The frontier of finance is shifting. In the face of AI, at least one thing is clear: human investors need new methods to stay competitive. Robo-investors are often powered by machine learning models that are trained on historical data, enabling them to predict market movements and identify ideal investment opportunities. How can human investors make equivalent or better decisions?

The answer is that in order to compete, humans need the same access to data and the ability to understand it. That’s why, in this race against robo-investors, investment professionals need to prioritize both acquiring all of the data necessary to complete and methods to quickly and effectively understand it. 

A new entrant to the invest-tech market, insytz, strives to bring an AI-level breadth of data along with easy-to-analyze dashboards, giving humans an opportunity to compete. They boast eight decades of market data alongside real-time trend tracking and macro market insights, enabling humans to make investment strategies that are powered by perspective, not just prediction.

Because AI Gets it Wrong

Between November 2017 and June 2023, EquBot’s AI Powered Equity ETF (AIEQ) returned 6.7% per annum with a standard deviation of 23%, placing it in the 97th percentile of performance. In comparison, Vanguard’s Total Stock Market Index Fund (VTSMX) returned 11.2% with a standard deviation of 18.5%, meaning AIEQ underperformed.

AIEQ is powered by IBM’s Watson, which gained media attention for defeating Brad Rutter and Ken Jennings in 2011’s Jeopardy! Challenge. As Andrew Berkin and Larry Swedroe explain in their book The Incredible Shrinking Alpha, technology like Watson excels at outperforming humans in one-on-one situations, such as chess or trivia. 

But individual people aren’t actually the competition when it comes to investing.

Machine learning models in the financial market are competing with the collective strategy of millions of people trading around the world, all day, every day. What’s more, if a headline or whitepaper hits an email chain, AI can’t see that information. Human eyes, however, do. That means savvy human beings will consider that information alongside all of the other sources of information they have access to, and if they choose to act on it, they will immediately influence the market.

The Power of Historical Data in Predictive Modeling

Clearly, historical data is a crucial tool for forecasting future market movements. If it weren’t, AI wouldn’t need it. As advanced analytics and robo-investors increasingly shape the financial landscape, investors must adapt to stay competitive. And, as AI-powered investment tools become increasingly prevalent, access to comprehensive, long-term data is more important than ever—as is the ability to quickly understand it.

At the core of the insytz platform lies a massive cache of unbiased, historical market data, its foundation for sophisticated, predictive modeling. This historical data offers the same kinds of pattern identification and trends that AI models rely on. By combining this data with human intuition and expertise, investors can make informed decisions that not only rival but exceed those of their AI counterparts.

And if that sounds like an abundance of information only AI could comprehend in seconds, you’ve identified the crux of what makes this invest-tech company interesting. Their big differentiator isn’t that they provide a ton of data (lots of companies do that); where they’ve made their mark is in the way the data is displayed.

We’ve all begun to associate red, yellow, and green with traffic lights—stop, slow, go. Now, this color scheme is a core component of insytz’s platform. They present a completely holistic view of the global markets and uncomplicate that view with color-coded visual models that illustrate market movements and draw attention to opportunities. This three-color system enables investors, analysts, and wealth managers with a holistic view of the markets at a glance—offering people, not robots, the opportunity to make fast, informed, and confident decisions.  

Empowering Human Investors

As the invest-tech landscape continues to evolve, human expertise in navigating the complexities of modern financial markets remains key. While AI-powered robo-investors and machine-learning models can offer unprecedented speed and accuracy in analyzing vast amounts of data, they lack the intuition, experience, and contextual understanding human investors bring to the table. 

Investment-tech platforms that synthesize immense amounts of data for easy human use bridge the gap between artificial intelligence and human insight, providing investors with the tools they need to make informed, data-driven decisions. By harnessing the power of historical data and advanced analytics and combining it with their own expertise, investors can not only compete with AI-driven strategies but also identify unique opportunities that machines overlook. 

As we move forward in this era of algorithmic investing, those who successfully integrate cutting-edge technology with human intelligence will be best positioned to maximize returns, deliver superior results for their clients, and thrive.