The Market's First AI-Managed ETF Lets An Algorithm Inform Its Trading Decisions — And It's Not As Crazy As It Sounds

Investors have seen a few AI-guided exchange-traded funds (ETFs) pop up amid the recent wave of AI hype. These funds use AI and machine learning to select stocks, but the actual trading is still left to humans. But now, at a time when fears of letting AI take over are high, Kaiju ETF Advisors made the bold decision to build the first actively managed ETF that’s fully managed by AI. It picks the stocks and it makes the trading decisions. Here’s how the specific AI works and why it’s potentially among the most efficient use cases for the emerging tech investors have heard so far. 

AI Can Do a Lot, But It Can’t Do Everything

The promise and risks of AI have been making headlines more and more in the past few months, and it’s often blown out of proportion or poorly understood. The exciting new tech may deliver medical advice in a friendlier manner than some doctors, for example. But it’s often inaccurate or not up-to-date and doesn’t factor in the patient’s relevant medical history. As such, fears that AI will push doctors out of a job appear largely unfounded at the moment.

In finance, AI can serve as a high-powered data analytics tool and it’s equally great at pattern recognition. So if you feed it intraday trading data, it can find the patterns and make predictions based on those with impressive accuracy. 

But it still has no intuition and a limited ability to understand context, apply common sense to new situations, or create completely new ideas. So when it comes to more nuanced aspects of financial decision-making like making longer-term forecasts, understanding the possible implications of geopolitical events, or anticipating market shifts, it becomes a lot less reliable.

Kaiju ETF Advisors Bets On AI’s Strengths While Understanding Its Limits

Kaiju ETF Advisors uses AI to drive primarily technical trading strategies — often considered to be where a systematic, fast, and accurate approach can be the key to success. Technical trading relies less on intuition and common sense than, say, a strategy based on news or market sentiment where a human understanding of human behavior and reactions is key to successfully interpreting signals. 

This makes it a prime field for applying AI. Where humans are comparatively slow to analyze data, liable to make mistakes if they’re tired or distracted, and prone to changing their trades based on gut feelings or bias, machine learning evades all of those risks. Once a human establishes the rules it should follow, it can execute them quickly, accurately, and repeatedly across massive datasets. 

This approach to AI turns a theoretically sound but practically hard-to-implement strategy like buying the dip — the strategy behind Kaiju’s inaugural Buy the Dip (DIP) ETF — into a potentially potent and repeatable strategy for generating returns. 

To buy the dip, an investor needs to find stocks that are trading artificially below their mean. Then, sell them when they bounce back toward that mean. Above all, they need to repeat that process often enough to generate meaningful returns. 

On paper, the strategy is simple. But, in practice, sifting through thousands of stocks to find dips and then trying to separate authentic dips from stocks that were actually artificially inflated and are now falling back toward their mean is a slow and painstaking process. Even when investors are good at it, they’re likely working at a snail’s pace compared to the speed at which AI can do the same thing. 

In the time it takes a human to set the parameters on their stock screener in the morning, AI can sift through massive volumes of data, and attempt to pick out the dips to facilitate trade execution.

That’s exactly what DIP is designed to do. The team behind Kaiju’s tech leveraged their expertise in mathematics, financial behavior, data science, and computer programming to build an AI that finds stocks and makes trade decisions according to a specific and clearly defined buy-the-dip strategy that accounts for over 25 quantitative factors. 

Rather than trying to time the market, the proprietary algorithm is trained to simply recognize the patterns that indicate an individual stock is temporarily oversold, regardless of larger market conditions. This leverages its ability to parse data, recognize patterns, and make short-term predictions without asking it to do more intuitive or creative work. 

The team’s depth and breadth of knowledge shaped the strategy, but it’s the AI that has the power to examine billions of data points and apply that strategy in seconds, paving the way for swift trade execution and allowing the strategy to repeat itself over and over again.

Featured photo by Kevin Ku on Unsplash.

This post contains sponsored advertising content. This content is for informational purposes only and is not intended to be investing advice. 

Investors should consider the investment objectives, risks, charges and expenses carefully before investing. For a prospectus or summary prospectus with this and other information about the Fund, please call (800) 617-0004 or visit our website at dipetf.com. Read the prospectus or summary prospectus carefully before investing.

The Fund is distributed by Quasar Distributors, LLC. Exchange Traded Concepts, LLC (the “Adviser”) serves as the Fund’s investment adviser. Kaiju ETF Advisors (the “Sub-Adviser”) serves as the Fund’s investment sub-adviser.

Investing involves risk, including loss of principal. The Fund is subject to numerous risks including but not limited to: Equity Risk, Large Cap Risk, Management Risk, and Trading Risk. The Fund is actively managed and may not meet its investment objective based on the Sub-Adviser’s success or failure to implement investment strategies for the Fund. The Fund’s principal investment strategies are dependent on the Sub-Adviser’s understanding of artificial intelligence. The Fund relies heavily on a proprietary artificial intelligence selection model as well as data and information supplied by third parties that are utilized by such a model. Specifically, the Fund relies on the Kaiju Algorithm to implement its principal investment strategies. To the extent the model does not perform as designed or as intended, the Fund’s strategy may not be successfully implemented and the Fund may lose value. A “value” style of investing could produce poor performance results relative to other funds, even in a rising market, if the methodology used by the Fund to determine a company’s “value” or prospects for exceeding earnings expectations or market conditions is wrong. In addition, “value stocks” can continue to be undervalued by the market for long periods of time. The Fund is expected to actively and frequently trade securities or other instruments in its portfolio to carry out its investment strategies. A high portfolio turnover rate increases transaction costs, which may increase the Fund’s expenses. Frequent trading may also cause adverse tax consequences for investors in the Fund due to an increase in short-term capital gains. The fund is new, with a limited operating history.