department. The second wave, in the 90s, came from mathematics and physics, not discretionary hedge funds. They brought a hypothesis driven quantitative approach to investing. The third wave, Autonomous Learning Investment Strategies (ALIS), exploits the confluence of data, data science, machine learning, cheap computing power, combined with intelligent human beings. ALIS managers’ brains are wired differently. They are often physicists, scientists, hackers, or computer gamers with a healthy disrespect for convention. They are poised to make today’s investment Ferrari look like yesterday’s horse and buggy.
In 2002, Equitas Capital Advisors, LLC was established as a unique company that blends the resources of a large global corporation with the flexibility of a small boutique firm. The registered service mark of Equitas Capital Advisors is Engineering Financial Solutions® and the purpose of Equitas is to design, build, and deliver investment solutions to meet the goals and objectives of our investors. Equitas Capital Advisors, LLC located in New Orleans, has over 200 years of combined investment management consulting experience providing professional investment management services to investors such as foundations, endowments, insurance companies, oil companies, universities, corporate retirement plans, and high net worth family offices.
Disclosures and Disclaimers:
Above information is for illustrative purposes only and has been obtained from reliable sources but no guarantee is made with regard to accuracy or completeness. It is not an offer to sell or solicitation to buy any security. The specific securities used are for illustrative purposes only and not a recommendation or solicitation to purchase or sell any individual security.
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Information presented is believed to be factual and up-to-date, but we do not guarantee its accuracy and it should not be regarded as a complete analysis of the subjects discussed. All expressions of opinion reflect the judgment of the author on the date of publication and are subject to change. This publication does not involve the rendering of personalized investment advice.
Charts and references to returns do not represent the performance achieved by Equitas Capital Advisors, LLC, or any of its clients.
Asset allocation and diversification do not assure or guarantee better performance and cannot eliminate the risk of investment losses.
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