The U.S. Exchange Traded Fund (ETF) industry has become a behemoth in the stock market in recent years, surpassing a record high of $5.5 trillion dollars in assets under management in 2020.
Part of its proliferation is due to a growing popularity in thematic investing – more specifically, with thematic ETFs, which account for 1.9% of the total ETF market share (Palanrani, 2021). They allow investors to ride a trend that essentially carries an entire sector. Trends like clean energy, cybersecurity, and cloud computing have spiked interests and proven to be successful ETF products.
With increasing adoption of new technologies and trends, some may agree the possibilities for thematic ETFs are endless. However, research shows that investors should think twice before buying another trendy thematic ETF.
What are Thematic ETFs?
To start, thematic ETFs focus on macro-level trends and identify areas of growth in disruptive and evolving industries. According to the Thematic ETF Report Q4 2020 by the Global X ETFs’ research team, there were a total of 148 thematic ETFs with $104.1B in assets under management in Q4 2020. according to Global X, this is up 78% from Q3 of 2020. (Palanrani, 2021)
Disruptive technologies may include Fintech, Digital Content, Big Data, Robotics, and Mobility – many of which have underlying themes like Electric Vehicles, Blockchain, Cybersecurity, and AI. Other evolving industries include consumer trends like Cannabis or Sports Betting and environmental issues like Clean & Renewable Energy and CleanTech.
The Problem with Thematic ETFs
The potential issue with thematic ETFs comes down to risk. The current landscape of specialized ETFs, particularly thematic ETFs, have attracted greater media exposure than broad-based ETFs and thus may have driven the price up with irrational expectations. According to the research paper, financial innovation in the ETF industry, once the hype or trend dies down, most thematic ETFs end up underperforming the market and delivering negative risk-adjusted returns. (Ben-DavidItzhak, 2021) For example, browse through any financial news online and you’ll quickly find that Cathie Wood has been grabbing headlines with ARK ETFs. With that being said, most thematic ETFs are narrowly focused and not diversified enough and the greatest pain point might be that they depend heavily on certain sectors to drive growth.
Looking at the exponential growth of ARK investments, you’ll find the company owns significant amounts of stock in a single sector, carrying all the risk of that sector. As of 09/13/21, ARK Innovation ETF (NYSE: ARKK) holds more than 10% in Tesla stocks so far. If the tech sector suddenly drops in price, ARK funds will most likely have a dramatic impact. This is just one example. Other thematic ETFs cover stocks related to COVID-19 vaccines, work from home trend, and even Black Lives Matter movement. Once the underlying factors for those trends die down, there is a potential risk of their price dropping significantly.
On the other hand, broad-based ETFs are likely more favored by institutional investors. Broad-based ETFs are funds that track broad market indices. They are considered more beneficial in that they reduce transaction costs and provide higher diversification. While specialized ETFs may attract unsophisticated investors who chase performances and disregard fundamentals, broad-based index-tracking ETFs often offer low-risk and low-cost exposure to greater market segments.
The Impact of AI on Actively Managed ETFs
Some may argue that thematic ETFs, while risky, do offer higher returns than broad-based ETFs, at least in the short-term. This is true in many cases, where some investors just want to ride market momentum until the trend is over.
But what if there’s an alternative way to find excess return without taking excessive risk? In the broad-based active ETF category, there is such a thing called AI powered ETFs. AI powered ETFs are managed directly by AI. In other words, no humans manage the fund.
What is an AI powered fund? To put it simply, it is a fund that uses AI technology to construct an investment strategy, apply that to a universe of stocks (e.g.: large cap stocks), choose the stocks that meet the strategy, and finally give the weights of those stocks in the ETF portfolio.
With the broad-based, active approach, AI powered ETFs can potentially provide excess returns compared to their index. Additionally, due to the potential lower volatility of allocating in multiple sectors, it can potentially provide better risk-adjusted returns as well.
Qraft AI ETFs, which consist of four listed AI powered ETFs on the New York Stock Exchange, built a proprietary AI technology that automatically finds alpha factors to potentially bring excess returns.
Qraft’s core research technology, called Alpha Factory, leverages AutoML and Deep Learning technology to find market factors apply them to the large cap holdings in the fund. The AI automatically rebalances the weightings of the stocks each month to favor potential outperformance against the benchmark index.
Once example of this is when Qraft AI-Enhanced U.S. Large Cap Momentum ETF (NYSE: AMOM) held Tesla as its biggest position for three consecutive months starting in November 2020. However, Qraft’s AI system automatically reduced Tesla shares to zero beginning of September when the stock plummeted by 13.9% and did not buy back until November when the stock became bullish again. This may very well be just a mere coincidence. However, this could also be indicative of the potential predictive power of AI’s solution for building high performing ETFs.
For information purposes only. Not meant to represent the Fund. Past performance does not guarantee future results. For AMOM top ten holdings, click https://qraftaietf.com/amom.
