AI in My ETF!

By Bryan Novak, Astor Investment Management

Artificial intelligence continues to arrive in the ETF space. On Wednesday, the Artificial Intelligence run ETF powered by IBM’s Watson launched on the NYSE. The fund, AIEQ, is managed by EquBot and brought to market by ETF Managers Group.

It’s hard to deny the lure of such an elegant concept behind an ETF in what is now undeniably a technological revolution within the asset management space. The supercomputing machine, Watson, has been learning finance applications for some time and is now set to manage a portfolio of stocks based on what it has learned. It’s important to point out the machine learning component of this. There are numerous applications of AI hitting various parts of the marketplace and how the machines are taught and learn is important to the outcome. The analogy was of two chefs who have the same ingredient but different skills. The end meal will come out different. However there’s no denying the application of this type of approach in investment management space.

The ETF itself is targeted toward active managers. The value proposition enhanced by the AI application is that it allows the computer system to analyze information faster and more efficient than a group of analysts. Efficient market hypothesis postulates that a stock’s market price is reflective of all publicly available data. With the increase in volume but availability of information and subjectivity of the integrity and reliability of the information, this has created efficiencies and challenges to the art and science of investment analysis. A system that can more effectively filter through this information conceptually seems to be a value proposition, for sure. The technology behind the ETF is also designed to create trade points based on the metrics.

Where Does it Fit?

The objective is a more efficient approach toward active management, designed to accelerate current methods of data analysis, potentially improving outcomes under the Efficient Market Hypothesis. The parameters and constraints on the allocation will shape the portfolio. Additionally, what is has learned and what it will learn will effect the outcome and its ability to outperform other active managers and indexed strategies, whether that be traditional passive or smart / strategic beta.

Related: Artificial Intelligence Powered ETF Debuts on NYSE