Benzinga’s Aaron Bry sat down with the CEO of Kaiju ETF Advisors, Ryan Pannell, to talk about the ethics and misconceptions around AI and the future of rules-based investing for retail investors. Kaiju ETF Advisors’ inaugural Buy the Dip ETF (NYSE: DIP) is an AI-managed fund trained to identify and act on short-term dips in individual stocks to generate returns. The team behind the innovative ETF has been working with AI for years and working in quantitative trading even longer. “We shifted from quantitative trading into AI early on,” Pannell told Bry, adding that they moved to the space at a time when others in the industry doubted the technology’s potential. “It’s the topic du jour today, but you go back to 2018 and it was like we were practicing the dark arts.” Seeing the technology get more attention and gradually broader adoption, especially in the investment space, has been exciting for the Kaiju team. “2023 has been validating for us,” Pannell added.
Kaiju CEO Says Data Crunching Is AI’s Greatest Strength
Pannell told Bry that the role of AI in its AI-managed fund really comes down to pattern recognition and data analysis. “Where it can assist [fund managers] is in cutting through an enormous amount of data and bubbling up what the investment manager is looking for to the top,” Pannell said.
For a strategy like buying the dip, that ability to crunch data in real time is key. The theory is simple: buy stocks that are temporarily trading below the price they should be and then sell when they rebound. But executing the strategy precisely and repeatedly is tough for a human because it requires the ability to scan the entire market and apply layers of criteria to correctly differentiate between a dip and a dive.
Processing that data at a speed fast enough to actually make short-term trading decisions would be all but impossible. But for AI, it can be done in a matter of nanoseconds. “We’re talking about 220 terabytes of data that we’re looking at every day,” Pannell said. “We ingest the entire stock market at the tick level.”
Kaiju ETF Advisors Wants To Bring AI-Powered Investing To Retail Investors
While Kaiju’s team was one of a few early adopters, AI has largely been out of reach for retail traders. That’s largely due to cost, according to Kaiju’s CEO. “It’s incredibly expensive,” he told Bry, “it’s just not cost-effective to employ for small investment advisors or small portfolio managers.”
There’s also a steep learning curve to deploy AI effectively, and the tech is so new that there just aren’t many people with the training or experience to do it. That was even more so the case back when Kaiju began working with AI. “Five years ago, there was no such thing [as financial AI data scientists]. There is still no degree in AI.”
Instead, Kaiju assembled a team of mathematicians, physicists, computer programmers, financial behaviorists and other experts from fields where AI was already being used in some capacity. Merging the expertise from those diverse fields helped create a proprietary Buy the Dip algorithm that can identify authentic dips and optimize trading decisions using over 25 factors in fractions of a second. And it can repeat the process relentlessly.
By packaging that AI power into an ETF, Kaiju could offer retail investors a chance to leverage that technology and the massive amounts of data behind it in their portfolios in a way that just wasn’t cost-effective or practical for them to do in the past.
Kaiju Envisions A Future Of Responsible AI-Powered Investing
Controversies around ChatGPT and other AI tools have led to a broader public discussion on the ethics of AI. Pannell and Bry touched on some of these issues during the interview. For Pannell, some of the biggest ethical questions are around the data that are fed to the AI. “How do you train these models? Where did you get your data and how did you pay for them in order to train these massive models?” Pannell asked. “If you programmed a machine to scrape the internet and view some other artist’s content to create novel images, you didn’t really pay for the data.”
That doesn’t have to be an issue in finance, though, where data service providers like ICE or Morningstar sell the massive amounts of data needed to make informed investment decisions. “You buy the data to train the engine on,” Pannell said.
Instead, the bigger ethical issue in investment management is in the actual application of AI. “Black box systems that don’t take anything into consideration are potentially dangerous because [AI is] not sentient. [It doesn’t] have a broad understanding of context in the market,” Pannell explained. “So it’s really up to the human portfolio manager to apply some guardrails and keep everybody safe.”
Pannell Sees AI-Managed ETFs Dominating The Quantitative Investing Space In The Future
Pannell predicts AI will touch nearly every industry in the future and that the tech is just going to keep getting better. So even though there are still doubts about AI right now, he’s confident that will change. “The skepticism will start to shift as the performance kicks in and you see the results,” Pannell said.
While bottom-tier portfolio managers might struggle to keep up with AI, Pannell says it likely won’t threaten every professional in the investment space. “There’s some job security for fixed income, global macro, and things like that that AI has a tough time with,” he noted. “But when it comes to quantitative investing and pattern recognition, I think you’re going to see it all over the place and even trickling down to the retail level.”
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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.