Investors have seen several AI-guided exchange-traded funds (ETFs) emerge during the recent wave of AI hype. These funds use AI and machine learning to choose stocks, but humans still execute the trading. However, in a time when concerns about letting AI take over are prevalent, Kaiju ETF Advisors took the brave step of creating the first actively managed ETF that is entirely run by AI. It selects the stocks and makes the trading decisions. Here’s a look at how the specific AI operates and why it is potentially one of the most efficient uses of the emerging technology that investors have heard of so far.
AI Can Do a Lot, But It Can’t Do Everything
AI has been receiving increasing attention in recent months, with both its potential and risks being discussed. Unfortunately, it is often exaggerated or misunderstood. For example, while it may be able to offer medical advice in a more approachable manner than some doctors, it is often inaccurate or not current and does not take into account the patient’s specific medical history.
In finance, AI can be a powerful tool for data analysis and pattern recognition. By analyzing intraday trading data, it can identify patterns and make predictions with impressive accuracy. However, its ability to understand context, apply common sense to new scenarios, or generate completely new ideas is limited, and it lacks intuition. As a result, it is less reliable when it comes to more nuanced aspects of financial decision-making such as forecasting long-term trends, comprehending the potential implications of geopolitical events, or predicting market fluctuations.
Kaiju ETF Advisors Bets On AI’s Strengths While Understanding Its Limits
Kaiju ETF Advisors utilizes AI to drive primarily technical trading strategies. This approach is often regarded as the key to success, as it is systematic, fast, and accurate. Unlike trading strategies based on news or market sentiment, technical trading relies less on intuition and common sense. Instead, it requires a deep understanding of human behavior and reactions, which is where a human’s expertise comes into play.
Applying AI to technical trading makes perfect sense. Humans are comparatively slow to analyze data and prone to making mistakes when tired or distracted. Additionally, they often change their trades based on gut feelings or bias. In contrast, machine learning evades all of these risks. Once a human establishes the rules to follow, the machine can execute those rules quickly, accurately, and repeatedly across massive datasets.
This AI-driven approach turns a theoretically sound but practically hard-to-implement strategy, like buying the dip, 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 and sell them when they bounce back toward that mean. Above all, investors need to repeat this process often enough to generate meaningful returns.
Kaiju’s inaugural Buy the Dip (DIP) ETF is based on this strategy, which can potentially generate significant returns.
The strategy seems straightforward on paper, but in practice, it can be a slow and tedious process to sift through thousands of stocks to find dips and distinguish genuine dips from artificially inflated stocks that are now returning to their mean. Even the most skilled investors are likely to be working at a much slower pace compared to AI, which can quickly sift through massive amounts of data and identify dips, facilitating trade execution.
This is precisely what DIP is designed to do. Kaiju’s technology team used their expertise in mathematics, finance, data science, and computer programming to design an AI that identifies stocks and makes trade decisions based on a specific and clearly defined buy-the-dip strategy that considers over 25 quantitative factors.
Rather than trying to time the market, the proprietary algorithm is trained to simply recognize patterns that indicate an individual stock is temporarily oversold, regardless of larger market conditions. This allows the AI to parse data, recognize patterns, and make short-term predictions without requiring it to do more intuitive or creative work.
The team’s extensive knowledge helped shape the strategy, but it’s the AI’s ability to examine billions of data points and apply the strategy in seconds that allows for quick trade execution and enables the strategy to repeat itself.
In-Article Image CreditsAI financial trading decisions computer screens via Unsplash by Kevin Ku with usage type - News Release Media
Featured Image CreditAI financial trading decisions computer screens via Unsplash by Kevin Ku with usage type - News Release Media