Mobile investing and trading may seem cutting edge in 2020, but current technology will likely seem archaic in the not-so-distant future.
The real revolution in the financial markets will likely arrive as artificial intelligence (AI) and machine learning (ML) become more prevalent in the industry.
Artificial intelligence refers to sentient machines, which likely won’t appear for some time, if ever. Instead, the more practically applicable “machine learning” technology will lead the transformation through advanced computer modeling, predictions and analytics.
In practice, machine learning is represented by computer algorithms that improve themselves with “experience.” Algorithms, as they relate to the financial markets, are mathematical models that leverage data sets in order to generate forecasts or predictions that can assist with decision-making. The machine learning field is also commonly referred to as “predictive analytics.”
Machine learning projects are typically paired with disciplines such as computational statistics, mathematical optimization and data mining—resulting in an integrated approach that allows for the creation of the most effective and accurate algorithms possible.
The “learning” component of machine learning refers to the fact that an algorithm is only as good as the data it relies upon. Machine learning is therefore most effective when it draws upon deep pools of diverse data.
That requirement is one reason the investment/trading industry is such a good fit for machine learning-focused innovation; because there’s so much available data. Data in the financial markets ranges from technical indicators like price and volume, to fundamental data relating to profitability, growth and competitive landscape.
On top of those categories, qualitative information stemming from news and global macro events can also be incorporated into machine learning algorithms.
With such vast pools of diverse data, it’s easy to see the value in a machine that can track, process and analyze all of it. There’s simply no way the human mind, as amazing as it is, can compete.
In terms of practical application, one of the most revolutionary methods of applying machine learning in the investment industry is the rise of machine learning-directed investment and trading strategies.
So-called “black boxes” have been used on Wall Street for some time, but the advent of advanced machine learning has opened this approach to a wider group of investment strategies and trading approaches—mainly due to advancements in machine learning hardware and software.
In that regard, the investment industry is no different than any other industry experiencing significant advancements as a result of cutting-edge technology.
The term “black box” traditionally refers to an algorithmic trading strategy that takes in a series of inputs (i.e. data) and processes them into actionable outputs—usually resulting in trading/investment decisions. Advanced machine learning has made it possible to create even more robust models that leverage a greater degree of automation.
Today, a great example of machine learning being applied in the markets is represented by J4 Capital in Seattle, which uses an investment algorithm to direct all of its investment decisions.
The founder of J4, Jeff Glickman, refers to the machine charged with managing the firm’s capital as “an autodidactic superintelligence that can reprogram itself.”
Glickman claims J4 has consistently produced attractive returns because of the algorithm’s ability to reliably predict the direction of individual stocks.
Most readers will also be familiar with the concept of a “Robo-advisor,” or digital platforms that dispense market advice using minimal human intervention. Many of these platforms also rely on customized investment algorithms that are programmed to mirror a given investor’s unique risk profile. Robo-advising first appeared in 2008 as a commercialized concept when the company Betterment launched its platform during the Great Recession.
That’s not to suggest a Robo-advisor is suitable for all investors, much less active traders, but one can at least see how the trend toward machine learning fits the Robo-advising niche like a glove.
Given the hyper-competitive nature of Wall Street, there’s no doubting the fact that companies in the investments industry are currently working furiously to find an edge over their competitors using machine learning. The graphic below provides a rough overview of machine learning capabilities across the quantitative niche of the industry at this time.
Of course, the utilization of machine learning isn’t limited to investment-focused algorithms. There are many other ways that firms in the financial industry can leverage machine learning, including:
- Process automation
- Enhanced security monitoring
- Predictive modeling
- Fraud protection
- Risk management
- Data management
- Data analytics
Machine learning is an emerging trend, which means companies are currently working intensely to ascertain how and where to utilize this technology.
It’s currently estimated that the majority of the finance and investment industries are working on discrete proof-of-concept machine learning projects at this time. With the fintech space gathering tons of momentum in 2020, it’s assured that new breakthroughs will be coming to the market soon.
Investors and traders would be wise to keep abreast of these developments, not only because it could affect the way they interact with the market in terms of analysis and execution, but also because of associated investment/trading opportunities in the market. For example, the upcoming initial public offering (IPO) of the well-known Chinese fintech Ant Group, a division of e-commerce giant Alibaba (BABA).
Readers seeking to expand their understanding of emerging trends relating to quantitative finance and technology may also want to follow the tastytrade series The Skinny on Quantitative Finance for periodic updates.
Andrew Prochnow, an avid, longtime options trader, has written extensively on progressional tennis and has contributed articles to Bleacher Report and Yahoo!Sports. Readers can direct questions about any of the topics covered in this blog post, or any other trading-related subject, to email@example.com.