BTD Capital Fund (NYSE: DIP) has shot up over the last two months, pushing its year-to-date gross performance to 6.07% as of this writing. The AI-powered ETF from Kaiju is the first to use AI not just to pick stocks but to actually direct trading decisions and this recent almost uninterrupted rally that began at the end of May demonstrates the potential of what properly curated and directed AI can do for investors.
A Classic Investment Strategy With An AI Edge
Kaiju designed DIP based on the timeless buy-the-dip strategy. The simple strategy requires buying dips—stocks that are temporarily trading below their value—and then selling them when they later rebound. But actually executing this approach can be slow and inefficient in practice. Even with a well-honed research-based strategy, it’s simply impractical for a human investor to scan the entire market at all times in search of individual dips.
This is what made buying the dip an ideal candidate for Kaiju’s first AI-powered ETF. It’s a purely science-based strategy that relies heavily on pattern recognition and analyzing large quantities of data, two of the things machines are best at.
The AI behind DIP was built on a proprietary buy-the-dip algorithm designed by a team of data scientists, financial behaviorists, mathematicians, and other experts. Then, it was trained on 15 years of intraday market data to find dips and identify the optimal entry and exit signals for trading those dips.
Now, the machine scans the entire market, flags the most promising dips for purchase and the human fund manager simply executes those transactions as prescribed by the AI when the portfolio undergoes its daily rebalance. And the ETF’s steady 7.36 % growth over the last three months certainly seems to be proof that Kaiju’s founder and CEO, Ryan Pannell isn’t completely insane for letting a machine direct the trading decisions.
Not All AI-Powered Investment Tools Are Created Equal
The terms AI and machine learning are being thrown around so much these days that they’re starting to lose all meaning. But to understand what DIP is doing and how it works, it’s important to understand what AI actually is.
Before all the AI buzz, algorithmic trading was already happening. This approach translated a trading strategy into an algorithm that a computer could execute. But it typically relied on traditional programming, not machine learning. That is, the human investor clearly defined a set of rules, including what technical indicators to check and when to enter or exit a position. The computer just carried out those commands over and over without adapting or learning as it went.
Other recent applications of the technology have seen AI being used to recommend stocks and create personalized investment portfolios. But, often, there’s no learning involved here either. The AI might be trained on historical data or the latest research on how to build a solid portfolio, but it’s usually not generating any new insights. It’s just picking out the stocks or designing the portfolio that fits the parameters it was taught.
True machine learning enters the picture when you get a computer that can actually adjust its own programming based on new information. It can identify new patterns, draw new connections, and hones its strategy to improve its performance as it goes. That’s what DIP does.
It’s built for deep learning via a series of neural networks — interconnected processors that work together sort of like a brain. What makes neural networks so powerful, especially when it comes to complex tasks like making trading decisions, is the way that they can layer information and functions coming from each individual processor.
In facial recognition, for example, one neural net might be dedicated to detecting noses while others each exclusively detect another feature like mouths, eyes and eyebrows. Then, yet another layer would detect whether all of those features are arranged in a way that indicates a human face.
Similarly, in DIP, each neural net is independently tracking and analyzing billions of data points. Together, they can apply a complex filter of over 25 factors to screen for stocks that are temporarily trading below their mean price and then identify the entry and exit signals to optimize the trade. Above all, they can do all of this in fractions of a second, helping it to capture more dips than a human investor could if they had to do all of this research and trade optimization manually.
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Performance data quoted represents past performance and is no guarantee of future results. Investment return and principal value of an investment will fluctuate so that an investor’s shares, when redeemed, may be worth more or less than the original cost. Current performance may be lower or higher than the original cost. Returns for periods of less than one year are not annualized. For the most recent month-end performance, call (800) 617-0004 or visit the fund’s website at www.dipetf.com.
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.