With all investments in the stock market, there will always be a risk factor involved. In the advent of thematic ETFs, investors are exposed to trendy themes with the possibility of holding attention seeking and overvalued stocks that may potentially result in lower risk-adjusted returns. As a viable alternative to specialized ETFs, investors can add AI ETFs that are broad-based and provide diversification with the added benefit of AI integrating alpha factors in search of excess return strategies.
1. Macro level trends – A macro trend is a pervasive and persistent shift in the direction of some phenomenon on a global level. Examples of current macro trends include urbanization, automation, and changing demographics.
2. Risk-adjusted returns – A risk-adjusted return is a calculation of the profit or potential profit from an investment that takes into account the degree of risk that must be accepted in order to achieve it.
3. Broad-based ETFs – Broad-based ETFs are all ETFs that track broad market indices.
4. Alpha – Alpha is a measure of the active return on an investment, the performance of that investment compared with a suitable market index.
6. Factor Factory – Qraft’s core AI technology that automatically finds factors that could bring excess returns. Factor Factory is able to produce at least 10 factors per day without any human intervention.
7. AutoML – Short for Automated Machine Learning, AutoML is the automation of the machine learning process to make machine learning jobs simpler, easier, and faster.
8. Deep Learning - a type of machine learning based on artificial neural networks in which multiple layers of processing are used to extract progressively higher level features from data.
Ben-David, I. a. (2021). Competition for Attention in the ETF Space. Swiss Finance Institue Research Paper Series, 66.
Palanrani, P. (2021, January 7). Thematic ETF Report: Q4 2020. Retrieved from Globalxetfs Website: https://www.globalxetfs.com/thematic-etf-report-q4-2020/
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 1-855-973-7880 or visit our website at www.qraftaietf.com. Read the prospectus or summary prospectus carefully before investing.
The Funds are distributed by Foreside Fund Services, LLC
Investing involves risk, including loss of principal. The Funds are subject to numerous risks including but not limited to: Equity Risk, Sector Risk, Large Cap Risk, Management Risk, and Trading Risk. The Funds rely heavily on a proprietary artificial intelligence selection model as well as data and information supplied by third parties that are utilized by such model. To the extent the model does not perform as designed or as intended, the Fund’s strategy may not be successfully implemented and the Funds may lose value. Additionally, the funds are non-diversified, which means that they may invest more of their assets in the securities of a single issuer or a smaller number of issuers than if they were a diversified fund. As a result, each Fund may be more exposed to the risks associated with and developments affecting an individual issuer or a smaller number of issuers than a fund that invests more widely. A new or smaller fund's performance may not represent how the fund is expected to or may perform in the long term if and when it becomes larger and has fully implemented its investment strategies. Read the prospectus for additional details regarding risks.
QRAFT AI-Enhanced U.S. Large Cap ETF: Companies in the health care sector are subject to extensive government regulation and their profitability can be significantly affected by restrictions on government reimbursement for medical expenses, rising costs of medical products and services, pricing pressure (including price discounting), limited product lines and an increased emphasis on the delivery of health care through outpatient services.
QRAFT AI-Enhanced U.S. Large Cap Momentum ETF: The Fund is subject to the risk that market or economic factors impacting technology companies and companies that rely heavily on technology advances could have a major effect on the value of the Fund’s investments. The value of stocks of technology companies and companies that rely heavily on technology is particularly vulnerable to rapid changes in technology product cycles, rapid product obsolescence, the loss of patent, copyright and trademark protections, government regulation and competition, both domestically and internationally, including competition from foreign competitors with lower production costs. Technology companies and companies that rely heavily on technology, especially those of smaller, less-seasoned companies, tend to be more volatile than the overall market.
QRAFT AI-Enhanced US High Dividend ETF: Securities that pay dividends, as a group, may be out of favor with the market and underperform the overall equity market or stocks of companies that do not pay dividends. In addition, changes in the dividend policies of the companies held by the Fund or the capital resources available for such company’s dividend payments may adversely affect the Fund. In the event a company reduces or eliminates its dividend, the Fund may not only lose the dividend payout but the stock price of the company may also fall.
QRAFT AI-Enhanced U.S. Next Value ETF: The value approach to investing involves the risk that stocks may remain undervalued, undervaluation may become more severe, or perceived undervaluation may actually represent intrinsic value. Value stocks may underperform the overall equity market while the market concentrates on growth stocks. The small- and mid-capitalization companies in which the Fund invests may be more vulnerable to adverse business or economic evens than larger, more established companies, and may underperform other segments of the market or the equity market as a whole. Securities of small- and mid-capitalization companies generally trade in lower volumes, are often more vulnerable to market volatility, and are subject to greater and more unpredictable price changes than larger capitalization stocks or the stock market as a whole.
Alpha – Alpha is a measure of the active return on an investment, the performance of that investment compared with a suitable market index.
AutoML – Short for Automated Machine Learning, AutoML is the automation of the machine learning process to make machine learning jobs simpler, easier, and faster.
Kirin API - Developed by Qraft’s data scientists, integrates multiple vendors to provide both macroeconomic and company fundamentals with the correct point-in-time data.