Diversification is one of the most basic principles of investing. But to be truly diversified, investors need to be open to more than just new asset classes or sectors – they need to be open to new ways to invest. As technology continues to disrupt different aspects of our lives (i.e. communication, travel, health & wellness), investing could be next. In 2019, some investors are already turning towards new technologies that leverage AI and machine learning for an edge in the stock market.
Today, we’ll be taking a look at the rise of machine learning in the investing world – and whether or not it has staying power.
The Growth of Machine Learning and AI Research
In July, Microsoft announced it would be investing $1 billion in OpenAI, an AI research company, to “…support the development of artificial general intelligence (AGI) — AI with the capacity to learn any intellectual task that a human can.”
“AI is one of the most transformative technologies of our time and has the potential to help solve many of our world’s most pressing challenges.”
The article goes on to explain the growing computational power of AI,
“According to Brockman, the partnership was motivated in part by OpenAI’s continued pursuit of enormous computational power. Its researchers recently released analysis showing that from 2012 to 2018 the amount of compute used in the largest AI training runs grew by more than 300,000 times, with a 3.5-month doubling time, far exceeding the pace of Moore’s Law.”
How Does Machine Learning Enhance Investing?
In the context of investing, machine learning is about analyzing data, recognizing patterns, and inferring future trends – something that computers can do far faster and far more efficiently than humans.
Simply put, the human mind can only take into consideration so many data points when developing an investment thesis about a company or sector. Machines, on the other hand, have the potential to process millions of data points at any given moment.
But machine learning offers the potential for more than just faster, more accurate stock trend analysis… it can render emotions – the investor’s Achilles heel – a non-factor.
Mike Chen, an equity portfolio manager at Boston-based PanAgora, whose quantitative investment firm manages about $43 billion in assets, said in a CNN article,
“[Machine learning] takes emotion out of [investing]. Everything is rational.”
Major Investment Institutions Embrace Machine Learning
A CNN Business article titled How elite investors use artificial intelligence and machine learning to gain an edge, takes a look at how large banks and hedge funds are already leveraging machine learning tools,
“Citigroup (C) uses machine learning to make portfolio recommendations to clients. High-frequency trading firms rely on machine learning tools to rapidly read and react to financial markets. And quant shops like PanAgora Asset Management have developed complex algorithms to test sophisticated investment ideas.”
CNN Business continues,
“Domeyard, a Boston hedge fund that focuses on high-frequency trading, depends on machine learning to decipher 300 million data points in the New York Stock Exchange’s opening hour of trading alone.”
But it’s not just quant shops and hedge funds that have access to machine learning trading technologies – they’re becoming increasingly available to the retail investor.
Take BlackRock’s “Aladdin Risk Platform”, for example.
“According to BlackRock the [Aladdin Risk] platform enables individual investors and asset managers to assess the levels of risk or returns in a particular portfolio of investments. The company claims that [using machine learning] Aladdin can automatically monitor over 2,000 risk-related factors per day (like interest rates or currencies rates) and test portfolio performance under different economic conditions.”
Machine Learning Investment Technologies Fail To Live Up To Expectations
But despite the hype surrounding machine learning and AI investment technologies, they aren’t delivering stellar returns – at least, not yet. In a Bloomberg article titled Why Machine Learning Hasn’t Made Investors Smarter, Nishant Kumar outlines the current limitations and challenges facing machine learning investment technologies,
“Finding patterns isn’t that hard; finding ones that work reliably in the real world is. Financial data is very noisy, markets are not stationary and powerful tools require deep understanding and talent that’s hard to get. One quantitative analyst, or quant, estimates the failure rate in live tests at about 90%.”
“Man AHL, a quant unit of Man Group, needed three years of work to gain enough confidence in a machine-learning strategy to devote client money to it. It later extended its use to four of its main money pools.”
Like many flavor of the month investing tools, machine learning investment technologies often fail to beat leading index funds, as shown in the chart above.
Nevertheless, machine learning is coming to a financial institution near you. The article continues,
“Fifty-eight percent of managers in one survey said machine learning will have a medium-to-large impact on the industry. Hedge fund giant Bridgewater Associates and Man Group Plc as well as Highbridge Capital Management and Simplex Asset Management in Japan are among firms developing machine learning or investing in it.”
Machine learning and AI can help create predictive outcomes for a variety of applications by spotting patterns and testing potential scenarios against massive data sets. However, many of today’s machine learning investment technologies are currently underperforming when compared to traditional trading methods. Given time for improvement, we believe that machine learning and AI powered investment solutions have the potential to become powerful trading alternatives – even for the most self-directed retail investor.
All the best with your investments,
If you’re not already a member of our newsletter and you invest in TSX Venture and CSE stocks, what are you waiting for? Subscribe today. Only our best content will land in your inbox.