When the Science of Machine Learning Collides with the Art of Stock Picking

December 20, 2017
By Vlad Karpel

Robo Street – December 20, 2017 – New FREE Newsletter from Tradespoon

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Welcome to Robo Street, a free e-letter canvasing the future of investing using transformational advances in technology. Hi, this is Vlad Karpel, founder and creator of Tradespoon.com and formerly Chief Technology Officer of Options Express prior to it being acquired by Charles Schwab. My aim with this weekly publication is to familiarize, educate and inspire investors like you to harness the power of the various high-tech tools and methods available to apply to your portfolio in an effort to crush the market averages, week-after-week, month-after-month, year-after-year.

Every Friday I’ll be providing some market commentary, trend analysis and insights as to where the best investing opportunities lie according to my proprietary models that continuously adapt to all market conditions. My models never stop learning and only get smarter every day. It’s exciting to share my pursuit of truly making the machines do all the heavy lifting when it comes to investing. Times have changed – and changed for the good – when it comes to your investible capital and risk/reward. Money never sleeps and neither do my machines. So let’s embark on our intelligent journey together and make 2018 a banner year for your portfolio.

Going back several decades, Wall Street and Main Street investors alike have been enamored with the keen ability of some unique people that have had the Midas touch with picking winning stocks and posting consistent annual returns that exceed the market averages. The greater investing community likes to keep a short list of the greatest stock investors of all time simply because achieving market-beating returns year-after-year is an amazing feat in of itself.

This exclusive club includes the likes of Benjamin Graham, Warren Buffet, John Templeton, Peter Lynch, Julian Robertson and Stanley Druckenmiller. I would put these and other notable mentions under the category of “old school” investors, those that use pure human-driven fundamental analysis that determine stock selection.

Fast forward to the twenty-first century and we find a select set of pools of capital, namely within the hedge fund community, with snazzy names I would ascribe to as “new school” investors. This is where fundamental analysis, while still utilized, takes a back seat to computer generated mathematical formulas modeled to eliminate fundamental risk while bringing forth a high degree of technical certainty about a stock’s future direction. For decades, investors imagined a time when data-driven traders would dominate financial markets. That day has arrived.

While humans build these systems from the start and modify them as needed, their creation identifies stock and ETF candidates entirely on its own, drawing on multiple forms of Artificial Intelligence (AI) that leverage advanced artificial neural networks which constantly self-learn in order to apply the best-fitting models to a particular stock or ETF in order to generate highly accurate signals. Hence, the rise of data-centric funds is gradually replacing much of the human brain contribution to stock and ETF selection.

Hedge funds have long relied on computers to help make trades, 1,360 hedge funds make a majority of their trades with help from computer models which are applied to investible assets. Hedge fund assets under management at the end of the third quarter 2017 totaled $3.37 trillion. But this typically involves data scientists, or “quants,” in Wall Street lingo—using machines to build large statistical models to predict market trends and pinpoint particular trades.

The early funds employing quantitative analysis were somewhat static and as the market changes, may not work as well as human-based actively managed styles. The chart below clearly illustrates this position, however recent advances in AI where systems keep improving on their own around the clock are sure to bring an edge to hedge fund performance.

Hedge funds like Two Sigma and Renaissance Technologies have said they rely on AI. Bridgewater Associates and Point72 Asset Management, run by big Wall Street names Ray Dalio and Steven A. Cohen are moving in the same direction. Newer entrants like Hong Kong based Aidyia and San Francisco startup Sentient Technologies have systems in place that not only pick the stocks but also do all the trading on an automated system completely independent of human interaction.

While this futuristic trading and investing approach is all well and good for accredited investors, but where are typical retail investors to go to benefit from this powerful and transformational wave of robot-based Investing. Wall Street was quick to come up with its own offerings in recent years/ that fall under the catchy title of robo-advisor. A robo-advisor is an online, automated portfolio management service. Because these companies use computer algorithms, a set of rules to choose appropriate investments based on your risk tolerance and time horizon, robo-advisors can offer their services for a fraction of the cost of a human financial advisor.

The robo-advisor has officially gone mainstream, with a series of automated advisory launches over the past year by incumbent online brokers. E-Trade put out E-Trade Adaptive Portfolio, Fidelity now has Fidelity Go, and TD Ameritrade launched TD Ameritrade Essential Portfolios. Plus, the vast majority of asset allocation involves the use of index funds and ETFs and little in the way of stock picking.

And yet despite, or perhaps because of their ties to established heavy hitters in the field, these newcomers brought little innovation. Most fail to stand apart from independent startups like Betterment and Wealthfront and are merely mimicking those services at a higher price. For those investors that want to hand off the job of investing to others with the objective of hopefully just matching market returns the concept of the robo-advisor might make good sense.

For investors that want to apply a hands-on approach to optimizing portfolio performance, the passive robo-advisor is not a good fit. Fortunately, new and creative trading and investing systems that incorporate all the state-of-the-art technology with the human touch of tutoring on how to maximize the use of AI and deep learning have made their way to the marketplace. Consider it the sweet spot for self-directed investors and these tools are now readily available for low annual fees. In the weeks ahead, I’ll be providing you with a road map on how to put my lifetime of work and knowledge to formidable use to build your wealth with a high level of confidence. Thank you and good investing for 2018.

– Vlad Karpel




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