How Qraft and Kaiju Are Using AI to Enhance Returns

 Some ETF issuers are not only investing in AI, but are instead investing with AI.

Utilizing machine learning, AI-driven strategies can adapt to market environments better than human managers.

Notably, using AI in the investment space is not a new thing. Hedge funds have long been using deep learning engines to analyze market data and separate the signals from the noise to identify investment opportunities, Francis Oh, CEO of Qraft Technologies, said during VettaFi’s AI Symposium on August 30.

Oh said ETF issuers like Qraft Technologies and Kaiju are utilizing AI to duplicate the human portfolio managers investment and decision making processes.

The additional benefit of AI, however, is the lack of emotional bias. AI is able to make methodical, calculated decisions without being influenced by emotion. AI uses a data-driven approach to enhance returns, limiting human behavioral mistakes, Oh said.

“We all know that emotional biases can be one of the performance draggers, in the long term,” Oh said.

Importantly, AI gets smarter over time. Oh cited ChatGPT as an example of AI’s continued advancement.

When ChatGPT was first introduced a couple years ago, its uses were limited and it was riddled with errors. However, by 2023, ChatGPT has become advanced enough to have many real-life applications and uses.

“Similar cases can be [seen]in the AI models in the financial markets, as well,” Oh said.

There are two ways in which AI will get smarter over time. As new data becomes available, AI models will process the data and learn from it. Additionally, the tremendous advancements in technology will continue to advance AI and enhance its accuracy in stock prediction in financial markets.

AI ETFs Available to Investors

Three compelling ETFs run using AI include the QRAFT AI Enhanced U.S. Large Cap ETF (QRFT), the QRAFT AI-Enhanced U.S. Large Cap Momentum ETF (AMOM), and the BTD Capital Fund (DIP). 

QRFT and AMOM leverage AI models’ prediction power to deliver better returns over time.

“Instead of a thematic approach, what QRFT and AMOM are trying to do is trying to elevate the return from the markets,” Oh said.

While the underlying strategies are similar, QRFT is a broader approach than AMOM. QRFT is trying to beat the S&P 500. The fund comprises 350 stocks which are chosen and rebalanced on a monthly basis. The model selects the stocks that have the highest probability of beating the S&P 500.

On the other hand, AMOM has a focus on momentum and is a more concentrated portfolio. AMOM comprises 50 stocks and, like QRFT, rebalances monthly. Oh said AMOM has more flexibility in getting in and out of positions, so investors can expect to see the fund profit taking when a stock has rallied well. 

DIP uses AI to capitalize on quick-return opportunities in the market. DIP identifies market dislocations, initiates buys, and then instructs when to sell rebounded shares in short order — replacing a significant portion of the ETF’s holdings every day.

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