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Data-Driven Methods to Identify Companies Involved in Artificial Intelligence

Companies and Markets

By Hiroki Miyahara  |  August 16, 2023

Investors watching the popularity and potential of AI take off across more businesses are asking key portfolio questions:

  • Which sector participants are poised to capitalize on AI and its related technologies such as generative AI, Large Language Models, and Machine Learning?

  • Is the potential for higher revenues and business transformation limited to companies directly involved in AI?

  • Could supply-chain relationships indicate companies that might benefit from the AI tsunami?

  • Is there a data-driven way to identify and evaluate potential winners and losers from an investment perspective?

This article examines methods to identify companies involved in AI, with a focus on known supply-chain relationships and the associated textual metadata. First, we will evaluate the performance of early beneficiaries of AI: chipmaker NVIDIA and its suppliers. Next, we will identify AI companies by their supply-chain keywords and highlight their performance over time.

The superstar stock price of NVIDIA

In a major surprise to the investing world, NVIDIA significantly revised its earnings guidance upward after market close on May 24, 2023. The company said its anticipated revenue for the second quarter would be roughly $11 billion, whereas the market consensus, according to FactSet Consensus Estimates, targeted $7.2 billion. NVIDIA’s stock price jumped 24% the next day.

The change in guidance has also significantly impacted expectations across semiconductor manufacturers. For example, the PHLX Semiconductor Index rose 6.81% on May 25 and 6.26% on May 26 this year.

Suppliers’ performance

It is reasonable to assume NVIDIA’s suppliers would experience revenue growth because of increased orders from the company. Figure 1 displays the cumulative return of NVIDIA (blue), the Global Total Market Benchmark (orange), and a market-value weighted average of NVIDIA’s suppliers (green) over the month starting May 24, 2023. As anticipated following NVIDIA’s revised guidance, the suppliers outperformed the benchmark, although their combined performance wasn’t as impressive as NVIDIA’s.

Figure 1: Cumulative stock-price returns for NVIDIA and its suppliers against the benchmark


Source: FactSet

It is interesting to note that suppliers NVIDIA disclosed (direct disclosure) had lower stock-price performance compared to those not disclosed but who reported the relationship themselves (reverse disclosure). Figure 2 illustrates the cumulative return of both groups of suppliers.

Figure 2: Cumulative returns for direct and reverse disclosure companies


Source: FactSet

Performance among AI companies

The rise of generative AI has not only provided a financial tailwind to semiconductor companies and their suppliers. It has also bolstered businesses that offer AI-related services and products.

To assess the performance of these companies, we must identify the universe of AI-related businesses. Since a wide range of industries use AI technologies, no single sector encompasses all AI companies. Thus, we will utilize supply chain relationship keywords to determine companies that offer AI-related products and services.

Supply chain relationship keywords are collected from source documents, and they provide contextual information about the products or services two companies exchange. For example, NVIDIA reported in its Q4 FY23 investor presentation that Deutsche Bank is utilizing NVIDIA AI Enterprise software “to accelerate the use of AI and Machine Learning.”

This was captured as “AI and Machine Learning” in the relationship keyword metadata. Similarly, Jaguar Land Rover (Tata Motor) announced their new vehicles will be built on a NVIDIA DRIVE software-defined platform. Supply chain relationship keywords captured “next-generation automated driving systems,” “AI-enabled services,” and “NVIDIA DRIVE software-defined platform” as keywords. As both cases have the keyword “AI,” we will consider NVIDIA as an AI company for the purpose of our analysis.

We employed the same techniques for cleaning the relationship keyword data as described in our December FactSet Insight article, “Innovations of Tomorrow: Analyzing Intercompany Research Partnerships for Investable Themes and Beneficiaries.” First, we lemmatized each word to standardize the form (e.g., “Intelligences” to “Intelligence”). Then we filtered suppliers to those containing the term “AI” / “Artificial Intelligence.” As a result, we found 154 distinct suppliers (36 in Global Market Benchmark) as of June 2022. The list includes large tech companies such as Microsoft, Amazon, NVIDIA, Alphabet, Meta, and Adobe. From here on, we’ll refer to these companies as “AI companies.”

Figure 3 shows how a portfolio of these AI companies performed from June 2022 to June 2023. The portfolio consists of AI companies that are constituents of the Global Market benchmark and is weighted based on market value. There has been an upward trend in performance since January 2023. It is worth noting that this was just one month after OpenAI launched ChatGPT, which may have played a role in the portfolio’s success.

Figure 3: AI portfolio performance from June 2022 to June 2023


Source: FactSet

Since January 2023, the AI suppliers portfolio has outperformed the market benchmark. However, this may be due to the portfolio being heavily allocated towards growing technology sectors rather than from select companies that provide AI-related products. Figure 4 shows the portfolio’s average weight and total return by RBICS L2 sector alongside the Benchmark L2 sector returns to determine if the outcome came solely from sector allocation.

The return horizon begins on the same date as the launch of ChatGPT, November 30, 2022, as the recent AI boom is assumed to have started with ChatGPT. The portfolio has the largest exposure in the Software and Consulting sector, including companies such as Microsoft, Meta, Adobe, Baidu, and Snowflake. The figure shows that all portfolio sectors except Hardware have outperformed the benchmark sector returns, implying that the market expects higher growth in products and services leveraging or serving AI technology, regardless of business type.

Figure 4: Average weight and total return by RBICS L2 sector alongside the benchmark L2 sector returns


Source: FactSet

Now that we observed how NVIDIA's stock trend correlated with its suppliers, let’s investigate if there is similar correlation between the performance of AI companies and their suppliers. Among the 154 AI companies we identified earlier, we created a portfolio of companies with at least four customers—a total of 220 firms we'll call tier 2 AI suppliers.

Among those, 45 are constituents of the global market benchmark. Figure 5 shows the average weight and total return of tier 2 AI suppliers in each sector of the global market benchmark between November 2022 and June 2023. Although the total return is lower than that of AI companies, the results indicate that the tier 2 AI suppliers portfolio outperformed the benchmark in all sectors. This suggests that the AI boom has indeed spread to suppliers of companies providing AI services.

Figure 5: Tier 2 AI suppliers portfolio outperformance


Source: FactSet


In this article, we analyzed how NVIDIA's guidance surprise impacted its suppliers and revealed they meaningfully outperformed the market—particularly in cases where the relationship was reverse disclosed by the supplier. We also explored the use of supply chain relationship keywords to identify AI companies. We analyzed AI companies’ and suppliers' performance during the initial generative AI boom after the launch of ChatGPT in 2022. This analysis helped to confirm the accuracy of the derived universe in capturing the market trend.

To learn more about the related data feed, visit FactSet Supply Chain Relationships

Colin Smith, Product Manager for Data Solutions, also contributed to this article.

This blog post is for informational purposes only. The information contained in this blog post is not legal, tax, or investment advice. FactSet does not endorse or recommend any investments and assumes no liability for any consequence relating directly or indirectly to any action or inaction taken based on the information contained in this article.

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Hiroki Miyahara

Senior Product Manager, Japan

Mr. Hiroki Miyahara is a Senior Product Manager at FactSet, based on Tokyo, Japan. In this role, he covers the Asia Pacific region for FactSet proprietary content sets including supply chain, RBICS, GeoRev, shipping, and FactSet Data Management Solution. Mr. Miyahara joined FactSet in 2011 and previously held roles as an account executive and product developer. He earned an MSc in economics from the University of Essex.


The information contained in this article is not investment advice. FactSet does not endorse or recommend any investments and assumes no liability for any consequence relating directly or indirectly to any action or inaction taken based on the information contained in this article.