Differentiating your AI exposure from the Nasdaq-100The Nasdaq-100 has recorded its best H1 since the inception of the index in 1985, propelled by the year-to-date rally in the biggest tech stocks riding the artificial intelligence (AI) wave. For investors looking to benefit from the long-term growth offered by the AI megatrend, this period presents an opportunity to analyse how the AI-focused thematic strategies have fared in such a market and how they could perform going forward.
In the first part of this two-part blog series, we discussed the case for a targeted AI strategy vs achieving exposure to AI through the Nasdaq-100. The key factors in favour of an AI strategy included a more comprehensive exposure to the breadth of AI activities, a potential inclusion of the mega caps of tomorrow, and diversification benefits. In this blog, we look at the AI space in Europe and discuss the signposts for investors in selecting a sound AI-focused thematic strategy and the importance of such a selection.
The drivers of return dispersion in the AI peer group
One of the simplest and most compelling arguments that not all AI strategies are created equal can be made by looking at the dispersion of returns within the AI peer group. The dispersion of returns across strategies aiming to harness the same theme is something that we continuously observe across a range of 42 themes tracked in our WisdomTree Thematic Universe1. This confirms that this phenomenon is not just specific to the AI theme.
Based on the 15 AI strategies with available year-to-date history in Europe, we have observed that the year-to-date return experience across the AI strategies has been quite different - ranging from around 13% to up to 43%. The average return across the strategies was around 29.75%, or 9.4% lower than the return of the Nasdaq-100. Given that returns of a range of AI stocks have been boosted by the growing enthusiasm around ChatGPT and generative AI, we would view relative outperformance vs the average return in the AI peer group as one of the factors potentially suggesting a promising AI strategy. However, an important question to answer here is if a given AI strategy has been driven by the performance of the same key stocks driving the performance of the Nasdaq-100, or if it has been propelled by other return drivers. If the latter is true, such a strategy can present a return enhancement play for investors holding the Nasdaq-100 as the broad tech benchmark.
While it is not always feasible to run performance attribution for each fund in the AI peer group, and assess how different its return drivers have been in contrast to the Nasdaq-100, we have observed that this dispersion boils down to strategy design, and how each fund is meant to capture the opportunities offered by the proliferation of AI.
For example, in our WisdomTree Artificial Intelligence UCITS ETF (WTAI), enhancers (that is, the companies that are a prominent force in AI but with a smaller portion of products and revenues associated with the theme) receive only 10% weight during each semi-annual period. This means that tech giants that dominate the top 10 in the Nasdaq-100, jointly can receive only up to 10% weight. At the same time, more pure-play opportunities in the space (known as ‘engagers’) receive 50% weight at the rebalance, ensuring a certain degree of theme purity.
The importance of a robust selection framework
At WisdomTree, we have previously singled out five building blocks that comprise the selection framework for thematic strategies first proposed in our thematic white paper. In short, we invite investors to first focus on selecting strategies with a clear focus on the theme of interest, assess if the subject matter expertise is part of the strategy design and, if possible, evaluate the purity of the suggested exposure. All these signposts are more qualitative in nature, unlike the next step, which involves testing the shortlisted strategies for the level of differentiation they offer vs broad benchmarks, other themes, and each other.
Let’s have a look at WTAI vs the best performing fund in the AI peer group year-to-date, that is, Fund A, and see if these two funds are differentiated vs the Nasdaq-100. One easy analysis that investors can do to assess the degree of differentiation is to look at the overlap weight vs a broad benchmark and the percentage of common and unique holdings vs the same benchmark. In Figure 2, the analysis suggests that WTAI has relatively low overlap with the Nasdaq-100 and holds only 29% weight in the holdings common with the broad tech gauge. In contrast, Fund A has a relatively high overlap of around 40% and has invested around 62% weight in the stocks represented in the Nasdaq-100.
We can extend our comparison further and resort to performance attribution, as both strategies are offered in an exchange-traded fund (ETF) wrapper and have to report their holdings on a daily basis. The top 10 holdings contributing to the year-to-date performance within WTAI and Fund A, exposes an interesting contrast between the two strategies. Within the top 10 contributors of Fund A, investors can find eight stocks in dark blue that have also been the top 10 year-to-date contributors in the Nasdaq-100. The average weight of the top 10 contributors in Fund A has been 45.6%, accounting for around 70% of the strategy’s return. Due to its market cap-driven weighting, the majority of holdings in Fund A that have posted year-to-date returns above 50%, and even above 100%, have received weights below 0.20% each. This has limited Fund A’s ability to benefit from the AI stocks not included in the Nasdaq-100.
In turn, in WTAI, only Nvidia, also represented in the top 10 in the Nasdaq-100, has been within the top 10 year-to-date contributors. The average weight of the top 10 contributors has comprised only 23.5%, and the top 10 have jointly contributed only 56% to the strategy’s year-to-date return. Notably, the best-performing stock in WTAI was C3.ai and not Nvidia. Fund A had its own stock, Wistron, that has also beaten Nvidia with 204.7% year-to-date return, but the fund had only 0.08% average weight in it. Furthermore, in contrast to Fund A, 4 out of the 5 best-performing stocks in WTAI had average weights between 2.3% and 2.8%. This highlights that the strategy design behind WTAI has allowed the fund to not only benefit from strong returns posted by a range of AI stocks year-to-date, but also to differentiate its key return contributors from the broad tech benchmark.
Sources
1 Please see page 8 of the WisdomTree European thematic monthly update for an overview of the WisdomTree Thematic Universe and page 4 for the dispersions of returns across the themes.
This material is prepared by WisdomTree and its affiliates and is not intended to be relied upon as a forecast, research or investment advice, and is not a recommendation, offer or solicitation to buy or sell any securities or to adopt any investment strategy. The opinions expressed are as of the date of production and may change as subsequent conditions vary. The information and opinions contained in this material are derived from proprietary and non-proprietary sources. As such, no warranty of accuracy or reliability is given and no responsibility arising in any other way for errors and omissions (including responsibility to any person by reason of negligence) is accepted by WisdomTree, nor any affiliate, nor any of their officers, employees or agents. Reliance upon information in this material is at the sole discretion of the reader. Past performance is not a reliable indicator of future performance.
