Algorithms And Artificial Intelligence: How We Are A GameChanger
- Our Proprietary Algorithm System combined with Artificial Intelligence And Human Genius is producing Trade Ideas with an Accuracy Ratio of 80 percent. The findings of a new groundbreaking report clearly proves that Machine Learning can be used to predict the future evolution of chaotic systems out to stunningly distant horizons
- 50 years ago, the pioneers o the chaos theory discovered that the “butterfly effect” makes long-term prediction impossible. Even the smallest perturbation to a complex system (like the weather, the economy or just about anything else) can touch off a concatenation of events that leads to a dramatically divergent future
- The findings come from veteran chaos theorist Edward Ott and four collaborators at the University of Maryland. They employed a machine-learning algorithm called reservoir computing to “learn” the dynamics of an archetypal chaotic system called the Kuramoto-Sivashinsky equation. The evolving solution to this equation behaves like a flame front, flickering as it advances through a combustible medium
- After training itself on data from the past evolution of the Kuramoto-Sivashinsky equation, the researchers’ reservoir computer could then closely predict how the flamelike system would continue to evolve out to eight “Lyapunov times” into the future, eight times further ahead than previous methods allowed, loosely speaking. The Lyapunov time represents how long it takes for two almost-identical states of a chaotic system to exponentially diverge. As such, it typically sets the horizon of predictability
- The algorithm knows nothing about the Kuramoto-Sivashinsky equation itself; it only sees data recorded about the evolving solution to the equation. This makes the machine-learning approach powerful; in many cases, the equations describing a chaotic system aren’t known, crippling dynamicists’ efforts to model and predict them. Ott and company’s results suggest you don’t need the equations — only data
- Six or seven years ago, when the powerful algorithm known as “deep learning” was starting to master AI tasks like image and speech recognition, they started reading up on machine learning and thinking of clever ways to apply it to chaos. They learned of a handful of promising results predating the deep-learning revolution
- Most importantly, in the early 2000s, Jaeger and fellow German chaos theorist Harald Haas made use of a network of randomly connected artificial neurons — which form the “reservoir” in reservoir computing — to learn the dynamics of three chaotically coevolving variables
- After training on the three series of numbers, the network could predict the future values of the three variables out to an impressively distant horizon
- Parallelization allows the reservoir computing approach to handle chaotic systems of almost any size, as long as proportionate computer resources are dedicated to the task
- In a paper soon to be published in Chaos, Parlitz and a collaborator applied reservoir computing to predict the dynamics of “excitable media,” such as cardiac tissue. Parlitz suspects that deep learning, while being more complicated and computationally intensive than reservoir computing, will also work well for tackling chaos, as will other machine-learning algorithms. Recently, researchers at the Massachusetts Institute of Technology and ETH Zurich achieved similar results as the Maryland team using a “long short-term memory” neural network, which has recurrent loops that enable it to store temporary information for a long time
- In new research accepted for publication in Chaos, they showed that improved predictions of chaotic systems like the Kuramoto-Sivashinsky equation become possible by hybridizing the data-driven, machine-learning approach and traditional model-based prediction
- Barron’s/AI: Artificial intelligence starts with a machine learning data, as a child might, as opposed to being programmed to execute a specific task. Machines learn by being fed data via a set of statistical techniques, which are unique and proprietary. The term “Big Data” refers to the bazillions of data points—where you are at any given moment, how much you’ve spent on coffee this month, even who your friends are—that could be available to data scientists to help create and feed into these statistical models
- One issue relating to neural network-based AI applications in investment management is the black box, in which the workings of an algorithm are not understood by its user and lead to potentially unintended actions or consequences. It’s a well-known headache for regulators trying to ensure market stability. Although some attempts have been made to check the source code of algorithmic traders, the most effective protection against algorithmic errors are circuit breakers on markets that limit the damage a failing algorithm can cause
- Barron’s/Deep Learning: Deep learning is a sub-methodology of machine learning, where the biggest breakthroughs are happening now. Deep learning is basically teaching computers to process information more like humans do. Natural language processing, for instance, means computers can come to actually understand and develop responses to language, rather than react according to a series of programmed rules. Computer vision allows a computer to see and recognize visual images, not only making the distinction between a photo of an apple, a drawing of an apple, and an actual apple, but also distinguishing a multitude of variations of each. This type of AI has enabled self-driving cars, and machines that can identify tumors or choose the best grapes to make a Cabernet. And, of course, augment financial data analysis
- Some of the most interesting developments in AI are in portfolio management. Machines have already commandeered the passive investing trend: The $3 trillion exchange-traded fund industry couldn’t have happened without modern computing, and the newest ETFs are likely to make even greater use of AI
- Passive investing is simply the buying and selling according to a set of rules on a particular schedule. The best-known passive investment, a Standard & Poor’s 500 index fund, only adjusts its holdings according to its criteria around market value. Others, like the Russell indexes, rebalance annually. But funds using much more complicated—though still technically passive—rules are being launched, and embraced. These new products are passive in that the securities they own and when they’re bought or sold adhere to a set of rules, but those rules have become so complex that they are essentially active products. AI can take the decision-making even further, using Big Data—satellite images of foot traffic on New York’s Fifth Avenue or the shadows cast by oil tankers—to process massive amounts of information, filter out the “noise,” and seize on the “signals” to buy or sell
- AI will make the distinction between active and passive even more subtle, perhaps subsuming the debate into a completely new form of investing, says Jeff Shen, co-head of BlackRock’s scientific active equity group. “We just don’t have a name for it yet.”
- Machine learning is starting to show up in more mainstream products, but early efforts are a little underwhelming. BlackRock launched seven new sector ETFs last month, such as the iShares Evolved U.S. Technology ETF (ticker: IETC) and iShares Evolved U.S. Consumer Staples ETF (IECS). The firm uses natural language processing to sift through public filings for specific words and phrases describing the business. That determines which companies go in which ETFs and at what weighting. The end result is similar to traditional sector categories, though some companies appear in more than one sector. IBM’s Watson is also on the ETF bandwagon. The $134 million large-company AI Powered Equity ETF (AIEQ) launched six months ago
- “A massive amount of data is required to make AI work, and machine learning is not just one thing, but many different things,” says Campbell Harvey, a Duke University professor and Man Group consultant. “This is a common problem with robo-advisors. People are fooled thinking it’s algorithmic or whatever, but some are just garbage. Be very careful.”
- Plus, markets are complicated. “The market is a biological system, not an immutable one, and we don’t have laws that can explain how this ecology evolves over the course of a year or even decades,” says Lo. “AI can address the who, what, when, where and how, but the underlying logic of the decision—which is what makes Warren Buffett successful—is lacking.”
- That uniquely human ability to reason means that Buffett, Fidelity’s Will Danoff, DoubleLine Capital’s Jeffrey Gundlach, and the like can breathe easy. But it’s not just about portfolio management: Some financial firms are betting that AI can accurately ascertain behavioral traits and predict individual reactions to financial and market events
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