Active ETF and AI: A Promising or Perilous Combination?

Exchange-traded funds, or ETFs, are popular investment vehicles that offer exposure to a basket of securities, such as stocks, bonds, commodities, or cryptocurrencies. ETFs trade on exchanges like stocks, making them easy and convenient to buy and sell. Most ETFs are passively managed, meaning they track an index or a benchmark and aim to replicate its performance. However, some ETFs are actively managed, meaning they have a fund manager who makes decisions on what securities to include in the portfolio and when to buy or sell them. The goal of active ETFs is to outperform the index or the benchmark, rather than just match it.

Active ETFs have been around for more than a decade, but they have gained more attention and popularity in recent years, thanks to the emergence of new technologies and innovations. One of these innovations is artificial intelligence, or AI, which refers to the ability of machines or software to perform tasks that normally require human intelligence, such as learning, reasoning, and decision making. AI has been applied to various fields and industries, including finance and investing. Some active ETFs have started to use AI as a tool or a strategy to enhance their performance and gain an edge over the market.

But how does AI work in active ETFs? And what are the benefits and risks of this combination? I will offer my point of view on these questions, based on the information I gathered from various sources.

How AI Works in Active ETFs

There are different ways that AI can be used in active ETFs, depending on the type and the objective of the fund. Here are some examples:

  • AI can be used to analyze large amounts of data, such as market trends, economic indicators, company fundamentals, and social media sentiment, and generate insights and predictions that can help the fund manager make better investment decisions. For instance, the AI Powered Equity ETF (NYSE: AIEQ ) uses an AI system called IBM Watson to build predictive models on the universe of U.S. equities and identify stocks that have the highest potential for capital appreciation. It is equal to 1,000 research analysts, traders and quants working around the clock.
  • AI can be used to automate the trading process, such as executing orders, rebalancing the portfolio, and adjusting the risk exposure, based on predefined rules and algorithms. This can reduce human errors, biases, and emotions, and increase efficiency and speed. For example, the BTD Capital Fund (NYSE: DIP ) uses an AI algorithm to trade U.S. stocks based on momentum, volatility, and trend-following factors.
  • AI can be used to create new and innovative investment strategies, such as using natural language processing to analyze the transcripts of corporate earnings calls and identify signals of future performance, or using machine learning to discover hidden patterns and correlations among different asset classes and markets. For instance, the WisdomTree International AI Enhanced Value Fund (NYSE: AIVI ) uses an AI model to enhance the value factor by incorporating alternative data sources, such as patent filings, web traffic, and news sentiment.

What Are the Benefits of AI in Active ETFs

The main benefit of using AI in active ETFs is that it can potentially improve the performance and the returns of the fund, by providing more accurate and timely information, by exploiting market inefficiencies and opportunities, and by adapting to changing market conditions. AI can also lower the cost and the risk of active management, by reducing the need for human intervention, by optimizing the portfolio allocation and the trading execution, and by diversifying the sources of alpha. Furthermore, AI can offer more transparency and accountability, by disclosing the methodology and the rationale behind the investment decisions, and by providing performance attribution and feedback.

Some evidence suggests that AI can indeed enhance the performance. For example, an article on CNBC mentioned that using IBM’s Watson platform, the AI Powered Equity ETF (AIEQ) is among the first ETFs to rely on AI for stock selection. It also mention that the information edge of AI is still unclear, but there are signs that some AI-based funds are doing better than their conventional peers. In another research paper published in 2022 by Rui Chen and Jinjuan Ren, titled ‘Do AI-powered mutual funds perform better’, which examined the broader AI capability in the mutual fund domain. They analysed the prospectuses from the EDGAR database of the US Securities and Exchange Commission. It matched them with AI-powered funds using mutual fund data from the CRSP Survivor-Bias-Free US Mutual Fund Database from January 2009 to December 2019. They discovered that these funds do not beat the market in general. However, a comparison reveals that AI-powered funds outperform human-managed peer funds significantly.

What Are the Risks of AI in Active ETFs

However, using AI in active ETFs also comes with some risks and challenges. One of these risks is that AI is not infallible, and it can make mistakes or errors, especially when dealing with complex, uncertain, and dynamic situations. AI can also be affected by data quality and availability issues, such as noise, bias, or gaps, which can impair its accuracy and reliability. Moreover, AI can be vulnerable to cyberattacks, hacking, or manipulation, which can compromise its security and integrity.

Another risk is that AI can be difficult to understand and explain, especially when using advanced and sophisticated techniques, such as deep learning or neural networks. This can create a lack of trust and confidence among investors, regulators, and auditors, who may not be able to verify or validate the logic and the outcomes of the AI system. This can also raise ethical and legal issues, such as accountability, liability, and fairness, when the AI system makes decisions that have significant impacts or consequences.

A third risk is that AI can create new and unforeseen problems or risks, such as market instability, systemic risk, or social and environmental harm. For example, AI can amplify market volatility and contagion, by triggering feedback loops, herd behavior, or flash crashes, especially when many funds use similar or correlated AI strategies. AI can also disrupt the market structure and the competitive landscape, by creating new winners and losers, by increasing the concentration and the power of a few players, or by displacing or replacing human workers.

Summing Up

In conclusion, active ETFs and AI are a promising or perilous combination, depending on how they are used and regulated.

On the one hand, AI can offer significant advantages and opportunities, by enhancing their performance, lowering their cost and risk, and increasing their transparency and accountability. On the other hand, AI can pose significant challenges and threats, by introducing errors and uncertainties, creating trust and ethical issues, and generating new and unforeseen problems and risks.

Therefore, investors, fund managers, and regulators need to be aware and cautious of the benefits and the risks of AI in active ETFs, and adopt appropriate measures and safeguards to ensure that AI is used in a responsible and sustainable manner. For myself, I hope to see how this will work on Bitcoin ETFs. I will try to collect more data to give you a follow up analysis on that.

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