Can AI Drive Alpha For ETFs?

In our previous post we touched on the potential of an ETF bubble. The exponential growth of ETFs, especially from younger investors who want to set-it-and-forget-it, means there’s an opportunity for providers to increasingly use Artificial Intelligence in smart alpha and active products. But what can AI do for your business and investment strategy?

Like Humans, Only Better, Faster, Smarter

AI tools can intake data, learn from it, and act on it to meet specific objectives. But they can do it more quickly and efficiently. In fact, machines running AI algorithms can process large amounts of data in the blink of an eye. Market data is dynamic. Machines can react instantly to fluctuations to best identify ideal investment strategies. They can also read through thousands of pages of market reports in seconds, while simultaneously connecting new market signals with recent ones detected in other markets. It would take a fund manager hours to do the same thing a machine can do in split seconds.  

AI Has No Ego or Emotion

Investors tend to make poor decisions because it’s their money they could lose. Money is emotional. But machines don’t get stressed, tired, or angry. There’s no winning or losing. They operate in a purely logical manner and make decisions based only on evidence and indicators. When you remove emotion from the equation, you make better decisions. There’s no holding onto a position because you think it might change. There’s only analyzing the facts and deciding based on what is happening, not what might happen.

Disrupting the ETF Industry

ETF positions are decided on by an AI system that processes market signals, news articles, and social media posts. Daily trade recommendations in an AI capacity are not only easy, but cost-effective. Smaller fintechs and individual developers have unprecedented access to this technology. Perhaps you read about AIIQ from EquBot, the first exchange-traded fund to use AI technology to pick stocks from developed markets outside of the U.S. It leverages IBM’s Watson capabilities to build predictive models that identify 30-70 U.S. stocks every day that have the best appreciation potential.

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