Financial markets have existed for centuries. But what gets traded, and how markets operate, has evolved over time. Look at the past 50 years: Those iconic images of stockbrokers yelling orders in a trading pit are (mostly) a thing of the past. Now, professional traders are more likely to use advanced algorithms to find trades than a well-suited broker. As for the next 50 years, we’re likely to see artificial intelligence play a much bigger role in how we invest.

At Revo, we work with investment research partners well-versed in artificial intelligence. To understand why this type of cutting-edge thinking benefits you, it’s helpful to understand some of the technology that underpins our financial system.

How Nasdaq changed the game with electronic trading

While the New York Stock Exchange wasn’t the first stock exchange in the world (that title goes to London), it’s probably the most well-known. Officially formed in 1792, the NYSE rented a room on Wall Street and allowed brokers to come together twice a day to trade based on a list of 30 stocks and bonds.

Over time, the number of securities grew, and the NYSE moved to a large trading floor that stayed open all day. That same basic system continued well into the 1970s. In 1971, however, the Nasdaq Stock Market opened with no trading floor—it processed trades electronically and was based entirely on computers. This offered a few perks.

• Reduced costs: Automation and efficiency lowered the cost of trades.
• Greater access: No physical trading floor meant people could trade from anywhere.
• Improved liquidity: Better access meant more trades and a more liquid market.
• Transparency: Electronic trading has, in general, increased visibility and awareness of what happens in the market.

Of course, we’ve evolved quite a bit since the introduction of electronic trading in the 70s. Some might argue we’re at another inflection point, as machine learning and artificial intelligence become more prevalent.

How does artificial intelligence work?

Artificial intelligence (AI) refers to a machine’s ability to simulate human intelligence. In essence, it’s meant to replicate the human thought process. Machine learning is a type of AI that’s designed to improve without hands-on human programming.

To do this, the computer identifies patterns in historical data, then uses human-programmed algorithms like decision trees, ultimately allowing it to “learn.” The computer then makes decisions based on those learnings.

Machine learning and the stock market

As you might expect, the stock market—rich in historical data—has been a focal point of AI and machine learning. While the abundance of historical data can certainly help machines learn patterns and make predictions, there’s one catch: Past performance doesn’t predict future results. Markets can be unpredictable and volatile, just like the humans trading the shares.

That doesn’t make machine learning useless, however. In fact, it’s become an essential tool for many traders in recent years, as it can help forecast changes in the market and economy and even predict the emotional responses of traders in reaction to certain events… all based on an analysis of how similar events have played out in the past.

What’s more, computers can process unfathomable amounts of data in a matter of seconds, then apply machine learning to that huge pool of information. This approach can help investment professionals identify areas of risk they need to manage, spot new investment opportunities, and get a sense of where things may be headed.

How we use machine learning at Revo

Revo Financial works with a research partner—Helios Quantitative—that uses machine learning as part of its market analysis and investment research.

Helios starts with a wide pool of investment and economic data, which it uses to help identify the type of market we’re in, based on volatility and returns. That’s learning number one. From there, the algorithms use that first learning as a guide to the rest of the data, narrowing it down to the 50 most relevant data points—learning number two. Finally, the algorithms analyze those data points to make recommendations around allocations. For instance, their research might show large cap stocks are more likely to outperform in the next few months than international stocks.

We think including this cutting-edge technology along with fundamental best practices for building portfolios and managing investment provides our clients with the best chance of successfully reaching their investment goals. As such, we use AI-driven research from Helios to help guide our decisions when managing client investments.

At the end of the day, however, data analysis is just a tool. Our clients’ goals, risk tolerance, and preference matter more than machine learning. If you have more detailed questions about how we think about AI or use machine learning, don’t hesitate to contact us.