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INVESTING IN THEMES

Thematic investing: Tomorrow’s themes, today

Investment themes are highly dynamic, shouldn’t your investment process be too? Learn about a systematic framework to help with identifying and investing across the themes driving markets.

Key points

  • 01

    Decoding thematic investing

    The rise of thematic investing raises key questions for allocators: how can investors identify the themes to invest in, determine entry and exit points, and reliably assess their return potential?

  • 02

    Investing in dynamic themes

    Our data-driven approach leverages large language models and vast sets of data to seek to uncover companies with direct and indirect exposure to themes ranging from structural mega forces to short-lived trends.

  • 03

    Building a thematic rotation portfolio

    The iShares U.S. Thematic Rotation Active ETF (THRO) seeks long-term capital appreciation by investing across the themes with the most compelling return potential.

The case for thematic investing

Thematic investing has grown in prominence as a way for investors to align portfolios with the evolving trends shaping the future economy. Over the last 10 years, assets managed in the U.S.-listed thematic funds have grown nearly ten times from $9.7bn in 2014 to $92.7bn in 2024.1

Themes can be described as topics that are front-of-mind for investors, persist over time, and have demonstrated an ability to drive equity market returns. We define themes as meeting three criteria:

  • Themes are dynamic. The top themes driving markets are constantly shifting, requiring a timely and scalable approach to identify the most important themes in equity markets today.
  • Themes impact generally unrelated companies. Capturing thematic alpha2 requires anticipating new linkages across stocks. The traditional groupings by sector, industry, or country may overlook the cross-market returns driven by themes.
  • Themes have the potential to drive meaningful returns. Investors need a framework for evaluating the return potential of themes while seeking to allocate across the strongest opportunity set.

We can begin to understand how themes evolve by assessing the attention they receive using natural language processing (NLP) techniques. By nature, themes create a persistent flow of news. They can be topics that investors are researching, corporate officers are addressing in conference calls, the press is publishing, and government officials are often responding to. Leveraging these text sources, we aim to quantify the flow of information to determine a theme’s relevance and life span.

Take for example the release of ChatGPT in late 2022. We view artificial intelligence as a persistent structural theme that will drive innovation for years to come. However, the launch of this consumer-facing technology led all aspects of the AI value chain—from GPUs and large language models to data centers and power generation—to surge in popularity.3 Figure 1 shows how we measure this with a proprietary attention score which currently exceeds historical highs of “big data”, “clean tech”, and “SPACs,” and is only modestly below “credit defaults” in the Great Financial Crisis (GFC).

We also find that some themes persist while others rapidly decay. Generating consistent returns requires seeking alpha opportunities across the broadest possible range of thematic opportunities. While long-term themes can align a portfolio with transformational change and innovation, many short-term themes can crowd out investor flows and attention—and importantly, may not be directly accessible in an off-the-shelf investment vehicle. The ability to incorporate new thematic ideas into portfolios requires data, technology, and scale to effectively pursue this spectrum.

Thematic rotation: thematic + dynamic

Armed with a deep understanding of themes, our systematic framework used within the iShares U.S. Thematic Rotation Active ETF (THRO) seeks to generate consistent alpha by addressing the unique challenges of thematic investing: How do you identify which themes to invest in? What measures should be used for a theme’s entry and exit point? How should an allocator benchmark thematic returns?

Figure 2: A systematic framework for theme identification, rotation, and return generation

Visual shows a three-step process for approaching thematic investing, including identifying themes to invest in, measuring the optimal entry and exit point, and benchmarking thematic returns.

Source: BlackRock Systematic, for illustrative purposes only.

Our process leverages data and technology to monitor how themes develop and interact in real-time, incorporating a breadth of insights to navigate the thematic landscape.

How do you identify themes to invest in?

Our systematic approach starts with collecting vast amounts of data. We analyze over one million financial news articles each year and more than five thousand earnings call transcripts every quarter, as shown in Figure 3. Our models seek to link companies to the potential themes identified. This paints a detailed picture of the current thematic landscape — potentially before the market can piece together evolving trends.

