Investors Can Now Let AI Buy The Dip For Them Thanks To A New AI-Powered ETF

BTD Capital Fund DIP, the actively managed ETF, is powered by an AI that’s been trained to apply the classic “buy the dip” strategy to manage the fund’s portfolio. While other AI-driven ETFs have also deployed emerging tech in stock selection, DIP is the first fund on the market to use AI for both picking the stocks and directing the trades. 

Kaiju ETF Advisors, the company behind the AI-powered active ETF, says it chose a “buy the dip” strategy for its first ETF as a kind of proof-of-concept of its proprietary AI. The relatively conservative trading strategy tasks the algorithm with finding large-cap stocks that are temporarily trading below their mean and then selling them a few days later when they bounce back up. Here’s how DIP works and what separates it from the other AI-driven ETFs. 

How Kaiju’s Dip-Seeking AI Works

The AI behind DIP has been trained to find stocks and plan trades using over 25 factors that the team of financial behaviorists, mathematicians, and data scientists who built it have identified as predictors that a price is both artificially low and likely to move high in the next few days. 

The fund will hold 25-100 positions, but it’s not intended to hold any of them for long periods because it’s not designed to time the market. Instead, the AI is programmed to identify individual stocks that look like they’re priced temporarily and artificially below their mean. 

Then, it exits each position after a few days when the price has, ideally, moved higher, and starts looking for the next dip to trade. The AI relentlessly repeats the strategy over and over and over with the goal of generating returns by capturing as many dips as possible. 

Capturing just a piece of the price action in each trade like this rather than trying to ride an entire anticipated rebound is a risk management strategy, but also a likely recognition of the limits of AI. The powerful tool has proven itself pretty formidable when it comes to predictive analytics, but its accuracy decreases with longer time horizons or larger, more complex conditions. 

Take ChatGPT’s hallucination problem as an example. It’s impressively good at deciding which word should come next when it’s generating a sentence — so good that it sounds eerily human. But when you take its response as a whole, it’s prone to spitting out repetitive, contradictory, or even entirely made-up statements.

Likewise, in finance, AI can be an impressive tool for figuring out where the next bar in a bar chart might pop up, but less accurate at providing larger forecasts of where the overall market is headed or even where individual stocks are likely to be in the future. 

Buy-The-Dip Is A Timeless Strategy That’s Hard For Humans To Execute

Buying the dip is a classic strategy. Buy stocks that are priced cheaper than they should be and sell them when they recover to generate a profit. The theory is solid, but the problem is in the execution. There are thousands of publicly traded companies in the U.S. market alone. Monitoring all of them for potential dips is not feasible for any human being. 

Even if it were possible, planning the trades without letting bias or past experience cloud judgment is nearly impossible. Even if a trader has crafted a solid strategy, they will likely be tempted to view specific companies they like as undervalued or oversold without a deep understanding of the possibility that it is reverting to the mean or correcting from a high.

This can make it difficult to generate consistent returns with a buy-the-dip strategy. But that’s exactly where an AI can help. It can parse through billions of data points in seconds and follow a set of rules without deviation. So it’s just the right kind of tool to help an investor find more dips than they could find on their own and stick to the parameters of a strategy – without letting human error get in the way. 

Kaiju chose this strategy for that reason. It’s a simple concept that can be set up as a purely technical strategy, relying on AI’s pattern recognition and predictive analytics without tasking it with trying to forecast longer horizons or more complex market conditions. 

It’s also a relatively conservative methodology, as it’s just trying to identify oversold stocks rather than anticipate trends or predict explosive growth opportunities. The hope is that a more conservative (by focusing on dips in large-cap stocks and ETFs) AI-powered ETF can help build trust among investors who are wary of some of the relatively more outlandish claims that have been made about AI’s market-beating abilities. 

But DIP is just the first in what’s expected to be a growing portfolio of AI-managed funds. While there’s not much word yet on when the next ETF will be launched, Kaiju says it’s working on more AI-powered ETFs for retail and institutional investors alike.

Learn more about Kaiju ETF advisors here.

Featured photo by Anna Nekrashevich on Pexels.

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.