Figure 3: We turn unstructured textual information into meaningful relationships across stocks & themes

Visual shows examples of text data sources and how systematic models are able to extract insights on investment themes.

Source: BlackRock, as of Sept. 30, 2024. 1 Information on BlackRock’s systematic investing capabilities in using natural language processing (NLP), artificial intelligence, and large language models available at https://www.blackrock.com/us/individual/insights/ai-investing. 2 For illustrative purposes only and subject to change. Examples are hypothetical and reflect sample natural language processing which searches for keywords in financial news, earnings transcripts, broker research, broker baskets and other data points.

Our analytical capabilities can do more than detect themes—they can identify the most relevant companies tied to each trend. Let’s consider a theme like generative AI, for example. Our model constructs a custom basket using a range of data sources, first including existing sell-side baskets and thematic funds, tracking stocks that brokers and portfolio managers have identified as relevant. These sources tend to consist of first-order exposures and capture a highly concentrated cohort of stocks. For AI, this could include firms that develop AI technology and semiconductor manufacturers who create the hardware necessary for AI model training.

Then, using NLP techniques, we expand the initial cohort to include a broader group of stocks that spans both direct and indirect relationships to the theme, revealing more subtle linkages. We extract keywords from broker research, and search for those keywords across sources such as conference call transcripts and financial news. This text-mining can uncover names not originally captured. For example, we may discover companies cutting costs by enhancing back-office tasks, asset managers investing in the space, or even firms who seem relatively resilient to the technological change given the physical nature of their processes.

What are the entry and exit points?

Part of an alpha-seeking, systematic approach to thematic investing is the consistent and timely analysis of existing themes. This requires the daily evaluation of the return potential of all themes in the investible universe that we’ve identified—something that most investors are not equipped to do on their own.

Proprietary systematic signals may yield dynamic and timely return forecasts that shape the current view of each theme. These signals tend to favor themes with positive investor sentiment and strong stock price performance, while also accounting for factors like over-crowding and over-valuation. We rank themes by combining these insights, as shown in Figure 4. Each signal feeds into the theme’s overall rating, and the aggregated view helps determine how we rotate across themes within a portfolio.

Figure 4: We evaluate and rank each theme daily using proprietary signals

Image shows how themes are ranked and scored, informing thematic positioning at the aggregate portfolio level.

Source: BlackRock, as of September 30, 2024. Thematic positions provided for illustrative purposes only. Not representative of actual fund holdings. Positioning represented as follows: Green: strongly positive return forecast; light green: positive return forecast; yellow: negative return forecast; orange: strongly negative return forecast. Not representative of actual fund holdings. 1 Other insights include quantitative data such as financial metrics and alternative data sets. For illustrative purposes only and subject to change.

This lays the groundwork for portfolio implementation. To build the thematic rotation portfolio, we use an optimization that incorporates both our alpha forecasts for each individual theme and the interactions, or correlations, across stocks and themes. The resulting portfolio may reflect overweights in the themes where we have greater conviction and underweights in themes where we may forecast less supportive outlooks. Additionally, our implementation is focused on diversification and risk management,4 accounting for interactions between themes and transaction costs.

How do you benchmark thematic returns?

With broad US equities as our reference benchmark, our approach to thematic rotation seeks to generate consistent and compelling excess returns above and beyond the market.

As themes such as the pandemic and generative AI have dominated headlines in recent years, it’s no surprise that thematic investing has garnered attention. Our analysis indicates that a dynamic thematic strategy rooted in a systematic framework may enhance portfolio exposure while complementing other investment allocations.

By transforming a wealth of data into valuable insights, a systematic approach offers the potential to capture thematic alpha with precision. Investors can access diversified exposure to a spectrum of timely and transformational themes — making a systematic framework the future of thematic investing, in our view.

Linus Franngard
Senior Researcher for BlackRock Systematic
Scott Gladstone
Senior Strategist for BlackRock Systematic

Systematic investing

Explore a new way to invest that combines big data, scientific research, and deep human expertise to make sense of market complexity.
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